Characterizing COVID-19 Transmission Chains for Precision Mitigation Using Epidemiological Survey Data (DP – CHAIN)

Xiaofan Liu at the City University of Hong Kong and colleagues will reconstruct COVID-19 transmission chains between individuals in communities and households using statistical methods applied to existing datasets to more reliably estimate COVID-19 transmission characteristics, such as reproduction rates, that are critical for planning effective control measures. Currently, transmission characteristics are estimated using aggregated-level data, which leads to inaccuracies. Ideally, data on how COVID-19 is transmitted between individuals are needed. They will curate an existing collection of datasets containing over 40,000 COVID-19 cases in five Asian countries with person-to-person transmission evidence to reconstruct transmission chains. They will then apply statistical tests and an analytical methodology called regression analysis to identify the most important transmission risk factors, which may include virus strain, transmission media, population density, and climate conditions.

 

To improve control measures for COVID-19 by applying statistical methods to existing datasets containing over 40,000 COVID-19 cases from five Asian countries to reconstruct transmission chains between individuals in households and communities.

Driver Project 8 - Characterizing COVID-19 Transmission Chains for Precision Mitigation Using Epidemiological Survey Data