Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity

TL;DR

This study improves a dengue virus test prediction model by combining patient data with population-level factors like climate and disease spread in Thailand.

Contribution

The novel integration of population-level data with clinical data enhances prediction of dengue virus test positivity.

Findings

Adding climate data improved model performance for dengue prediction.

Reconstructed susceptibility and force of infection estimates enhanced predictive accuracy.

A recent case clustering metric significantly improved model performance in cross-validation.

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.

Addition of population-level data to clinical data improved performance of a prediction model for dengue test positivity.

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