Hayley A Thompson1, Andria Mousa1, Amy Dighe1, Han Fu1, Alberto Arnedo-Pena2,3, Peter Barrett4,5, Juan Bellido-Blasco2,3,6, Qifang Bi7, Antonio Caputi8, Liling Chaw9, Luigi De Maria8, Matthias Hoffmann10, Kiran Mahapure11, Kangqi Ng12, Jagadesan Raghuram12, Gurpreet Singh13, Biju Soman13, Vicente Soriano14, Francesca Valent15, Luigi Vimercati8, Liang En Wee16, Justin Wong9,17, Azra C Ghani1, Neil M Ferguson1. 1. MRC Centre for Global Infectious Disease Analysis & World Health Organization Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK. 2. Sección de Epidemiología, Centro de Salud Pública de Castellón, Valencia, Spain. 3. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Valencia, Spain. 4. School of Public Health, University College Cork, Cork, Ireland. 5. Irish Centre for Maternal and Child Health Research (INFANT), University College Cork, Cork, Ireland. 6. Facultad de Ciencias de la Salud, Universitat Jaime I (UJI), Castelló, Spain. 7. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. 8. Interdisciplinary Department of Medicine, University of Bari, Unit of Occupational Medicine, University Hospital of Bari, Bari, Italy. 9. PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Jalan Tungku Link, Brunei. 10. Division of General Internal Medicine, Infectious Diseases and Hospital Epidemiology, Cantonal Hospital Olten, Olten, Switzerland. 11. Department of Plastic Surgery, Dr Prabhakar Kore Hospital and MRC, Belgaum, Karnataka, India. 12. Changi General Hospital, Singapore. 13. Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India. 14. UNIR Health Sciences School & Medical Center, Madrid, Spain. 15. SOC Istituto di Igiene ed Epidemiologia Clinica, Azienda Sanitaria Universitaria Friuli Centrale, Udine, Italy. 16. Department of Infectious Diseases, Singapore General Hospital, Singapore, Singapore. 17. Disease Control Division, Ministry of Health, Brunei.
Abstract
BACKGROUND: Understanding the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is crucial for control policies, but evidence of transmission rates in different settings remains limited. METHODS: We conducted a systematic review to estimate secondary attack rates (SARs) and observed reproduction numbers (Robs) in different settings exploring differences by age, symptom status, and duration of exposure. To account for additional study heterogeneity, we employed a beta-binomial model to pool SARs across studies and a negative-binomial model to estimate Robs. RESULTS: Households showed the highest transmission rates, with a pooled SAR of 21.1% (95% confidence interval [CI]:17.4-24.8). SARs were significantly higher where the duration of household exposure exceeded 5 days compared with exposure of ≤5 days. SARs related to contacts at social events with family and friends were higher than those for low-risk casual contacts (5.9% vs 1.2%). Estimates of SARs and Robs for asymptomatic index cases were approximately one-seventh, and for presymptomatic two-thirds of those for symptomatic index cases. We found some evidence for reduced transmission potential both from and to individuals younger than 20 years of age in the household context, which is more limited when examining all settings. CONCLUSIONS: Our results suggest that exposure in settings with familiar contacts increases SARS-CoV-2 transmission potential. Additionally, the differences observed in transmissibility by index case symptom status and duration of exposure have important implications for control strategies, such as contact tracing, testing, and rapid isolation of cases. There were limited data to explore transmission patterns in workplaces, schools, and care homes, highlighting the need for further research in such settings.
BACKGROUND: Understanding the drivers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is crucial for control policies, but evidence of transmission rates in different settings remains limited. METHODS: We conducted a systematic review to estimate secondary attack rates (SARs) and observed reproduction numbers (Robs) in different settings exploring differences by age, symptom status, and duration of exposure. To account for additional study heterogeneity, we employed a beta-binomial model to pool SARs across studies and a negative-binomial model to estimate Robs. RESULTS: Households showed the highest transmission rates, with a pooled SAR of 21.1% (95% confidence interval [CI]:17.4-24.8). SARs were significantly higher where the duration of household exposure exceeded 5 days compared with exposure of ≤5 days. SARs related to contacts at social events with family and friends were higher than those for low-risk casual contacts (5.9% vs 1.2%). Estimates of SARs and Robs for asymptomatic index cases were approximately one-seventh, and for presymptomatic two-thirds of those for symptomatic index cases. We found some evidence for reduced transmission potential both from and to individuals younger than 20 years of age in the household context, which is more limited when examining all settings. CONCLUSIONS: Our results suggest that exposure in settings with familiar contacts increases SARS-CoV-2 transmission potential. Additionally, the differences observed in transmissibility by index case symptom status and duration of exposure have important implications for control strategies, such as contact tracing, testing, and rapid isolation of cases. There were limited data to explore transmission patterns in workplaces, schools, and care homes, highlighting the need for further research in such settings.
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