Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations

Abstract

A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynamics and estimate age-specific differences in biological susceptibility to infection, age-assortativeness in transmission mixing, and transition in rate of infectious contacts (and reproduction number R ₀) following introduction of mass interventions. The model estimated the infectious contact rate in early epidemic at 0.59 contacts/day (95% uncertainty interval-UI = 0.48-0.71). Relative to those 60-69 years, susceptibility was 0.06 in those ≤19 years, 0.34 in 20-29 years, 0.57 in 30-39 years, 0.69 in 40-49 years, 0.79 in 50-59 years, 0.94 in 70-79 years, and 0.88 in ≥80 years. Assortativeness in transmission mixing by age was limited at 0.004 (95% UI = 0.002-0.008). R ₀ rapidly declined from 2.1 (95% UI = 1.8-2.4) to 0.06 (95% UI = 0.05-0.07) following interventions' onset. Age appears to be a principal factor in explaining the transmission patterns in China. The biological susceptibility to infection seems limited among children but high among those >50 years. There was no evidence for differential contact mixing by age.

Keywords: COVID-19; China; Coronavirus; Epidemiology; Mathematical model; SARS-CoV-2.

Fig. 1

Model fitting of COVID-19 empirical data. Model fits to (A) the time-series of daily diagnosed cases, (B) the time-series of daily reported deaths, and (C) the age-stratified attack rate, that is the proportion of the population that has already been infected by February 11, 2020 stratified by age.

Fig. 2

Model predictions for the time evolution of the COVID-19 crude case fatality rate defined as the cumulative number of deaths out of the cumulative number of diagnosed infections.

Fig. 3

Model predictions for the age-stratified susceptibility profile to COVID-19 infection, relative to those 60–69 years of age.

Fig. 4

Model predictions for the degree of assortativeness in infection transmission mixing by age across the 500 uncertainty analysis simulation runs. This parameter, defined through the age mixing matrix, describes the mixing between the different age groups. Relevant equations pertaining to the age mixing matrix and its components can be found in Section 1 of Supporting Information.

Fig. 5

Model predictions for the time evolution of the basic reproduction number R₀ before and after onset of the interventions in China.