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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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Sah P, Fitzpatrick MC, Zimmer CF, Abdollahi E, Juden-Kelly L, Moghadas SM, Singer BH, Galvani AP. Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis. Proc Natl Acad Sci U S A 2021; 118:e2109229118. [PMID: 34376550 PMCID: PMC8403749 DOI: 10.1073/pnas.2109229118] [Citation(s) in RCA: 255] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Quantification of asymptomatic infections is fundamental for effective public health responses to the COVID-19 pandemic. Discrepancies regarding the extent of asymptomaticity have arisen from inconsistent terminology as well as conflation of index and secondary cases which biases toward lower asymptomaticity. We searched PubMed, Embase, Web of Science, and World Health Organization Global Research Database on COVID-19 between January 1, 2020 and April 2, 2021 to identify studies that reported silent infections at the time of testing, whether presymptomatic or asymptomatic. Index cases were removed to minimize representational bias that would result in overestimation of symptomaticity. By analyzing over 350 studies, we estimate that the percentage of infections that never developed clinical symptoms, and thus were truly asymptomatic, was 35.1% (95% CI: 30.7 to 39.9%). At the time of testing, 42.8% (95% prediction interval: 5.2 to 91.1%) of cases exhibited no symptoms, a group comprising both asymptomatic and presymptomatic infections. Asymptomaticity was significantly lower among the elderly, at 19.7% (95% CI: 12.7 to 29.4%) compared with children at 46.7% (95% CI: 32.0 to 62.0%). We also found that cases with comorbidities had significantly lower asymptomaticity compared to cases with no underlying medical conditions. Without proactive policies to detect asymptomatic infections, such as rapid contact tracing, prolonged efforts for pandemic control may be needed even in the presence of vaccination.
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Affiliation(s)
- Pratha Sah
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
| | - Meagan C Fitzpatrick
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Charlotte F Zimmer
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
| | - Elaheh Abdollahi
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Lyndon Juden-Kelly
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Seyed M Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Burton H Singer
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
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Abstract
BACKGROUND Asymptomatic infection seems to be a notable feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), but the prevalence is uncertain. PURPOSE To estimate the proportion of persons infected with SARS-CoV-2 who never develop symptoms. DATA SOURCES Searches of Google News, Google Scholar, medRxiv, and PubMed using the keywords antibodies, asymptomatic, coronavirus, COVID-19, PCR, seroprevalence, and SARS-CoV-2. STUDY SELECTION Observational, descriptive studies and reports of mass screening for SARS-CoV-2 that were either cross-sectional or longitudinal in design; were published through 17 November 2020; and involved SARS-CoV-2 nucleic acid or antibody testing of a target population, regardless of current symptomatic status, over a defined period. DATA EXTRACTION The authors collaboratively extracted data on the study design, type of testing performed, number of participants, criteria for determining symptomatic status, testing results, and setting. DATA SYNTHESIS Sixty-one eligible studies and reports were identified, of which 43 used polymerase chain reaction (PCR) testing of nasopharyngeal swabs to detect current SARS-CoV-2 infection and 18 used antibody testing to detect current or prior infection. In the 14 studies with longitudinal data that reported information on the evolution of symptomatic status, nearly three quarters of persons who tested positive but had no symptoms at the time of testing remained asymptomatic. The highest-quality evidence comes from nationwide, representative serosurveys of England (n = 365 104) and Spain (n = 61 075), which suggest that at least one third of SARS-CoV-2 infections are asymptomatic. LIMITATION For PCR-based studies, data are limited to distinguish presymptomatic from asymptomatic infection. Heterogeneity precluded formal quantitative syntheses. CONCLUSION Available data suggest that at least one third of SARS-CoV-2 infections are asymptomatic. Longitudinal studies suggest that nearly three quarters of persons who receive a positive PCR test result but have no symptoms at the time of testing will remain asymptomatic. Control strategies for COVID-19 should be altered, taking into account the prevalence and transmission risk of asymptomatic SARS-CoV-2 infection. PRIMARY FUNDING SOURCE National Institutes of Health.
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Affiliation(s)
- Daniel P Oran
- Scripps Research Translational Institute, La Jolla, California (D.P.O., E.J.T.)
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, California (D.P.O., E.J.T.)
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