A mechanistic statistical model of dengue dynamics in an endemic region

Background

Dengue is a major public health challenge, and predictive models are crucial for early warning systems. However, many current modeling practices rely exclusively on climatic factors or employ complex algorithms that lack the interpretability needed for informed public health decision-making. To address these shortcomings, we developed and validated a multidimensional, interpretable statistical model to predict monthly dengue incidence.

Methodology/Principal Findings

We used a Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution to analyze 14 years (2010–2023) of spatiotemporal data from 37 municipalities in Huila, Colombia, an endemic region. The model integrates non-linear and lagged effects of climatic, demographic, and socioeconomic factors. The final model underwent rigorous external validation on an independent test set (2021–2023). Our model demonstrated high predictive discrimination (R² = 0.743, Spearman’s ρ = 0.657), accurately capturing the timing of epidemic outbreaks. Key findings include the identification of an optimal thermal window for transmission at 27–28°C, a threshold effect for precipitation above 800 mm, and a saturation dynamic in outbreak autocorrelation.

Conclusions/Significance

This mechanistically-informed statistical approach provides a robust and transparent tool for epidemiological surveillance, successfully balancing high predictive performance with the explanatory power needed for effective, data-driven public health interventions.

Author Summary

Predicting dengue outbreaks is a complex challenge. To protect communities, public health authorities need tools that are not only accurate but also understandable. Many advanced prediction models are like ’black boxes,’ making it difficult to see why they are forecasting an increased risk, which hinders effective decision-making.

In our study, we built a statistical model that opens up this black box, identifying and explaining the key drivers behind dengue outbreaks. Using 14 years of data from a high-incidence region in Colombia, we integrated information on climate, geography, and social conditions to create a more complete picture of the disease’s behavior. We found clear patterns, such as an “ideal temperature” for dengue transmission and how periods of intense rainfall can trigger outbreaks.

The main advantage of our model is its transparency. It predicts with good accuracy when an outbreak is likely to start, but more importantly, it helps explain why the risk is changing. This allows health authorities to shift from a reactive to a proactive stance. By using specific insights, like an upcoming period of optimal temperature, they can guide targeted interventions, such as eliminating mosquito breeding sites or launching awareness campaigns, exactly where and when they are needed most.