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Bretaña NA, Kwon JA, Grant L, Galouzis J, McGrath C, Hoey W, Blogg J, Lloyd AR, Gray RT. Controlling COVID-19 outbreaks in the correctional setting: A mathematical modelling study. PLoS One 2024; 19:e0303062. [PMID: 38758971 PMCID: PMC11101071 DOI: 10.1371/journal.pone.0303062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
Correctional centres (termed here 'prisons') are at high risk of COVID-19 and have featured major outbreaks worldwide. Inevitable close contacts, frequent inmate movements, and a disproportionate burden of co-morbidities mean these environments need to be prioritised in any public health response to respiratory pathogens such as COVID-19. We developed an individual-based SARS-CoV-2 transmission model for the prison system in New South Wales, Australia - incorporating all 33 correctional centres, 13,458 inmates, 578 healthcare and 6,909 custodial staff. Potential COVID-19 disease outbreaks were assessed under various mitigation strategies, including quarantine on entry, isolation of cases, rapid antigen testing of staff, as well as immunisation.Without control measures, the model projected a peak of 472 new infections daily by day 35 across the prison system, with all inmates infected by day 120. The most effective individual mitigation strategies were high immunisation coverage and prompt lockdown of centres with infected inmates which reduced outbreak size by 62-73%. Other than immunisation, the combination of quarantine of inmates at entry, isolation of proven or suspected cases, and widespread use of personal protective equipment by staff and inmates was the most effective strategy. High immunisation coverage mitigates the spread of COVID-19 within and between correctional settings but is insufficient alone. Maintaining quarantine and isolation, along with high immunisation levels, will allow correctional systems to function with a low risk of outbreaks. These results have informed public health policy for respiratory pathogens in Australian correctional systems.
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Affiliation(s)
- Neil Arvin Bretaña
- Allied Health and Human Performance, University of South Australia, Australia
| | - Jisoo A. Kwon
- Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | | | | | - Colette McGrath
- Justice Health and Forensic Mental Health Network NSW, Australia
| | - Wendy Hoey
- Justice Health and Forensic Mental Health Network NSW, Australia
| | - James Blogg
- Justice Health and Forensic Mental Health Network NSW, Australia
| | - Andrew R. Lloyd
- Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia
| | - Richard T Gray
- Kirby Institute, UNSW Sydney, Sydney, New South Wales, Australia
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Saber LB, Kennedy SS, Yang Y, Moore KN, Wang Y, Hilton SP, Chang TY, Liu P, Phillips VL, Akiyama MJ, Moe CL, Spaulding AC. Correlation of SARS-CoV-2 in Wastewater and Individual Testing Results in a Jail, Atlanta, Georgia, USA. Emerg Infect Dis 2024; 30:S21-S27. [PMID: 38561638 PMCID: PMC10986836 DOI: 10.3201/eid3013.230775] [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] [Indexed: 04/04/2024] Open
Abstract
Institution-level wastewater-based surveillance was implemented during the COVID-19 pandemic, including in carceral facilities. We examined the relationship between COVID-19 diagnostic test results of residents in a jail in Atlanta, Georgia, USA (average population ≈2,700), and quantitative reverse transcription PCR signal for SARS-CoV-2 in weekly wastewater samples collected during October 2021‒May 2022. The jail offered residents rapid antigen testing at entry and periodic mass screenings by reverse transcription PCR of self-collected nasal swab specimens. We aggregated individual test data, calculated the Spearman correlation coefficient, and performed logistic regression to examine the relationship between strength of SARS-CoV-2 PCR signal (cycle threshold value) in wastewater and percentage of jail population that tested positive for COVID-19. Of 13,745 nasal specimens collected, 3.9% were COVID-positive (range 0%-29.5% per week). We observed a strong inverse correlation between diagnostic test positivity and cycle threshold value (r = -0.67; p<0.01). Wastewater-based surveillance represents an effective strategy for jailwide surveillance of COVID-19.
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Heidecke J, Fuhrmann J, Barbarossa MV. A mathematical model to assess the effectiveness of test-trace-isolate-and-quarantine under limited capacities. PLoS One 2024; 19:e0299880. [PMID: 38470895 DOI: 10.1371/journal.pone.0299880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/17/2024] [Indexed: 03/14/2024] Open
Abstract
Diagnostic testing followed by isolation of identified cases with subsequent tracing and quarantine of close contacts-often referred to as test-trace-isolate-and-quarantine (TTIQ) strategy-is one of the cornerstone measures of infectious disease control. The COVID-19 pandemic has highlighted that an appropriate response to outbreaks of infectious diseases requires a firm understanding of the effectiveness of such containment strategies. To this end, mathematical models provide a promising tool. In this work, we present a delay differential equation model of TTIQ interventions for infectious disease control. Our model incorporates the assumption of limited TTIQ capacities, providing insights into the reduced effectiveness of testing and tracing in high prevalence scenarios. In addition, we account for potential transmission during the early phase of an infection, including presymptomatic transmission, which may be particularly adverse to a TTIQ based control. Our numerical experiments inspired by the early spread of COVID-19 in Germany demonstrate the effectiveness of TTIQ in a scenario where immunity within the population is low and pharmaceutical interventions are absent, which is representative of a typical situation during the (re-)emergence of infectious diseases for which therapeutic drugs or vaccines are not yet available. Stability and sensitivity analyses reveal both disease-dependent and disease-independent factors that impede or enhance the success of TTIQ. Studying the diminishing impact of TTIQ along simulations of an epidemic wave, we highlight consequences for intervention strategies.
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Affiliation(s)
- Julian Heidecke
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Heidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany
| | - Jan Fuhrmann
- Institute of Applied Mathematics, Heidelberg University, Heidelberg, Germany
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Smith DRM, Chervet S, Pinettes T, Shirreff G, Jijón S, Oodally A, Jean K, Opatowski L, Kernéis S, Temime L. How have mathematical models contributed to understanding the transmission and control of SARS-CoV-2 in healthcare settings? A systematic search and review. J Hosp Infect 2023; 141:132-141. [PMID: 37734676 DOI: 10.1016/j.jhin.2023.07.028] [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: 03/24/2023] [Accepted: 07/04/2023] [Indexed: 09/23/2023]
Abstract
Since the onset of the COVID-19 pandemic, mathematical models have been widely used to inform public health recommendations regarding COVID-19 control in healthcare settings. The objective of this study was to systematically review SARS-CoV-2 transmission models in healthcare settings, and to summarize their contributions to understanding nosocomial COVID-19. A systematic search and review of published articles indexed in PubMed was carried out. Modelling studies describing dynamic inter-individual transmission of SARS-CoV-2 in healthcare settings, published by mid-February 2022 were included. Models have mostly focused on acute-care and long-term-care facilities in high-income countries. Models have quantified outbreak risk, showing great variation across settings and pandemic periods. Regarding surveillance, routine testing rather than symptom-based was highlighted as essential for COVID-19 prevention due to high rates of silent transmission. Surveillance impacts depended critically on testing frequency, diagnostic sensitivity, and turn-around time. Healthcare re-organization also proved to have large epidemiological impacts: beyond obvious benefits of isolating cases and limiting inter-individual contact, more complex strategies (staggered staff scheduling, immune-based cohorting) reduced infection risk. Finally, vaccination impact, while highly effective for limiting COVID-19 burden, varied substantially depending on assumed mechanistic impacts on infection acquisition, symptom onset and transmission. Modelling results form an extensive evidence base that may inform control strategies for future waves of SARS-CoV-2 and other viral respiratory pathogens. We propose new avenues for future models of healthcare-associated outbreaks, with the aim of enhancing their efficiency and contributions to decision-making.
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Affiliation(s)
- D R M Smith
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - S Chervet
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Université Paris-Cité, INSERM, IAME, F-75018, Paris, France
| | - T Pinettes
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - G Shirreff
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - S Jijón
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - A Oodally
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France; Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France
| | - K Jean
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
| | - L Opatowski
- Anti-infective Evasion and Pharmacoepidemiology Team, CESP, Université Paris-Saclay, UVSQ, INSERM U1018, Montigny-le-Bretonneux, France; Institut Pasteur, Université Paris-Cité, Epidemiology and Modelling of Antibiotic Evasion (EMAE), F-75015 Paris, France
| | - S Kernéis
- Université Paris-Cité, INSERM, IAME, F-75018, Paris, France; Equipe de Prévention du Risque Infectieux (EPRI), AP-HP, Hôpital Bichat, F-75018 Paris, France.
| | - L Temime
- Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers, F-75003 Paris, France; Unité PACRI, Institut Pasteur, Conservatoire National des Arts et Métiers, Paris, France
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Henriques HR, Sousa D, Faria J, Pinto J, Costa A, Henriques MA, Durão MC. Learning from the covid-19 outbreaks in long-term care facilities: a systematic review. BMC Geriatr 2023; 23:618. [PMID: 37784017 PMCID: PMC10546730 DOI: 10.1186/s12877-023-04319-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has devastatingly affected Long-Term Care Facilities (LTCF), exposing aging people, staff members, and visitors. The world has learned through the pandemic and lessons can be taken to adopt effective measures to deal with COVID-19 outbreaks in LTCF. We aimed to systematically review the available evidence on the effect of measures to minimize the risk of transmission of COVID-19 in LTCs during outbreaks since 2021. METHODS The search method was guided by the preferred reporting items for systematic reviews (PRISMA) and the reporting guideline synthesis without meta-analysis (SWiM) in systematic reviews. The search was performed in April 2023. Observational and interventional studies from the databases of PubMed, Web of Science, Scopus, Cochrane Systematic Reviews, CINAHL, and Academic Search were systematically reviewed. We included studies conducted in the LTCF with outbreaks that quantitatively assess the effect of non-pharmacological measures on cases of COVID-19. Two review authors independently reviewed titles for inclusion, extracted data, and undertook the risk of bias according to pre-specified criteria. The quality of studies was analyzed using the Joanna Briggs Institute Critical Appraisal. RESULTS Thirteen studies were included, with 8442 LTCF experiencing COVID-19 outbreaks and 598 thousand participants (residents and staff members). Prevention and control of COVID-19 infection interventions were grouped into three themes: strategic, tactical, and operational measures. The strategic measures reveal the importance of COVID-19 prevention and control as LTCF structural characteristics, namely the LTCF size, new admissions, infection control surveillance, and architectural structure. At the tactical level, the lack of personal and long staff shifts is related to COVID-19's spread. Operational measures with a favorable effect on preventing COVID-19 transmission are sufficient. Personal protective equipment stock, correct mask use, signaling, social distancing, and resident cohorting. CONCLUSIONS Operational, tactical, and strategic approaches may have a favorable effect on preventing the spread of COVID-19 in LTCFs experiencing outbreaks. Given the heterogeneous nature of the measures, performing a meta-analysis was not possible. Future research should use more robust study designs to explore similar infection control measures in LTCFs during endemic situations with comparable outbreaks. TRIAL REGISTRATION The protocol of this systematic review was registered in PROSPERO (CRD42020214566).
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Affiliation(s)
- Helga Rafael Henriques
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal.
| | - Diana Sousa
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - José Faria
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - Joana Pinto
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - Andreia Costa
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
- Instituto de Saúde Ambiental - ISAMB, Lisbon Medical School - Avenida Professor Egas Moniz MB, 1649-028, Lisbon, Portugal
| | - Maria Adriana Henriques
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
- Instituto de Saúde Ambiental - ISAMB, Lisbon Medical School - Avenida Professor Egas Moniz MB, 1649-028, Lisbon, Portugal
| | - Maria Cândida Durão
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
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6
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Dubey P, Hoover CM, Lu P, Blumberg S, Porco TC, Parsons TL, Worden L. Rates of SARS-CoV-2 transmission between and into California state prisons. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.24.23294583. [PMID: 37662306 PMCID: PMC10473789 DOI: 10.1101/2023.08.24.23294583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Correctional institutions are a crucial hotspot amplifying SARS-CoV-2 spread and disease disparity in the U.S. In the California state prison system, multiple massive outbreaks have been caused by transmission between prisons. Correctional staff are a likely vector for transmission into the prison system from surrounding communities. We used publicly available data to estimate the magnitude of flows to and between California state prisons, estimating rates of transmission from communities to prison staff and residents, among and between residents and staff within facilities, and between staff and residents of distinct facilities in the state's 34 prisons through March 22, 2021. We use a mechanistic model, the Hawkes process, reflecting the dynamics of SARS-CoV-2 transmission, for joint estimation of transmission rates. Using nested models for hypothesis testing, we compared the results to simplified models (i) without transmission between prisons, and (ii) with no distinction between prison staff and residents. We estimated that transmission between different facilities' staff is a significant cause of disease spread, and that staff are a vector of transmission between resident populations and outside communities. While increased screening and vaccination of correctional staff may help reduce introductions, large-scale decarceration remains crucially needed as more limited measures are not likely to prevent large-scale disease spread.
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Affiliation(s)
- Preeti Dubey
- Francis I. Proctor Foundation, University of California, San Francisco, Calif., USA
| | | | - Phoebe Lu
- Francis I. Proctor Foundation, University of California, San Francisco, Calif., USA
| | - Seth Blumberg
- Francis I. Proctor Foundation, University of California, San Francisco, Calif., USA
- Department of Medicine, Division of Hospital Medicine, University of California, San Francisco, Calif., USA
| | - Travis C. Porco
- Francis I. Proctor Foundation, University of California, San Francisco, Calif., USA
| | - Todd L. Parsons
- CNRS & Laboratoire de Probabilités, Statistique et Modélisation, Campus Pierre et Marie Curie, Sorbonne Université, Paris, France
| | - Lee Worden
- Francis I. Proctor Foundation, University of California, San Francisco, Calif., USA
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Bouba A, Helle KB, Schneider KA. Predicting the combined effects of case isolation, safe funeral practices, and contact tracing during Ebola virus disease outbreaks. PLoS One 2023; 18:e0276351. [PMID: 36649296 PMCID: PMC9844901 DOI: 10.1371/journal.pone.0276351] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/19/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The recent outbreaks of Ebola virus disease (EVD) in Uganda and the Marburg virus disease (MVD) in Ghana reflect a persisting threat of Filoviridae to the global health community. Characteristic of Filoviridae are not just their high case fatality rates, but also that corpses are highly contagious and prone to cause infections in the absence of appropriate precautions. Vaccines against the most virulent Ebolavirus species, the Zaire ebolavirus (ZEBOV) are approved. However, there exists no approved vaccine or treatment against the Sudan ebolavirus (SUDV) which causes the current outbreak of EVD. Hence, the control of the outbreak relies on case isolation, safe funeral practices, and contact tracing. So far, the effectiveness of these control measures was studied only separately by epidemiological models, while the impact of their interaction is unclear. METHODS AND FINDINGS To sustain decision making in public health-emergency management, we introduce a predictive model to study the interaction of case isolation, safe funeral practices, and contact tracing. The model is a complex extension of an SEIR-type model, and serves as an epidemic preparedness tool. The model considers different phases of the EVD infections, the possibility of infections being treated in isolation (if appropriately diagnosed), in hospital (if not properly diagnosed), or at home (if the infected do not present to hospital for whatever reason). It is assumed that the corpses of those who died in isolation are buried with proper safety measures, while those who die outside isolation might be buried unsafely, such that transmission can occur during the funeral. Furthermore, the contacts of individuals in isolation will be traced. Based on parameter estimates from the scientific literature, the model suggests that proper diagnosis and hence isolation of cases has the highest impact in reducing the size of the outbreak. However, the combination of case isolation and safe funeral practices alone are insufficient to fully contain the epidemic under plausible parameters. This changes if these measures are combined with contact tracing. In addition, shortening the time to successfully trace back contacts contribute substantially to contain the outbreak. CONCLUSIONS In the absence of an approved vaccine and treatment, EVD management by proper and fast diagnostics in combination with epidemic awareness are fundamental. Awareness will particularly facilitate contact tracing and safe funeral practices. Moreover, proper and fast diagnostics are a major determinant of case isolation. The model introduced here is not just applicable to EVD, but also to other viral hemorrhagic fevers such as the MVD or the Lassa fever.
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Affiliation(s)
- Aliou Bouba
- Hochschule Mittweida, University of Applied Sciences Mittweida, Mittweida, Germany
- African Institute for Mathematical Sciences (AIMS), Limbe, Cameroon
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Preuß B, Fischer L, Schmidt A, Seibert K, Hoel V, Domhoff D, Heinze F, Brannath W, Wolf-Ostermann K, Rothgang H. COVID-19 in German Nursing Homes: The Impact of Facilities' Structures on the Morbidity and Mortality of Residents-An Analysis of Two Cross-Sectional Surveys. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:610. [PMID: 36612931 PMCID: PMC9819748 DOI: 10.3390/ijerph20010610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/02/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic constitutes an exceptional risk to people living and working in nursing homes (NHs). There were numerous cases and deaths among NH residents, especially at the beginning of the pandemic when no vaccines had yet been developed. Besides regional differences, individual NHs showed vast differences in the number of cases and deaths: while in some, nobody was affected, in others, many people were infected or died. We examine the relationship between facility structures and their effect on infections and deaths of NH residents and infections of staff, while considering the influence of COVID-19 prevalence among the general population on the incidence of infection in NHs. Two nationwide German surveys were conducted during the first and second pandemic waves, comprising responses from n = 1067 NHs. Different hurdle models, with an assumed Bernoulli distribution for zero density and a negative binomial distribution for the count density, were fitted. It can be shown that the probability of an outbreak, and the number of cases/deaths among residents and staff, increased with an increasing number of staff and the general spread of the virus. Therefore, reverse isolation of NH residents was an inadequate form of protection, especially at the beginning of the pandemic.
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Affiliation(s)
- Benedikt Preuß
- SOCIUM Research Center on Inequality and Social Policy, University of Bremen, 28359 Bremen, Germany
| | - Lasse Fischer
- Competence Center for Clinical Trials Bremen (KKSB), University of Bremen, 28359 Bremen, Germany
| | - Annika Schmidt
- Institute for Public Health and Nursing Research, University of Bremen, 28359 Bremen, Germany
| | - Kathrin Seibert
- Institute for Public Health and Nursing Research, University of Bremen, 28359 Bremen, Germany
- Leibniz Science Campus Digital Public Health, 28359 Bremen, Germany
| | - Viktoria Hoel
- Institute for Public Health and Nursing Research, University of Bremen, 28359 Bremen, Germany
- Leibniz Science Campus Digital Public Health, 28359 Bremen, Germany
| | - Dominik Domhoff
- Institute for Public Health and Nursing Research, University of Bremen, 28359 Bremen, Germany
- Leibniz Science Campus Digital Public Health, 28359 Bremen, Germany
| | - Franziska Heinze
- SOCIUM Research Center on Inequality and Social Policy, University of Bremen, 28359 Bremen, Germany
| | - Werner Brannath
- Competence Center for Clinical Trials Bremen (KKSB), University of Bremen, 28359 Bremen, Germany
| | - Karin Wolf-Ostermann
- Institute for Public Health and Nursing Research, University of Bremen, 28359 Bremen, Germany
- Leibniz Science Campus Digital Public Health, 28359 Bremen, Germany
| | - Heinz Rothgang
- SOCIUM Research Center on Inequality and Social Policy, University of Bremen, 28359 Bremen, Germany
- Leibniz Science Campus Digital Public Health, 28359 Bremen, Germany
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Rosello A, Barnard RC, Smith DRM, Evans S, Grimm F, Davies NG, Deeny SR, Knight GM, Edmunds WJ. Impact of non-pharmaceutical interventions on SARS-CoV-2 outbreaks in English care homes: a modelling study. BMC Infect Dis 2022; 22:324. [PMID: 35365070 PMCID: PMC8972713 DOI: 10.1186/s12879-022-07268-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 03/13/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND COVID-19 outbreaks still occur in English care homes despite the interventions in place. METHODS We developed a stochastic compartmental model to simulate the spread of SARS-CoV-2 within an English care home. We quantified the outbreak risk with baseline non-pharmaceutical interventions (NPIs) already in place, the role of community prevalence in driving outbreaks, and the relative contribution of all importation routes into a fully susceptible care home. We also considered the potential impact of additional control measures in care homes with and without immunity, namely: increasing staff and resident testing frequency, using lateral flow antigen testing (LFD) tests instead of polymerase chain reaction (PCR), enhancing infection prevention and control (IPC), increasing the proportion of residents isolated, shortening the delay to isolation, improving the effectiveness of isolation, restricting visitors and limiting staff to working in one care home. We additionally present a Shiny application for users to apply this model to their facility of interest, specifying care home, outbreak and intervention characteristics. RESULTS The model suggests that importation of SARS-CoV-2 by staff, from the community, is the main driver of outbreaks, that importation by visitors or from hospitals is rare, and that the past testing strategy (monthly testing of residents and daily testing of staff by PCR) likely provides negligible benefit in preventing outbreaks. Daily staff testing by LFD was 39% (95% 18-55%) effective in preventing outbreaks at 30 days compared to no testing. CONCLUSIONS Increasing the frequency of testing in staff and enhancing IPC are important to preventing importations to the care home. Further work is needed to understand the impact of vaccination in this population, which is likely to be very effective in preventing outbreaks.
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Affiliation(s)
- Alicia Rosello
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Rosanna C Barnard
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - David R M Smith
- Epidemiology and Modelling of Antibiotic Evasion (EMAE), Institut Pasteur, Paris, France
- Anti-infective Evasion and Pharmacoepidemiology Team, Université Paris-Saclay, UVSQ, CESP, Montigny-Le-Bretonneux, Inserm, France
- Modélisation, Épidémiologie Et Surveillance Des Risques Sanitaires (MESuRS), Conservatoire National Des Arts Et Métiers, Paris, France
| | - Stephanie Evans
- Healthcare Associated Infection and Antimicrobial Resistance Department, Public Health England, London, England
| | | | - Nicholas G Davies
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Gwenan M Knight
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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10
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Biby A, Wang X, Liu X, Roberson O, Henry A, Xia X. Rapid testing for coronavirus disease 2019 (COVID-19). MRS COMMUNICATIONS 2022; 12:12-23. [PMID: 35075405 PMCID: PMC8769796 DOI: 10.1557/s43579-021-00146-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 09/29/2021] [Indexed: 05/03/2023]
Abstract
Rapid testing, generally refers to the paper-based diagnostic platform known as "lateral flow assay" (LFA), has emerged as a critical asset to the containment of coronavirus disease 2019 (COVID-19) around the world. LFA technology stands out amongst peer platforms due to its cost-effective design, user-friendly interface, and low sample-to-readout times. This article aims to introduce its design, use, and practicality for the purpose of diagnosing SARS-CoV-2 infection. A connection is made from the normal COVID-19 immune response to the design and efficacy of rapid testing. Interference in test results is a challenge shared by most diagnostic platforms and can be rooted in various underlying issues. The current knowledge and situation about interference in rapid COVID-19 tests due to variant strains as well as vaccination are discussed. The cost and societal impact are reviewed as they play important roles in determining how to properly implement public testing practices. Perspectives on improving the performance, especially detection sensitivity, of LFA for COVID-19 are provided.
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Affiliation(s)
- Alexander Biby
- Department of Chemistry, University of Central Florida, Orlando, FL 32816 USA
| | - Xiaochuan Wang
- School of Social Work, University of Central Florida, Orlando, FL 32816 USA
| | - Xinliang Liu
- School of Global Health Management & Informatics, University of Central Florida, Orlando, FL 32816 USA
| | - Olivia Roberson
- Department of Chemistry, University of Central Florida, Orlando, FL 32816 USA
| | - Allya Henry
- School of Social Work, University of Central Florida, Orlando, FL 32816 USA
| | - Xiaohu Xia
- Department of Chemistry, University of Central Florida, Orlando, FL 32816 USA
- NanoScience Technology Center, University of Central Florida, Orlando, FL 32816 USA
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11
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Akashi Y, Kiyasu Y, Takeuchi Y, Kato D, Kuwahara M, Muramatsu S, Ueda A, Notake S, Nakamura K, Ishikawa H, Suzuki H. Evaluation and clinical implications of the time to a positive results of antigen testing for SARS-CoV-2. J Infect Chemother 2021; 28:248-251. [PMID: 34799237 PMCID: PMC8577995 DOI: 10.1016/j.jiac.2021.10.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/21/2021] [Accepted: 10/30/2021] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Antigen tests for severe acute respiratory coronavirus 2 sometimes show positive lines earlier than their specified read time, although the implication of getting the results at earlier time is not well understood. METHODS We prospectively collected additional nasopharyngeal samples from patients who had already tested positive for SARS-CoV-2 by reverse transcription PCR. The swab was used for an antigen test, QuickNavi™-COVID19 Ag, and the time periods to get positive results were measured. RESULTS In 84 of 96 (87.5%) analyzed cases, the results of QuickNavi™-COVID19 Ag were positive. The time to obtain positive results was 15.0 seconds in median (inter quartile range: 12.0-33.3, range 11-736) and was extended in samples with higher cycle thresholds (p < 0.001). Positive lines appeared within a minute in 85.7% of cases and within 5 min in 96.4%. CONCLUSION QuickNavi™-COVID19 Ag immediately showed positive results in most cases, and the time to a positive reaction may have indicated the viral load.
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Affiliation(s)
- Yusaku Akashi
- Division of Infectious Diseases, Department of Medicine, Tsukuba Medical Center Hospital, 1-3-1 Amakubo Tsukuba, Ibaraki, 3058558, Japan; Akashi Internal Medicine Clinic, 3-1-63 Asahigaoka, Kashiwara, Osaka, 5820026, Japan.
| | - Yoshihiko Kiyasu
- Division of Infectious Diseases, Department of Medicine, Tsukuba Medical Center Hospital, 1-3-1 Amakubo Tsukuba, Ibaraki, 3058558, Japan; Department of Infectious Diseases, University of Tsukuba Hospital, 2-1-1 Amakubo, Tsukuba, Ibaraki, 3058576, Japan.
| | - Yuto Takeuchi
- Division of Infectious Diseases, Department of Medicine, Tsukuba Medical Center Hospital, 1-3-1 Amakubo Tsukuba, Ibaraki, 3058558, Japan; Department of Infectious Diseases, University of Tsukuba Hospital, 2-1-1 Amakubo, Tsukuba, Ibaraki, 3058576, Japan.
| | - Daisuke Kato
- Research & Development Division, Reagent R&D Department, Gosen site, Denka Co., Ltd., 1-2-2 Minamihoncho, Gosen-shi, Niigata, 9591695, Japan.
| | - Miwa Kuwahara
- Research & Development Division, Reagent R&D Department, Gosen site, Denka Co., Ltd., 1-2-2 Minamihoncho, Gosen-shi, Niigata, 9591695, Japan.
| | - Shino Muramatsu
- Research & Development Division, Reagent R&D Department, Gosen site, Denka Co., Ltd., 1-2-2 Minamihoncho, Gosen-shi, Niigata, 9591695, Japan.
| | - Atsuo Ueda
- Department of Clinical Laboratory, Tsukuba Medical Center Hospital, 1-3-1 Amakubo, Tsukuba, Ibaraki, 3058558, Japan.
| | - Shigeyuki Notake
- Department of Clinical Laboratory, Tsukuba Medical Center Hospital, 1-3-1 Amakubo, Tsukuba, Ibaraki, 3058558, Japan.
| | - Koji Nakamura
- Department of Clinical Laboratory, Tsukuba Medical Center Hospital, 1-3-1 Amakubo, Tsukuba, Ibaraki, 3058558, Japan.
| | - Hiroichi Ishikawa
- Department of Respiratory Medicine, Tsukuba Medical Center Hospital, 1-3-1 Amakubo, Tsukuba, Ibaraki, 3058558, Japan.
| | - Hiromichi Suzuki
- Division of Infectious Diseases, Department of Medicine, Tsukuba Medical Center Hospital, 1-3-1 Amakubo Tsukuba, Ibaraki, 3058558, Japan; Department of Infectious Diseases, University of Tsukuba Hospital, 2-1-1 Amakubo, Tsukuba, Ibaraki, 3058576, Japan; Department of Infectious Diseases, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 3058575, Japan.
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12
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Stratil JM, Biallas RL, Burns J, Arnold L, Geffert K, Kunzler AM, Monsef I, Stadelmaier J, Wabnitz K, Litwin T, Kreutz C, Boger AH, Lindner S, Verboom B, Voss S, Movsisyan A. Non-pharmacological measures implemented in the setting of long-term care facilities to prevent SARS-CoV-2 infections and their consequences: a rapid review. Cochrane Database Syst Rev 2021; 9:CD015085. [PMID: 34523727 PMCID: PMC8442144 DOI: 10.1002/14651858.cd015085.pub2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Starting in late 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2, spread around the world. Long-term care facilities are at particularly high risk of outbreaks, and the burden of morbidity and mortality is very high among residents living in these facilities. OBJECTIVES To assess the effects of non-pharmacological measures implemented in long-term care facilities to prevent or reduce the transmission of SARS-CoV-2 infection among residents, staff, and visitors. SEARCH METHODS On 22 January 2021, we searched the Cochrane COVID-19 Study Register, WHO COVID-19 Global literature on coronavirus disease, Web of Science, and CINAHL. We also conducted backward citation searches of existing reviews. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies that assessed the effects of the measures implemented in long-term care facilities to protect residents and staff against SARS-CoV-2 infection. Primary outcomes were infections, hospitalisations and deaths due to COVID-19, contaminations of and outbreaks in long-term care facilities, and adverse health effects. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles, abstracts and full texts. One review author performed data extractions, risk of bias assessments and quality appraisals, and at least one other author checked their accuracy. Risk of bias and quality assessments were conducted using the ROBINS-I tool for cohort and interrupted-time-series studies, the Joanna Briggs Institute (JBI) checklist for case-control studies, and a bespoke tool for modelling studies. We synthesised findings narratively, focusing on the direction of effect. One review author assessed certainty of evidence with GRADE, with the author team critically discussing the ratings. MAIN RESULTS We included 11 observational studies and 11 modelling studies in the analysis. All studies were conducted in high-income countries. Most studies compared outcomes in long-term care facilities that implemented the measures with predicted or observed control scenarios without the measure (but often with baseline infection control measures also in place). Several modelling studies assessed additional comparator scenarios, such as comparing higher with lower rates of testing. There were serious concerns regarding risk of bias in almost all observational studies and major or critical concerns regarding the quality of many modelling studies. Most observational studies did not adequately control for confounding. Many modelling studies used inappropriate assumptions about the structure and input parameters of the models, and failed to adequately assess uncertainty. Overall, we identified five intervention domains, each including a number of specific measures. Entry regulation measures (4 observational studies; 4 modelling studies) Self-confinement of staff with residents may reduce the number of infections, probability of facility contamination, and number of deaths. Quarantine for new admissions may reduce the number of infections. Testing of new admissions and intensified testing of residents and of staff after holidays may reduce the number of infections, but the evidence is very uncertain. The evidence is very uncertain regarding whether restricting admissions of new residents reduces the number of infections, but the measure may reduce the probability of facility contamination. Visiting restrictions may reduce the number of infections and deaths. Furthermore, it may increase the probability of facility contamination, but the evidence is very uncertain. It is very uncertain how visiting restrictions may adversely affect the mental health of residents. Contact-regulating and transmission-reducing measures (6 observational studies; 2 modelling studies) Barrier nursing may increase the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent cleaning and environmental hygiene measures may reduce the number of infections, but the evidence is very uncertain. It is unclear how contact reduction measures affect the probability of outbreaks. These measures may reduce the number of infections, but the evidence is very uncertain. Personal hygiene measures may reduce the probability of outbreaks, but the evidence is very uncertain. Mask and personal protective equipment usage may reduce the number of infections, the probability of outbreaks, and the number of deaths, but the evidence is very uncertain. Cohorting residents and staff may reduce the number of infections, although evidence is very uncertain. Multicomponent contact -regulating and transmission -reducing measures may reduce the probability of outbreaks, but the evidence is very uncertain. Surveillance measures (2 observational studies; 6 modelling studies) Routine testing of residents and staff independent of symptoms may reduce the number of infections. It may reduce the probability of outbreaks, but the evidence is very uncertain. Evidence from one observational study suggests that the measure may reduce, while the evidence from one modelling study suggests that it probably reduces hospitalisations. The measure may reduce the number of deaths among residents, but the evidence on deaths among staff is unclear. Symptom-based surveillance testing may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Outbreak control measures (4 observational studies; 3 modelling studies) Separating infected and non-infected residents or staff caring for them may reduce the number of infections. The measure may reduce the probability of outbreaks and may reduce the number of deaths, but the evidence for the latter is very uncertain. Isolation of cases may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent measures (2 observational studies; 1 modelling study) A combination of multiple infection-control measures, including various combinations of the above categories, may reduce the number of infections and may reduce the number of deaths, but the evidence for the latter is very uncertain. AUTHORS' CONCLUSIONS This review provides a comprehensive framework and synthesis of a range of non-pharmacological measures implemented in long-term care facilities. These may prevent SARS-CoV-2 infections and their consequences. However, the certainty of evidence is predominantly low to very low, due to the limited availability of evidence and the design and quality of available studies. Therefore, true effects may be substantially different from those reported here. Overall, more studies producing stronger evidence on the effects of non-pharmacological measures are needed, especially in low- and middle-income countries and on possible unintended consequences of these measures. Future research should explore the reasons behind the paucity of evidence to guide pandemic research priority setting in the future.
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Affiliation(s)
- Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke L Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Laura Arnold
- Academy of Public Health Services, Duesseldorf, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Angela M Kunzler
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Anna Helen Boger
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Saskia Lindner
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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13
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Helle KB, Sadiku A, Zelleke GM, Ibrahim TB, Bouba A, Tsoungui Obama HC, Appiah V, Ngwa GA, Teboh-Ewungkem MI, Schneider KA. Is increased mortality by multiple exposures to COVID-19 an overseen factor when aiming for herd immunity? PLoS One 2021; 16:e0253758. [PMID: 34270576 PMCID: PMC8284653 DOI: 10.1371/journal.pone.0253758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 06/13/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Governments across the globe responded with different strategies to the COVID-19 pandemic. While some countries adopted measures, which have been perceived controversial, others pursued a strategy aiming for herd immunity. The latter is even more controversial and has been called unethical by the WHO Director-General. Inevitably, without proper control measures, viral diversity increases and multiple infectious exposures become common, when the pandemic reaches its maximum. This harbors not only a potential threat overseen by simplified theoretical arguments in support of herd immunity, but also deserves attention when assessing response measures to increasing numbers of infection. METHODS AND FINDINGS We extend the simulation model underlying the pandemic preparedness web interface CovidSim 1.1 (http://covidsim.eu/) to study the hypothetical effect of increased morbidity and mortality due to 'multi-infections', either acquired at by successive infective contacts during the course of one infection or by a single infective contact with a multi-infected individual. The simulations are adjusted to reflect roughly the situation in the USA. We assume a phase of general contact reduction ("lockdown") at the beginning of the epidemic and additional case-isolation measures. We study the hypothetical effects of varying enhancements in morbidity and mortality, different likelihoods of multi-infected individuals to spread multi-infections and different susceptibility to multi-infections in different disease phases. It is demonstrated that multi-infections lead to a slight reduction in the number of infections, as these are more likely to get isolated due to their higher morbidity. However, the latter substantially increases the number of deaths. Furthermore, simulations indicate that a potential second lockdown can substantially decrease the epidemic peak, the number of multi-infections and deaths. CONCLUSIONS Enhanced morbidity and mortality due to multiple disease exposure is a potential threat in the COVID-19 pandemic that deserves more attention. Particularly it underlines another facet questioning disease management strategies aiming for herd immunity.
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Affiliation(s)
- Kristina Barbara Helle
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
| | - Arlinda Sadiku
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
| | - Girma Mesfin Zelleke
- African Institute for Mathematical Sciences Cameroon, Limbe, Cameroon
- Department of Mathematics, University of Buea, Buea, Cameroon
| | - Toheeb Babatunde Ibrahim
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
- African Institute for Mathematical Sciences Cameroon, Limbe, Cameroon
| | - Aliou Bouba
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
- African Institute for Mathematical Sciences Cameroon, Limbe, Cameroon
| | | | - Vincent Appiah
- West African Centre for Cell Biology of Infectious Pathogens, University of Ghana, Accra, Ghana
| | - Gideon Akumah Ngwa
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
- Department of Mathematics, University of Buea, Buea, Cameroon
| | | | - Kristan Alexander Schneider
- Department of Applied Computer- and Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
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