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

TL;DR

This study introduces a framework to assess how under-reporting affects the understanding of COVID-19's spatiotemporal spread, using healthcare worker infection data to reveal biases and improve epidemic modeling.

Contribution

The paper presents a novel framework for analyzing the impact of under-reporting on COVID-19 distribution, validated with healthcare worker data from Wuhan and Hubei.

Findings

Lognormal distribution best describes epidemic evolution over time.

Reported peak infection times lag behind healthcare worker infection peaks.

Under-reporting has a greater impact in early pandemic stages and in early onset areas.

Abstract

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of cases is widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged 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-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (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. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.