Intra-vector infection dynamics challenge how to model the extrinsic incubation period for major arboviruses: dengue, Zika, and chikungunya.

Arboviruses represent a significant threat to the health of humans, animals, and plants worldwide. Mechanistic modeling has proven useful for elucidating the transmission, anticipating the spread, and predicting the response of arboviruses to control measures. However, most models approximate the intra-vector infection dynamic (IVD), which occurs during the extrinsic incubation period (EIP), by a single stage with an average duration. At the end of this stage, all exposed vectors are expected to become infectious. Strong assumption is hidden behind this: that the EIP is exponentially distributed in the vector population. To assess the validity of this assumption, we developed a stochastic compartmental model that represents successive IVD stages, associated with the crossing or not of the three within-mosquito barriers (infection, dissemination, and transmission). We calibrated the model using an ABC-SMC (Approximate Bayesian Computation - Sequential Monte Carlo) method, which includes model selection. We searched for literature data on experimental infections of Aedes mosquitoes infected by dengue, chikungunya, or Zika viruses. We demonstrated the large discrepancy between the exponential hypothesis and observed EIP distributions for dengue and Zika viruses, and identified more relevant EIP distributions. This work provides a generic modeling framework that can be applied to other arboviruses for which similar data are available. Our model also can be linked to population-scale models to aid future arbovirus control efforts.