ArticleBMC medical research methodology2025
An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.
Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
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4 authors.
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Abstract
introductionMachine learning models have been employed to predict COVID-19 infections and mortality, but many models were built on training and testing sets from different periods. The purpose of this study is to investigate the impact of temporality, i.e., the temporal gap between training and testing sets, on model performances for predicting COVID-19 infections and mortality. Furthermore, this study seeks to understand the causes of the impact of temporality.
methodsThis study used a COVID-19 surveillance dataset collected from Brazil in year 2020, 2021 and 2022, and built prediction models for COVID-19 infections and mortality using random forest and logistic regression, with 20 model features. Models were trained and tested based on data from different years and the same year as well, to examine the impact of temporality. To further explain the impact of temporality and its driving factors, Shapley values are employed to quantify individual contributions to model predictions.
resultsFor the infection model, we found that the temporal gap had a negative impact on prediction accuracy. On average, the loss in accuracy was 0.0256 for logistic regression and 0.0436 for random forest when there was a temporal gap between the training and testing sets. For the mortality model, the loss in accuracy was 0.0144 for logistic regression and 0.0098 for random forest, which means the impact of temporality was not as strong as in the infection model. Shapley values uncovered the reason behind such differences between the infection and mortality models.
conclusionsOur study confirmed the negative impact of temporality on model performance for predicting COVID-19 infections, but it did not find such negative impact of temporality for predicting COVID-19 mortality. Shapley value revealed that there was a fixed set of four features that made predominant contributions for the mortality model across data in three years (2020-2022), while for the infection model there was no such fixed set of features across different years.
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