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O'Brien SF, Asamoah-Boaheng M, Grunau B, Krajden M, Buckeridge DL, Goldfarb DM, Anderson M, Germain M, Brown P, Stein DR, Kandola K, Tipples G, Awadalla P, Lang A, Behl L, Fitzpatrick T, Drews SJ. Canada's approach to SARS-CoV-2 sero-surveillance: Lessons learned for routine surveillance and future pandemics. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2024:10.17269/s41997-024-00901-w. [PMID: 38981961 DOI: 10.17269/s41997-024-00901-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/24/2024] [Indexed: 07/11/2024]
Abstract
SETTING In Canada's federated healthcare system, 13 provincial and territorial jurisdictions have independent responsibility to collect data to inform health policies. During the COVID-19 pandemic (2020-2023), national and regional sero-surveys mostly drew upon existing infrastructure to quickly test specimens and collect data but required cross-jurisdiction coordination and communication. INTERVENTION There were 4 national and 7 regional general population SARS-CoV-2 sero-surveys. Survey methodologies varied by participant selection approaches, assay choices, and reporting structures. We analyzed Canadian pandemic sero-surveillance initiatives to identify key learnings to inform future pandemic planning. OUTCOMES Over a million samples were tested for SARS-CoV-2 antibodies from 2020 to 2023 but siloed in 11 distinct datasets. Most national sero-surveys had insufficient sample size to estimate regional prevalence; differences in methodology hampered cross-regional comparisons of regional sero-surveys. Only four sero-surveys included questionnaires. Sero-surveys were not directly comparable due to different assays, sampling methodologies, and time-frames. Linkage to health records occurred in three provinces only. Dried blood spots permitted sample collection in remote populations and during stay-at-home orders. IMPLICATIONS To provide timely, high-quality information for public health decision-making, routine sero-surveillance systems must be adaptable, flexible, and scalable. National capability planning should include consortiums for assay design and validation, defined mechanisms to improve test capacity, base documents for data linkage and material transfer across jurisdictions, and mechanisms for real-time communication of data. Lessons learned will inform incorporation of a robust sero-survey program into routine surveillance with strategic sampling and capacity to adapt and scale rapidly as a part of a comprehensive national pandemic response plan.
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Affiliation(s)
- Sheila F O'Brien
- Epidemiology & Surveillance, Canadian Blood Services, Ottawa, ON, Canada.
- School of Epidemiology & Public Health, University of Ottawa, Ottawa, ON, Canada.
| | - Michael Asamoah-Boaheng
- Department of Emergency Medicine, University of British Columbia, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, St. Paul's Hospital, Vancouver, BC, Canada
| | - Brian Grunau
- Department of Emergency Medicine, University of British Columbia, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, St. Paul's Hospital, Vancouver, BC, Canada
- British Columbia Emergency Health Services, Vancouver, BC, Canada
| | - Mel Krajden
- Public Health Laboratory, British Columbia Centre for Disease Control, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David L Buckeridge
- School of Population and Global Health, McGill University, Montreal, QC, Canada
| | - David M Goldfarb
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, BC Children's Hospital, Vancouver, BC, Canada
| | - Maureen Anderson
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
- Ministry of Health, Government of Saskatchewan, Regina, SK, Canada
| | - Marc Germain
- Affaires médicales et innovation, Héma-Québec, Québec, QC, Canada
| | - Patrick Brown
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Derek R Stein
- Cadham Provincial Laboratory, Winnipeg, MB, Canada
- Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Kami Kandola
- Government of Northwest Territories, Yellowknife, NT, Canada
| | - Graham Tipples
- Public Health Laboratory, Alberta Precision Laboratories, Edmonton, AB, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB, Canada
- Department of Medical Microbiology and Immunology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada
| | - Amanda Lang
- Roy Romanow Provincial Laboratory, Saskatchewan Health Authority, Regina, SK, Canada
| | - Lesley Behl
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
| | - Tiffany Fitzpatrick
- Public Health Ontario, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Steven J Drews
- Medical Microbiology, Canadian Blood Services, Edmonton, AB, Canada
- Division of Diagnostics and Applied Microbiology, Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada
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Costa ACGD, Martins TF, da Silva VZM, Leite CF, Castro SSD, Cipriano G, Cipriano GFB. Standardization use of the international classification of functioning, disability and health in the determination of health status in patients with post-acute COVID-19 syndrome. Disabil Rehabil 2024:1-13. [PMID: 38835177 DOI: 10.1080/09638288.2024.2358897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/10/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE To propose a standardized method for the use of the International Classification of Functioning, Disability and Health (ICF) to describe the health status in Post-Acute COVID-19 Syndrome (PACS) and investigate interrater agreement in the linking process in instruments and clinical exams using the ICF categories. MATERIALS AND METHODS Cross-sectional and interrater agreement study that followed the Guidelines for Reporting Reliability and Agreement Studies. Two raters performed the linking coding process in instruments of quality of life, anxiety and depression, fatigue and pulmonary function, inspiratory muscle strength and cardiopulmonary exercise testing. The codes were qualified by standards defined to each instrument and exams. RESULTS The instrument with the lowest Cohen's Kappa coefficient was anxiety and depression (k = 0.57). Forty ICF codes were linked to clinical instruments and exams. The fatigue instrument presented a higher degree of disability by the qualification process, from severe to complete, in the linked codes. CONCLUSION The study presents a standardized method for the assessment of the health status of patients with PACS through ICF. Restriction in work performance, socialization and family relationships as well as disabilities in physical endurance, fatigue and exercise tolerance were found in the sample. The agreement between the raters was moderate to perfect, demonstrating that the method can be reproducible.
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Affiliation(s)
| | | | - Vinicius Zacarias Maldaner da Silva
- Physiotherapy department, University of Brasília, Brazil
- Postgraduate Program in Human Moviment and Rehabilitation, UniEVANGÉLICA, Anápolis, Brazil
| | - Camila Ferreira Leite
- Master Program in Physiotherapy and Functioning, Federal University of Ceara, Brazil
| | | | - Gerson Cipriano
- Postgraduate Program in Health Sciences and Technologies, University of Brasília, Brazil
- Postgraduate Program in Rehabilitation Science, University of Brasília, Brazil
- Physiotherapy department, University of Brasília, Brazil
- Postgraduate Program in Human Moviment and Rehabilitation, UniEVANGÉLICA, Anápolis, Brazil
| | - Graziella França Bernardelli Cipriano
- Postgraduate Program in Health Sciences and Technologies, University of Brasília, Brazil
- Postgraduate Program in Rehabilitation Science, University of Brasília, Brazil
- Physiotherapy department, University of Brasília, Brazil
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3
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Lei J, MacNab Y. Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic. Spat Spatiotemporal Epidemiol 2024; 49:100658. [PMID: 38876569 DOI: 10.1016/j.sste.2024.100658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/25/2024] [Accepted: 05/08/2024] [Indexed: 06/16/2024]
Abstract
The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.
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Affiliation(s)
- Jingxin Lei
- School of Public Health, University of British Columbia, 2206 East Mall, Vancouver, V6T 1Z3, BC, Canada.
| | - Ying MacNab
- School of Public Health, University of British Columbia, 2206 East Mall, Vancouver, V6T 1Z3, BC, Canada
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4
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Starke JC, Bell NS, Martinez CM, Friberg IK, Lawley C, Sriskantharajah V, Hirschberg DL. Measuring SARS-CoV-2 RNA concentrations in neighborhood wastewater. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 926:172021. [PMID: 38552966 DOI: 10.1016/j.scitotenv.2024.172021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/05/2024]
Abstract
Longitudinal wastewater sampling during the COVID-19 pandemic was an important aspect of disease surveillance, adding to a more complete understanding of infection dynamics and providing important data for community public health monitoring and intervention planning. This was largely accomplished by testing SARS-CoV-2 RNA concentrations in samples from municipal wastewater treatment plants (WWTPs). We evaluated the utility of testing for virus levels upstream from WWTP within the residential neighborhoods that feed into the WWTP. We propose that monitoring virus dynamics across residential neighborhoods could reveal important public health-relevant information about community sub-group heterogeneity in virus concentrations. PRINCIPAL RESULTS: Virus concentration patterns display heterogeneity within neighborhoods and between neighborhoods over time. Sewage SARS-CoV-2 RNA concentrations as measured by RT-qPCR also corresponded closely to verified COVID-19 infection counts within individual neighborhoods. More importantly, our data suggest the loss of disease-relevant public health information when sampling occurs only at the level of WWTP instead of upstream in neighborhoods. Spikes in SARS-CoV-2 RNA concentrations in neighborhoods are often masked by dilution from other neighborhoods in the WWTP samples. MAJOR CONCLUSIONS: Wastewater-based epidemiology (WBE) employed at WWTP reliably detects SARS-CoV-2 in a city-sized population but provides less actionable public health information about neighborhoods experiencing greater viral infection and disease. Neighborhood sewershed sampling reveals important population-based information about local virus dynamics and improves opportunities for public health intervention. Longitudinally employed, neighborhood sewershed surveillance may provide a 3-6 day early warning of SARS-CoV-2 infection spikes and, importantly, highly specific information on subpopulations in a community particularly at higher risk at different points in time. Sampling in neighborhoods may thus provide timely and cost-saving information for targeted interventions within communities.
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Affiliation(s)
| | - Nicole S Bell
- RAIN Incubator, Tacoma, WA, USA; Squally Creek, LLC, Tacoma, WA, USA
| | - Chloe Mae Martinez
- RAIN Incubator, Tacoma, WA, USA; University of Washington-Tacoma, Tacoma, WA, USA
| | | | | | | | - David L Hirschberg
- RAIN Incubator, Tacoma, WA, USA; School of Engineering and Technology, University of Washington-Tacoma, Tacoma, WA, USA
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5
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Hussain H, Jilani A, Salah N, Memić A, Ansari MO, Alshahrie A. Carbon dioxide-activated mesoporous date palm fronds carbon integrated with MnO 2/polyaniline for highly efficient capacitive deionization of water. WATER ENVIRONMENT RESEARCH : A RESEARCH PUBLICATION OF THE WATER ENVIRONMENT FEDERATION 2024; 96:e11038. [PMID: 38797821 DOI: 10.1002/wer.11038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 04/22/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024]
Abstract
The continuous population growth and drying up the freshwater reservoirs around the world are increasing the demand for fresh water. Therefore, there is an urgent need to explore newer technologies able to purify water on large scales for human usage. Capacitive deionization is one of the most promising approaches to generate fresh water by the removal of salt ions from brackish water. In this work, we prepared three different capacitive deionization electrodes using carbonized palm tree fronds (PFC). These PFC activation was achieved using CO2 at 900°C. To generate the deionization electrodes, PFC activated carbon was combined with either polyaniline (PANI), MnO2, or both (PFC-PANI, PFC-MnO2, and PFC-MnO2-PANI). The MnO2 and PANI provided additional functionality and enhanced electrical conductivity, which resulted in much higher Na+ and Cl- ions adsorption. The BET surface area of PFC-MnO2-PANI was estimated to be 208.56 m2/g, which is approximately three times that of PCF-PANI and PFC-MnO2 alone. The morphological analysis showed that the PANI and MnO2 nanorods were well dispersed throughout the PFC network. Although PANI and MnO2 is largely embedded inside the PFC network, some remnants are visible on the surface of the electrodes. The cyclic voltammetry (CV) curves showed capacitive behavior of all electrodes in which PFC-MnO2-PANI showed highest specific capacitance of 84 F/g, while the PFC-MnO2 and PFC-PANI showed 42 and 43 F/g, respectively. Owing to its enhanced functionality and CV characteristics, the PFC-MnO2-PANI showed maximum salt adsorption capacity of 10.5 mg/g in contrast to 3.72 and 5.64 mg/g for PFC-MnO2 and PFC-PANI, respectively. Moreover, the measured contact angle for PFC-MnO2-PANI was ~51°, which indicates the hydrophilic nature of electrode that improved ions adsorption. PRACTITIONER POINTS: Date tree fronds were converted into mesopores carbon using CO2 as activation agent. Three composites were prepared with PANI, MnO2, and date palm fronds activated carbon (PFC-MnO2, PFC-MnO2-PANI, and PFC-PANI). Surface area, pore profile, surface morphology, electrochemical behavior, desalination performance, and hydrophilicity of all the electrodes were investigated. The PFC-MnO2-PANI showed maximum salt adsorption capacity of 10.5 mg/g in contrast to 3.72 and 5.64 mg/g for PFC-MnO2 and PFC-PANI, respectively.
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Affiliation(s)
- Humair Hussain
- Center of Nanotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
- Physics Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Asim Jilani
- Center of Nanotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Numan Salah
- Center of Nanotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adnan Memić
- Center of Nanotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Ahmed Alshahrie
- Center of Nanotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
- Physics Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
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Robotti C, Costantini G, Saggio G, Cesarini V, Calastri A, Maiorano E, Piloni D, Perrone T, Sabatini U, Ferretti VV, Cassaniti I, Baldanti F, Gravina A, Sakib A, Alessi E, Pietrantonio F, Pascucci M, Casali D, Zarezadeh Z, Zoppo VD, Pisani A, Benazzo M. Machine Learning-based Voice Assessment for the Detection of Positive and Recovered COVID-19 Patients. J Voice 2024; 38:796.e1-796.e13. [PMID: 34965907 PMCID: PMC8616736 DOI: 10.1016/j.jvoice.2021.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/17/2021] [Accepted: 11/18/2021] [Indexed: 12/12/2022]
Abstract
Many virological tests have been implemented during the Coronavirus Disease 2019 (COVID-19) pandemic for diagnostic purposes, but they appear unsuitable for screening purposes. Furthermore, current screening strategies are not accurate enough to effectively curb the spread of the disease. Therefore, the present study was conducted within a controlled clinical environment to determine eventual detectable variations in the voice of COVID-19 patients, recovered and healthy subjects, and also to determine whether machine learning-based voice assessment (MLVA) can accurately discriminate between them, thus potentially serving as a more effective mass-screening tool. Three different subpopulations were consecutively recruited: positive COVID-19 patients, recovered COVID-19 patients and healthy individuals as controls. Positive patients were recruited within 10 days from nasal swab positivity. Recovery from COVID-19 was established clinically, virologically and radiologically. Healthy individuals reported no COVID-19 symptoms and yielded negative results at serological testing. All study participants provided three trials for multiple vocal tasks (sustained vowel phonation, speech, cough). All recordings were initially divided into three different binary classifications with a feature selection, ranking and cross-validated RBF-SVM pipeline. This brough a mean accuracy of 90.24%, a mean sensitivity of 91.15%, a mean specificity of 89.13% and a mean AUC of 0.94 across all tasks and all comparisons, and outlined the sustained vowel as the most effective vocal task for COVID discrimination. Moreover, a three-way classification was carried out on an external test set comprised of 30 subjects, 10 per class, with a mean accuracy of 80% and an accuracy of 100% for the detection of positive subjects. Within this assessment, recovered individuals proved to be the most difficult class to identify, and all the misclassified subjects were declared positive; this might be related to mid and short-term vocal traces of COVID-19, even after the clinical resolution of the infection. In conclusion, MLVA may accurately discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies should test MLVA among larger populations and asymptomatic positive COVID-19 patients to validate this novel screening technology and test its potential application as a potentially more effective surveillance strategy for COVID-19.
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Affiliation(s)
- Carlo Robotti
- Department of Otolaryngology - Head and Neck Surgery, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy.
| | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
| | - Valerio Cesarini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Anna Calastri
- Department of Otolaryngology - Head and Neck Surgery, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Eugenia Maiorano
- Department of Otolaryngology - Head and Neck Surgery, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Davide Piloni
- Pneumology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Tiziano Perrone
- Department of Internal Medicine, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Umberto Sabatini
- Department of Internal Medicine, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Virginia Valeria Ferretti
- Clinical Epidemiology and Biometry Unit, Fondazione IRCCS Policlinico San Matteo Foundation, Pavia, Italy
| | - Irene Cassaniti
- Molecular Virology Unit, Microbiology and Virology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Fausto Baldanti
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Molecular Virology Unit, Microbiology and Virology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Andrea Gravina
- Otorhinolaryngology Department, University of Rome Tor Vergata, Rome, Italy
| | - Ahmed Sakib
- Otorhinolaryngology Department, University of Rome Tor Vergata, Rome, Italy
| | - Elena Alessi
- Internal Medicine Unit, Ospedale dei Castelli ASL Roma 6, Ariccia, Italy
| | | | - Matteo Pascucci
- Internal Medicine Unit, Ospedale dei Castelli ASL Roma 6, Ariccia, Italy
| | - Daniele Casali
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Zakarya Zarezadeh
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Vincenzo Del Zoppo
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Antonio Pisani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy; IRCCS Mondino Foundation, Pavia, Italy
| | - Marco Benazzo
- Department of Otolaryngology - Head and Neck Surgery, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
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7
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Milwid RM, Gabriele-Rivet V, Ogden NH, Turgeon P, Fazil A, London D, de Montigny S, Rees EE. A methodology for estimating SARS-CoV-2 importation risk by air travel into Canada between July and November 2021. BMC Public Health 2024; 24:1088. [PMID: 38641571 PMCID: PMC11027292 DOI: 10.1186/s12889-024-18563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 04/09/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Estimating rates of disease importation by travellers is a key activity to assess both the risk to a country from an infectious disease emerging elsewhere in the world and the effectiveness of border measures. We describe a model used to estimate the number of travellers infected with SARS-CoV-2 into Canadian airports in 2021, and assess the impact of pre-departure testing requirements on importation risk. METHODS A mathematical model estimated the number of essential and non-essential air travellers infected with SARS-CoV-2, with the latter requiring a negative pre-departure test result. The number of travellers arriving infected (i.e. imported cases) depended on air travel volumes, SARS-CoV-2 exposure risk in the departure country, prior infection or vaccine acquired immunity, and, for non-essential travellers, screening from pre-departure molecular testing. Importation risk was estimated weekly from July to November 2021 as the number of imported cases and percent positivity (PP; i.e. imported cases normalised by travel volume). The impact of pre-departure testing was assessed by comparing three scenarios: baseline (pre-departure testing of all non-essential travellers; most probable importation risk given the pre-departure testing requirements), counterfactual scenario 1 (no pre-departure testing of fully vaccinated non-essential travellers), and counterfactual scenario 2 (no pre-departure testing of non-essential travellers). RESULTS In the baseline scenario, weekly imported cases and PP varied over time, ranging from 145 to 539 cases and 0.15 to 0.28%, respectively. Most cases arrived from the USA, Mexico, the United Kingdom, and France. While modelling suggested that essential travellers had a higher weekly PP (0.37 - 0.65%) than non-essential travellers (0.12 - 0.24%), they contributed fewer weekly cases (62 - 154) than non-essential travellers (84 - 398 per week) given their lower travel volume. Pre-departure testing was estimated to reduce imported cases by one third (counterfactual scenario 1) to one half (counterfactual scenario 2). CONCLUSIONS The model results highlighted the weekly variation in importation by traveller group (e.g., reason for travel and country of departure) and enabled a framework for measuring the impact of pre-departure testing requirements. Quantifying the contributors of importation risk through mathematical simulation can support the design of appropriate public health policy on border measures.
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Affiliation(s)
- Rachael M Milwid
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Vanessa Gabriele-Rivet
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada.
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada.
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Patricia Turgeon
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, Guelph, ON, Canada
| | - David London
- Physique Des Particules, Université de Montréal, Faculté Des Arts Et Des Sciences, Montréal, QC, Canada
| | - Simon de Montigny
- Emergency Management Branch, Global Public Health Intelligence Network Tiger Team, Public Health Agency of Canada, Ottawa, ON, Canada
| | - Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St-Hyacinthe, QC, Canada
- Department of Pathology and Microbiology, Faculty of Veterinary Medicine, Université de Montréal, Saint-Hyacinthe, QC, Canada
- Epidemiology of Zoonoses and Public Health Research Unit, Faculté de médecine vétérinaire, Université de Montréal, Saint-Hyacinthe, QC, Canada
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8
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Jiang N, Kolozsvary C, Li Y. Artificial Neural Network Prediction of COVID-19 Daily Infection Count. Bull Math Biol 2024; 86:49. [PMID: 38558267 DOI: 10.1007/s11538-024-01275-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.
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Affiliation(s)
- Ning Jiang
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA
| | - Charles Kolozsvary
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA
| | - Yao Li
- Department of Mathematics and Statistics, University of Massachusetts, 710 N Pleasant St, Amherst, 01003, MA, USA.
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9
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Majumdar R, Taye B, Bjornberg C, Giljork M, Lynch D, Farah F, Abdullah I, Osiecki K, Yousaf I, Luckstein A, Turri W, Sampathkumar P, Moyer AM, Kipp BR, Cattaneo R, Sussman CR, Navaratnarajah CK. From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota. Heliyon 2024; 10:e27974. [PMID: 38515669 PMCID: PMC10955309 DOI: 10.1016/j.heliyon.2024.e27974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Traditionally, public health surveillance relied on individual-level data but recently wastewater-based epidemiology (WBE) for the detection of infectious diseases including COVID-19 became a valuable tool in the public health arsenal. Here, we use WBE to follow the course of the COVID-19 pandemic in Rochester, Minnesota (population 121,395 at the 2020 census), from February 2021 to December 2022. We monitored the impact of SARS-CoV-2 infections on public health by comparing three sets of data: quantitative measurements of viral RNA in wastewater as an unbiased reporter of virus level in the community, positive results of viral RNA or antigen tests from nasal swabs reflecting community reporting, and hospitalization data. From February 2021 to August 2022 viral RNA levels in wastewater were closely correlated with the oscillating course of COVID-19 case and hospitalization numbers. However, from September 2022 cases remained low and hospitalization numbers dropped, whereas viral RNA levels in wastewater continued to oscillate. The low reported cases may reflect virulence reduction combined with abated inclination to report, and the divergence of virus levels in wastewater from reported cases may reflect COVID-19 shifting from pandemic to endemic. WBE, which also detects asymptomatic infections, can provide an early warning of impending cases, and offers crucial insights during pandemic waves and in the transition to the endemic phase.
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Affiliation(s)
| | - Biruhalem Taye
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | - Iris Yousaf
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Priya Sampathkumar
- Division of Infectious Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ann M. Moyer
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Benjamin R. Kipp
- Advanced Diagnostics Laboratory, Mayo Clinic, Rochester, MN, USA
- Division of Laboratory Genetics and Genomics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Roberto Cattaneo
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Caroline R. Sussman
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
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10
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Fonseca I Casas P, Garcia I Subirana J, Corominas L, Bosch LM. Applying a Digital Twin and wastewater analysis for robust validation of COVID-19 pandemic forecasts: insights from Catalonia. JOURNAL OF WATER AND HEALTH 2024; 22:584-600. [PMID: 38557573 DOI: 10.2166/wh.2024.345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/16/2024] [Indexed: 04/04/2024]
Abstract
Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.
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Affiliation(s)
- Pau Fonseca I Casas
- Universitat Politècncia de Catalunya - Barcelona Tech, Barcelona, Catalunya 08034, Spain E-mail:
| | - Joan Garcia I Subirana
- Universitat Politècncia de Catalunya - Barcelona Tech, Barcelona, Catalunya 08034, Spain
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11
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Hou Y, Bidkhori H. Multi-feature SEIR model for epidemic analysis and vaccine prioritization. PLoS One 2024; 19:e0298932. [PMID: 38427619 PMCID: PMC10906911 DOI: 10.1371/journal.pone.0298932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/02/2024] [Indexed: 03/03/2024] Open
Abstract
The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.
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Affiliation(s)
- Yingze Hou
- Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Hoda Bidkhori
- Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Data Sciences, George Mason University, Fairfax, Virginia, United States of America
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12
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Adhikari B, Abdia Y, Ringa N, Clemens F, Mak S, Rose C, Janjua NZ, Otterstatter M, Irvine MA. Visible minority status and occupation were associated with increased COVID-19 infection in Greater Vancouver British Columbia between June and November 2020: an ecological study. Front Public Health 2024; 12:1336038. [PMID: 38481842 PMCID: PMC10935735 DOI: 10.3389/fpubh.2024.1336038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/16/2024] [Indexed: 05/12/2024] Open
Abstract
Background The COVID-19 pandemic has highlighted health disparities, especially among specific population groups. This study examines the spatial relationship between the proportion of visible minorities (VM), occupation types and COVID-19 infection in the Greater Vancouver region of British Columbia, Canada. Methods Provincial COVID-19 case data between June 24, 2020, and November 7, 2020, were aggregated by census dissemination area and linked with sociodemographic data from the Canadian 2016 census. Bayesian spatial Poisson regression models were used to examine the association between proportion of visible minorities, occupation types and COVID-19 infection. Models were adjusted for COVID-19 testing rates and other sociodemographic factors. Relative risk (RR) and 95% Credible Intervals (95% CrI) were calculated. Results We found an inverse relationship between the proportion of the Chinese population and risk of COVID-19 infection (RR = 0.98 95% CrI = 0.96, 0.99), whereas an increased risk was observed for the proportions of the South Asian group (RR = 1.10, 95% CrI = 1.08, 1.12), and Other Visible Minority group (RR = 1.06, 95% CrI = 1.04, 1.08). Similarly, a higher proportion of frontline workers (RR = 1.05, 95% CrI = 1.04, 1.07) was associated with higher infection risk compared to non-frontline. Conclusion Despite adjustments for testing, housing, occupation, and other social economic status variables, there is still a substantial association between the proportion of visible minorities, occupation types, and the risk of acquiring COVID-19 infection in British Columbia. This ecological analysis highlights the existing disparities in the burden of diseases among different visible minority populations and occupation types.
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Affiliation(s)
| | | | - Notice Ringa
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | | | - Sunny Mak
- BC Centre for Disease Control, Vancouver, BC, Canada
| | - Caren Rose
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Naveed Z. Janjua
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michael Otterstatter
- BC Centre for Disease Control, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Michael A. Irvine
- BC Centre for Disease Control, Vancouver, BC, Canada
- Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada
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13
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Macaluso M, Rothenberg ME, Ferkol T, Kuhnell P, Kaminski HJ, Kimberlin DW, Benatar M, Chehade M. Impact of the COVID-19 Pandemic on People Living With Rare Diseases and Their Families: Results of a National Survey. JMIR Public Health Surveill 2024; 10:e48430. [PMID: 38354030 PMCID: PMC10868638 DOI: 10.2196/48430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 09/20/2023] [Accepted: 12/15/2023] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND With more than 103 million cases and 1.1 million deaths, the COVID-19 pandemic has had devastating consequences for the health system and the well-being of the entire US population. The Rare Diseases Clinical Research Network funded by the National Institutes of Health was strategically positioned to study the impact of the pandemic on the large, vulnerable population of people living with rare diseases (RDs). OBJECTIVE This study was designed to describe the characteristics of COVID-19 in the RD population, determine whether patient subgroups experienced increased occurrence or severity of infection and whether the pandemic changed RD symptoms and treatment, and understand the broader impact on respondents and their families. METHODS US residents who had an RD and were <90 years old completed a web-based survey investigating self-reported COVID-19 infection, pandemic-related changes in RD symptoms and medications, access to care, and psychological impact on self and family. We estimated the incidence of self-reported COVID-19 and compared it with that in the US population; evaluated the frequency of COVID-19 symptoms according to self-reported infection; assessed infection duration, complications and need for hospitalization; assessed the influence of the COVID-19 pandemic on RD symptoms and treatment, and whether the pandemic influenced access to care, special food and nutrition, or demand for professional psychological assistance. RESULTS Between May 2, 2020, and December 15, 2020, in total, 3413 individuals completed the survey. Most were female (2212/3413, 64.81%), White (3038/3413, 89.01%), and aged ≥25 years (2646/3413, 77.53%). Overall, 80.6% (2751/3413) did not acquire COVID-19, 2.08% (71/3413) acquired it, and 16.58% (566/3413) did not know. Self-reported cases represented an annual incidence rate of 2.2% (95% CI 1.7%-2.8%). COVID-19 cases were more than twice the expected (71 vs 30.3; P<.001). COVID-19 was associated with specific symptoms (loss of taste: odds ratio [OR] 38.9, 95% CI 22.4-67.6, loss of smell: OR 30.6, 95% CI 17.7-53.1) and multiple symptoms (>9 symptoms vs none: OR 82.5, 95% CI 29-234 and 5-9: OR 44.8, 95% CI 18.7-107). Median symptom duration was 16 (IQR 9-30) days. Hospitalization (7/71, 10%) and ventilator support (4/71, 6%) were uncommon. Respondents who acquired COVID-19 reported increased occurrence and severity of RD symptoms and use or dosage of select medications; those who did not acquire COVID-19 reported decreased occurrence and severity of RD symptoms and use of medications; those who did not know had an intermediate pattern. The pandemic made it difficult to access care, receive treatment, get hospitalized, and caused mood changes for respondents and their families. CONCLUSIONS Self-reported COVID-19 was more frequent than expected and was associated with increased prevalence and severity of RD symptoms and greater use of medications. The pandemic negatively affected access to care and caused mood changes in the respondents and family members. Continued surveillance is necessary.
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Affiliation(s)
- Maurizio Macaluso
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Marc E Rothenberg
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Thomas Ferkol
- Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Pierce Kuhnell
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Henry J Kaminski
- Department of Neurology and Rehabilitation Medicine, George Washington University, Washington, DC, United States
| | - David W Kimberlin
- Division of Pediatric Infectious Diseases, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Michael Benatar
- Department of Neurology, University of Miami, Miami, FL, United States
| | - Mirna Chehade
- Mount Sinai Center for Eosinophilic Disorders, Departments of Pediatrics and Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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14
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Staadegaard L, Dückers M, van Summeren J, van Gameren R, Demont C, Bangert M, Li Y, Casalegno JS, Caini S, Paget J. Determining the timing of respiratory syncytial virus (RSV) epidemics: a systematic review, 2016 to 2021; method categorisation and identification of influencing factors. Euro Surveill 2024; 29. [PMID: 38304952 PMCID: PMC10835753 DOI: 10.2807/1560-7917.es.2024.29.5.2300244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/23/2023] [Indexed: 02/03/2024] Open
Abstract
BackgroundThere is currently no standardised approach to estimate respiratory syncytial virus (RSV) epidemics' timing (or seasonality), a critical information for their effective prevention and control.AimWe aimed to provide an overview of methods to define RSV seasonality and identify factors supporting method choice or interpretation/comparison of seasonal estimates.MethodsWe systematically searched PubMed and Embase (2016-2021) for studies using quantitative approaches to determine the start and end of RSV epidemics. Studies' features (data-collection purpose, location, regional/(sub)national scope), methods, and assessment characteristics (case definitions, sampled population's age, in/outpatient status, setting, diagnostics) were extracted. Methods were categorised by their need of a denominator (i.e. numbers of specimens tested) and their retrospective vs real-time application. Factors worth considering when choosing methods and assessing seasonal estimates were sought by analysing studies.ResultsWe included 32 articles presenting 49 seasonality estimates (18 thereof through the 10% positivity threshold method). Methods were classified into eight categories, two requiring a denominator (1 retrospective; 1 real-time) and six not (3 retrospective; 3 real-time). A wide range of assessment characteristics was observed. Several studies showed that seasonality estimates varied when methods differed, or data with dissimilar assessment characteristics were employed. Five factors (comprising study purpose, application time, assessment characteristics, healthcare system and policies, and context) were identified that could support method choice and result interpretation.ConclusionMethods and assessment characteristics used to define RSV seasonality are heterogeneous. Our categorisation of methods and proposed framework of factors may assist in choosing RSV seasonality methods and interpretating results.
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Affiliation(s)
- Lisa Staadegaard
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | - Michel Dückers
- ARQ National Psychotrauma Centre, Diemen, The Netherlands
- Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | | | - Rob van Gameren
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | | | | | - You Li
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom
- National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jean-Sebastien Casalegno
- Hospices Civils de Lyon; Hôpital de la Croix-Rousse; Centre de Biologie Nord; Institut des Agents Infectieux; Laboratoire de Virologie, Lyon; France
| | - Saverio Caini
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
| | - John Paget
- Netherlands Institute for Health Services Research (Nivel), Utrecht, The Netherlands
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15
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Matias WR, Fulcher IR, Sauer SM, Nolan CP, Guillaume Y, Zhu J, Molano FJ, Uceta E, Collins S, Slater DM, Sánchez VM, Moheed S, Harris JB, Charles RC, Paxton RM, Gonsalves SF, Franke MF, Ivers LC. Disparities in SARS-CoV-2 Infection by Race, Ethnicity, Language, and Social Vulnerability: Evidence from a Citywide Seroprevalence Study in Massachusetts, USA. J Racial Ethn Health Disparities 2024; 11:110-120. [PMID: 36652163 PMCID: PMC9847437 DOI: 10.1007/s40615-022-01502-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Uncovering and addressing disparities in infectious disease outbreaks require a rapid, methodical understanding of local epidemiology. We conducted a seroprevalence study of SARS-CoV-2 infection in Holyoke, Massachusetts, a majority Hispanic city with high levels of socio-economic disadvantage to estimate seroprevalence and identify disparities in SARS-CoV-2 infection. METHODS We invited 2000 randomly sampled households between 11/5/2020 and 12/31/2020 to complete questionnaires and provide dried blood spots for SARS-CoV-2 antibody testing. We calculated seroprevalence based on the presence of IgG antibodies using a weighted Bayesian procedure that incorporated uncertainty in antibody test sensitivity and specificity and accounted for household clustering. RESULTS Two hundred eighty households including 472 individuals were enrolled. Three hundred twenty-eight individuals underwent antibody testing. Citywide seroprevalence of SARS-CoV-2 IgG was 13.1% (95% CI 6.9-22.3) compared to 9.8% of the population infected based on publicly reported cases. Seroprevalence was 16.1% (95% CI 6.2-31.8) among Hispanic individuals compared to 9.4% (95% CI 4.6-16.4) among non-Hispanic white individuals. Seroprevalence was higher among Spanish-speaking households (21.9%; 95% CI 8.3-43.9) compared to English-speaking households (10.2%; 95% CI 5.2-18.0) and among individuals in high social vulnerability index (SVI) areas based on the CDC SVI (14.4%; 95% CI 7.1-25.5) compared to low SVI areas (8.2%; 95% CI 3.1-16.9). CONCLUSIONS The SARS-CoV-2 IgG seroprevalence in a city with high levels of social vulnerability was 13.1% during the pre-vaccination period of the COVID-19 pandemic. Hispanic individuals and individuals in communities characterized by high SVI were at the highest risk of infection. Public health interventions should be designed to ensure that individuals in high social vulnerability communities have access to the tools to combat COVID-19.
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Affiliation(s)
- Wilfredo R Matias
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA.
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, USA.
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA.
| | - Isabel R Fulcher
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Sara M Sauer
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Cody P Nolan
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yodeline Guillaume
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Jack Zhu
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Francisco J Molano
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth Uceta
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Shannon Collins
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Damien M Slater
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Vanessa M Sánchez
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Serina Moheed
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Jason B Harris
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Richelle C Charles
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | - Molly F Franke
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Louise C Ivers
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Harvard Global Health Institute, Cambridge, MA, USA
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16
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Cerda A, Rivera M, Armijo G, Ibarra-Henriquez C, Reyes J, Blázquez-Sánchez P, Avilés J, Arce A, Seguel A, Brown AJ, Vásquez Y, Cortez-San Martín M, Cubillos FA, García P, Ferres M, Ramírez-Sarmiento CA, Federici F, Gutiérrez RA. An Open One-Step RT-qPCR for SARS-CoV-2 detection. PLoS One 2024; 19:e0297081. [PMID: 38271448 PMCID: PMC10810446 DOI: 10.1371/journal.pone.0297081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
The COVID-19 pandemic has resulted in millions of deaths globally, and while several diagnostic systems were proposed, real-time reverse transcription polymerase chain reaction (RT-PCR) remains the gold standard. However, diagnostic reagents, including enzymes used in RT-PCR, are subject to centralized production models and intellectual property restrictions, which present a challenge for less developed countries. With the aim of generating a standardized One-Step open RT-qPCR protocol to detect SARS-CoV-2 RNA in clinical samples, we purified and tested recombinant enzymes and a non-proprietary buffer. The protocol utilized M-MLV RT and Taq DNA pol enzymes to perform a Taqman probe-based assay. Synthetic RNA samples were used to validate the One-Step RT-qPCR components, demonstrating sensitivity comparable to a commercial kit routinely employed in clinical settings for patient diagnosis. Further evaluation on 40 clinical samples (20 positive and 20 negative) confirmed its comparable diagnostic accuracy. This study represents a proof of concept for an open approach to developing diagnostic kits for viral infections and diseases, which could provide a cost-effective and accessible solution for less developed countries.
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Affiliation(s)
- Ariel Cerda
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Maira Rivera
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Grace Armijo
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Catalina Ibarra-Henriquez
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Reyes
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Paula Blázquez-Sánchez
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javiera Avilés
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Aníbal Arce
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Aldo Seguel
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
| | - Alexander J. Brown
- Department of Biomedical Research, National Jewish Health, Denver, CO, United States of America
- Department of Immunology & Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Yesseny Vásquez
- Escuela de Ciencias Médicas, Facultad de Medicina, Universidad de Santiago de Chile, USACH, Santiago, Chile
| | - Marcelo Cortez-San Martín
- Departamento de Biología, Facultad de Química y Biología, Universidad de Santiago de Chile, USACH, Santiago, Chile
| | - Francisco A. Cubillos
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Departamento de Biología, Facultad de Química y Biología, Universidad de Santiago de Chile, USACH, Santiago, Chile
| | - Patricia García
- Departamento de Laboratorios Clínicos, Escuela de Medicina, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Marcela Ferres
- Departamento de Laboratorios Clínicos, Escuela de Medicina, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - César A. Ramírez-Sarmiento
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Fernán Federici
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Rodrigo A. Gutiérrez
- ANID—Millennium Science Initiative Program—Millennium Institute for Integrative Biology (iBio), Santiago, Chile
- FONDAP Center for Genome Regulation, Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Santiago, Chile
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17
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Ling-Hu T, Simons LM, Dean TJ, Rios-Guzman E, Caputo MT, Alisoltani A, Qi C, Malczynski M, Blanke T, Jennings LJ, Ison MG, Achenbach CJ, Larkin PM, Kaul KL, Lorenzo-Redondo R, Ozer EA, Hultquist JF. Integration of individualized and population-level molecular epidemiology data to model COVID-19 outcomes. Cell Rep Med 2024; 5:101361. [PMID: 38232695 PMCID: PMC10829796 DOI: 10.1016/j.xcrm.2023.101361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/07/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk.
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Affiliation(s)
- Ted Ling-Hu
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Lacy M Simons
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Taylor J Dean
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Estefany Rios-Guzman
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Matthew T Caputo
- Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Arghavan Alisoltani
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Chao Qi
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael Malczynski
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Timothy Blanke
- Diagnostic Molecular Biology Laboratory, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Lawrence J Jennings
- Clinical Microbiology Laboratory, Department of Pathology, Northwestern Memorial Hospital, Chicago, IL 60611, USA
| | - Michael G Ison
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Chad J Achenbach
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Paige M Larkin
- Department of Molecular Microbiology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Karen L Kaul
- Department of Pathology, Northshore University HealthSystem, Evanston, IL 60201, USA
| | - Ramon Lorenzo-Redondo
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Egon A Ozer
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA
| | - Judd F Hultquist
- Department of Medicine, Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Center for Pathogen Genomics and Microbial Evolution, Northwestern University Havey Institute for Global Health, Chicago, IL 60611, USA.
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18
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Nash D, Srivastava A, Shen Y, Penrose K, Kulkarni SG, Zimba R, You W, Berry A, Mirzayi C, Maroko A, Parcesepe AM, Grov C, Robertson MM. Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults. Sci Rep 2024; 14:644. [PMID: 38182731 PMCID: PMC10770061 DOI: 10.1038/s41598-023-51029-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
This study used repeat serologic testing to estimate infection rates and risk factors in two overlapping cohorts of SARS-CoV-2 N protein seronegative U.S. adults. One mostly unvaccinated sub-cohort was tracked from April 2020 to March 2021 (pre-vaccine/wild-type era, n = 3421), and the other, mostly vaccinated cohort, from March 2021 to June 2022 (vaccine/variant era, n = 2735). Vaccine uptake was 0.53% and 91.3% in the pre-vaccine and vaccine/variant cohorts, respectively. Corresponding seroconversion rates were 9.6 and 25.7 per 100 person-years. In both cohorts, sociodemographic and epidemiologic risk factors for infection were similar, though new risk factors emerged in the vaccine/variant era, such as having a child in the household. Despite higher incidence rates in the vaccine/variant cohort, vaccine boosters, masking, and social distancing were associated with substantially reduced infection risk, even through major variant surges.
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Affiliation(s)
- Denis Nash
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA.
- CUNY Graduate School of Public Health and Health Policy, 55 W. 125th St., 6th Floor, New York, NY, 10027, USA.
| | - Avantika Srivastava
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Yanhan Shen
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Kate Penrose
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Sarah G Kulkarni
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Rebecca Zimba
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - William You
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Amanda Berry
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Chloe Mirzayi
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Andrew Maroko
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Angela M Parcesepe
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christian Grov
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - McKaylee M Robertson
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
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19
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Milanesi S, De Nicolao G. Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions. PLoS One 2023; 18:e0295079. [PMID: 38060513 PMCID: PMC10703316 DOI: 10.1371/journal.pone.0295079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded.
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Affiliation(s)
- Simone Milanesi
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Division of Infectious Diseases I, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
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20
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Taylor KM, Ricks KM, Kuehnert PA, Eick-Cost AA, Scheckelhoff MR, Wiesen AR, Clements TL, Hu Z, Zak SE, Olschner SP, Herbert AS, Bazaco SL, Creppage KE, Fan MT, Sanchez JL. Seroprevalence as an Indicator of Undercounting of COVID-19 Cases in a Large Well-Described Cohort. AJPM FOCUS 2023; 2:100141. [PMID: 37885754 PMCID: PMC10598697 DOI: 10.1016/j.focus.2023.100141] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Introduction Reported confirmed cases represent a small portion of overall true cases for many infectious diseases. The undercounting of true cases can be considerable when a significant portion of infected individuals are asymptomatic or minimally symptomatic, as is the case with COVID-19. Seroprevalence studies are an efficient way to assess the extent to which true cases are undercounted during a large-scale outbreak and can inform efforts to improve case identification and reporting. Methods A longitudinal seroprevalence study of active duty U.S. military members was conducted from May 2020 through June 2021. A random selection of service member serum samples submitted to the Department of Defense Serum Repository was analyzed for the presence of antibodies reactive to SARS-CoV-2. The monthly seroprevalence rates were compared with those of cumulative confirmed cases reported during the study period. Results Seroprevalence was 2.3% in May 2020 and increased to 74.0% by June 2021. The estimated true case count based on seroprevalence was 9.3 times greater than monthly reported cases at the beginning of the study period and fell to 1.7 by the end of the study. Conclusions In our sample, confirmed case counts significantly underestimated true cases of COVID-19. The increased availability of testing over the study period and enhanced efforts to detect asymptomatic and minimally symptomatic cases likely contributed to the fall in the seroprevalence to reported case ratio.
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Affiliation(s)
- Kevin M. Taylor
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
- Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Keersten M. Ricks
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Paul A. Kuehnert
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Angelia A. Eick-Cost
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Mark R. Scheckelhoff
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Andrew R. Wiesen
- Health Readiness Policy and Oversight, Office of the Assistant Secretary of Defense for Health Affairs, Washington, District of Columbia
| | - Tamara L. Clements
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Zheng Hu
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Samantha E. Zak
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Scott P. Olschner
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Andrew S. Herbert
- United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland
| | - Sara L. Bazaco
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Kathleen E. Creppage
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Michael T. Fan
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
| | - Jose L. Sanchez
- Armed Forces Health Surveillance Division, Defense Health Agency, Silver Spring, Maryland
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21
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Orban ZS, Visvabharathy L, Perez Giraldo GS, Jimenez M, Koralnik IJ. SARS-CoV-2-Specific Immune Responses in Patients With Postviral Syndrome After Suspected COVID-19. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2023; 10:e200159. [PMID: 37612134 PMCID: PMC10448973 DOI: 10.1212/nxi.0000000000200159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/28/2023] [Indexed: 08/25/2023]
Abstract
BACKGROUND AND OBJECTIVES Millions of Americans were exposed to SARS-CoV-2 early in the pandemic but could not get diagnosed with COVID-19 due to testing limitations. Many have developed a postviral syndrome (PVS) including neurologic manifestations similar to those with postacute sequelae of SARS-CoV-2 infection (Neuro-PASC). Owing to those circumstances, proof of SARS-CoV-2 infection was not required for evaluation at Northwestern Medicine's Neuro COVID-19 clinic. We sought to investigate clinical and immunologic findings suggestive of SARS-CoV-2 exposure in patients with PVS. METHODS We measured SARS-CoV-2-specific humoral and cell-mediated immune responses against Nucleocapsid and Spike proteins in 29 patients with PVS after suspected COVID-19, 32 confirmed age-matched/sex-matched Neuro-PASC (NP) patients, and 18 unexposed healthy controls. Neurologic symptoms and signs, comorbidities, quality of life, and cognitive testing data collected during clinic visits were studied retrospectively. RESULTS Of 29 patients with PVS, 12 (41%) had detectable humoral or cellular immune responses consistent with prior exposure to SARS-CoV-2. Of 12 PVS responders (PVS+), 75% harbored anti-Nucleocapsid and 50% harbored anti-Spike responses. Patients with PVS+ had similar neurologic symptoms as patients with NP, but clinic evaluation occurred 5.3 months later from the time of symptom onset (10.7 vs 5.4 months; p = 0.0006). Patients with PVS+ and NP had similar subjective impairments in quality of life measures including cognitive function and fatigue. Patients with PVS+ had similar results in objective cognitive measures of processing speed, attention, and executive function and better results in working memory than patients with NP. DISCUSSION Antibody and T-cell assays showed evidence of prior SARS-CoV-2 exposure in approximately 40% of the PVS group. Three-quarters of patients with PVS+ had detectable anti-Nucleocapsid and one-half anti-Spike responses, highlighting the importance of multitargeted COVID-19 immunologic evaluation and the limitations of commercially available diagnostic tests. Despite their persistent symptoms, lack of COVID-19 diagnosis likely delayed clinical care in patients with PVS. Our data suggest that millions of Americans presenting with PVS resembling Neuro-PASC were indeed exposed to SARS-CoV-2 at the beginning of the pandemic, and they deserve the same access to care and inclusion in research studies as patients with NP with confirmed COVID-19 diagnosis.
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Affiliation(s)
- Zachary S Orban
- From the Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Lavanya Visvabharathy
- From the Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Gina S Perez Giraldo
- From the Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Millenia Jimenez
- From the Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Igor J Koralnik
- From the Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL.
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22
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Lythgoe KA, Golubchik T, Hall M, House T, Cahuantzi R, MacIntyre-Cockett G, Fryer H, Thomson L, Nurtay A, Ghafani M, Buck D, Green A, Trebes A, Piazza P, Lonie LJ, Studley R, Rourke E, Smith D, Bashton M, Nelson A, Crown M, McCann C, Young GR, Andre Nunes dos Santos R, Richards Z, Tariq A, Fraser C, Diamond I, Barrett J, Walker AS, Bonsall D. Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey. Proc Biol Sci 2023; 290:20231284. [PMID: 37848057 PMCID: PMC10581763 DOI: 10.1098/rspb.2023.1284] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/08/2023] [Indexed: 10/19/2023] Open
Abstract
The Office for National Statistics Coronavirus (COVID-19) Infection Survey (ONS-CIS) is the largest surveillance study of SARS-CoV-2 positivity in the community, and collected data on the United Kingdom (UK) epidemic from April 2020 until March 2023 before being paused. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing the sequenced samples collected by the ONS-CIS during this period. We observed a series of sweeps or partial sweeps, with each sweeping lineage having a distinct growth advantage compared to their predecessors, although this was also accompanied by a gradual fall in average viral burdens from June 2021 to March 2023. The sweeps also generated an alternating pattern in which most samples had either S-gene target failure (SGTF) or non-SGTF over time. Evolution was characterized by steadily increasing divergence and diversity within lineages, but with step increases in divergence associated with each sweeping major lineage. This led to a faster overall rate of evolution when measured at the between-lineage level compared to within lineages, and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens.
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Affiliation(s)
- Katrina A. Lythgoe
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Department of Biology, University of Oxford, Oxford OX1 3SZ, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tanya Golubchik
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Sydney Infectious Diseases Institute (Sydney ID), School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Thomas House
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - Roberto Cahuantzi
- Department of Mathematics, University of Manchester, Manchester M13 9PL, UK
| | - George MacIntyre-Cockett
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Helen Fryer
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Laura Thomson
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Anel Nurtay
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - Mahan Ghafani
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
| | - David Buck
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Angie Green
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Amy Trebes
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Paolo Piazza
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | - Lorne J. Lonie
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
| | | | | | - Darren Smith
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Bashton
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Andrew Nelson
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Matthew Crown
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Clare McCann
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Gregory R. Young
- The Hub for Biotechnology in the Built Environment, Department of Applied Sciences, Faculty of Health and Life Sciences, Nothumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Rui Andre Nunes dos Santos
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Zack Richards
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | - Adnan Tariq
- Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
| | | | | | - Christophe Fraser
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | | | - Jeff Barrett
- Wellcome Sanger Institute, Cambridge CB10 1SA, UK
| | - Ann Sarah Walker
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
- The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- MRC Clinical Trials Unit at UCL, UCL, London, UK
| | - David Bonsall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Biomedical Research Centre, University of Oxford, Old Road Campus, Oxford OX3 7BN, UK
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK
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23
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Nash D, Srivastava A, Shen J, Penrose K, Kulkarni SG, Zimba R, You W, Berry A, Mirzayi C, Maroko A, Parcesepe AM, Grov C, Robertson MM. Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.29.23296142. [PMID: 37873066 PMCID: PMC10593054 DOI: 10.1101/2023.09.29.23296142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background Infectious disease surveillance systems, which largely rely on diagnosed cases, underestimate the true incidence of SARS-CoV-2 infection, due to under-ascertainment and underreporting. We used repeat serologic testing to measure N-protein seroconversion in a well-characterized cohort of U.S. adults with no serologic evidence of SARS-CoV-2 infection to estimate the incidence of SARS-CoV-2 infection and characterize risk factors, with comparisons before and after the start of the SARS-CoV-2 vaccine and variant eras. Methods We assessed the incidence rate of infection and risk factors in two sub-groups (cohorts) that were SARS-CoV-2 N-protein seronegative at the start of each follow-up period: 1) the pre-vaccine/wild-type era cohort (n=3,421), followed from April to November 2020; and 2) the vaccine/variant era cohort (n=2,735), followed from November 2020 to June 2022. Both cohorts underwent repeat serologic testing with an assay for antibodies to the SARS-CoV-2 N protein (Bio-Rad Platelia SARS-CoV-2 total Ab). We estimated crude incidence and sociodemographic/epidemiologic risk factors in both cohorts. We used multivariate Poisson models to compare the risk of SARS-CoV-2 infection in the pre-vaccine/wild-type era cohort (referent group) to that in the vaccine/variant era cohort, within strata of vaccination status and epidemiologic risk factors (essential worker status, child in the household, case in the household, social distancing). Findings In the pre-vaccine/wild-type era cohort, only 18 of the 3,421 participants (0.53%) had ≥1 vaccine dose by the end of follow-up, compared with 2,497/2,735 (91.3%) in the vaccine/variant era cohort. We observed 323 and 815 seroconversions in the pre-vaccine/wild-type era and the vaccine/variant era and cohorts, respectively, with corresponding incidence rates of 9.6 (95% CI: 8.3-11.5) and 25.7 (95% CI: 24.2-27.3) per 100 person-years. Associations of sociodemographic and epidemiologic risk factors with SARS-CoV-2 incidence were largely similar in the pre-vaccine/wild-type and vaccine/variant era cohorts. However, some new epidemiologic risk factors emerged in the vaccine/variant era cohort, including having a child in the household, and never wearing a mask while using public transit. Adjusted incidence rate ratios (aIRR), with the entire pre-vaccine/wild-type era cohort as the referent group, showed markedly higher incidence in the vaccine/variant era cohort, but with more vaccine doses associated with lower incidence: aIRRun/undervaccinated=5.3 (95% CI: 4.2-6.7); aIRRprimary series only=5.1 (95% CI: 4.2-7.3); aIRRboosted once=2.5 (95% CI: 2.1-3.0), and aIRRboosted twice=1.65 (95% CI: 1.3-2.1). These associations were essentially unchanged in risk factor-stratified models. Interpretation In SARS-CoV-2 N protein seronegative individuals, large increases in incidence and newly emerging epidemiologic risk factors in the vaccine/variant era likely resulted from multiple co-occurring factors, including policy changes, behavior changes, surges in transmission, and changes in SARS-CoV-2 variant properties. While SARS-CoV-2 incidence increased markedly in most groups in the vaccine/variant era, being up to date on vaccines and the use of non-pharmaceutical interventions (NPIs), such as masking and social distancing, remained reliable strategies to mitigate the risk of SARS-CoV-2 infection, even through major surges due to immune evasive variants. Repeat serologic testing in cohort studies is a useful and complementary strategy to characterize SARS-CoV-2 incidence and risk factors.
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Affiliation(s)
- Denis Nash
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Avantika Srivastava
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Jenny Shen
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Kate Penrose
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Sarah Gorrell Kulkarni
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Rebecca Zimba
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - William You
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Amanda Berry
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Chloe Mirzayi
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Andrew Maroko
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Angela M. Parcesepe
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christian Grov
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - McKaylee M. Robertson
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
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Lee J, Acosta N, Waddell BJ, Du K, Xiang K, Van Doorn J, Low K, Bautista MA, McCalder J, Dai X, Lu X, Chekouo T, Pradhan P, Sedaghat N, Papparis C, Buchner Beaudet A, Chen J, Chan L, Vivas L, Westlund P, Bhatnagar S, Stefani S, Visser G, Cabaj J, Bertazzon S, Sarabi S, Achari G, Clark RG, Hrudey SE, Lee BE, Pang X, Webster B, Ghali WA, Buret AG, Williamson T, Southern DA, Meddings J, Frankowski K, Hubert CRJ, Parkins MD. Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community. WATER RESEARCH 2023; 244:120469. [PMID: 37634459 DOI: 10.1016/j.watres.2023.120469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/29/2023]
Abstract
Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.
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Affiliation(s)
- Jangwoo Lee
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Nicole Acosta
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Barbara J Waddell
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Kristine Du
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Kevin Xiang
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Jennifer Van Doorn
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Kashtin Low
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Maria A Bautista
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Janine McCalder
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Xiaotian Dai
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Xuewen Lu
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada
| | - Thierry Chekouo
- Department of Mathematics and Statistics, University of Calgary, Calgary, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, USA
| | - Puja Pradhan
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Navid Sedaghat
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Chloe Papparis
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Alexander Buchner Beaudet
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Jianwei Chen
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Leslie Chan
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Laura Vivas
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | | | - Srijak Bhatnagar
- Department of Biological Sciences, University of Calgary, Calgary, Canada; Faculty of Science and Technology, Athabasca University, Athabasca, Alberta, Canada
| | - September Stefani
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Gail Visser
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada
| | - Jason Cabaj
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; Provincial Population & Public Health, Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada
| | | | - Shahrzad Sarabi
- Department of Geography, University of Calgary, Calgary, Canada
| | - Gopal Achari
- Department of Civil Engineering, University of Calgary, Calgary, Canada
| | - Rhonda G Clark
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Steve E Hrudey
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Analytical and Environmental Toxicology, University of Alberta, Edmonton, Alberta, Canada
| | - Bonita E Lee
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada; Women & Children's Health Research Institute, Li Ka Shing Institute of Virology, Edmonton, Alberta, Canada
| | - Xiaoli Pang
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada; Alberta Precision Laboratories, Public Health Laboratory, Alberta Health Services, Edmonton, Alberta, Canada; Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alberta, Canada
| | - Brendan Webster
- Occupational Health Staff Wellness, University of Calgary, Calgary, Canada
| | - William Amin Ghali
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Andre Gerald Buret
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Tyler Williamson
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Danielle A Southern
- Department of Community Health Sciences, University of Calgary, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada; Centre for Health Informatics, University of Calgary, Calgary, Canada
| | - Jon Meddings
- Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada
| | - Kevin Frankowski
- Advancing Canadian Water Assets, University of Calgary, Calgary, Canada
| | - Casey R J Hubert
- Department of Biological Sciences, University of Calgary, Calgary, Canada
| | - Michael D Parkins
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, 3330 Hospital Drive, NW, Calgary, Alberta T2N 2V5, Canada; Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Canada; O'Brien Institute for Public Health, University of Calgary, Calgary, Canada.
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Romanescu RG, Hu S, Nanton D, Torabi M, Tremblay-Savard O, Haque MA. The effective reproductive number: Modeling and prediction with application to the multi-wave Covid-19 pandemic. Epidemics 2023; 44:100708. [PMID: 37499586 DOI: 10.1016/j.epidem.2023.100708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 07/04/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
Classical compartmental models of infectious disease assume that spread occurs through a homogeneous population. This produces poor fits to real data, because individuals vary in their number of epidemiologically-relevant contacts, and hence in their ability to transmit disease. In particular, network theory suggests that super-spreading events tend to happen more often at the beginning of an epidemic, which is inconsistent with the homogeneity assumption. In this paper we argue that a flexible decay shape for the effective reproductive number (Rt) indexed by the susceptible fraction (St) is a theory-informed modeling choice, which better captures the progression of disease incidence over human populations. This, in turn, produces better retrospective fits, as well as more accurate prospective predictions of observed epidemic curves. We extend this framework to fit multi-wave epidemics, and to accommodate public health restrictions on mobility. We demonstrate the performance of this model by doing a prediction study over two years of the SARS-CoV2 pandemic.
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Affiliation(s)
- Razvan G Romanescu
- Department of Community Health Sciences, University of Manitoba, Canada; Center for Healthcare Innovation, University of Manitoba, Canada.
| | - Songdi Hu
- Department of Computer Science, University of Manitoba, Canada
| | - Douglas Nanton
- Center for Healthcare Innovation, University of Manitoba, Canada
| | - Mahmoud Torabi
- Department of Community Health Sciences, University of Manitoba, Canada
| | | | - Md Ashiqul Haque
- Department of Community Health Sciences, University of Manitoba, Canada
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26
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Case BKM, Young JG, Hébert-Dufresne L. Accurately summarizing an outbreak using epidemiological models takes time. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230634. [PMID: 37771961 PMCID: PMC10523082 DOI: 10.1098/rsos.230634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.
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Affiliation(s)
- B. K. M. Case
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
| | - Jean-Gabriel Young
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT 05405, USA
| | - Laurent Hébert-Dufresne
- Vermont Complex Systems Center, University of Vermont, Burlington, VT 05405, USA
- Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
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Bugalia S, Tripathi JP. Assessing potential insights of an imperfect testing strategy: Parameter estimation and practical identifiability using early COVID-19 data in India. COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION 2023; 123:107280. [PMID: 37207195 PMCID: PMC10148719 DOI: 10.1016/j.cnsns.2023.107280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/27/2023] [Accepted: 04/25/2023] [Indexed: 05/21/2023]
Abstract
A deterministic model with testing of infected individuals has been proposed to investigate the potential consequences of the impact of testing strategy. The model exhibits global dynamics concerning the disease-free and a unique endemic equilibrium depending on the basic reproduction number when the recruitment of infected individuals is zero; otherwise, the model does not have a disease-free equilibrium, and disease never dies out in the community. Model parameters have been estimated using the maximum likelihood method with respect to the data of early COVID-19 outbreak in India. The practical identifiability analysis shows that the model parameters are estimated uniquely. The consequences of the testing rate for the weekly new cases of early COVID-19 data in India tell that if the testing rate is increased by 20% and 30% from its baseline value, the weekly new cases at the peak are decreased by 37.63% and 52.90%; and it also delayed the peak time by four and fourteen weeks, respectively. Similar findings are obtained for the testing efficacy that if it is increased by 12.67% from its baseline value, the weekly new cases at the peak are decreased by 59.05% and delayed the peak by 15 weeks. Therefore, a higher testing rate and efficacy reduce the disease burden by tumbling the new cases, representing a real scenario. It is also obtained that the testing rate and efficacy reduce the epidemic's severity by increasing the final size of the susceptible population. The testing rate is found more significant if testing efficacy is high. Global sensitivity analysis using partial rank correlation coefficients (PRCCs) and Latin hypercube sampling (LHS) determine the key parameters that must be targeted to worsen/contain the epidemic.
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Affiliation(s)
- Sarita Bugalia
- Department of Mathematics, Central University of Rajasthan, Bandar Sindri, Kishangarh 305817, Ajmer, Rajasthan, India
| | - Jai Prakash Tripathi
- Department of Mathematics, Central University of Rajasthan, Bandar Sindri, Kishangarh 305817, Ajmer, Rajasthan, India
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Nersesjan V, Christensen RHB, Kondziella D, Benros ME. COVID-19 and Risk for Mental Disorders Among Adults in Denmark. JAMA Psychiatry 2023; 80:778-786. [PMID: 37223890 PMCID: PMC10209831 DOI: 10.1001/jamapsychiatry.2023.1265] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/15/2023] [Indexed: 05/25/2023]
Abstract
Importance Psychiatric outcomes after COVID-19 have been of high concern during the pandemic; however, studies on a nationwide level are lacking. Objective To estimate the risk of mental disorders and use of psychotropic medication among individuals with COVID-19 compared with individuals not tested, individuals with SARS-CoV-2-negative test results, and those hospitalized for non-COVID-19 infections. Design, Setting, and Participants This nationwide cohort study used Danish registries to identify all individuals who were alive, 18 years or older, and residing in Denmark between January 1 and March 1, 2020 (N = 4 152 792), excluding individuals with a mental disorder history (n = 616 546), with follow-up until December 31, 2021. Exposures Results of SARS-CoV-2 polymerase chain reaction (PCR) testing (negative, positive, and never tested) and COVID-19 hospitalization. Main Outcomes and Measures Risk of new-onset mental disorders (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes F00-F99) and redeemed psychotropic medication (Anatomical Therapeutic Chemical classification codes N05-N06) was estimated through survival analysis using a Cox proportional hazards model, with a hierarchical time-varying exposure, reporting hazard rate ratios (HRR) with 95% CIs. All outcomes were adjusted for age, sex, parental history of mental illness, Charlson Comorbidity Index, educational level, income, and job status. Results A total of 526 749 individuals had positive test results for SARS-CoV-2 (50.2% men; mean [SD] age, 41.18 [17.06] years), while 3 124 933 had negative test results (50.6% women; mean [SD] age, 49.36 [19.00] years), and 501 110 had no tests performed (54.6% men; mean [SD] age, 60.71 [19.78] years). Follow-up time was 1.83 years for 93.4% of the population. The risk of mental disorders was increased in individuals with positive (HRR, 1.24 [95% CI, 1.17-1.31]) and negative (HRR, 1.42 [95% CI, 1.38-1.46]) test results for SARS-CoV-2 compared with those never tested. Compared with individuals with negative test results, the risk of new-onset mental disorders in SARS-CoV-2-positive individuals was lower in the group aged 18 to 29 years (HRR, 0.75 [95% CI, 0.69-0.81]), whereas individuals 70 years or older had an increased risk (HRR, 1.25 [95% CI, 1.05-1.50]). A similar pattern was seen regarding psychotropic medication use, with a decreased risk in the group aged 18 to 29 years (HRR, 0.81 [95% CI, 0.76-0.85]) and elevated risk in those 70 years or older (HRR, 1.57 [95% CI, 1.45-1.70]). The risk for new-onset mental disorders was substantially elevated in hospitalized patients with COVID-19 compared with the general population (HRR, 2.54 [95% CI, 2.06-3.14]); however, no significant difference in risk was seen when compared with hospitalization for non-COVID-19 respiratory tract infections (HRR, 1.03 [95% CI, 0.82-1.29]). Conclusion and Relevance In this Danish nationwide cohort study, overall risk of new-onset mental disorders in SARS-CoV-2-positive individuals did not exceed the risk among individuals with negative test results (except for those aged ≥70 years). However, when hospitalized, patients with COVID-19 had markedly increased risk compared with the general population, but comparable to risk among patients hospitalized for non-COVID-19 infections. Future studies should include even longer follow-up time and preferentially include immunological biomarkers to further investigate the impact of infection severity on postinfectious mental disorder sequelae.
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Affiliation(s)
- Vardan Nersesjan
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rune H. B. Christensen
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel Kondziella
- Department of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael E. Benros
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Grønkjær CS, Christensen RHB, Kondziella D, Benros ME. Long-term neurological outcome after COVID-19 using all SARS-CoV-2 test results and hospitalisations in Denmark with 22-month follow-up. Nat Commun 2023; 14:4235. [PMID: 37454151 PMCID: PMC10349860 DOI: 10.1038/s41467-023-39973-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Hospitalisation with COVID-19 is associated with an increased risk of neurological sequelae; however, representative nationwide studies comparing to other infections with similar severity and also including milder SARS-CoV-2 infections have been lacking. Using the nationwide Danish registers including all SARS-CoV-2 PCR test results and hospitalisations between March 1, 2020, and December 31, 2021, we estimate the risk of any first neurological disorder diagnosed in inpatient, outpatient, or emergency room settings. We show that positive tests increase the rate of neurological disorders by a hazard ratio of 1.96 (95% confidence interval: 1.88-2.05) compared to individuals not tested and by a hazard ratio of 1.11 (95% confidence interval: 1.07-1.16) compared to individuals with negative tests only. However, there is no evidence that the risk of neurological disorders is higher for individuals who test positive compared to non-COVID-19 infections treated with anti-infective medication. The risk of neurological disorders is increased after COVID-19-hospitalisation compared to no COVID-19 hospital admission; however, these risks are comparable to hospitalisation with other respiratory infections (P value 0.328). In conclusion, COVID-19 is associated with an increased risk of neurological disorders, but no more than that observed after other infections of similar severity.
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Affiliation(s)
- Clara S Grønkjær
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Rune H B Christensen
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Daniel Kondziella
- Departments of Neurology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Michael E Benros
- Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Qasmieh SA, Robertson MM, Teasdale CA, Kulkarni SG, Jones HE, Larsen DA, Dennehy JJ, McNairy M, Borrell LN, Nash D. The prevalence of SARS-CoV-2 infection and other public health outcomes during the BA.2/BA.2.12.1 surge, New York City, April-May 2022. COMMUNICATIONS MEDICINE 2023; 3:92. [PMID: 37391483 DOI: 10.1038/s43856-023-00321-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 06/09/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Routine case surveillance data for SARS-CoV-2 are incomplete, unrepresentative, missing key variables of interest, and may be increasingly unreliable for timely surge detection and understanding the true burden of infection. METHODS We conducted a cross-sectional survey of a representative sample of 1030 New York City (NYC) adult residents ≥18 years on May 7-8, 2022. We estimated the prevalence of SARS-CoV-2 infection during the preceding 14-day period. Respondents were asked about SARS-CoV-2 testing, testing outcomes, COVID-like symptoms, and contact with SARS-CoV-2 cases. SARS-CoV-2 prevalence estimates were age- and sex-adjusted to the 2020 U.S. POPULATION We triangulated survey-based prevalence estimates with contemporaneous official SARS-CoV-2 counts of cases, hospitalizations, and deaths, as well as SARS-CoV-2 wastewater concentrations. RESULTS We show that 22.1% (95% CI 17.9-26.2%) of respondents had SARS-CoV-2 infection during the two-week study period, corresponding to ~1.5 million adults (95% CI 1.3-1.8 million). The official SARS-CoV-2 case count during the study period is 51,218. Prevalence is estimated at 36.6% (95% CI 28.3-45.8%) among individuals with co-morbidities, 13.7% (95% CI 10.4-17.9%) among those 65+ years, and 15.3% (95% CI 9.6-23.5%) among unvaccinated persons. Among individuals with a SARS-CoV-2 infection, hybrid immunity (history of both vaccination and infection) is 66.2% (95% CI 55.7-76.7%), 44.1% (95% CI 33.0-55.1%) were aware of the antiviral nirmatrelvir/ritonavir, and 15.1% (95% CI 7.1-23.1%) reported receiving it. Hospitalizations, deaths and SARS-CoV-2 virus concentrations in wastewater remained well below that during the BA.1 surge. CONCLUSIONS Our findings suggest that the true magnitude of NYC's BA.2/BA.2.12.1 surge may have been vastly underestimated by routine case counts and wastewater surveillance. Hybrid immunity, bolstered by the recent BA.1 surge, likely limited the severity of the BA.2/BA.2.12.1 surge.
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Affiliation(s)
- Saba A Qasmieh
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - McKaylee M Robertson
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Chloe A Teasdale
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Sarah G Kulkarni
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Heidi E Jones
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - David A Larsen
- Department of Public Health, Falk College, Syracuse University, Syracuse, NY, USA
| | - John J Dennehy
- Department of Biology, Queens College, City University of New York, Queens, NY, USA
| | - Margaret McNairy
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Center for Global Health and Division of General Internal Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Denis Nash
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA.
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Boumar I, Deliorman M, Sukumar P, Qasaimeh MA. Spike- and nucleocapsid-based gold colloid assay toward the development of an adhesive bandage for rapid SARS-CoV-2 immune response detection and screening. MICROSYSTEMS & NANOENGINEERING 2023; 9:82. [PMID: 37351273 PMCID: PMC10281977 DOI: 10.1038/s41378-023-00554-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/02/2023] [Accepted: 05/14/2023] [Indexed: 06/24/2023]
Abstract
Immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies are important biomarkers used for the diagnosis and screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in both symptomatic and asymptomatic individuals. These antibodies are highly specific to the spike (S) and nucleocapsid (N) proteins of the SARS-CoV-2 virus. This paper outlines the development steps of a novel hybrid (vertical-lateral-vertical) flow assay in the form of a finger-stick point-of-care device, similar to an adhesive bandage, designed for the timely detection and screening of IgM and IgG immune responses to SARS-CoV-2 infections. The assay, comprising a vertically stacked plasma/serum separation membrane, conjugate pad, and detection (readout) zone, utilizes gold nanoparticles (AuNPs) conjugated with SARS-CoV-2 S and N proteins to effectively capture IgM and IgG antibodies from a pinprick (~15 µL) of blood in just one step and provides results of no immune IgM-/IgG-, early immune IgM+/IgG-, active immune IgM+/IgG+ or immune IgM-/IgG+ in a short amount of time (minutes). The adhesive bandage-like construction is an example of the design of rapid, low-cost, disposable, and easy-to-use tests for large-scale detection and screening in households. Furthermore, the bandage can be easily adjusted and optimized to detect different viral infections as they arise by simply selecting appropriate antigens related to pandemics and outbreaks.
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Affiliation(s)
- Imen Boumar
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
| | | | - Pavithra Sukumar
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
| | - Mohammad A. Qasaimeh
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, UAE
- NYU Tandon School of Engineering, New York University, New York, USA
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Cimmino C, Rodrigues Capítulo L, Lerman A, Silva A, Von Haften G, Comino AP, Cigoy L, Scagliola M, Poncet V, Caló G, Uez O, Berón CM. Presence of SARS-CoV-2 in urban effluents in south-east Buenos Aires, Argentina, May 2020 to March 2022. Rev Panam Salud Publica 2023; 47:e94. [PMID: 37324201 PMCID: PMC10261580 DOI: 10.26633/rpsp.2023.94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 03/14/2023] [Indexed: 06/17/2023] Open
Abstract
Objectives To implement and evaluate the use of wastewater sampling for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in two coastal districts of Buenos Aires Province, Argentina. Methods In General Pueyrredon district, 400 mL of wastewater samples were taken with an automatic sampler for 24 hours, while in Pinamar district, 20 L in total (2.2 L at 20-minute intervals) were taken. Samples were collected once a week. The samples were concentrated based on flocculation using polyaluminum chloride. RNA purification and target gene amplification and detection were performed using reverse transcription polymerase chain reaction for clinical diagnosis of human nasopharyngeal swabs. Results In both districts, the presence of SARS-CoV-2 was detected in wastewater. In General Pueyrredon, SARS-CoV-2 was detected in epidemiological week 28, 2020, which was 20 days before the start of an increase in coronavirus virus disease 2019 (COVID-19) cases in the first wave (epidemiological week 31) and 9 weeks before the maximum number of laboratory-confirmed COVID-19 cases was recorded. In Pinamar district, the virus genome was detected in epidemiological week 51, 2020 but it was not possible to carry out the sampling again until epidemiological week 4, 2022, when viral circulation was again detected. Conclusions It was possible to detect SARS-CoV-2 virus genome in wastewater, demonstrating the usefulness of the application of wastewater epidemiology for long-term SARS-CoV-2 detection and monitoring.
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Affiliation(s)
- Carlos Cimmino
- Instituto Nacional de Epidemiología “Dr. Juan H. Jara”Mar del PlataArgentinaInstituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina.
| | - Leandro Rodrigues Capítulo
- Centro de Estudios Integrales de la Dinámica ExógenaUniversidad Nacional de La PlataLa PlataArgentinaCentro de Estudios Integrales de la Dinámica Exógena, Universidad Nacional de La Plata, La Plata, Argentina.
| | - Andrea Lerman
- Instituto Nacional de Epidemiología “Dr. Juan H. Jara”Mar del PlataArgentinaInstituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina.
| | - Andrea Silva
- Instituto Nacional de Epidemiología “Dr. Juan H. Jara”Mar del PlataArgentinaInstituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina.
| | - Gabriela Von Haften
- Obras Sanitarias Sociedad de EstadoMar del PlataArgentinaObras Sanitarias Sociedad de Estado, Mar del Plata, Argentina.
| | - Ana P. Comino
- Obras Sanitarias Sociedad de EstadoMar del PlataArgentinaObras Sanitarias Sociedad de Estado, Mar del Plata, Argentina.
| | - Luciana Cigoy
- Obras Sanitarias Sociedad de EstadoMar del PlataArgentinaObras Sanitarias Sociedad de Estado, Mar del Plata, Argentina.
| | - Marcelo Scagliola
- Obras Sanitarias Sociedad de EstadoMar del PlataArgentinaObras Sanitarias Sociedad de Estado, Mar del Plata, Argentina.
| | - Verónica Poncet
- Instituto Nacional de Epidemiología “Dr. Juan H. Jara”Mar del PlataArgentinaInstituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina.
| | - Gonzalo Caló
- Instituto de Investigaciones en Biodiversidad y Biotecnología and FIBAMar del PlataArgentinaInstituto de Investigaciones en Biodiversidad y Biotecnología and FIBA, Mar del Plata, Argentina.
| | - Osvaldo Uez
- Instituto Nacional de Epidemiología “Dr. Juan H. Jara”Mar del PlataArgentinaInstituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina.
| | - Corina M. Berón
- Instituto de Investigaciones en Biodiversidad y Biotecnología and FIBAMar del PlataArgentinaInstituto de Investigaciones en Biodiversidad y Biotecnología and FIBA, Mar del Plata, Argentina.
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Brett TS, Bansal S, Rohani P. Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state. PLoS Comput Biol 2023; 19:e1011263. [PMID: 37379328 PMCID: PMC10335681 DOI: 10.1371/journal.pcbi.1011263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 07/11/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.
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Affiliation(s)
- Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, D.C., United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
- Center for Influenza Disease & Emergence Research (CIDER), Athens, Georgia, United States of America
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Sandie AB, Ngo Sack F, Medi Sike CI, Mendimi Nkodo J, Ngegni H, Ateba Mimfoumou HG, Lobe SA, Choualeu Noumbissi D, Tchuensou Mfoubi F, Tagnouokam Ngoupo PA, Ayong L, Njouom R, Tejiokem MC. Spread of SARS-CoV-2 Infection in Adult Populations in Cameroon: A Repeated Cross-Sectional Study Among Blood Donors in the Cities of Yaoundé and Douala. J Epidemiol Glob Health 2023; 13:266-278. [PMID: 37129837 PMCID: PMC10152017 DOI: 10.1007/s44197-023-00102-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 04/11/2023] [Indexed: 05/03/2023] Open
Abstract
Over a period of about 9 months, we conducted three serosurveys in the two major cities of Cameroon to determine the prevalence of SARS-COV-2 antibodies and to identify factors associated with seropositivity in each survey. We conducted three independent cross-sectional serosurveys of adult blood donors at the Central Hospital in Yaoundé (CHY), the Jamot Hospital in Yaoundé (JHY) and at the Laquintinie Hospital in Douala (LHD) who consented in writing to participate. Before blood sampling, a short questionnaire was administered to participants to collect their sociodemographic and clinical characteristics. We included a total of 743, 1202, and 1501 participants in the first (January 25-February 15, 2021), second (May 03-28, 2021), and third (November 29-December 31, 2021) surveys, respectively. The adjusted seroprevalence increased from 66.3% (95% CrI 61.1-71.3) in the first survey to 87.2% (95% CrI 84.0-90.0) in the second survey, and 98.4% (95% CrI 96.8-99.7) in the third survey. In the first survey, study site, participant occupation, and comorbid conditions were associated with SARS-CoV-2 seropositivity, whereas only study site remained associated in the second survey. None of the factors studied was significantly associated with seropositivity in the third survey. Together, the data suggest a rapid initial spread of SARS-CoV-2 in the study population, independent of the sociodemographic parameters assessed.
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Affiliation(s)
- Arsène Brunelle Sandie
- Service d’épidémiologie Et de Santé Publique, Centre Pasteur du Cameroun, 451, Street 2005, Yaounde 2, P.O. Box: 1274, Yaoundé, Cameroon
- African Population and Health Research Center, Dakar, Senegal
| | | | | | | | | | | | | | - Diane Choualeu Noumbissi
- Service d’épidémiologie Et de Santé Publique, Centre Pasteur du Cameroun, 451, Street 2005, Yaounde 2, P.O. Box: 1274, Yaoundé, Cameroon
| | - Fabrice Tchuensou Mfoubi
- Service d’épidémiologie Et de Santé Publique, Centre Pasteur du Cameroun, 451, Street 2005, Yaounde 2, P.O. Box: 1274, Yaoundé, Cameroon
| | | | - Lawrence Ayong
- Service de Paludisme, Centre Pasteur du Cameroun, Yaoundé, Cameroon
| | - Richard Njouom
- Service de Virologie, Centre Pasteur du Cameroun, Yaoundé, Cameroon
| | - Mathurin Cyrille Tejiokem
- Service d’épidémiologie Et de Santé Publique, Centre Pasteur du Cameroun, 451, Street 2005, Yaounde 2, P.O. Box: 1274, Yaoundé, Cameroon
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Lippi G, Mattiuzzi C, Henry BM. Uncontrolled confounding in COVID-19 epidemiology. Diagnosis (Berl) 2023; 10:200-202. [PMID: 36474317 DOI: 10.1515/dx-2022-0128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University of Verona, Verona, Italy
| | - Camilla Mattiuzzi
- Service of Clinical Governance, Provincial Agency for Social and Sanitary Services (APSS), Trento, Italy
| | - Brandon M Henry
- Clinical Laboratory, Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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García-Carreras B, Hitchings MDT, Johansson MA, Biggerstaff M, Slayton RB, Healy JM, Lessler J, Quandelacy T, Salje H, Huang AT, Cummings DAT. Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S. Nat Commun 2023; 14:2235. [PMID: 37076502 PMCID: PMC10115837 DOI: 10.1038/s41467-023-37944-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.
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Affiliation(s)
- Bernardo García-Carreras
- Department of Biology, University of Florida, Gainesville, FL, USA.
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
| | - Matt D T Hitchings
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Michael A Johansson
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Matthew Biggerstaff
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rachel B Slayton
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica M Healy
- COVID-19 Response, US Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Justin Lessler
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Carolina Population Center, Chapel Hill, NC, USA
| | | | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Angkana T Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
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Caniza H, Forriol F, Pangrazio O, Gil-Conesa M. Safety of International Professional Sports Competitions During the COVID-19 Pandemic: The Association Football Experience. Sports Med 2023; 53:765-768. [PMID: 36167919 PMCID: PMC9514706 DOI: 10.1007/s40279-022-01763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2022] [Indexed: 11/26/2022]
Abstract
Major sporting events were suspended during the most acute phase of the COVID-19 pandemic. Competitions are resuming with enhanced hygiene protocols and altered mechanics. While risks for players and staff have been studied, the impact of large-scale tournaments on the communities that host them remains largely unstudied. CONMEBOL Copa América is one of the first wide-scale international tournaments to be conducted in its original format since the beginning of the COVID-19 pandemic. The tournament saw 10 national teams compete in four Brazilian cities during a period of heightened viral transmission. The analysis of over 28,000 compulsory PCR tests showed that positive cases did not lead to the uncontrolled spread of the disease among staff and players. More importantly, the data indicate that locally hired staff were not exposed to increased risk while working. The Copa América experience shows that international sporting competitions can be conducted safely even under unfavourable epidemiological situations.
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Desta BN, Ota S, Gournis E, Pires SM, Greer AL, Dodd W, Majowicz SE. Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020. J Public Health Res 2023; 12:22799036231174133. [PMID: 37197719 PMCID: PMC10184215 DOI: 10.1177/22799036231174133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/16/2023] [Indexed: 05/19/2023] Open
Abstract
Background Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.
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Affiliation(s)
- Binyam N Desta
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Binyam N Desta, School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.
| | - Sylvia Ota
- Toronto Public Health, Toronto, ON, Canada
| | | | - Sara M Pires
- Risk-Benefit Research Group, Technical University of Denmark, Lyngby, Denmark
| | - Amy L Greer
- Department of Population Medicine, University of Guelph, Guelph, ON, Canada
| | - Warren Dodd
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shannon E Majowicz
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Drain PK, Dalmat RR, Hao L, Bemer MJ, Budiawan E, Morton JF, Ireton RC, Hsiang TY, Marfatia Z, Prabhu R, Woosley C, Gichamo A, Rechkina E, Hamilton D, Montaño M, Cantera JL, Ball AS, Golez I, Smith E, Greninger AL, McElrath MJ, Thompson M, Grant BD, Meisner A, Gottlieb GS, Gale M. Duration of viral infectiousness and correlation with symptoms and diagnostic testing in non-hospitalized adults during acute SARS-CoV-2 infection: A longitudinal cohort study. J Clin Virol 2023; 161:105420. [PMID: 36913789 PMCID: PMC9981266 DOI: 10.1016/j.jcv.2023.105420] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/07/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023]
Abstract
BACKGROUND Guidelines for SARS-CoV-2 have relied on limited data on duration of viral infectiousness and correlation with COVID-19 symptoms and diagnostic testing. METHODS We enrolled ambulatory adults with acute SARS-CoV-2 infection and performed serial measurements of COVID-19 symptoms, nasal swab viral RNA, nucleocapsid (N) and spike (S) antigens, and replication-competent SARS-CoV-2 by viral growth in culture. We determined average time from symptom onset to a first negative test result and estimated risk of infectiousness, as defined by positive viral growth in culture. RESULTS Among 95 adults, median [interquartile range] time from symptom onset to first negative test result was 9 [5] days, 13 [6] days, 11 [4] days, and >19 days for S antigen, N antigen, culture growth, and viral RNA by RT-PCR, respectively. Beyond two weeks, virus growth and N antigen titers were rarely positive, while viral RNA remained detectable among half (26/51) of participants tested 21-30 days after symptom onset. Between 6-10 days from symptom onset, N antigen was strongly associated with culture positivity (relative risk=7.61, 95% CI: 3.01-19.22), whereas neither viral RNA nor symptoms were associated with culture positivity. During the 14 days following symptom onset, the presence of N antigen remained strongly associated (adjusted relative risk=7.66, 95% CI: 3.96-14.82) with culture positivity, regardless of COVID-19 symptoms. CONCLUSIONS Most adults have replication-competent SARS-CoV-2 for 10-14 after symptom onset. N antigen testing is a strong predictor of viral infectiousness and may be a more suitable biomarker, rather than absence of symptoms or viral RNA, to discontinue isolation within two weeks from symptom onset.
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Affiliation(s)
- Paul K Drain
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States; Division of Allergy and Infectious Diseases, Department of Medicine, School of Medicine, University of Washington, Seattle, WA, United States.
| | - Ronit R Dalmat
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States; Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, United States
| | - Linhui Hao
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Meagan J Bemer
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Elvira Budiawan
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Jennifer F Morton
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Renee C Ireton
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Tien-Ying Hsiang
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Zarna Marfatia
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Roshni Prabhu
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Claire Woosley
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Adanech Gichamo
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Elena Rechkina
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Daphne Hamilton
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | - Michalina Montaño
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States
| | | | | | - Inah Golez
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Elise Smith
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Alexander L Greninger
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
| | - M Juliana McElrath
- Division of Allergy and Infectious Diseases, Department of Medicine, School of Medicine, University of Washington, Seattle, WA, United States; Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Matthew Thompson
- Department of Family Medicine, School of Medicine, University of Washington, Seattle, WA, United States
| | | | - Allison Meisner
- International Clinical Research Center, Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States; Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Geoffrey S Gottlieb
- Division of Allergy and Infectious Diseases, Department of Medicine, School of Medicine, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States; Department of Global Health, Schools of Medicine and Public Health, University of Washington, Seattle, WA, United States; Environmental Health & Safety Department, University of Washington, Seattle, WA, United States
| | - Michael Gale
- Department of Immunology, Center for Innate Immunity and Immune Disease, University of Washington, Seattle, WA, United States; Center for Emerging & Re-emerging Infectious Diseases, University of Washington, Seattle, WA, United States
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Kalachev L, Landguth EL, Graham J. Revisiting classical SIR modelling in light of the COVID-19 pandemic. Infect Dis Model 2023; 8:72-83. [PMID: 36540893 PMCID: PMC9755423 DOI: 10.1016/j.idm.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/09/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
Background Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms. Objective We aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined, and recovered cases) for situations where just one data type is available to fit the model. Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment. Methods We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed (SIR) modelling framework. We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy: a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County, Montana, USA. Results We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data. We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification. Using a classical example of influenza epidemics in an England boarding school, we argue that the Susceptible-Infected-Quarantined-Recovered (SIQR) model is more appropriate than the commonly employed SIR model given the data collected (number of active cases). Similarly, we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected. Conclusions We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data. For both a classical influenza data set and a COVID-19 case data set, we demonstrate the implications of using the "right" data in the "wrong" model. The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections, as well as minimal vaccination requirements.
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Affiliation(s)
- Leonid Kalachev
- Mathematical Sciences, University of Montana, Missoula, USA
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USA
| | - Erin L. Landguth
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USA
| | - Jon Graham
- Mathematical Sciences, University of Montana, Missoula, USA
- Center for Population Health Research, School of Public and Community Health Sciences, University of Montana, Missoula, USA
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41
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Bedekar P, Kearsley AJ, Patrone PN. Prevalence estimation and optimal classification methods to account for time dependence in antibody levels. J Theor Biol 2023; 559:111375. [PMID: 36513210 DOI: 10.1016/j.jtbi.2022.111375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/14/2022] [Accepted: 11/29/2022] [Indexed: 12/14/2022]
Abstract
Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement from a random sample changes due to changing prevalence (i.e., seroprevalence, or fraction of individuals exhibiting an immune response) of the disease in the population. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes the error associated with classifying a value as positive (history of infection) or negative (no such history) on a given day since the start of the pandemic. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.
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Affiliation(s)
- Prajakta Bedekar
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
| | - Anthony J Kearsley
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
| | - Paul N Patrone
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
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Marquez-Zavala E, Utrilla J. Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy? Microb Biotechnol 2023; 16:990-999. [PMID: 36808834 PMCID: PMC10128133 DOI: 10.1111/1751-7915.14233] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 01/26/2023] [Indexed: 02/20/2023] Open
Abstract
The elimination of the expression of cellular functions that are not needed in a certain well-defined artificial environment, such as those used in industrial production facilities, has been the goal of many cellular minimization projects. The generation of a minimal cell with reduced burden and less host-function interactions has been pursued as a tool to improve microbial production strains. In this work, we analysed two cellular complexity reduction strategies: genome and proteome reduction. With the aid of an absolute proteomics data set and a genome-scale model of metabolism and protein expression (ME-model), we quantitatively assessed the difference of reducing genome to the correspondence of reducing proteome. We compare the approaches in terms of energy consumption, defined in ATP equivalents. We aim to show what is the best strategy for improving resource allocation in minimized cells. Our results show that genome reduction by length is not proportional to reducing resource use. When we normalize calculated energy savings, we show that strains with the larger calculated proteome reduction show the largest resource use reduction. Furthermore, we propose that reducing highly expressed proteins should be the target as the translation of a gene uses most of the energy. The strategies proposed here should guide cell design when the aim of a project is to reduce the maximum amount or cellular resources.
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Affiliation(s)
- Elisa Marquez-Zavala
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, Norway.,Synthetic Biology Program, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Jose Utrilla
- Synthetic Biology Program, Center for Genomic Sciences, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
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43
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Zhong H, Liu S, Zhu J, Wu L. Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk. Int J Cancer 2023; 153:103-110. [PMID: 36757187 DOI: 10.1002/ijc.34466] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/14/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies, which is featured by systematic metabolism. Thus, a better understanding of metabolic dysregulation in PDAC is important to better characterize its etiology. Here, we performed a large metabolome-wide association study (MWAS) to systematically explore associations between genetically predicted metabolite levels in blood and PDAC risk. Using data from 881 subjects of European descent in the TwinsUK Project, comprehensive genetic models were built to predict serum metabolite levels. These prediction models were applied to the genetic data of 8275 cases and 6723 controls included in the PanScan (I, II and III) and PanC4 consortia. After assessing the metabolite-PDAC risk associations by a slightly modified TWAS/FUSION framework, we identified five metabolites (including two dipeptides) showing significant associations with PDAC risk at false discovery rate (FDR) <0.05. Integrated with gut microbial information, two-sample Mendelian randomization (MR) analyses were further performed to investigate the relationship among serum metabolites, gut microbiome features and PDAC. The flavonoid-degrading bacteria Flavonifractor sp90199495 was found to be associated with metabolite X-21849 and it was also shown to be associated with PDAC risk. Collectively, our study identified novel candidate metabolites for PDAC risk, which could lead to new insights into the etiology of PDAC and improved treatment options.
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Affiliation(s)
- Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA
| | - Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA
| | - Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA
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44
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Li N, Dong L. Real-time digital data of international passengers will shine in the precaution of epidemics. INTELLIGENT MEDICINE 2023; 3:44-45. [PMID: 36312891 PMCID: PMC9595419 DOI: 10.1016/j.imed.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/27/2022] [Accepted: 10/10/2022] [Indexed: 11/05/2022]
Abstract
International movement plays an important role in spatial spread of infectious diseases. Here, we share two successful COVID-19 interventions based on real-time digital information collected from international passengers, which have been performed in Greece and China respectively. Both of the interventions demonstrated good performance and showed the potential of real-time digital data in containing the spread. However, several key points should not be ignored when we promote similar strategies.
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Affiliation(s)
- Naizhe Li
- MOE Key Laboratory For Biodiversity Science And Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing 100091, China.,State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100091, China
| | - Lu Dong
- MOE Key Laboratory For Biodiversity Science And Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing 100091, China
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Qasmieh SA, Robertson MM, Rane MS, Shen Y, Zimba R, Picchio CA, Parcesepe AM, Chang M, Kulkarni SG, Grov C, Nash D. The Importance of Incorporating At-Home Testing Into SARS-CoV-2 Point Prevalence Estimates: Findings From a US National Cohort, February 2022. JMIR Public Health Surveill 2022; 8:e38196. [PMID: 36240020 PMCID: PMC9822564 DOI: 10.2196/38196] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 09/30/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Passive, case-based surveillance underestimates the true extent of active infections in the population due to undiagnosed and untested cases, the exclusion of probable cases diagnosed point-of-care rapid antigen tests, and the exclusive use of at-home rapid tests which are not reported as part of case-based surveillance. The extent in which COVID-19 surveillance may be underestimating the burden of infection is likely due to time-varying factors such as decreased test-seeking behaviors and increased access to and availability of at-home testing. OBJECTIVE The objective of this study is to estimate the prevalence of SARS-CoV-2 based on different definitions of a case to ascertain the extent to which cases of SARS-CoV-2 may be underestimated by case-based surveillance. METHODS A survey on COVID-19 exposure, infection, and testing was administered to calculate point prevalence of SARS-CoV-2 among a diverse sample of cohort adults from February 8, 2022, to February 22, 2022. Three-point prevalence estimates were calculated among the cohort, as follows: (1) proportion positives based on polymerase chain reaction (PCR) and rapid antigen tests; (2) proportion positives based on testing exclusively with rapid at-home tests; and (3) proportion of probable undiagnosed cases. Test positivity and prevalence differences across booster status were also examined. RESULTS Among a cohort of 4328, there were a total of 644 (14.9%) cases. The point prevalence estimate based on PCR or rapid antigen tests was 5.5% (95% CI 4.8%-6.2%), 3.7% (95% CI 3.1%-4.2%) based on at-home rapid tests, and 5.7% (95% CI 5.0%-6.4%) based on the case definition of a probable case. The total point prevalence across all definitions was 14.9% (95% CI 13.8%-16.0%). The percent positivity among PCR or rapid tests was 50.2%. No statistically significant differences were observed in prevalence between participants with a COVID-19 booster compared to fully vaccinated and nonboosted participants except among exclusive at-home rapid testers. CONCLUSIONS Our findings suggest a substantial number of cases were missed by case-based surveillance systems during the Omicron B.1.1.529 surge, when at-home testing was common. Point prevalence surveys may be a rapid tool to be used to understand SARS-CoV-2 prevalence and would be especially important during case surges to measure the scope and spread of active infections in the population.
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Affiliation(s)
- Saba A Qasmieh
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
| | - McKaylee M Robertson
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
| | - Madhura S Rane
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
| | - Yanhan Shen
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
| | - Rebecca Zimba
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
| | - Camila A Picchio
- Barcelona Institute for Global Health (ISGlobal), Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Angela M Parcesepe
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Department of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, United States
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Mindy Chang
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
| | - Sarah G Kulkarni
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
| | - Christian Grov
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
| | - Denis Nash
- Institute for Implementation Science in Population Health, City University of New York, New York, NY, United States
- Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States
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Wang C, Huang X, Lau EHY, Cowling BJ, Tsang TK. Association Between Population-Level Factors and Household Secondary Attack Rate of SARS-CoV-2: A Systematic Review and Meta-analysis. Open Forum Infect Dis 2022; 10:ofac676. [PMID: 36655186 PMCID: PMC9835764 DOI: 10.1093/ofid/ofac676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Background Accurate estimation of household secondary attack rate (SAR) is crucial to understand the transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The impact of population-level factors, such as transmission intensity in the community, on SAR estimates is rarely explored. Methods In this study, we included articles with original data to compute the household SAR. To determine the impact of transmission intensity in the community on household SAR estimates, we explored the association between SAR estimates and the incidence rate of cases by country during the study period. Results We identified 163 studies to extract data on SARs from 326 031 cases and 2 009 859 household contacts. The correlation between the incidence rate of cases during the study period and SAR estimates was 0.37 (95% CI, 0.24-0.49). We found that doubling the incidence rate of cases during the study period was associated with a 1.2% (95% CI, 0.5%-1.8%) higher household SAR. Conclusions Our findings suggest that the incidence rate of cases during the study period is associated with higher SAR. Ignoring this factor may overestimate SARs, especially for regions with high incidences, which further impacts control policies and epidemiological characterization of emerging variants.
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Affiliation(s)
- Can Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaotong Huang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China,Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- Correspondence: Tim K. Tsang, PhD, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Pokfulam, Hong Kong Special Administrative Region, China ()
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Barua S, Newbolt CH, Ditchkoff SS, Johnson C, Zohdy S, Smith R, Wang C. Absence of SARS-CoV-2 in a captive white-tailed deer population in Alabama, USA. Emerg Microbes Infect 2022; 11:1707-1710. [PMID: 35707965 PMCID: PMC9246038 DOI: 10.1080/22221751.2022.2090282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Subarna Barua
- College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Chad H Newbolt
- College of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | - Stephen S Ditchkoff
- College of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | - Calvin Johnson
- College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Sarah Zohdy
- College of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | - Rachel Smith
- College of Veterinary Medicine, Auburn University, Auburn, AL, USA
| | - Chengming Wang
- College of Veterinary Medicine, Auburn University, Auburn, AL, USA
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Kevrekidis GA, Rapti Z, Drossinos Y, Kevrekidis PG, Barmann MA, Chen QY, Cuevas-Maraver J. Backcasting COVID-19: a physics-informed estimate for early case incidence. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220329. [PMID: 36533196 PMCID: PMC9748501 DOI: 10.1098/rsos.220329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea and Brazil. We assume that the second wave was more accurately monitored, even though we acknowledge that behavioural changes occurred during the pandemic and region- (or country-) specific monitoring protocols evolved. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50%. On the other hand, in South Korea-which had a proactive mitigation approach-a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 18% predicted underestimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases.
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Affiliation(s)
- G. A. Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Z. Rapti
- Department of Mathematics and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
| | - Y. Drossinos
- European Commission, Joint Research Centre, I-21027 Ispra (VA), Italy
| | - P. G. Kevrekidis
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - M. A. Barmann
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Q. Y. Chen
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - J. Cuevas-Maraver
- Grupo de Física No Lineal, Departamento de Física Aplicada I, Universidad de Sevilla. Escuela Politécnica Superior, C/ Virgen de África, 7, 41012 Sevilla, Spain
- Instituto de Matemáticas de la Universidad de Sevilla (IMUS). Edificio Celestino Mutis. Avda. Reina Mercedes s/n, 41012 Sevilla, Spain
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Tuncer N, Timsina A, Nuno M, Chowell G, Martcheva M. Parameter identifiability and optimal control of an SARS-CoV-2 model early in the pandemic. JOURNAL OF BIOLOGICAL DYNAMICS 2022; 16:412-438. [PMID: 35635313 DOI: 10.1080/17513758.2022.2078899] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 04/28/2022] [Indexed: 06/15/2023]
Abstract
We fit an SARS-CoV-2 model to US data of COVID-19 cases and deaths. We conclude that the model is not structurally identifiable. We make the model identifiable by prefixing some of the parameters from external information. Practical identifiability of the model through Monte Carlo simulations reveals that two of the parameters may not be practically identifiable. With thus identified parameters, we set up an optimal control problem with social distancing and isolation as control variables. We investigate two scenarios: the controls are applied for the entire duration and the controls are applied only for the period of time. Our results show that if the controls are applied early in the epidemic, the reduction in the infected classes is at least an order of magnitude higher compared to when controls are applied with 2-week delay. Further, removing the controls before the pandemic ends leads to rebound of the infected classes.
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Affiliation(s)
- Necibe Tuncer
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | - Archana Timsina
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | - Miriam Nuno
- Department of Biostatistics, University of California, Davis, CA, USA
| | - Gerardo Chowell
- Department of Population Health Sciences, Georgia State University, Atlanta, GA, USA
| | - Maia Martcheva
- Department of Mathematics, University of Florida, Gainesville, FL, USA
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50
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Jiang B, Yang Y, Chen L, Liu X, Wu X, Chen B, Webster C, Sullivan WC, Larsen L, Wang J, Lu Y. Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States. LANDSCAPE AND URBAN PLANNING 2022; 228:104583. [PMID: 36158763 PMCID: PMC9485427 DOI: 10.1016/j.landurbplan.2022.104583] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 05/10/2023]
Abstract
The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.
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Affiliation(s)
- Bin Jiang
- Urban Environments and Human Health Lab, HKUrbanLabs, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
- Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Yuwen Yang
- Urban Environments and Human Health Lab, HKUrbanLabs, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
- Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Long Chen
- Department of Architecture and Civil Engineering, College of Engineering, City University of Hong Kong, Hong Kong Special Administrative Region
| | - Xueming Liu
- Urban Environments and Human Health Lab, HKUrbanLabs, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
- Division of Landscape Architecture, Department of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Xueying Wu
- Department of Architecture and Civil Engineering, College of Engineering, City University of Hong Kong, Hong Kong Special Administrative Region
| | - Bin Chen
- Future Urbanity & Sustainable Environment (FUSE) Lab, Division of Landscape Architecture, Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
- Urban Systems Institute, The University of Hong Kong, Hong Kong Special Administrative Region
- HKU Musketeers Foundation Institute of Data Science, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Chris Webster
- HKUrbanLabs, Faculty of Architecture, The University of Hong Kong, Hong Kong Special Administrative Region
| | - William C Sullivan
- Smart, Healthy Communities Initiative, University of Illinois at Urbana-Champaign, USA
- Department of Landscape Architecture, University of Illinois at Urbana-Champaign, USA
| | - Linda Larsen
- Smart Energy Design Assistance Center, University of Illinois at Urbana-Champaign, USA
| | - Jingjing Wang
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region
| | - Yi Lu
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region
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