A timely understanding of the spatiotemporal pattern and development trend in the early stages of the of COVID-19 epidemic is 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 are 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 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 estimation of the spatiotemporal characteristics of COVID-19 , such as the infection peak and infection duration, is of great significance for the accurate prevention and control of the pandemic . However, under-reporting of cases is very common in fields associated with public health. It is also possible to draw biased inferences if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan) were considered as a small sample, and a novel framework was proposed to explore the impact of under-reporting on COVID-19 infection peak and duration estimation in Wuhan and Hubei (excluding Wuhan) . The results show that the infection peak estimated by the reported cases in Wuhan may lag the actual infection peak by 9~10 days , and the infection duration estimated by the reported cases in Wuhan may be 16 days shorter than the actual infection duration. The infection peak estimated by the reported cases in Hubei (excluding Wuhan) may lag the actual infection peak by 1~2 days , and the infection duration estimated by the reported cases in Hubei (excluding Wuhan) may be 12.1 days shorter than the actual infection duration. Additionally, we confirmed differences in the spatial distribution of COVID-19 statistical characteristics .
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pattern and development trend in the early stages of the of
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are considered as accurate small samples to analyze
in Wuhan and Hubei (excluding Wuhan) were considered as a small sample, and a novel framework was proposed to explore
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the spatiotemporal distribution of
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from three perspectives: parameter estimation , spatiotemporal correlation, and spatiotemporal lag
infection peak and duration estimation in Wuhan and Hubei (excluding Wuhan)
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infection peak estimated by the
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lagged the peak infection time of the healthcare worker cases
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estimated infection time interval of
infection duration estimated by
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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
in Wuhan may be 16 days shorter than the actual infection duration. The infection peak estimated by the reported cases in Hubei (excluding Wuhan) may lag the actual infection peak by 1~2 days
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impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower
infection duration estimated by the reported cases in Hubei (excluding Wuhan) may be 12.1 days shorter
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number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases
infection duration. Additionally, we confirmed differences in the spatial distribution of COVID-19 statistical characteristics