Comparison of two detailed models of Aedes aegypti population dynamics

Abstract

The success of control programs for mosquito borne diseases can be enhanced by crucial information provided by models of the mosquito populations. Models, however, can differ in their structure, complexity and biological assumptions, and these differences impact their predictions. Unfortunately, it is typically difficult to determine why two complex models make different predictions because we lack structured side-by-side comparisons of models using comparable parameterization. Here we present a detailed comparison of two complex, spatially-explicit, stochastic models of the population dynamics of Aedes aegypti, the main vector of dengue, yellow fever, chikungunya and Zika viruses. Both models describe the mosquito's biological and ecological characteristics, but differ in complexity and specific assumptions. We compare the predictions of these models in two selected climatic settings, a tropical and weakly seasonal climate in Iquitos, Peru, and a temperate and strongly seasonal climate in Buenos Aires, Argentina. Both models were calibrated to operate at identical average densities in unperturbed conditions in both settings, by adjusting parameters regulating densities in each model (number of larval development sites and amount of nutritional resources). We show that the models differ in their sensitivity to environmental conditions (temperature and rainfall), and trace differences to specific model assumptions. Temporal dynamics of the Ae. aegypti populations predicted by the two models differ more markedly under strongly seasonal Buenos Aires conditions. We use both models to simulate killing of larvae and/or adults with insecticides in selected areas. We show that predictions of population recovery by the models differ substantially, an effect likely related to model assumptions regarding larval development and (direct or delayed) density dependence. Our methodical comparison provides important guidance for model improvement by identifying key areas of Ae. aegypti ecology that substantially affect model predictions, and revealing the impact of model assumptions on population dynamics predictions in unperturbed and perturbed conditions.

Modelling population dynamics based on experimental trials with genetically modified (RIDL) mosquitoes

“…The epidemiological situation after the intervention could then be worse than before the intervention. Some detailed models like SkeeterBuster predict overshooting of mosquito populations going above the equilibrium value after interventions, other like aedesBA do not [55], it is a matter under discussion that depends on modelling decisions under uncertainty. We must conclude that the possibility of a renewed risk for epidemics after the intervention has ceased should be taken into account.…”

“…Iquitos has been the locus of a long term research project in dengue and Aedes aegypti, but even in such conditions, parametrisation of the model is difficult [54]: We read in the conclusions "CIMSiM and DENSiM are most sensitive to parameters for which little empirical information exists, and although this information can be extremely valuable for setting research priorities, it also places constraints on the use and interpretation of model outputs." When compared to early versions of aedesBA (a compartmental model that uses an intermediate level of information) no definite advantage was observed for any of the models [55]. The difference between modelling philosophies has been briefly discussed in [56].…”

Predicting the spatial dynamics of Wolbachia infections in Aedes aegypti arbovirus vector populations in heterogeneous landscapes

“…We performed local sensitivity analysis to investigate how changes in mosquito demographic parameters affect three aspects of the dynamics of our metapopulation model: (1) the distance that Wolbachia spreads beyond the release zone in 20 months following the final release (using 0.5 Wolbachia frequency contour as described above); (2) the total mosquito population size and (3) the average per capita female fecundity across the entire spatial region ([2] and [3] are evaluated the equilibrium in the absence of Wolbachia ). We chose to vary three demographic parameters that are known to be important to the predictions of mosquito population dynamic models, but are poorly quantified for mosquito populations (Hancock, White, et al, ; Legros et al, ; Schofield, ): the daily larval mortality, µ L the daily pupal and adult mortality, µ A , and the daily probability of adult dispersal, p d (Table ). We assume in this analysis that Wolbachia infection does not affect mosquito fitness and that the mosquito habitat quality landscape is temporally invariant.…”

Nutritional availability and larval density dependence in Aedes aegypti

“…Ae. aegypti is an excellent study species both because of its importance as a vector of human disease (Higa, 2011;Powell et al, 2018) and its pervasiveness in mathematical models of mosquitoes and mosquito-borne disease (Dye, 1984;Legros et al, 2016). Consequently, a comprehensive understanding of density dependence in Ae.…”