Abstract
Extrinsic environmental factors influence the spatio-temporal dynamics of many organisms, including insects that transmit the pathogens responsible for vector-borne diseases (VBDs). Temperature is an especially important constraint on the fitness of a wide variety of insects, as they are primarily ectotherms. Temperature constrains the distribution of ectotherms and therefore of the infections that they spread in both space and time. More concretely, a mechanistic understanding of how temperature impacts traits of ectotherms to predict the distribution of ectotherms and vector-borne infections is key to predicting the consequences of climate change on transmission of VBDs like malaria. However, the response of transmission to temperature and other drivers is complex, as thermal traits of ectotherms are typically non-linear, and they interact to determine transmission constraints. In this study, we assess and compare the effect of temperature on the transmission of two malaria parasites, Plasmodium falciparum and Plasmodium vivax, by two malaria vector species, Anopheles gambiae and Anopheles stephensi. We model the non-linear responses of temperature dependent mosquito and parasite traits (mosquito development rate, bite rate, fecundity, egg to adult survival, vector competence, mortality rate, and parasite development rate) and incorporate these traits into a suitability metric based on a model for the basic reproductive number across temperatures. Our model predicts that the optimum temperature for transmission suitability is similar for the four mosquito-parasite combinations assessed in this study. The main differences are found at the thermal limits. More specifically, we found significant differences in the upper thermal limit between parasites spread by the same mosquito (An. stephensi) and between mosquitoes carrying P. falciparum. In contrast, at the lower thermal limit the significant differences were primarily between the mosquito species that both carried the same pathogen (e.g., An. stephensi and An. gambiae both with P. falciparum). Using prevalence data from Africa and Asia, we show that the transmission suitability metric S(T) calculated from our mechanistic model is an important predictor of malaria prevalence. We mapped risk to illustrate the areas in Africa and Asia that are suitable for malaria transmission year-round based temperature.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Updated figures, shortened main manuscript, and moved some to the supplementary materials.
Terms and abbreviations
- BIC
- Bayesian information criterion
- D2
- Squared Deviance, the proportion of deviance explained by the model
- Model pr.
- Model probability based on BIC
- ηi
- linear predictor;
- β0
- Intercept; β1,…βn Regression parameters
- lpop den
- log(population density)
- lGDP
- log(per capita gross domestic product)
- SGTZ
- Probability of S(T)>0
- Pfal
- Plasmodium falciparum
- Pviv
- Plasmodium vivax
- 𝟙AFR
- An indicator function that returns 1 if the condition in the subscript is TRUE and zero if FALSE. In our notation, the variable AFR is TRUE (=1) if the location is in Africa and if FALSE (=0) otherwise (i.e., if the location is in Asia).
