From lab to outbreak: experimental mosquito extrinsic incubation period distributions shape dengue epidemic dynamics

TL;DR

This study demonstrates that using experimentally derived distributions for mosquito extrinsic incubation periods in dengue models significantly alters epidemic predictions, making them more realistic and potentially more useful for public health planning.

Contribution

The paper introduces a stochastic dengue transmission model comparing exponential and experimentally derived EIP distributions, highlighting the impact on epidemic dynamics.

Findings

Experimentally derived EIP delays epidemic peaks

Realistic EIP distributions produce lower, prolonged peaks

Outbreak probability remains largely unchanged

Abstract

Dengue virus transmission models commonly assume an exponential distribution for the mosquito extrinsic incubation period (EIP), potentially oversimplifying biological variability. We developed a stochastic mechanistic dengue transmission model comparing epidemic dynamics under commonly assumed exponential (EXP) versus experimentally derived (ED) EIP distributions. Our results show that using an experimentally derived EIP distribution delays and flattens epidemic peaks, resulting in lower but more prolonged peaks, slightly prolongs crisis durations, and reduces peak intensity compared to the exponential assumption, while outbreak probability remains largely unaffected. These differences are modulated by mosquito mortality and human recovery principally. Incorporating experimentally informed EIP distributions enhances the biological realism of models and may improve predictions of dengue epidemic dynamics, informing more effective vector control strategies and public health responses.