ArticleScience advances2024
Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 0 citations in OpenAlex.
- Development and External Validation of a Prognostic Prediction Model for Hospitalization in SARS-CoV-2-Infected Ambulatory Patients.Open forum infectious diseases · 2026Article
- Proof-of-Concept Machine Learning Framework for Arboviral Disease Classification Using Literature-Derived Synthetic Data: Methodological Development Preceding Clinical Validation.Healthcare (Basel, Switzerland) · 2026Article
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15 authors at 7 institutions in 3 countries.
Funding
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.
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