1
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Almeida F, Correia S, Leal C, Guedes M, Duro R, Andrade P, Pedrosa A, Rocha-Pereira N, Lima-Alves C, Azevedo A. Contextual Hospital Conditions and the Risk of Nosocomial SARS-CoV-2 Infection: A Matched Case-Control Study with Density Sampling in a Large Portuguese Hospital. J Clin Med 2024; 13:5251. [PMID: 39274464 PMCID: PMC11396589 DOI: 10.3390/jcm13175251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/21/2024] [Accepted: 09/03/2024] [Indexed: 09/16/2024] Open
Abstract
Objective: Knowledge of the role of hospital conditions in SARS-CoV-2 transmission should inform strategies for the prevention of nosocomial spread of this pathogen and of similarly transmitted viruses. This study aimed to identify risk factors for nosocomial acquisition of SARS-CoV-2. Methods: We ran a nested case-control study with incidence density sampling among adult patients hospitalized for >7 days (August-December 2020). Patients testing positive for SARS-CoV-2 after the 7th day of hospitalization were defined as cases and matched with controls (1:4) by date of admission, hospitalization duration until index date, and type of department. Individual and contextual characteristics were gathered, including admission characteristics and exposures during the risk period. Conditional logistic regression was used to estimate the odds ratios (ORs) with respective 95% confidence intervals (CI) separately for probable (diagnosed on day 8-13) and definitive (diagnosed after day 14) nosocomial sets. Results: We identified 65 cases (31 probable; 34 definitive) and 219 controls. No individual characteristic was related to nosocomial acquisition of SARS-CoV-2. Contextual risk factors for nosocomial acquisition were staying in a non-refurbished room (probable nosocomial: OR = 3.6, 1.18-10.87), contact with roommates with newly diagnosed SARS-CoV-2 (probable nosocomial: OR = 9.9, 2.11-46.55; definitive nosocomial: OR = 3.4, 1.09-10.30), and contact with roommates with a first positive test 21-90 days before the beginning of contact (probable nosocomial: OR = 10.7, 1.97-57.7). Conclusions: Hospital conditions and contact with recently infected patients modulated nosocomial SARS-CoV-2 transmission. These results alert us to the importance of the physical context and of agile screening procedures to shorten contact with patients with recent infection.
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Affiliation(s)
- Francisco Almeida
- Unidade de Prevenção e Controlo de Infeção e Resistência aos Antimicrobianos, Centro de Epidemiologia Hospitalar, Centro Hospitalar de São João, 4200-319 Porto, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, 4200-319 Porto, Portugal
- Laboratório Para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, 4200-319 Porto, Portugal
| | - Sofia Correia
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, 4200-319 Porto, Portugal
- Laboratório Para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, 4200-319 Porto, Portugal
| | - Cátia Leal
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, 4200-319 Porto, Portugal
- Laboratório Para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, 4200-319 Porto, Portugal
| | - Mariana Guedes
- Unidade de Prevenção e Controlo de Infeção e Resistência aos Antimicrobianos, Centro de Epidemiologia Hospitalar, Centro Hospitalar de São João, 4200-319 Porto, Portugal
- Infectious Diseases and Microbiology Division, Hospital Universitario Virgen Macarena, Department of Medicine, Biomedicine Institute of Sevilla (IBiS)/CSIC, University of Sevilla, 41004 Sevilla, Spain
| | - Raquel Duro
- Unidade Local do Programa de Controlo de Infeção e Resistência aos Antimicrobianos, Unidade Local de Saúde do Tâmega e Sousa, 4560-136 Penafiel, Portugal
- Serviço de Doenças Infeciosas, Unidade Local de Saúde do Tâmega e Sousa, 4560-136 Penafiel, Portugal
| | - Paulo Andrade
- Unidade de Prevenção e Controlo de Infeção e Resistência aos Antimicrobianos, Centro de Epidemiologia Hospitalar, Centro Hospitalar de São João, 4200-319 Porto, Portugal
- Serviço de Doenças Infeciosas, Centro Hospitalar de São João, 4200-319 Porto, Portugal
| | - Afonso Pedrosa
- Serviço de Inteligência de Dados, Centro Hospitalar Universitário São João, 4200-319 Porto, Portugal
| | - Nuno Rocha-Pereira
- Unidade de Prevenção e Controlo de Infeção e Resistência aos Antimicrobianos, Centro de Epidemiologia Hospitalar, Centro Hospitalar de São João, 4200-319 Porto, Portugal
- Departamento de Medicina, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal
| | - Carlos Lima-Alves
- Unidade de Prevenção e Controlo de Infeção e Resistência aos Antimicrobianos, Centro de Epidemiologia Hospitalar, Centro Hospitalar de São João, 4200-319 Porto, Portugal
- INFARMED-Autoridade Nacional do Medicamento e Produtos de Saúde, I.P., 1749-004 Lisboa, Portugal
| | - Ana Azevedo
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, 4200-319 Porto, Portugal
- EPIUnit, Instituto de Saúde Pública da Universidade do Porto, 4200-319 Porto, Portugal
- Laboratório Para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Universidade do Porto, 4200-319 Porto, Portugal
- Centro de Epidemiologia Hospitalar, Centro Hospitalar Universitário São João, 4200-319 Porto, Portugal
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2
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Demongeot J, Magal P. Data-driven mathematical modeling approaches for COVID-19: A survey. Phys Life Rev 2024; 50:166-208. [PMID: 39142261 DOI: 10.1016/j.plrev.2024.08.004] [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: 07/15/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).
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Affiliation(s)
- Jacques Demongeot
- Université Grenoble Alpes, AGEIS EA7407, La Tronche, F-38700, France.
| | - Pierre Magal
- Department of Mathematics, Faculty of Arts and Sciences, Beijing Normal University, Zhuhai, 519087, China; Univ. Bordeaux, IMB, UMR 5251, Talence, F-33400, France; CNRS, IMB, UMR 5251, Talence, F-33400, France
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3
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Chan LYH, Morris SE, Stockwell MS, Bowman NM, Asturias E, Rao S, Lutrick K, Ellingson KD, Nguyen HQ, Maldonado Y, McLaren SH, Sano E, Biddle JE, Smith-Jeffcoat SE, Biggerstaff M, Rolfes MA, Talbot HK, Grijalva CG, Borchering RK, Mellis AM. Estimating the generation time for influenza transmission using household data in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.17.24312064. [PMID: 39228738 PMCID: PMC11370535 DOI: 10.1101/2024.08.17.24312064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially post the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days completing daily symptom diaries and collecting nasal swabs, which were tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission before symptomatic onset, infectious period, and latent period. We estimated a mean intrinsic generation time of 3.2 (95% credible interval, CrI: 2.9-3.6) days, with a realized household generation time of 2.8 (95% CrI: 2.7-3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variation in incubation periods. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.
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Affiliation(s)
| | - Sinead E Morris
- Centers for Disease Control and Prevention
- Goldbelt Professional Services
| | | | | | - Edwin Asturias
- University of Colorado School of Medicine and Children's Hospital Colorado
| | - Suchitra Rao
- University of Colorado School of Medicine and Children's Hospital Colorado
| | | | | | | | | | | | - Ellen Sano
- Columbia University Irving Medical Center
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4
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Zhang X, Zhang L, Li H, Xu Y, Meng L, Liang G, Wang B, Liu L, Guan T, Guo C, He Y. Weak Value Amplification Based Optical Sensor for High Throughput Real-Time Immunoassay of SARS-CoV-2 Spike Protein. BIOSENSORS 2024; 14:332. [PMID: 39056608 PMCID: PMC11274545 DOI: 10.3390/bios14070332] [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: 05/06/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
The demand for accurate and efficient immunoassays calls for the development of precise, high-throughput analysis methods. This paper introduces a novel approach utilizing a weak measurement interface sensor for immunoassays, offering a solution for high throughput analysis. Weak measurement is a precise quantum measurement method that amplifies the weak value of a system in the weak interaction through appropriate pre- and post-selection states. To facilitate the simultaneous analysis of multiple samples, we have developed a chip with six flow channels capable of conducting six immunoassays concurrently. We can perform real-time immunoassay to determine the binding characteristics of spike protein and antibody through real-time analysis of the flow channel images and calculating the relative intensity. The proposed method boasts a simple structure, eliminating the need for intricate nano processes. The spike protein concentration and relative intensity curve were fitted using the Log-Log fitting regression equation, and R2 was 0.91. Utilizing a pre-transformation approach to account for slight variations in detection sensitivity across different flow channels, the present method achieves an impressive limit of detection(LOD) of 0.85 ng/mL for the SARS-CoV-2 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, with a system standard deviation of 5.61. Furthermore, this method has been successfully verified for monitoring molecular-specific binding processes and differentiating binding capacities.
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Affiliation(s)
- Xiaonan Zhang
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Lizhong Zhang
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Han Li
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Yang Xu
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Lingqin Meng
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Gengyu Liang
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Bei Wang
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
| | - Le Liu
- Institute of Materials Research, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
| | - Tian Guan
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Cuixia Guo
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
| | - Yonghong He
- Shenzhen Key Laboratory for Minimal Invasive Medical Technologies, Institute of Optical Imaging and Sensing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (X.Z.); (H.L.); (Y.X.); (L.M.); (G.L.); (B.W.); (T.G.); (Y.H.)
- Jilin Fuyuan Guan Food Group Joint Research Center, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
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5
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Ogi-Gittins I, Hart WS, Song J, Nash RK, Polonsky J, Cori A, Hill EM, Thompson RN. A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. Epidemics 2024; 47:100773. [PMID: 38781911 DOI: 10.1016/j.epidem.2024.100773] [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: 09/12/2023] [Revised: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.
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Affiliation(s)
- I Ogi-Gittins
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - W S Hart
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - J Song
- Communicable Disease Surveillance Centre, Health Protection Division, Public Health Wales, Cardiff CF10 4BZ, UK
| | - R K Nash
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London W2 1PG, UK
| | - J Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland
| | - A Cori
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College, London W2 1PG, UK
| | - E M Hill
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - R N Thompson
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.
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6
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Lau KY, Kang J, Park M, Leung G, Wu JT, Leung K. Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study. JMIR Public Health Surveill 2024; 10:e46687. [PMID: 38345850 PMCID: PMC10863650 DOI: 10.2196/46687] [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: 02/21/2023] [Revised: 12/01/2023] [Accepted: 01/10/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures. OBJECTIVE This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses. METHODS We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020. RESULTS The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong. CONCLUSIONS Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.
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Affiliation(s)
- Kitty Y Lau
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China (Hong Kong)
| | - Jian Kang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China (Hong Kong)
| | - Minah Park
- Department of Health Convergence, Ewha Womans University, Seoul, Republic of Korea
| | - Gabriel Leung
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China (Hong Kong)
| | - Joseph T Wu
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China (Hong Kong)
- The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
| | - Kathy Leung
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, China (Hong Kong)
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, China (Hong Kong)
- The University of Hong Kong - Shenzhen Hospital, Shenzhen, China
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7
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Lin KY, Pan SC, Wang JT, Fang CT, Liao CH, Cheng CY, Tseng SH, Yang CH, Chen YC, Chang SC. Preventing and controlling intra-hospital spread of COVID-19 in Taiwan - Looking back and moving forward. J Formos Med Assoc 2024; 123 Suppl 1:S27-S38. [PMID: 37268473 PMCID: PMC10201313 DOI: 10.1016/j.jfma.2023.05.018] [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: 02/22/2023] [Accepted: 05/18/2023] [Indexed: 06/04/2023] Open
Abstract
COVID-19 has exposed major weaknesses in the healthcare settings. The surge in COVID-19 cases increases the demands of health care, endangers vulnerable patients, and threats occupational safety. In contrast to a hospital outbreak of SARS leading to a whole hospital quarantined, at least 54 hospital outbreaks following a COVID-19 surge in the community were controlled by strengthened infection prevention and control measures for preventing transmission from community to hospitals as well as within hospitals. Access control measures include establishing triage, epidemic clinics, and outdoor quarantine stations. Visitor access restriction is applied to inpatients to limit the number of visitors. Health monitoring and surveillance is applied to healthcare personnel, including self-reporting travel declaration, temperature, predefined symptoms, and test results. Isolation of the confirmed cases during the contagious period and quarantine of the close contacts during the incubation period are critical for containment. The target populations and frequency of SARS-CoV-2 PCR and rapid antigen testing depend on the level of transmission. Case investigation and contact tracing should be comprehensive to identify the close contacts to prevent further transmission. These facility-based infection prevention and control strategies help reduce hospital transmission of SARS-CoV-2 to a minimum in Taiwan.
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Affiliation(s)
- Kuan-Yin Lin
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Sung-Ching Pan
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jann-Tay Wang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chi-Tai Fang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chun-Hsing Liao
- Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Chien-Yu Cheng
- Department of Infectious Diseases, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan; Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shu-Hui Tseng
- Taiwan Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Chin-Hui Yang
- Taiwan Centers for Disease Control, Ministry of Health and Welfare, Taipei, Taiwan
| | - Yee-Chun Chen
- Center for Infection Control, National Taiwan University Hospital, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
| | - Shan-Chwen Chang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
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8
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Bradbury NV, Hart WS, Lovell-Read FA, Polonsky JA, Thompson RN. Exact calculation of end-of-outbreak probabilities using contact tracing data. J R Soc Interface 2023; 20:20230374. [PMID: 38086402 PMCID: PMC10715912 DOI: 10.1098/rsif.2023.0374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/17/2023] [Indexed: 12/18/2023] Open
Abstract
A key challenge for public health policymakers is determining when an infectious disease outbreak has finished. Following a period without cases, an estimate of the probability that no further cases will occur in future (the end-of-outbreak probability) can be used to inform whether or not to declare an outbreak over. An existing quantitative approach (the Nishiura method), based on a branching process transmission model, allows the end-of-outbreak probability to be approximated from disease incidence time series, the offspring distribution and the serial interval distribution. Here, we show how the end-of-outbreak probability under the same transmission model can be calculated exactly if data describing who-infected-whom (the transmission tree) are also available (e.g. from contact tracing studies). In that scenario, our novel approach (the traced transmission method) is straightforward to use. We demonstrate this by applying the method to data from previous outbreaks of Ebola virus disease and Nipah virus infection. For both outbreaks, the traced transmission method would have determined that the outbreak was over earlier than the Nishiura method. This highlights that collection of contact tracing data and application of the traced transmission method may allow stringent control interventions to be relaxed quickly at the end of an outbreak, with only a limited risk of outbreak resurgence.
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Affiliation(s)
- N. V. Bradbury
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), University of Warwick, Coventry CV4 7AL, UK
| | - W. S. Hart
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | | | - J. A. Polonsky
- Geneva Centre of Humanitarian Studies, University of Geneva, Geneva 1205, Switzerland
| | - R. N. Thompson
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
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9
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Susvitasari K, Tupper P, Stockdale JE, Colijn C. A method to estimate the serial interval distribution under partially-sampled data. Epidemics 2023; 45:100733. [PMID: 38056165 DOI: 10.1016/j.epidem.2023.100733] [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: 02/16/2023] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023] Open
Abstract
The serial interval of an infectious disease is an important variable in epidemiology. It is defined as the period of time between the symptom onset times of the infector and infectee in a direct transmission pair. Under partially sampled data, purported infector-infectee pairs may actually be separated by one or more unsampled cases in between. Misunderstanding such pairs as direct transmissions will result in overestimating the length of serial intervals. On the other hand, two cases that are infected by an unseen third case (known as coprimary transmission) may be classified as a direct transmission pair, leading to an underestimation of the serial interval. Here, we introduce a method to jointly estimate the distribution of serial intervals factoring in these two sources of error. We simultaneously estimate the distribution of the number of unsampled intermediate cases between purported infector-infectee pairs, as well as the fraction of such pairs that are coprimary. We also extend our method to situations where each infectee has multiple possible infectors, and show how to factor this additional source of uncertainty into our estimates. We assess our method's performance on simulated data sets and find that our method provides consistent and robust estimates. We also apply our method to data from real-life outbreaks of four infectious diseases and compare our results with published results. With similar accuracy, our method of estimating serial interval distribution provides unique advantages, allowing its application in settings of low sampling rates and large population sizes, such as widespread community transmission tracked by routine public health surveillance.
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Affiliation(s)
| | - Paul Tupper
- Department of Mathematics, Simon Fraser University, Canada
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10
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Doran JWG, Thompson RN, Yates CA, Bowness R. Mathematical methods for scaling from within-host to population-scale in infectious disease systems. Epidemics 2023; 45:100724. [PMID: 37976680 DOI: 10.1016/j.epidem.2023.100724] [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: 02/20/2023] [Revised: 06/29/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023] Open
Abstract
Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.
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Affiliation(s)
- James W G Doran
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom.
| | - Robin N Thompson
- Mathematics Institute, Zeeman Building, University of Warwick, Coventry, CV4 7AL, United Kingdom; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, CV4 7AL, United Kingdom; Mathematical Institute, University of Oxford, Oxford, OX2 6GG, United Kingdom
| | - Christian A Yates
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
| | - Ruth Bowness
- Centre for Mathematical Biology, Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom
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11
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Hart WS, Park H, Jeong YD, Kim KS, Yoshimura R, Thompson RN, Iwami S. Analysis of the risk and pre-emptive control of viral outbreaks accounting for within-host dynamics: SARS-CoV-2 as a case study. Proc Natl Acad Sci U S A 2023; 120:e2305451120. [PMID: 37788317 PMCID: PMC10576149 DOI: 10.1073/pnas.2305451120] [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: 04/13/2023] [Accepted: 09/07/2023] [Indexed: 10/05/2023] Open
Abstract
In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses.
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Affiliation(s)
- William S. Hart
- Mathematical Institute, University of Oxford, OxfordOX2 6GG, United Kingdom
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
| | - Hyeongki Park
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
| | - Yong Dam Jeong
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
- Department of Mathematics, Pusan National University, Busan46241, South Korea
| | - Kwang Su Kim
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
- Department of Scientific Computing, Pukyong National University, Busan48513, South Korea
| | - Raiki Yoshimura
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
| | - Robin N. Thompson
- Mathematical Institute, University of Oxford, OxfordOX2 6GG, United Kingdom
- Mathematics Institute, University of Warwick, CoventryCV4 7AL, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, CoventryCV4 7AL, United Kingdom
| | - Shingo Iwami
- lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya464-8602, Japan
- Institute of Mathematics for Industry, Kyushu University, Fukuoka819-0395, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto606-8501, Japan
- Interdisciplinary Theoretical and Mathematical Sciences Program, RIKEN, Saitama351-0198, Japan
- NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo135-8550, Japan
- Science Groove Inc., Fukuoka810-0041, Japan
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12
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Ji J, Viloria Winnett A, Shelby N, Reyes JA, Schlenker NW, Davich H, Caldera S, Tognazzini C, Goh YY, Feaster M, Ismagilov RF. Index cases first identified by nasal-swab rapid COVID-19 tests had more transmission to household contacts than cases identified by other test types. PLoS One 2023; 18:e0292389. [PMID: 37796850 PMCID: PMC10553276 DOI: 10.1371/journal.pone.0292389] [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: 04/04/2023] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
At-home rapid COVID-19 tests in the U.S. utilize nasal-swab specimens and require high viral loads to reliably give positive results. Longitudinal studies from the onset of infection have found infectious virus can present in oral specimens days before nasal. Detection and initiation of infection-control practices may therefore be delayed when nasal-swab rapid tests are used, resulting in greater transmission to contacts. We assessed whether index cases first identified by rapid nasal-swab COVID-19 tests had more transmission to household contacts than index cases who used other test types (tests with higher analytical sensitivity and/or non-nasal specimen types). In this observational cohort study, 370 individuals from 85 households with a recent COVID-19 case were screened at least daily by RT-qPCR on one or more self-collected upper-respiratory specimen types. A two-level random intercept model was used to assess the association between the infection outcome of household contacts and each covariable (household size, race/ethnicity, age, vaccination status, viral variant, infection-control practices, and whether a rapid nasal-swab test was used to initially identify the household index case). Transmission was quantified by adjusted secondary attack rates (aSAR) and adjusted odds ratios (aOR). An aSAR of 53.6% (95% CI 38.8-68.3%) was observed among households where the index case first tested positive by a rapid nasal-swab COVID-19 test, which was significantly higher than the aSAR for households where the index case utilized another test type (27.2% 95% CI 19.5-35.0%, P = 0.003 pairwise comparisons of predictive margins). We observed an aOR of 4.90 (95% CI 1.65-14.56) for transmission to household contacts when a nasal-swab rapid test was used to identify the index case, compared to other test types. Use of nasal-swab rapid COVID-19 tests for initial detection of infection and initiation of infection control may be less effective at limiting transmission to household contacts than other test types.
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Affiliation(s)
- Jenny Ji
- California Institute of Technology, Pasadena, California, United States of America
| | - Alexander Viloria Winnett
- California Institute of Technology, Pasadena, California, United States of America
- University of California Los Angeles–California Institute of Technology Medical Scientist Training Program, Los Angeles, California, United States of America
| | - Natasha Shelby
- California Institute of Technology, Pasadena, California, United States of America
| | - Jessica A. Reyes
- California Institute of Technology, Pasadena, California, United States of America
| | - Noah W. Schlenker
- California Institute of Technology, Pasadena, California, United States of America
| | - Hannah Davich
- California Institute of Technology, Pasadena, California, United States of America
| | - Saharai Caldera
- California Institute of Technology, Pasadena, California, United States of America
| | - Colten Tognazzini
- Pasadena Public Health Department, Pasadena, California, United States of America
| | - Ying-Ying Goh
- Pasadena Public Health Department, Pasadena, California, United States of America
| | - Matt Feaster
- Pasadena Public Health Department, Pasadena, California, United States of America
| | - Rustem F. Ismagilov
- California Institute of Technology, Pasadena, California, United States of America
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13
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Li A, Wu J, Moghadas SM. Epidemic dynamics with time-varying transmission risk reveal the role of disease stage-dependent infectiousness. J Theor Biol 2023; 573:111594. [PMID: 37549785 DOI: 10.1016/j.jtbi.2023.111594] [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: 04/25/2023] [Revised: 07/19/2023] [Accepted: 08/02/2023] [Indexed: 08/09/2023]
Abstract
A key characteristic of acute communicable diseases is the infectiousness that varies over time as the infection dynamics evolve within a host, which influences the risk of transmission in different stages of the disease. Despite the evidence of time-varying transmission risk, most dynamic models of epidemics assume a constant transmission rate during the infectious period. Recent work has shown the difference in epidemic dynamics when this assumption is relaxed and different transmission rates are used by discretizing the infectious period into multiple sub-periods. Here, we develop an age-structured model to integrate a continuous time-varying transmission risk, based on an established correlation between the viral dynamics and infectiousness profile. Taking into account the natural history and parameter estimates of COVID-19 caused by the original strain of SARS-CoV-2, we demonstrate the difference in temporal epidemic dynamics when a continuous time-varying transmission probability is used as compared to multiple constant transmission probabilities. Our results show a significant difference between the incidence curves in terms of the magnitude and peak time, even when the reproduction number and total number of infections are the same for continuous and discrete transmission probabilities. Finally, we demonstrate the spurious outcome of preventing an epidemic through the isolation of infectious individuals when constant transmission probabilities are used, highlighting the importance of integrating a continuous time-dependent transmission parameter in dynamic models. These findings suggest a more cautious interpretation of model outcomes, especially those that are intended to evaluate the effectiveness of interventions and inform policy decisions for disease mitigation strategies.
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Affiliation(s)
- Ao Li
- Agent-Based Modelling Laboratory, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Jianhong Wu
- Department of Mathematics and Statistics, York University, Toronto, Ontario, M3J 1P3, Canada
| | - Seyed M Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, Ontario, M3J 1P3, Canada.
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14
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Dolezalova N, Gkrania-Klotsas E, Morelli D, Moore A, Cunningham AC, Booth A, Plans D, Reed AB, Aral M, Rennie KL, Wareham NJ. Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity. Sci Rep 2023; 13:10581. [PMID: 37386099 PMCID: PMC10310739 DOI: 10.1038/s41598-023-37301-y] [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: 03/06/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.
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Affiliation(s)
| | - Effrossyni Gkrania-Klotsas
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Infectious Diseases, Addenbrooke's Hospital, Box 25, Cambridge, UK
| | - Davide Morelli
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Alex Moore
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK.
| | | | - Adam Booth
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - David Plans
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- INDEX Group, Department of Science, Innovation, Technology, and Entrepreneurship, University of Exeter, Exeter, UK
| | - Angus B Reed
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Mert Aral
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Kirsten L Rennie
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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15
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Gao S, Shen M, Wang X, Wang J, Martcheva M, Rong L. A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US. J Theor Biol 2023; 565:111468. [PMID: 36940811 PMCID: PMC10027298 DOI: 10.1016/j.jtbi.2023.111468] [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: 06/23/2022] [Revised: 02/08/2023] [Accepted: 03/16/2023] [Indexed: 03/23/2023]
Abstract
COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.
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Affiliation(s)
- Shasha Gao
- School of Mathematics and Statistics, Jiangxi Normal University, Nanchang, 330000, China; Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America
| | - Mingwang Shen
- China-Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xueying Wang
- Department of Mathematics and Statistics, Washington State University, Pullman, WA 99163, United States of America
| | - Jin Wang
- Department of Mathematics, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States of America
| | - Maia Martcheva
- Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America
| | - Libin Rong
- Department of Mathematics, University of Florida, Gainesville, FL 32611, United States of America.
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16
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Viloria Winnett A, Akana R, Shelby N, Davich H, Caldera S, Yamada T, Reyna JRB, Romano AE, Carter AM, Kim MK, Thomson M, Tognazzini C, Feaster M, Goh YY, Chew YC, Ismagilov RF. Extreme differences in SARS-CoV-2 viral loads among respiratory specimen types during presumed pre-infectious and infectious periods. PNAS NEXUS 2023; 2:pgad033. [PMID: 36926220 PMCID: PMC10013338 DOI: 10.1093/pnasnexus/pgad033] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/18/2023] [Accepted: 01/24/2023] [Indexed: 03/16/2023]
Abstract
SARS-CoV-2 viral-load measurements from a single-specimen type are used to establish diagnostic strategies, interpret clinical-trial results for vaccines and therapeutics, model viral transmission, and understand virus-host interactions. However, measurements from a single-specimen type are implicitly assumed to be representative of other specimen types. We quantified viral-load timecourses from individuals who began daily self-sampling of saliva, anterior-nares (nasal), and oropharyngeal (throat) swabs before or at the incidence of infection with the Omicron variant. Viral loads in different specimen types from the same person at the same timepoint exhibited extreme differences, up to 109 copies/mL. These differences were not due to variation in sample self-collection, which was consistent. For most individuals, longitudinal viral-load timecourses in different specimen types did not correlate. Throat-swab and saliva viral loads began to rise as many as 7 days earlier than nasal-swab viral loads in most individuals, leading to very low clinical sensitivity of nasal swabs during the first days of infection. Individuals frequently exhibited presumably infectious viral loads in one specimen type while viral loads were low or undetectable in other specimen types. Therefore, defining an individual as infectious based on assessment of a single-specimen type underestimates the infectious period, and overestimates the ability of that specimen type to detect infectious individuals. For diagnostic COVID-19 testing, these three single-specimen types have low clinical sensitivity, whereas a combined throat-nasal swab, and assays with high analytical sensitivity, was inferred to have significantly better clinical sensitivity to detect presumed pre-infectious and infectious individuals.
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Affiliation(s)
| | - Reid Akana
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Natasha Shelby
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Hannah Davich
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Saharai Caldera
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Taikun Yamada
- Pangea Laboratory LLC, 14762 Bentley Cir, Tustin, CA 92780, USA.,Zymo Research Corp., 17062 Murphy Ave, Irvine, CA 92614, USA
| | | | - Anna E Romano
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Alyssa M Carter
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Mi Kyung Kim
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Matt Thomson
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
| | - Colten Tognazzini
- Pasadena Public Health Department, 1845 N. Fair Oaks Ave, Pasadena, CA 91103, USA
| | - Matthew Feaster
- Pasadena Public Health Department, 1845 N. Fair Oaks Ave, Pasadena, CA 91103, USA
| | - Ying-Ying Goh
- Pasadena Public Health Department, 1845 N. Fair Oaks Ave, Pasadena, CA 91103, USA
| | - Yap Ching Chew
- Pangea Laboratory LLC, 14762 Bentley Cir, Tustin, CA 92780, USA.,Zymo Research Corp., 17062 Murphy Ave, Irvine, CA 92614, USA
| | - Rustem F Ismagilov
- California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA
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17
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Song M, Hong S, Lee LP. Multiplexed Ultrasensitive Sample-to-Answer RT-LAMP Chip for the Identification of SARS-CoV-2 and Influenza Viruses. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2207138. [PMID: 36398425 DOI: 10.1002/adma.202207138] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/21/2022] [Indexed: 06/16/2023]
Abstract
Prompt on-site diagnosis of SARS-CoV-2 with other respiratory infections will have minimized the global impact of the COVID-19 pandemic through rapid, effective management. However, no such multiplex point-of-care (POC) chip has satisfied a suitable sensitivity of gold-standard nucleic acid amplification tests (NAATs). Here, a rapid multiplexed ultrasensitive sample-to-answer loop-mediated isothermal amplification (MUSAL) chip operated by simple LED-driven photothermal amplification to detect six targets from single-swab sampling is presented. First, the MUSAL chip allows ultrafast on-chip sample preparation with ≈500-fold preconcentration at a rate of 1.2 mL min-1 . Second, the chip enables contamination-free amplification using autonomous target elution into on-chip reagents by photothermal activation. Finally, the chip accomplishes multiplexed on-chip diagnostics of SARS-CoV-2 and influenza viruses with a limit of detection (LoD) of 0.5 copies µL-1 . The rapid, ultrasensitive, cost-effective sample-to-answer chip with a multiplex capability will allow timely management of various pandemics situations that may be faced shortly.
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Affiliation(s)
- Minsun Song
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Division of Engineering in Medicine and Renal Division, Department of Medicine, Brigham Women's Hospital, Boston, MA, 02115, USA
| | - SoonGweon Hong
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Division of Engineering in Medicine and Renal Division, Department of Medicine, Brigham Women's Hospital, Boston, MA, 02115, USA
| | - Luke P Lee
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Division of Engineering in Medicine and Renal Division, Department of Medicine, Brigham Women's Hospital, Boston, MA, 02115, USA
- Department of Bioengineering, Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, 94720, USA
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University (SKKU), Suwon, Gyeonggi-do, 16419, Republic of Korea
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18
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Hawkins LPA, Pallett SJC, Mazzella A, Anton-Vazquez V, Rosas L, Jawad SM, Shakespeare D, Breathnach AS. Transmission dynamics and associated mortality of nosocomial COVID-19 throughout 2021: a retrospective study at a large teaching hospital in London. J Hosp Infect 2023; 133:62-69. [PMID: 36632897 PMCID: PMC9827730 DOI: 10.1016/j.jhin.2022.12.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 12/20/2022] [Accepted: 12/27/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND The impact of nosocomial SARS-CoV-2 infections has changed significantly since 2020. However, there is a lack of up-to-date evidence of the epidemiology of these infections which is essential in order to appropriately guide infection control policy. AIMS To identify the secondary attack rate of SARS-CoV-2 infection and associated mortality across different variants of concern. METHODS A single-centre retrospective study of all nosocomial SARS-CoV-2 exposure events was conducted between 31st December 2020 and 31st December 2021. A secondary attack rate was calculated for nosocomial acquisition of SARS-CoV-2 infection and time to positivity. Positive contacts were assessed for all-cause 30-day mortality. RESULTS A total of 346 sequential index exposure events were examined, and 1378 susceptible contacts identified. Two hundred susceptible contacts developed SARS-CoV-2 infection (secondary attack rate of 15.5%). The majority of index cases (59%) did not result in any secondary SARS-CoV-2 infection. Where close contacts developed SARS-CoV-2 infection, 80% were detected within the first five days since last contact with the index case. The overall associated mortality among positive contacts across 2021 was 9%, with an estimated reduction of 68% when comparing periods of high Omicron versus Alpha transmission. CONCLUSION Our findings describe that most SARS-CoV-2 infections are detected within five days of contact with an index case; we have also demonstrated a considerably lower mortality rate with the Omicron variant in comparison to previous variants. These findings have important implications for informing and supporting infection control protocols to allow movement through the hospital, and ensure patients access care safely.
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Affiliation(s)
- L P A Hawkins
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK.
| | - S J C Pallett
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
| | - A Mazzella
- Institute for Infection and Immunity, St George's University of London, London, UK
| | - V Anton-Vazquez
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
| | - L Rosas
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
| | - S M Jawad
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
| | - D Shakespeare
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
| | - A S Breathnach
- Infection Care Group, Department of Microbiology, St George's Hospital, London, UK
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19
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Creswell R, Robinson M, Gavaghan D, Parag KV, Lei CL, Lambert B. A Bayesian nonparametric method for detecting rapid changes in disease transmission. J Theor Biol 2023; 558:111351. [PMID: 36379231 DOI: 10.1016/j.jtbi.2022.111351] [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: 07/01/2022] [Revised: 10/11/2022] [Accepted: 11/01/2022] [Indexed: 11/15/2022]
Abstract
Whether an outbreak of infectious disease is likely to grow or dissipate is determined through the time-varying reproduction number, Rt. Real-time or retrospective identification of changes in Rt following the imposition or relaxation of interventions can thus contribute important evidence about disease transmission dynamics which can inform policymaking. Here, we present a method for estimating shifts in Rt within a renewal model framework. Our method, which we call EpiCluster, is a Bayesian nonparametric model based on the Pitman-Yor process. We assume that Rt is piecewise-constant, and the incidence data and priors determine when or whether Rt should change and how many times it should do so throughout the series. We also introduce a prior which induces sparsity over the number of changepoints. Being Bayesian, our approach yields a measure of uncertainty in Rt and its changepoints. EpiCluster is fast, straightforward to use, and we demonstrate that it provides automated detection of rapid changes in transmission, either in real-time or retrospectively, for synthetic data series where the Rt profile is known. We illustrate the practical utility of our method by fitting it to case data of outbreaks of COVID-19 in Australia and Hong Kong, where it finds changepoints coinciding with the imposition of non-pharmaceutical interventions. Bayesian nonparametric methods, such as ours, allow the volume and complexity of the data to dictate the number of parameters required to approximate the process and should find wide application in epidemiology. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Richard Creswell
- Department of Computer Science, University of Oxford, Oxford, United Kingdom.
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Kris V Parag
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom; NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, United Kingdom
| | - Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, Macao Special Administrative Region of China
| | - Ben Lambert
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
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20
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Whittaker DG, Herrera-Reyes AD, Hendrix M, Owen MR, Band LR, Mirams GR, Bolton KJ, Preston SP. Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models. J Theor Biol 2023; 558:111337. [PMID: 36351493 PMCID: PMC9637393 DOI: 10.1016/j.jtbi.2022.111337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/16/2022] [Accepted: 10/26/2022] [Indexed: 11/08/2022]
Abstract
During the SARS-CoV-2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are mis-specified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly mis-specify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Dominic G Whittaker
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | | | - Maurice Hendrix
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK; Digital Research Service, University of Nottingham, University Park, Nottingham, NG8 1BB, UK
| | - Markus R Owen
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Leah R Band
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Gary R Mirams
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Kirsty J Bolton
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
| | - Simon P Preston
- School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
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21
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Whitfield CA, Hall I. Modelling the impact of repeat asymptomatic testing policies for staff on SARS-CoV-2 transmission potential. J Theor Biol 2023; 557:111335. [PMID: 36334850 PMCID: PMC9626407 DOI: 10.1016/j.jtbi.2022.111335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
Repeat asymptomatic testing in order to identify and quarantine infectious individuals has become a widely-used intervention to control SARS-CoV-2 transmission. In some workplaces, and in particular health and social care settings with vulnerable patients, regular asymptomatic testing has been deployed to staff to reduce the likelihood of workplace outbreaks. We have developed a model based on data available in the literature to predict the potential impact of repeat asymptomatic testing on SARS-CoV-2 transmission. The results highlight features that are important to consider when modelling testing interventions, including population heterogeneity of infectiousness and correlation with test-positive probability, as well as adherence behaviours in response to policy. Furthermore, the model based on the reduction in transmission potential presented here can be used to parameterise existing epidemiological models without them having to explicitly simulate the testing process. Overall, we find that even with different model paramterisations, in theory, regular asymptomatic testing is likely to be a highly effective measure to reduce transmission in workplaces, subject to adherence. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Carl A Whitfield
- Department of Mathematics, University of Manchester, United Kingdom.
| | - Ian Hall
- Department of Mathematics, University of Manchester, United Kingdom
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22
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Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data. Epidemiol Infect 2022; 151:e5. [PMID: 36524247 PMCID: PMC9837419 DOI: 10.1017/s0950268822001947] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95% CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95% CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29-2.58) for Alpha and 2.74 days (95% CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.
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23
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Jaenisch T, Lamb MM, Gallichotte EN, Adams B, Henry C, Riess J, van Sickle JT, Hawkins KL, Montague BT, Coburn C, Conners Bauer L, Kovarik J, Hernandez MT, Bronson A, Graham L, James S, Hanenberg S, Kovacs J, Spencer JS, Zabel M, Fox PD, Pluss O, Windsor W, Winstanley G, Olson D, Barer M, Berman S, Ebel G, Chu M. Investigating transmission of SARS-CoV-2 using novel face mask sampling: a protocol for an observational prospective study of index cases and their contacts in a congregate setting. BMJ Open 2022; 12:e061029. [PMID: 36418127 PMCID: PMC9684274 DOI: 10.1136/bmjopen-2022-061029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION This study aims to measure how transmission of SARS-CoV-2 occurs in communities and to identify conditions that lend to increased transmission focusing on congregate situations. We will measure SARS-CoV-2 in exhaled breath of asymptomatic and symptomatic persons using face mask sampling-a non-invasive method for SARS-CoV-2 detection in exhaled air. We aim to detect transmission clusters and identify risk factors for SARS-CoV-2 transmission in presymptomatic, asymptomatic and symptomatic individuals. METHODS AND ANALYSIS In this observational prospective study with daily follow-up, index cases and their respective contacts are identified at each participating institution. Contact definitions are based on Centers for Disease Control and Prevention and local health department guidelines. Participants will wear masks with polyvinyl alcohol test strips adhered to the inside for 2 hours daily. The strips are applied to all masks used over at least 7 days. In addition, self-administered nasal swabs and (optional) finger prick blood samples are performed by participants. Samples are tested by standard PCR protocols and by novel antigen tests. ETHICS AND DISSEMINATION This study was approved by the Colorado Multiple Institutional Review Board and the WHO Ethics Review Committee. From the data generated, we will analyse transmission clusters and risk factors for transmission of SARS-CoV-2 in congregate settings. The kinetics of asymptomatic transmission and the evaluation of non-invasive tools for detection of transmissibility are of crucial importance for the development of more targeted control interventions-and ultimately to assist with keeping congregate settings open that are essential for our social fabric. TRIAL REGISTRATION NUMBER ClinicalTrials.gov (#NCT05145803).
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Affiliation(s)
- Thomas Jaenisch
- Center for Global Health and Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
- Heidelberg Institute of Global Health, University Hospital Heidelberg, Heidelberg, Germany
| | - Molly M Lamb
- Center for Global Health and Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
| | - Emily N Gallichotte
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Brian Adams
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Charles Henry
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, USA
| | - Jeannine Riess
- Office of Environmental Health Services, Colorado State University, Fort Collins, Colorado, USA
| | | | | | - Brian T Montague
- Occupational Health and Division of Infectious Diseases, University of Colorado School of Medicine, Denver, Colorado, USA
| | - Cody Coburn
- Occupational Health and Division of Infectious Diseases, University of Colorado School of Medicine, Denver, Colorado, USA
| | - Leisha Conners Bauer
- Health Promotion and Collegiate Recovery Center, University of Colorado Boulder, Boulder, Colorado, USA
| | - Jennifer Kovarik
- Health Promotion and Collegiate Recovery Center, University of Colorado Boulder, Boulder, Colorado, USA
| | - Mark T Hernandez
- Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, Colorado, USA
| | - Amy Bronson
- Office of the Vice President, Colorado Mesa University, Grand Junction, Colorado, USA
| | - Lucy Graham
- Department of Health Sciences, Colorado Mesa University, Grand Junction, Colorado, USA
| | - Stephanie James
- Department of Pharmaceutical Sciences, Regis University, Denver, Colorado, USA
| | - Stephanie Hanenberg
- Wellness Center, University of Colorado Colorado Springs, Colorado Springs, Colorado, USA
| | - James Kovacs
- Department of Chemistry and Biology, University of Colorado Colorado Springs, Colorado Springs, Colorado, USA
| | - John S Spencer
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Mark Zabel
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Philip D Fox
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Olivia Pluss
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - William Windsor
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Geoffrey Winstanley
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Daniel Olson
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Michael Barer
- Department of Infectious Diseases, University of Leicester, Leicester, Leicestershire, UK
| | - Stephen Berman
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
| | - Gregory Ebel
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - May Chu
- Center for Global Health, Colorado School of Public Health, Aurora, Colorado, USA
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24
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Creswell R, Augustin D, Bouros I, Farm HJ, Miao S, Ahern A, Robinson M, Lemenuel-Diot A, Gavaghan DJ, Lambert BC, Thompson RN. Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210308. [PMID: 35965464 PMCID: PMC9376709 DOI: 10.1098/rsta.2021.0308] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 05/04/2022] [Indexed: 05/02/2023]
Abstract
During infectious disease outbreaks, inference of summary statistics characterizing transmission is essential for planning interventions. An important metric is the time-dependent reproduction number (Rt), which represents the expected number of secondary cases generated by each infected individual over the course of their infectious period. The value of Rt varies during an outbreak due to factors such as varying population immunity and changes to interventions, including those that affect individuals' contact networks. While it is possible to estimate a single population-wide Rt, this may belie differences in transmission between subgroups within the population. Here, we explore the effects of this heterogeneity on Rt estimates. Specifically, we consider two groups of infected hosts: those infected outside the local population (imported cases), and those infected locally (local cases). We use a Bayesian approach to estimate Rt, made available for others to use via an online tool, that accounts for differences in the onwards transmission risk from individuals in these groups. Using COVID-19 data from different regions worldwide, we show that different assumptions about the relative transmission risk between imported and local cases affect Rt estimates significantly, with implications for interventions. This highlights the need to collect data during outbreaks describing heterogeneities in transmission between different infected hosts, and to account for these heterogeneities in methods used to estimate Rt. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- R. Creswell
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - D. Augustin
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - I. Bouros
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - H. J. Farm
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - S. Miao
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - A. Ahern
- Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK
| | - M. Robinson
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - A. Lemenuel-Diot
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel CH-4070, Switzerland
| | - D. J. Gavaghan
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - B. C. Lambert
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
| | - R. N. Thompson
- Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, UK
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25
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Vegvari C, Abbott S, Ball F, Brooks-Pollock E, Challen R, Collyer BS, Dangerfield C, Gog JR, Gostic KM, Heffernan JM, Hollingsworth TD, Isham V, Kenah E, Mollison D, Panovska-Griffiths J, Pellis L, Roberts MG, Scalia Tomba G, Thompson RN, Trapman P. Commentary on the use of the reproduction number R during the COVID-19 pandemic. Stat Methods Med Res 2022; 31:1675-1685. [PMID: 34569883 PMCID: PMC9277711 DOI: 10.1177/09622802211037079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since the beginning of the COVID-19 pandemic, the reproduction number [Formula: see text] has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, [Formula: see text] is defined as the average number of secondary infections caused by one primary infected individual. [Formula: see text] seems convenient, because the epidemic is expanding if [Formula: see text] and contracting if [Formula: see text]. The magnitude of [Formula: see text] indicates by how much transmission needs to be reduced to control the epidemic. Using [Formula: see text] in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of [Formula: see text] but many, and the precise definition of [Formula: see text] affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined [Formula: see text], there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate [Formula: see text] vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when [Formula: see text] is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of [Formula: see text], and the data and methods used to estimate it, can make [Formula: see text] a more useful metric for future management of the epidemic.
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Affiliation(s)
- Carolin Vegvari
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | - Sam Abbott
- Center for the Mathematical Modelling of Infectious Diseases, 4906London School of Hygiene & Tropical Medicine, UK
| | - Frank Ball
- School of Mathematical Sciences, 6123University of Nottingham, UK
| | - Ellen Brooks-Pollock
- Bristol Veterinary School, 1980University of Bristol, UK.,NIHR Health Protection Research Unit in Behavioural Science and Evaluation at the University of Bristol, UK
| | - Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK.,Somerset NHS Foundation Trust, UK
| | - Benjamin S Collyer
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, 4615Imperial College London, London, UK
| | | | - Julia R Gog
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
| | - Katelyn M Gostic
- Department of Ecology and Evolution, 2462University of Chicago, USA
| | - Jane M Heffernan
- Centre for Disease Modelling, Mathematics & Statistics, 7991York University, Canada.,COVID Modelling Task-Force, The Fields Institute, Canada
| | - T Déirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, 6396University of Oxford, UK
| | - Valerie Isham
- Department of Statistical Science, 4919University College London, UK
| | - Eben Kenah
- Division of Biostatistics, College of Public Health, 2647The Ohio State University, USA
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Wolfson Centre for Mathematical Biology, Mathematical Institute and The Queen's College, University of Oxford, Oxford, UK
| | - Lorenzo Pellis
- Department of Mathematics, 5292The University of Manchester, UK.,The Alan Turing Institute, UK
| | - Michael G Roberts
- School of Natural and Computational Sciences and New Zealand Institute for Advanced Study, Massey University, New Zealand
| | | | - Robin N Thompson
- Mathematics Institute, 2707University of Warwick, Coventry, UK.,Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, 2707University of Warwick, Coventry, UK
| | - Pieter Trapman
- Department of Mathematics, 7675Stockholm University, Sweden
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26
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SARS-CoV-2 viral load is associated with risk of transmission to household and community contacts. BMC Infect Dis 2022; 22:672. [PMID: 35931971 PMCID: PMC9354300 DOI: 10.1186/s12879-022-07663-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 07/29/2022] [Indexed: 12/23/2022] Open
Abstract
Background Factors that lead to successful SARS-CoV-2 transmission are still not well described. We investigated the association between a case’s viral load and the risk of transmission to contacts in the context of other exposure-related factors. Methods Data were generated through routine testing and contact tracing at a large university. Case viral loads were obtained from cycle threshold values associated with a positive polymerase chain reaction test result from October 1, 2020 to April 15, 2021. Cases were included if they had at least one contact who tested 3–14 days after the exposure. Case-contact pairs were formed by linking index cases with contacts. Chi-square tests were used to evaluate differences in proportions of contacts testing positive. Generalized estimating equation models with a log link were used to evaluate whether viral load and other exposure-related factors were associated with a contact testing positive. Results Median viral load among the 212 cases included in the study was 5.6 (1.8–10.4) log10 RNA copies per mL of saliva. Among 365 contacts, 70 (19%) tested positive following their exposure; 36 (51%) were exposed to a case that was asymptomatic or pre-symptomatic on the day of exposure. The proportion of contacts that tested positive increased monotonically with index case viral load (12%, 23% and 25% corresponding to < 5, 5–8 and > 8 log10 copies per mL, respectively; X2 = 7.18, df = 2, p = 0.03). Adjusting for cough, time between test and exposure, and physical contact, the risk of transmission to a close contact was significantly associated with viral load (RR = 1.27, 95% CI 1.22–1.32). Conclusions Further research is needed to understand whether these relationships persist for newer variants. For those variants whose transmission advantage is mediated through a high viral load, public health measures could be scaled accordingly. Index cases with higher viral loads could be prioritized for contact tracing and recommendations to quarantine contacts could be made according to the likelihood of transmission based on risk factors such as viral load.
Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07663-1.
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27
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Sender R, Bar-On Y, Woo Park S, Noor E, Dushoff J, Milo R. The unmitigated profile of COVID-19 infectiousness. eLife 2022; 11:79134. [PMID: 35913120 PMCID: PMC9391043 DOI: 10.7554/elife.79134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/27/2022] [Indexed: 11/21/2022] Open
Abstract
Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.
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Affiliation(s)
- Ron Sender
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Yinon Bar-On
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Sang Woo Park
- Department of Ecology and Evolutionary, Princeton University, Princeton, United States
| | - Elad Noor
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
| | | | - Ron Milo
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
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28
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The effect of notification window length on the epidemiological impact of COVID-19 contact tracing mobile applications. COMMUNICATIONS MEDICINE 2022; 2:74. [PMID: 35774530 PMCID: PMC9237034 DOI: 10.1038/s43856-022-00143-2] [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: 11/18/2021] [Accepted: 06/14/2022] [Indexed: 12/30/2022] Open
Abstract
Background The reduction in SARS-CoV-2 transmission facilitated by mobile contact tracing applications (apps) depends both on the proportion of relevant contacts notified and on the probability that those contacts quarantine after notification. The proportion of relevant contacts notified depends upon the number of days preceding an infector's positive test that their contacts are notified, which we refer to as an app's notification window. Methods We use an epidemiological model of SARS-CoV-2 transmission that captures the profile of infection to consider the trade-off between notification window length and active app use. We focus on 5-day and 2-day windows, the notification windows of the NHS COVID-19 app in England and Wales before and after 2nd August 2021, respectively. Results Our analyses show that at the same level of active app use, 5-day windows result in larger reductions in transmission than 2-day windows. However, short notification windows can be more effective at reducing transmission if they are associated with higher levels of active app use and adherence to isolation upon notification. Conclusions Our results demonstrate the importance of understanding adherence to interventions when setting notification windows for COVID-19 contact tracing apps.
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29
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Favero M, Scalia Tomba G, Britton T. Modelling preventive measures and their effect on generation times in emerging epidemics. J R Soc Interface 2022; 19:20220128. [PMID: 35702865 PMCID: PMC9198515 DOI: 10.1098/rsif.2022.0128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/12/2022] [Indexed: 12/21/2022] Open
Abstract
We present a stochastic epidemic model to study the effect of various preventive measures, such as uniform reduction of contacts and transmission, vaccination, isolation, screening and contact tracing, on a disease outbreak in a homogeneously mixing community. The model is based on an infectivity process, which we define through stochastic contact and infectiousness processes, so that each individual has an independent infectivity profile. In particular, we monitor variations of the reproduction number and of the distribution of generation times. We show that some interventions, i.e. uniform reduction and vaccination, affect the former while leaving the latter unchanged, whereas other interventions, i.e. isolation, screening and contact tracing, affect both quantities. We provide a theoretical analysis of the variation of these quantities, and we show that, in practice, the variation of the generation time distribution can be significant and that it can cause biases in the estimation of reproduction numbers. The framework, because of its general nature, captures the properties of many infectious diseases, but particular emphasis is on COVID-19, for which numerical results are provided.
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Affiliation(s)
- Martina Favero
- Department of Mathematics, Stockholm University, Stockholm, Sweden
| | | | - Tom Britton
- Department of Mathematics, Stockholm University, Stockholm, Sweden
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Firle C, Steinmetz A, Stier O, Stengel D, Ekkernkamp A. Aerosol emission from playing wind instruments and related COVID-19 infection risk during music performance. Sci Rep 2022; 12:8598. [PMID: 35597808 PMCID: PMC9124212 DOI: 10.1038/s41598-022-12529-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 04/29/2022] [Indexed: 12/29/2022] Open
Abstract
The pandemic of COVID-19 led to restrictions in all kinds of music activities. Airborne transmission of SARS-CoV-2 requires risk assessment of wind instrument playing in various situations. Previous studies focused on short-range transmission, whereas long-range transmission risk has not been assessed. The latter requires knowledge of aerosol emission rates from wind instrument playing. We measured aerosol concentrations in a hermetically closed chamber of 20 m3 in an operating theatre as resulting from 20 min standardized wind instrument playing (19 flute, 11 oboe, 1 clarinet, 1 trumpet players). We calculated aerosol emission rates showing uniform distribution for both instrument groups. Aerosol emission from wind instrument playing ranged from 11 ± 288 particles/second (P/s) up to 2535 ± 195 P/s, expectation value ± uncertainty standard deviation. The analysis of aerosol particle size distributions shows that 70-80% of emitted particles had a size of 0.25-0.8 µm and thus are alveolar. Masking the bell with a surgical mask did not reduce aerosol emission. Aerosol emission rates were higher from wind instrument playing than from speaking or breathing. Differences between instrumental groups could not be found but high interindividual variance, as expressed by uniform distribution of aerosol emission rates. Our findings indicate that aerosol emission depends on physiological factors and playing techniques rather than on the type of instrument, in contrast to some previous studies. Based on our results, we present transmission risk calculations for long-range transmission of COVID-19 for three typical woodwind playing situations.
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Affiliation(s)
- Carl Firle
- GP Practice, Dr. Claudia-Isabella Wildfeuer, 10715, Berlin, Germany.
| | - Anke Steinmetz
- Physical and Rehabilitation Medicine, Department of Trauma, Reconstructive Surgery and Rehabilitation Medicine, University Medicine Greifswald, Greifswald, Germany
| | | | - Dirk Stengel
- BG Kliniken-Klinikverbund Der Gesetzlichen Unfallversicherung gGmbH, Berlin, Germany
- BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany
| | - Axel Ekkernkamp
- Physical and Rehabilitation Medicine, Department of Trauma, Reconstructive Surgery and Rehabilitation Medicine, University Medicine Greifswald, Greifswald, Germany
- Department of Trauma, Reconstructive Surgery, and Rehabilitation Medicine, University Medicine Greifswald, Greifswald, Germany
- BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany
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Leng T, Hill EM, Thompson RN, Tildesley MJ, Keeling MJ, Dyson L. Assessing the impact of lateral flow testing strategies on within-school SARS-CoV-2 transmission and absences: A modelling study. PLoS Comput Biol 2022; 18:e1010158. [PMID: 35622860 PMCID: PMC9182264 DOI: 10.1371/journal.pcbi.1010158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 06/09/2022] [Accepted: 05/02/2022] [Indexed: 12/05/2022] Open
Abstract
Rapid testing strategies that replace the isolation of close contacts through the use of lateral flow device tests (LFTs) have been suggested as a way of controlling SARS-CoV-2 transmission within schools that maintain low levels of pupil absences. We developed an individual-based model of a secondary school formed of exclusive year group bubbles (five year groups, with 200 pupils per year) to assess the likely impact of strategies using LFTs in secondary schools over the course of a seven-week half-term on transmission, absences, and testing volume, compared to a policy of isolating year group bubbles upon a pupil returning a positive polymerase chain reaction (PCR) test. We also considered the sensitivity of results to levels of participation in rapid testing and underlying model assumptions. While repeated testing of year group bubbles following case detection is less effective at reducing infections than a policy of isolating year group bubbles, strategies involving twice weekly mass testing can reduce infections to lower levels than would occur under year group isolation. By combining regular testing with serial contact testing or isolation, infection levels can be reduced further still. At high levels of pupil participation in lateral flow testing, strategies replacing the isolation of year group bubbles with testing substantially reduce absences, but require a high volume of testing. Our results highlight the conflict between the goals of minimising within-school transmission, minimising absences and minimising testing burden. While rapid testing strategies can reduce school transmission and absences, they may lead to a large number of daily tests.
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Affiliation(s)
- Trystan Leng
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Edward M. Hill
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Robin N. Thompson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Matt J. Keeling
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
| | - Louise Dyson
- The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom
- JUNIPER – Joint UNIversities Pandemic and Epidemiological Research, https://maths.org/juniper/
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Generation time of the alpha and delta SARS-CoV-2 variants: an epidemiological analysis. THE LANCET INFECTIOUS DISEASES 2022; 22:603-610. [PMID: 35176230 PMCID: PMC8843191 DOI: 10.1016/s1473-3099(22)00001-9] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 12/06/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
Background In May, 2021, the delta (B.1.617.2) SARS-CoV-2 variant became dominant in the UK, superseded by the omicron (B.1.1.529) variant in December, 2021. The delta variant is associated with increased transmissibility compared with the alpha variant, which was the dominant variant in the UK between December, 2020, and May, 2021. To understand transmission and the effectiveness of interventions, we aimed to investigate whether the delta variant generation time (the interval between infections in infector–infectee pairs) is shorter—ie, transmissions are happening more quickly—than that of the alpha variant. Methods In this epidemiological analysis, we analysed transmission data from an ongoing UK Health Security Agency (UKHSA) prospective household study. Households were recruited to the study after an index case had a positive PCR test and genomic sequencing was used to determine the variant responsible. By fitting a mathematical transmission model to the data, we estimated the intrinsic generation time (which assumes a constant supply of susceptible individuals throughout infection) and the household generation time (which reflects realised transmission in the study households, accounting for susceptible depletion) for the alpha and delta variants. Findings Between February and August, 2021, 227 households consisting of 559 participants were recruited to the UKHSA study. The alpha variant was detected or assumed to be responsible for infections in 131 households (243 infections in 334 participants) recruited in February–May, and the delta variant in 96 households (174 infections in 225 participants) in May–August. The mean intrinsic generation time was shorter for the delta variant (4·7 days, 95% credible interval [CI] 4·1–5·6) than the alpha variant (5·5 days, 4·7–6·5), with 92% posterior probability. The mean household generation time was 28% (95% CI 0–48%) shorter for the delta variant (3·2 days, 95% CI 2·5–4·2) than the alpha variant (4·5 days, 3·7–5·4), with 97·5% posterior probability. Interpretation The delta variant transmits more quickly in households than the alpha variant, which can be attributed to faster depletion of susceptible individuals in households and a possible decrease in the intrinsic generation time. Interventions such as contact tracing, testing, and isolation might be less effective if transmission of the virus occurs quickly. Funding National Institute for Health Research, UK Health Security Agency, Engineering and Physical Sciences Research Council, and UK Research and Innovation.
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Proesmans K, Hancart S, Braeye T, Klamer S, Robesyn E, Djiena A, De Leeuw F, Mahieu R, Dreuw A, Hammami N, Wildemeersch D, Cornelissen L, Van Cauteren D. COVID-19 contact tracing in Belgium: main indicators and performance, January - September 2021. Arch Public Health 2022; 80:118. [PMID: 35418097 PMCID: PMC9005619 DOI: 10.1186/s13690-022-00875-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/31/2022] [Indexed: 12/20/2022] Open
Abstract
Background Contact tracing is one of the main public health tools in the control of coronavirus disease 2019 (COVID-19). A centralized contact tracing system was developed in Belgium in 2020. We aim to evaluate the performance and describe the results, between January 01, 2021, and September 30, 2021. The characteristics of COVID-19 cases and the impact of COVID-19 vaccination on testing and tracing are also described. Methods We combined laboratory diagnostic test data (molecular and antigen test), vaccination data, and contact tracing data. A descriptive analysis was done to evaluate the performance of contact tracing and describe insights into the epidemiology of COVID-19 by contact tracing. Results Between January and September 2021, 555.181 COVID-19 cases were reported to the central contact center and 91% were contacted. The average delay between symptom onset and contact tracing initiation was around 5 days, of which 4 days corresponded to pre-testing delay. High-Risk Contacts (HRC) were reported by 49% of the contacted index cases. The mean number of reported HRC was 2.7. In total, 666.869 HRC were reported of which 91% were successfully contacted and 89% of these were tested at least once following the interview. The estimated average secondary attack rate (SAR) among the contacts of the COVID-19 cases who reported at least one contact, was 27% and was significantly higher among household HRC. The proportion of COVID-19 cases who were previously identified as HRC within the central system was 24%. Conclusions The contact-tracing system contacted more than 90% of the reported COVID-19 cases and their HRC. This proportion remained stable between January 1 2021 and September 30 2021 despite an increase in cases in March–April 2021. We report high SAR, indicating that through contact tracing a large number of infections were prospectively detected. The system can be further improved by (1) reducing the delay between onset of illness and medical consultation (2) having more exhaustive reporting of HRC by the COVID-19 case. Supplementary Information The online version contains supplementary material available at 10.1186/s13690-022-00875-6.
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Affiliation(s)
| | - Sharon Hancart
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Toon Braeye
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Sofieke Klamer
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Emmanuel Robesyn
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | | | - Frances De Leeuw
- Department of Infectious Disease Prevention and Control, Common Community Commission, Brussels-Capital Region, Brussels, Belgium
| | - Romain Mahieu
- Department of Infectious Disease Prevention and Control, Common Community Commission, Brussels-Capital Region, Brussels, Belgium
| | - Alex Dreuw
- Ministry of the German-speaking Community, Eupen, Belgium
| | - Naima Hammami
- Agency for Care and Health, Infection Prevention and Control, Flemish Community, Brussels, Belgium
| | - Dirk Wildemeersch
- Agency for Care and Health, Infection Prevention and Control, Flemish Community, Brussels, Belgium
| | - Laura Cornelissen
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
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Karr J, Malik-Sheriff RS, Osborne J, Gonzalez-Parra G, Forgoston E, Bowness R, Liu Y, Thompson R, Garira W, Barhak J, Rice J, Torres M, Dobrovolny HM, Tang T, Waites W, Glazier JA, Faeder JR, Kulesza A. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:822606. [PMID: 36909847 PMCID: PMC10002468 DOI: 10.3389/fsysb.2022.822606] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.
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Affiliation(s)
- Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rahuman S. Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - James Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia
| | | | - Eric Forgoston
- Department of Applied Mathematics and Statistics, Montclair State University, Montclair, NJ, United States
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Robin Thompson
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Winston Garira
- Department of Mathematics and Applied Mathematics, Modelling Health and Environmental Linkages Research Group, University of Venda, Limpopo, South Africa
| | - Jacob Barhak
- Jacob Barhak Analytics, Austin, TX, United States
| | - John Rice
- Independent Retired Working Group Volunteer, Virginia Beach, VA, United States
| | - Marcella Torres
- Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, United States
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, United States
| | - Tingting Tang
- Department of Mathematics and Statistics in San Diego State University (SDSU) and SDSU Imperial Valley, Calexico, CA, United States
| | - William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
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Ganti K, Ferreri LM, Lee CY, Bair CR, Delima GK, Holmes KE, Suthar MS, Lowen AC. Timing of exposure is critical in a highly sensitive model of SARS-CoV-2 transmission. PLoS Pathog 2022; 18:e1010181. [PMID: 35333914 PMCID: PMC8986102 DOI: 10.1371/journal.ppat.1010181] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/06/2022] [Accepted: 03/09/2022] [Indexed: 01/19/2023] Open
Abstract
Transmission efficiency is a critical factor determining the size of an outbreak of infectious disease. Indeed, the propensity of SARS-CoV-2 to transmit among humans precipitated and continues to sustain the COVID-19 pandemic. Nevertheless, the number of new cases among contacts is highly variable and underlying reasons for wide-ranging transmission outcomes remain unclear. Here, we evaluated viral spread in golden Syrian hamsters to define the impact of temporal and environmental conditions on the efficiency of SARS-CoV-2 transmission through the air. Our data show that exposure periods as brief as one hour are sufficient to support robust transmission. However, the timing after infection is critical for transmission success, with the highest frequency of transmission to contacts occurring at times of peak viral load in the donor animals. Relative humidity and temperature had no detectable impact on transmission when exposures were carried out with optimal timing and high inoculation dose. However, contrary to expectation, trends observed with sub-optimal exposure timing and lower inoculation dose suggest improved transmission at high relative humidity or high temperature. In sum, among the conditions tested, our data reveal the timing of exposure to be the strongest determinant of SARS-CoV-2 transmission success and implicate viral load as an important driver of transmission.
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Affiliation(s)
- Ketaki Ganti
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Lucas M. Ferreri
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Chung-Young Lee
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Camden R. Bair
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Gabrielle K. Delima
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Kate E. Holmes
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Mehul S. Suthar
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory Vaccine Center, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Center for Childhood Infections and Vaccines of Children’s Healthcare of Atlanta, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory-UGA Center of Excellence for Influenza Research and Surveillance [CEIRS], Atlanta, Georgia, United States of America
| | - Anice C. Lowen
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America
- Emory-UGA Center of Excellence for Influenza Research and Surveillance [CEIRS], Atlanta, Georgia, United States of America
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Lovell-Read FA, Shen S, Thompson RN. Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study. J Theor Biol 2022; 535:110983. [PMID: 34915042 PMCID: PMC8670853 DOI: 10.1016/j.jtbi.2021.110983] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 12/02/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.
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Affiliation(s)
| | - Silvia Shen
- Mathematical Institute, University of Oxford, Oxford, United Kingdom; Pembroke College, University of Oxford, Oxford, United Kingdom
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Coventry, United Kingdom; The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
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Hart WS, Abbott S, Endo A, Hellewell J, Miller E, Andrews N, Maini PK, Funk S, Thompson RN. Inference of the SARS-CoV-2 generation time using UK household data. eLife 2022; 11:e70767. [PMID: 35138250 PMCID: PMC8967386 DOI: 10.7554/elife.70767] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 02/07/2022] [Indexed: 11/17/2022] Open
Abstract
The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.
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Affiliation(s)
- William S Hart
- Mathematical Institute, University of OxfordOxfordUnited Kingdom
| | - Sam Abbott
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Akira Endo
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Joel Hellewell
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Elizabeth Miller
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
- Immunisation and Countermeasures Division, UK Health Security AgencyLondonUnited Kingdom
| | - Nick Andrews
- Data and Analytical Sciences, UK Health Security AgencyLondonUnited Kingdom
| | - Philip K Maini
- Mathematical Institute, University of OxfordOxfordUnited Kingdom
| | - Sebastian Funk
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical MedicineLondonUnited Kingdom
| | - Robin N Thompson
- Mathematics Institute, University of WarwickCoventryUnited Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of WarwickCoventryUnited Kingdom
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Kreiss A, Van Keilegom I. Semi-parametric estimation of incubation and generation times by means of Laguerre polynomials. J Nonparametr Stat 2022. [DOI: 10.1080/10485252.2022.2028281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Alexander Kreiss
- Department of Statistics, London School of Economics, London, UK
- Faculty of Economics and Business (FEB), KU Leuven, Leuven, Belgium
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Carrouel F, Gadea E, Esparcieux A, Dimet J, Langlois ME, Perrier H, Dussart C, Bourgeois D. Saliva Quantification of SARS-CoV-2 in Real-Time PCR From Asymptomatic or Mild COVID-19 Adults. Front Microbiol 2022; 12:786042. [PMID: 35046915 PMCID: PMC8761670 DOI: 10.3389/fmicb.2021.786042] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/09/2021] [Indexed: 12/14/2022] Open
Abstract
The fast spread of COVID-19 is related to the highly infectious nature of SARS-CoV-2. The disease is suggested to be transmitted through saliva droplets and nasal discharge. The saliva quantification of SARS-CoV-2 in real-time PCR from asymptomatic or mild COVID-19 adults has not been fully documented. This study analyzed the relationship between salivary viral load on demographics and clinical characteristics including symptoms, co-morbidities in 160 adults diagnosed as COVID-19 positive patients recruited between September and December 2020 in four French centers. Median initial viral load was 4.12 log10 copies/mL (IQR 2.95-5.16; range 0-10.19 log10 copies/mL). 68.6% of adults had no viral load detected. A median load reduction of 23% was observed between 0-2 days and 3-5 days, and of 11% between 3-5 days and 6-9 days for the delay from onset of symptoms to saliva sampling. No significant median difference between no-symptoms vs. symptoms patients was observed. Charge was consistently similar for the majority of the clinical symptoms excepted for headache with a median load value of 3.78 log10 copies/mL [1.95-4.58] (P < 0.003). SARS-CoV-2 RNA viral load was associated with headache and gastro-intestinal symptoms. The study found no statistically significant difference in viral loads between age groups, sex, or presence de co-morbidity. Our data suggest that oral cavity is an important site for SARS-CoV-2 infection and implicate saliva as a potential route of SARS-CoV-2 transmission.
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Affiliation(s)
- Florence Carrouel
- Health, Systemic, Process, UR4129 Research Unit, University Claude Bernard Lyon 1, University of Lyon, Lyon, France
| | - Emilie Gadea
- Equipe SNA-EPIS, EA4607, University Jean Monnet, Saint-Etienne, France
- Clinical Research Unit, Emile Roux Hospital Center, Le Puy-en-Velay, France
| | - Aurélie Esparcieux
- Department of Internal Medicine and Infectious Diseases, Protestant Infirmary, Caluire-et-Cuire, France
| | - Jérome Dimet
- Clinical Research Center, Intercommunal Hospital Center of Mont de Marsan et du Pays des Sources, Mont de Marsan, France
| | - Marie Elodie Langlois
- Department of Internal Medicine and Infectious Diseases, Saint Joseph Saint Luc Hospital, Lyon, France
| | - Hervé Perrier
- Clinical Research Unit, Protestant Infirmary, Lyon, France
| | - Claude Dussart
- Health, Systemic, Process, UR4129 Research Unit, University Claude Bernard Lyon 1, University of Lyon, Lyon, France
| | - Denis Bourgeois
- Health, Systemic, Process, UR4129 Research Unit, University Claude Bernard Lyon 1, University of Lyon, Lyon, France
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Moses ME, Hofmeyr S, Cannon JL, Andrews A, Gridley R, Hinga M, Leyba K, Pribisova A, Surjadidjaja V, Tasnim H, Forrest S. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection. PLoS Comput Biol 2021; 17:e1009735. [PMID: 34941862 PMCID: PMC8740970 DOI: 10.1371/journal.pcbi.1009735] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/07/2022] [Accepted: 12/09/2021] [Indexed: 01/03/2023] Open
Abstract
A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.
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Affiliation(s)
- Melanie E. Moses
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
| | - Steven Hofmeyr
- Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Judy L. Cannon
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Akil Andrews
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Rebekah Gridley
- Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America
| | - Monica Hinga
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Kirtus Leyba
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
| | - Abigail Pribisova
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Vanessa Surjadidjaja
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Humayra Tasnim
- Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Stephanie Forrest
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America
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Sachak-Patwa R, Byrne HM, Dyson L, Thompson RN. The risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions. COMMUNICATIONS MEDICINE 2021; 1:39. [PMID: 35602220 PMCID: PMC9053223 DOI: 10.1038/s43856-021-00038-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/03/2021] [Indexed: 12/23/2022] Open
Abstract
Background Countries around the world have introduced travel restrictions to reduce SARS-CoV-2 transmission. As vaccines are gradually rolled out, attention has turned to when travel restrictions and other non-pharmaceutical interventions (NPIs) can be relaxed. Methods Using SARS-CoV-2 as a case study, we develop a mathematical branching process model to assess the risk that, following the removal of NPIs, cases arriving in low prevalence settings initiate a local outbreak. Our model accounts for changes in background population immunity due to vaccination. We consider two locations with low prevalence in which the vaccine rollout has progressed quickly – specifically, the Isle of Man (a British crown dependency in the Irish Sea) and the country of Israel. Results We show that the outbreak risk is unlikely to be eliminated completely when travel restrictions and other NPIs are removed. This general result is the most important finding of this study, rather than exact quantitative outbreak risk estimates in different locations. It holds even once vaccine programmes are completed. Key factors underlying this result are the potential for transmission even following vaccination, incomplete vaccine uptake, and the recent emergence of SARS-CoV-2 variants with increased transmissibility. Conclusions Combined, the factors described above suggest that, when travel restrictions are relaxed, it may still be necessary to implement surveillance of incoming passengers to identify infected individuals quickly. This measure, as well as tracing and testing (and/or isolating) contacts of detected infected passengers, remains useful to suppress potential outbreaks while global case numbers are high. The effectiveness of public health measures against COVID-19 has varied between countries, with some experiencing many infections and others containing transmission successfully. As vaccines are deployed, an important challenge is deciding when to relax measures. Here, we consider locations with few cases, and investigate whether vaccination can ever eliminate the risk of COVID-19 outbreaks completely, allowing measures to be removed risk-free. Using a mathematical model, we demonstrate that there is still a risk that imported cases initiate outbreaks when measures are removed, even if most of the population is fully vaccinated. This highlights the need for continued vigilance in low prevalence settings to prevent imported cases leading to local transmission. Until case numbers are reduced globally, so that SARS-CoV-2 spread between countries is less likely, the risk of outbreaks in low prevalence settings will remain. Sachak-Patwa et al. estimate the risk of SARS-CoV-2 outbreaks in low prevalence settings following the removal of travel restrictions and other non-pharmaceutical interventions, with the Isle of Man and Israel as case studies. Using a branching process mathematical model, the authors show that even after a large proportion of the population is vaccinated, there remains a risk of local outbreaks from imported cases.
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Kim C, Kim YM, Heo N, Park E, Choi S, Jang S, Kim N, Kwon D, Park YJ, Choi B, Ha B, Jung K, Park C, Park S, Lee H. COVID-19 Outbreak in a Military Unit in Korea. Epidemiol Health 2021; 43:e2021065. [PMID: 34525497 PMCID: PMC8689117 DOI: 10.4178/epih.e2021065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/08/2021] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVES This study presents the response of a military unit to the COVID-19 outbreak in Gyeonggi Province. As soon as two soldiers were identified as index cases, the infectious disease investigators of the Gyeonggi Provincial Government, Korea Disease Control and Prevention Agency and the Armed Forces Epidemiologic Investigation Center, discussed the investigation and response plan for an imminent massive outbreak. METHODS The joint immediate response team (IRT) conducted interviews with confirmed patients with COVID-19, reviewed medical records, performed contact tracing using global positioning system (GPS), and undertook a field investigation. For risk assessment, the joint IRT visited all eight sites of the military units and the army chaplain's church to evaluate the transmission risk of each site. The evaluation items included the size of the site, the use of air conditioning, whether windows were opened, and whether masks were worn. A pooled testing was used for a low-risk population to quickly detect the spread of COVID-19 in the military base. RESULTS A day before the symptom onset of the index case, the lecturer and >50% of the attendees were infected with COVID-19 while attending a lecture that lasted 2 h and 30 min. Attendees were not wearing masks and were in a poorly ventilated room. CONCLUSION Since the disease can be spread before symptom onset, contact tracing must be performed to investigate potential exposures prior to symptom onset and manage any exposed persons.
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Affiliation(s)
- Chanhee Kim
- Infectious Disease Control Center, Gyeonggi Provincial Government, Suwon, Korea
| | - Young-Man Kim
- Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Namwoo Heo
- Infectious Disease Control Center, Gyeonggi Provincial Government, Suwon, Korea
| | - Eunjung Park
- Infectious Disease Control Center, Gyeonggi Provincial Government, Suwon, Korea
| | - Sojin Choi
- Infectious Disease Control Center, Gyeonggi Provincial Government, Suwon, Korea
| | - Sehyuk Jang
- Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Nayoung Kim
- Regional Center for Disease Control and Prevention, Korea Disease Control and Prevention Agency, Seoul, Korea
| | - Donghyok Kwon
- Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Young-Joon Park
- Central Disease Control Headquarters, Korea Disease Control and Prevention Agency, Cheongju, Korea
| | - Byeongseop Choi
- Republic of Korea Armed Forces Medical Command, Korea Army, Seongnam, Korea
| | - Beomman Ha
- Republic of Korea Army Headquarters, Korea Army, Gyerong, Korea
| | - Kyounghwa Jung
- Republic of Korea Army Headquarters, Korea Army, Gyerong, Korea
| | - Changbo Park
- Republic of Korea Armed Forces Epidemiologic Investigation Center, Korea Army, Seongnam, Korea
| | - Sejin Park
- Republic of Korea Armed Forces Medical Command, Korea Army, Seongnam, Korea
| | - Heeyoung Lee
- Center for Preventive Medicine and Public Health, Seoul National University Bundang Hospital, Seongnam, Korea
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Akhmetzhanov AR, Jung SM, Cheng HY, Thompson RN. A hospital-related outbreak of SARS-CoV-2 associated with variant Epsilon (B.1.429) in Taiwan: transmission potential and outbreak containment under intensified contact tracing, January-February 2021. Int J Infect Dis 2021; 110:15-20. [PMID: 34146689 PMCID: PMC8214728 DOI: 10.1016/j.ijid.2021.06.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES A hospital-related cluster of 22 cases of coronavirus disease 2019 (COVID-19) occurred in Taiwan in January-February 2021. Rigorous control measures were introduced and could only be relaxed once the outbreak was declared over. Each day after the apparent outbreak end, we estimated the risk of future cases occurring in order to inform decision-making. METHODS Probabilistic transmission networks were reconstructed, and transmission parameters (the reproduction number R and overdispersion parameter k) were estimated. The reporting delay during the outbreak was estimated (Scenario 1). In addition, a counterfactual scenario with less effective interventions characterized by a longer reporting delay was considered (Scenario 2). Each day, the risk of future cases was estimated under both scenarios. RESULTS The values of R and k were estimated to be 1.30 ((95% credible interval (CI) 0.57-3.80) and 0.38 (95% CI 0.12-1.20), respectively. The mean reporting delays considered were 2.5 days (Scenario 1) and 7.8 days (Scenario 2). Following the final case, ttthe inferred probability of future cases occurring declined more quickly in Scenario 1 than Scenario 2. CONCLUSIONS Rigorous control measures allowed the outbreak to be declared over quickly following outbreak containment. This highlights the need for effective interventions, not only to reduce cases during outbreaks but also to allow outbreaks to be declared over with confidence.
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Affiliation(s)
| | - Sung-Mok Jung
- School of Public Health, Kyoto University, Kyoto, Japan; Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Hao-Yuan Cheng
- Epidemic Intelligence Centre, Taiwan Centres for Disease Control, Taipei, Taiwan
| | - Robin N Thompson
- Mathematics Institute, University of Warwick, Coventry, UK; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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