From lab to outbreak: experimental mosquito extrinsic incubation period distributions shape dengue epidemic dynamics
Léa Loisel, Sandie Arnoux, Gaël Beaunée, Pauline Ezanno
TLDR
This paper shows that using experimentally derived extrinsic incubation period distributions in dengue models delays and flattens epidemic peaks.
Key contributions
- Developed a stochastic mechanistic dengue transmission model.
- Compared epidemic dynamics using exponential vs. experimentally derived EIP distributions.
- Experimentally derived EIPs delay and flatten epidemic peaks, prolonging crisis duration.
- Differences in epidemic dynamics are primarily modulated by mosquito mortality and human recovery.
Why it matters
Incorporating experimentally informed EIP distributions enhances the biological realism of dengue models. This can improve predictions of epidemic dynamics, leading to more effective vector control strategies and public health responses.
Original 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.
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