  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.

Populations and Evolution