Optimal Scheduling of Dengue Vector Control

TL;DR

This paper develops an optimal control framework for scheduling dengue vector interventions, incorporating environmental variability and operational practices, to effectively reduce disease transmission risk.

Contribution

It introduces a novel, temperature-dependent, stage-structured model with intervention-specific timing profiles and an adjoint-based optimization method for real-time vector control.

Findings

Optimal intervention timing significantly reduces dengue transmission risk.

Seasonal temperature variations strongly influence intervention effectiveness.

Embedding in Model Predictive Control enables adaptive, real-time vector management.

Abstract

Dengue transmission is shaped by the population dynamics of the Aedes aegypti mosquito, making vector control a central strategy for disease mitigation. The impact of interventions such as larvicide, adulticide, and breeding-site reduction depends critically on their timing under fluctuating environmental conditions. We build on a high-fidelity, non-Markovian mechanistic model of the Aedes life cycle that captures stage-structured, temperature-dependent developmental delays, and mortality, and extend it to incorporate multiple vector control measures. Rather than using continuous abstract control amplitudes as in standard optimal control formulations, we introduce intervention-specific temporal profiles that better reflect operational practice. We then develop an adjoint-based gradient descent framework to compute the optimal timing of a sequence of interventions by minimizing the time-dependent dengue reproduction number, R0. Numerical simulations based on seasonal temperature data from Miami, Florida, show that appropriately timed combinations of interventions can substantially suppress transmission risk, with outcomes strongly influenced by seasonal temperature variation and intervention duration. We further propose embedding the resulting optimization framework within a Model Predictive Control architecture, yielding a closed-loop approach for real-time, surveillance-driven vector management under environmental and operational uncertainty.