Scrutinising the COVID-19 Data on 10.676.000 Cases. A Novel Method using Retrospective, Population-based Descriptive Study for Data Quality Surveillance and a Review at 181.426.000 Cases
Author(s): Oriol Gallemí Rovira
Background: Reports on the detected positive patients with COVID-19 are as per today the best estimation of a country spread of the pandemic. In order to evaluate the early indicators for true lethality and recovery time, the data where the model is built must be quality checked. Each country sets different procedures and criteria for fatality count due to COVID-19 and the health system is stressed due to insufficient testing capabilities, untracked infectious and premature discharges. In this paper the dynamics behind such data quality issues are discussed throughout the clinical course to support better modeling and decision-making processes in a stressed healthcare system.
Methods: Based on data compiled and relayed by the Johns Hopkins University, tracking COVID-19 over 10.675.596 infections (July, 1st, 2020), the data is clustered and compared with discrete regression. Regression parameters are restricted by a time interval of 1 day and must be consistent and explanatory on the diagnostic (i.e. a fatality cannot occur before the patient displays symptoms). Cumulative infection curves are taken and built by holding a zero when the infections were lowest at the northern hemisphere. Data is picked from JHU consolidated repository. Infection synthetic curves are built from the Fatality count and the Recovered patient count. The adjusted parameters are τ=time to fatality (days), δ=time to discharge of recovered patients (days) and φ=case fatality rate (CFR in per unit, P.U.). Therefore, the discharge rate (recovery rate) is forced to be (1- φ). Also, a recovery coverage is set in order to determine the number of untracked discharged patients.
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