Abstract

21:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/30036bedc5269936.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/cd5e572bc3e4eb92.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/d4ac9dcf27f6ff52.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 22:T526,Abstract Transmission heterogeneity is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics, though previous efforts to estimate how heterogeneity changes over time are limited. Using contact tracing data, we compared the epidemiology of SARS and COVID-19 infection in Hong Kong in 2003 and 2020-21 and estimated time-varying transmission heterogeneity (k_t) by fitting negative binomial models to offspring distributions generated across variable observation windows. k_t fluctuated over time for both COVID-19 and SARS on a continuous scale though SARS exhibited significantly greater (p < 0.001) heterogeneity compared to COVID-19 overall and in-time. For COVID-19, k_t declined over time and was significantly associated with increasingly stringent non-pharmaceutical interventions though similar evidence for SARS was inconclusive. Underdetection of sporadic COVID-19 cases led to a moderate overestimation of k_t, indicating COVID-19 heterogeneity of could be greater than observed. Time-varying or real-time estimates of transmission heterogeneity could become a critical indicator for epidemic intelligence in the future.

#	Concept	H-Index	Publications	Citations
1	Medicine	1	1	12
2	Public Health	1	1	12
3	Epidemiology	1	1	12
4	Clinical Epidemiology	1	1	12
5	Emerging Infectious Diseases	1	1	12

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