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

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 casesis 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.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

“…Second, we had 1.6% of cases with missing neighbourhood information and 12.4% cases were outbreak cases, which were not included in our analysis. Additionally, COVID-19 is often asymptomatic, under-reported 83 or lacks accurate information on the onset of the COVID-19, limiting the capacity of the analysis. However, the Bayesian spatiotemporal hierarchical models allowed us to compensate for the missing/unobserved covariates or missing data by incorporating the structured, unstructured random effects into the model 63 .…”

The weekly P25 of the age of the influenza-like illness shows a higher correlation with COVID-19 mortality than rapid tests and could predict the evolution of COVID-19 pandemics in sentinel surveillance, Piura, Perú, 2021

“…However, due to the limited availability of these tests, an underreporting of COVID-19 cases was generated in the country, as has been demonstrated. So the official data did not show the absolute magnitude of the pandemic [ 8 , 9 ].…”

“…The dynamic characteristics of the weekly ILI syndromic surveillance curve and its correlation with the weekly curve of confirmed cases with rapid tests and mortality from COVID-19 during the pandemic were also described. There are mathematical models that try to establish forecasts of the pandemic full of inaccuracies due to the underreporting of cases [ 8 , 19 ]; therefore, it is necessary to find other, perhaps more accurate, pandemic monitoring indicators. Thus, the aim is to evaluate the usefulness and scope of syndromic sentinel surveillance of ILI in the COVID-19 pandemic.…”