Mathematicians modelled global Covid-19 spread and recovery
At the start of the Covid-19 pandemic, researchers designed a mathematical model that accurately predicted Covid-19 transmission and recovery rates across the globe for the first time.
The model’s data matched the real-world data, which means it could be used to predict how Covid-19 might spread, and how people would recover in future.
They used a computer model called “Susceptible-Infected-Recovered” (SIR), which simulated the spread of Covid-19 around the world for 65 weeks, beginning on 1 March 2020. The simulation was based on actual global data available at the time.
From their model, the researchers found that rates of Covid-19 recovery and spread was in the range of the actual rates across the globe. They also found that their model can predict trustworthy rates up to 15 weeks.
Before this study, many researchers had used mathematical modelling to describe and track the spread of the disease. This model was the first however to match its predicted spread and recovery rates so accurately with actual rates, which means that this model is very reliable.
Models like this can help countries in Africa to better predict increases or decreases in Covid-19 cases, so that they can better manage the disease. South African and Finnish authors wrote this research paper.
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 SusceptibleInfected-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−1 and 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.
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