ABSTRACT:The relationship between the life cycle and climate conditions of the malaria vector mosquito Anopheles was modeled in order to describe its temporal and geographic distribution at a fine resolution. Since the mosquito grows through immature life stages in an aquatic environment, the model was designed to express the surface moisture conditions conducive to the mosquito's growth. The development of the mosquito was estimated using either air or water temperature, depending on the developmental stage of the mosquito's life cycle. To achieve this, the previous model was modified in order to improve the estimation of the water conditions of its habitat. As a result, the modified model was able to predict seasonal patterns of occurrence of Anopheles at representative sites with a more reasonable degree of accuracy than the previous model. This model was then applied using simple climate data in order to obtain the geographic distribution of the occurrence of various species of Anopheles in Monsoon Asia. The most notable results of the simulated spatio-temporal emergence of the Anopheles mosquito is that although the modified model did not include unique parameters of each species, distribution was clearly divided into sub-regions for each dominant species that corresponded to the climate conditions.

Previous research models have used climate data to explain habitat conditions of Anopheles mosquitoes transmitting malaria parasites. Although they can estimate mosquito populations with sufficient accuracy in many areas, observational data show that there is a tendency to underestimate the active growth and reproduction period of mosquitoes in semi-arid agricultural regions. In this study, a new, modified model that includes irrigation as a factor was developed to predict the active growing period of mosquitoes more precisely than the base model for ecophysiological and climatological distribution of mosquito generations (ECD-mg). Five sites with complete sets of observational data were selected in semi-arid regions of India for the comparison. The active growing period of mosquitoes determined from the modified ECD-mg model that incorporated the irrigation factor was in agreement with the observational data, whereas the active growing period was underestimated by the previous ECD-mg model that did not incorporate irrigation. This suggests that anthropogenic changes in the water supply due to extensive irrigation can encourage the growth of Anopheles mosquitoes through the alteration of the natural water balance in their habitat. In addition, it was found that the irrigation systems not only enable the active growth of mosquitoes in dry seasons but also play an important role in stabilizing the growth in rainy seasons. Consequently, the irrigation systems could lengthen the annual growing period of Anopheles mosquitoes and increase the maximum generation number of mosquitoes in semi-arid subtropical regions.

The magnitude of regional malaria risk is dependent primarily on the dynamics and distribution of the vector species, which are determined mainly by climate conditions. A coupled model with ecophysiological and climatological factors was developed to estimate the spatiotemporal distribution of the five species of dominant malaria vectors in monsoon Asia. Here, we examined how the potential distribution obtained from the model could explain trends in malaria incidence observed in India, which has the highest number of confirmed cases of malaria in Asia. Most notably, there was a significant positive correlation between annual malaria incidences and the maximum generation number of vectors for each state (p < 0.001). Malaria incidence tended to increase exponentially as vector generation number increased. In addition, the interannual variation in observed regional malaria incidences was synchronized with that of the potential number of vector generations. The observed seasonal peak of malaria incidences corresponded closely to the simulated appearance period of vector species, except for intensively irrigated areas that experience anthropogenic impacts on hydrologic conditions. Simulated vector distributions effectively expressed spatial and temporal prevalence of malaria in India. This novel approach to modeling based on vector ecology is an effective method for assessing malaria risk.

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