Robust estimation of diagnostic rate and real incidence of COVID-19 for European policymakers

Journal article

Altres ajuts: CP, PJC and MC received funding from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003. This project has been partially funded by the European Comission - DG Communications Networks, Content and Technology through the contract LC-01485746.Policymakers need clear, fast assessment of the real spread of the COVID-19 epidemic in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths indicate immediately that countries like Italy and Spain had the worst situation as of mid-April, 2020, reported cases alone do not provide a complete picture of the situation. Different countries diagnose differently and present very distinctive reported case fatality ratios. Similar levels of reported incidence and mortality might hide a very different underlying pictures. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain a uniform, unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empirical. The key assumption of the method is that the infection fatality ratio of COVID-19 in Europe is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor to the way total deaths are reported. The estimation protocol is dynamic, and it has been yielding converging numbers for diagnostic rates in all European countries as from mid-April, 2020. Using this diagnostic rate, policy makers can obtain Effective Potential Growth updated every day, providing an unbiased assessment of the countries at greater risk of experiencing an uncontrolled situation. The method developed has been and will be used to track possible improvements in the diagnostic rate in European countries as the epidemic evolves

Authors: Català, M; Pino, D; Marchena, M; Palacios, P; Urdiales, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLoS ONE
• Volume: 16
• Issue: 1
• Pages: e0243701-e0243701
• Publication date: 2021-01-07
• DOI: 10.1371/journal.pone.0243701
• EISSN: 1932-6203
• ISSN: 1932-6203
• Language: English
• Keywords: Coronavirus disease 2019 (COVID-19); Mortality rate; Estimation; Mathematics; Medicine; Economics; Environmental health; Case fatality rate; Demography; Incidence (geometry); Geography; Population