A probabilistic epidemiological model for infectious diseases: The case of COVID‐19 at global‐level. Issue 1 (19th May 2022)

• Record Type: Journal Article
• Authors: Duarte, Heitor Oliveira; Siqueira, Paulo Gabriel; Oliveira, Alexandre Calumbi Antunes; Moura, Márcio das Chagas
• Is Part Of: Risk analysis. Volume 43:Issue 1(2023)
• Page Start: 183
• Page End: 201
• Publication Date: 2022-05-19
• Subjects: COVID‐19 -- probabilistic epidemiological model -- world population; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13950
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: This study has developed a probabilistic epidemiological model a few weeks after the World Health Organization declared COVID‐19 a pandemic (based on the little data available at that time). The aim was to assess relative risks for future scenarios and evaluate the effectiveness of different management actions for 1 year ahead. We quantified, categorized, and ranked the risks for scenarios such as business as usual, and moderate and strong mitigation. We estimated that, in the absence of interventions, COVID‐19 would have a 100% risk of explosion (i.e., more than 25% infections in the world population) and 34% (2.6 billion) of the world population would have been infected until the end of simulation. We analyzed the suitability of model scenarios by comparing actual values against estimated values for the first 6 weeks of the simulation period. The results proved to be more suitable with a business‐as‐usual scenario in Asia and moderate mitigation in the other continents. If everything went on like this, we would have 55% risk of explosion and 22% (1.7 billion) of the world population would have been infected. Strong mitigation actions in all continents could reduce these numbers to, 7% and 3% (223 million), respectively. Although the results were based on the data available in March 2020, both the model and probabilistic approach proved to be practicable and could be a basis for risk assessment in future pandemic episodes with unknown virus, especially in the early stages, when data and literature are scarce.