Papers › Weekly sequential Bayesian updating improves prediction of deaths at an early epidemic stage
Weekly sequential Bayesian updating improves prediction of deaths at an early epidemic stage
Pedro Henrique da Costa Avelar, Natalia Del Coco, Luis C. Lamb, Sophia Tsoka, Jonathan Cardoso-Silva
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Background: 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. Methods: Our four proposed variations of the original method allow accessing data of daily reported infections and take into account the under-reporting of cases more explicitly. Two of the proposed versions also attempt to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021. That workflow allowed us to run a lighter version of the model after the first calibration week. Google Mobility data, weekly updated, were used as covariates to the model at each simulated run. Findings: The changes made the model significantly less reactive and more rapid in adapting to scenarios after a peak in deaths is observed. Assuming that 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. Interpretation: Although not as reliable as death statistics, case statistics, when modelled in conjunction with an "overestimate" parameter, provide a good alternative for improving the forecasting of models, especially in long-range predictions and after the peak of an infection wave.
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