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A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave

da Costa Avelar, Pedro Henrique, del Coco, Natalia, Lamb, Luis C., Tsoka, Sophia and Cardoso-Silva, Jonathan (2022) A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave. Healthcare Analytics, 2. ISSN 2772-4425

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Identification Number: 10.1016/


Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the “hot” nature of the data used) had a negligible impact on performance.

Item Type: Article
Official URL:
Additional Information: © 2022 The Author(s)
Divisions: Data Science Institute
Subjects: R Medicine > RA Public aspects of medicine
H Social Sciences > HV Social pathology. Social and public welfare. Criminology
Q Science > QA Mathematics
Date Deposited: 03 Jun 2024 16:30
Last Modified: 01 Jul 2024 23:14

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