{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/weekly-bayesian-modelling-strategy-to-predict","title":"Weekly sequential Bayesian updating improves prediction of deaths at an early epidemic stage","arxiv_id":"2104.01133","date":"2021-04-02","proceeding":null,"authors":["Pedro Henrique da Costa Avelar","Natalia Del Coco","Luis C. Lamb","Sophia Tsoka","Jonathan Cardoso-Silva"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2104.01133v2","url_pdf":"https://arxiv.org/pdf/2104.01133v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"weekly-bayesian-modelling-strategy-to-predict","repo_url":"https://github.com/Data-Science-Brigade/modelo-epidemiologico-sc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"weekly-bayesian-modelling-strategy-to-predict","repo_url":"https://github.com/Data-Science-Brigade/paper-covid19-modelling-2021-03","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"weekly-bayesian-modelling-strategy-to-predict","repo_url":"https://github.com/jonjoncardoso/jonjoncardoso","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}