2021/09/13 by Craig S. Carlson, David M. Rubin, Vilma Heikkilä +1 · 1 citation
Mathematics · Medicine · Biochemistry, Genetics and Molecular Biology · #COVID-19 epidemiological studies #Mathematical and Theoretical Epidemiology and Ecology Models #Evolution and Genetic Dynamics
paper · doi:10.1109/africon51333.2021.9570946
openalex publication_date 2021/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Accurately modelling the susceptibility, infection, and recovery of populations with regards to the COVID-19 pandemic is highly relevant for the implementation of countermeasures by governing bodies. In the past year, several thousands of articles on COVID-19 modelling were published. The spread of the pandemic has frequently been modelled using the Susceptible-Infected-Recovered (SIR) epidemic model owing to the low level of complexity. In recognition of its simplicity, we developed an SIR model to represent the spread of disease on a global scale, irrespective of mutation and countermeasures. The SIR parameters were reverse-engineered from aggregated global data. This model is the first to retrospectively deduce the initial incidence. The average transmission and recovery parameters were computed to be 0.33 week−1and 0.23 week−1, respectively. These values lie well within the range of reported values on COVID-19 determined from geographically different regions. The model was simulated in the Ventana®simulation environment Vensim®for a 65-weeks duration and an adjusted initial infection incidence, which was presumed three times the reported initial infection incidence. The simulated data visually aligns with the real incidence data. We attribute the discrepancy between the presumed initial value and the reported value to lack of testing facilities on the starting date of 1 March 2020. Our parameter extraction suggests a novel methodology to quantify undertesting retrospectively in epidemics.