The effective management of the COVID-19 pandemic relies on accurate reporting of COVID-19 data. However, the collected data generally contains errors, and tends to under-report case numbers due to a current inability to address the number of asymptomatic infected cases. If the analyzed data remains unreliable, it becomes impossible to determine judgement for timely and effective infection control.
In order to address this problem, Grace Yi and Wenqing He are working with graduate students to analyse COVID-19 data using measurement error models to eliminate discrepancies and highlight faults in the data. This will provide a clearer understanding of the case fatality rate, which is the ratio of the number of deaths to the number of infected cases. Strengthening the quality of the data that informs our knowledge of the virus will lead to tangible insights that aid health care professionals in the COVID-19 response effort.