Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data

Wang, Peixiao; Hu, Tao; Liu, Hongqiang; Zhu, Xinyan

A timely understanding of the spatiotemporal pattern and development trend in the early stages of the COVID-19 epidemic were critical for timely prevention and control. However, under-reporting is commonly prevalent in the early stages of an epidemic. In this study, confirmed cases of healthcare workers were considered as accurate small samples to analyze the impact of under-reporting on the spatiotemporal distribution of COVID-19 from three perspectives: parameter estimation, spatiotemporal correlation, and spatiotemporal lag. The results show that (1) the lognormal distribution was the most suitable to describe the evolution of the epidemic with time; (2) the estimated peak infection time of reported cases lagged behind the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reported cases on the early stages of the pandemic was greater than that on its later stages, and the impact on early onset areas was greater than that on late onset areas. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases.