Influence of Vectors’ Risk-Spreading Strategies and Environmental Stochasticity on the Epidemiology and Evolution of Vector-Borne Diseases: The Example of Chagas’ Disease

Abstract

Insects are known to display strategies that spread the risk of encountering unfavorable conditions, thereby decreasing the extinction probability of genetic lineages in unpredictable environments. To what extent these strategies influence the epidemiology and evolution of vector-borne diseases in stochastic environments is largely unknown. In triatomines, the vectors of the parasite Trypanosoma cruzi, the etiological agent of Chagas’ disease, juvenile development time varies between individuals and such variation most likely decreases the extinction risk of vector populations in stochastic environments. We developed a simplified multi-stage vector-borne SI epidemiological model to investigate how vector risk-spreading strategies and environmental stochasticity influence the prevalence and evolution of a parasite. This model is based on available knowledge on triatomine biodemography, but its conceptual outcomes apply, to a certain extent, to other vector-borne diseases. Model comparisons between deterministic and stochastic settings led to the conclusion that environmental stochasticity, vector risk-spreading strategies (in particular an increase in the length and variability of development time) and their interaction have drastic consequences on vector population dynamics, disease prevalence, and the relative short-term evolution of parasite virulence. Our work shows that stochastic environments and associated risk-spreading strategies can increase the prevalence of vector-borne diseases and favor the invasion of more virulent parasite strains on relatively short evolutionary timescales. This study raises new questions and challenges in a context of increasingly unpredictable environmental variations as a result of global climate change and human interventions such as habitat destruction or vector control.

A Spatially Explicit Metapopulation Model and Cattle Trade Analysis Suggests Key Determinants for the Recurrent Circulation of Rift Valley Fever Virus in a Pilot Area of Madagascar Highlands

“…More generally, deterministic approaches are less relevant for analyzing extinction probabilities than stochastic approaches [36] . Even if parameter estimation and model comparison is more complex (despite the recent advances obtained using Approximate Bayesian Computing methods [37] ), a stochastic implementation of the model should allow analyzing the extinction probability of RVFV circulation in the study area [38] , [39] .…”

Evolutionary ecology of Chagas disease; what do we know and what do we need?

“…The only contribution sets under the light of life‐history evolution is by Pelosse et al. (), and it shows that vector's risk spreading strategies can speed up the invasion of virulent strains in a stochastically variable environment. Although truly inspiring, this paper relies on numerical analyses that do not seek to provide the simple (analytical) foundations for a broad theory of T. cruzi virulence evolution.…”

“…Today, the persistence of vector transmission in many other places (Patterson & Guhl, ) raises new issues for the future of Chagas disease's control. These emerging challenges are associated with typical evolutionary processes that could significantly change the local and/or global patterns of the disease epidemiology: (i) the rise of insecticide resistance (Mougabure‐Cueto & Picollo, ; Pessoa, Vinãs, Rosa, & Diotaiuti, ), (ii) the adaptation of nondomiciliated vectors to the human habitat (Almeida et al., ; Reyes‐Lugo & Rodriguez‐Acosta, ; Waleckx, Gourbière, et al., ), and (iii) the potential evolution of T. cruzi virulence (Bull & Lauring, ; Pelosse et al., ).…”

Development time and fitness: is there an adaptive development delay in the Rhodnius prolixus fifth nymphal stage?

“…Development delays are a well-studied aspect of evolutionary strategies, and have been analyzed in insect pest species, such as the chestnut weevil (Rajon et al, 2014); however, they have been scarcely investigated in disease vectoring insects (Menu et al, 2010). Recently, Pelosse et al (2013) showed that bet-hedging strategies in Chagas disease vectors potentially affect the epidemiology and evolution of the parasite Trypanosoma cruzi Chagas, the etiologic agent of Chagas disease, which ultimately affects the control of Chagas disease transmission. These authors also demonstrated that environmental stochasticity associated with bet-hedging strategies in triatomine development time may increase the prevalence of vector-borne Chagas disease, favoring more virulent strains of parasites in a relatively short evolutionary time scale.…”