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During the dry season from October to February , temperature, humidity, and wind speed stay relatively low. Wind direction changes before and after the main rainy season.

Wind blows from the southwest during the main rainy season and from the east for most of the rest of the year. Wind direction changes gradually from east to southwest during the secondary rainy season. Coupling modules that describe mechanistically hydrology, entomology, and malaria transmission, HYDREMATS can simulate explicitly in space and time: 1 presence of vector breeding pools, 2 life cycle of Anopheles mosquitoes, 3 behaviors of adult mosquitoes, 4 development of parasites, and 5 development of human immunity Figure 4.


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Presence of vector breeding pools. The presence of vector breeding pools is simulated by resolving hydrological processes e. Near large reservoirs, groundwater tables are influenced by the water levels of the reservoir, and the groundwater flow direction can be approximated as one dimensional Text S2 Endo, The formation of the marginal pools can be explained by the combination of muddy rough surface, shallow GW table, and high capillary fringe, and thus simulated accordingly Text S2 and Figure S1.

Reservoir shoreline can be simulated based on the topography and the reservoir water levels. Life cycle of Anopheles mosquitoes. Mosquitoes goes through the aquatic stage to the adult stage. Behaviors of adult mosquitoes. Modeling of Anopheles' flight behavior is fully described in Endo and Eltahir All the activities of Anopheles mosquitoes are simulated in a spatially explicit manner.

Development of parasites. Malaria transmission requires Anopheles mosquitoes to undergo both mosquito stages also known as aka extrinsic incubation period or EIP and human liver stages aka intrinsic incubation period or IIP. During mosquito stages, gamatocytes ingested from infectious person need to develop to sporozoites to be injected to a person. Parameters used in this study are found in Text S2. Development of human immunity. Simulation of human immunity borrows from Yamana et al. People acquire immunity with infectious bites and gradually lose it with time.

The transmission efficiency and malaria recovery rate are simulated taking account the levels of human immunity. For details of the simulation results, see Text S3. The Anopheles populations and malaria transmission peaks twice in a year: around June and October. Environmental factors driving these dynamics are examined in the following method. In order to quantify contribution of environmental factors to malaria transmission dynamics around the Koka Reservoir, simulation studies were conducted, decomposing the impacts of environmental factors.

The simulation settings are summarized in Table 1.

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The fixed temp , fixed rh , fixed wspd , and random wdir models, removed variability of temperature, relative humidity, wind speed, and wind direction, respectively, from the Ejersa model, by using either fixed or random values instead of observed values i. In the fixed rh model, a fixed value of relative humidity— In the fixed wspd model, a fixed value of 0. The random wdir model applied a random wind direction every hour throughout the simulation period. The impact of each factor can be understood by the deviation between the Ejersa model and the respective simulation.

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Finally, the fixed sln model analyzed the impact of climatological factors, independent of the conditions of water bodies, by removing RFPs and GWPs and by fixing the location of shoreline. Large deviations from the Ejersa model are apparent in the fixed wspd model, the random wdir model, and the no MGPs model, suggesting significant contribution of the wind and marginal pools to the dynamics of Anopheles populations.

The gap between the Ejersa model black and the sln only model gray should be explained by the variability of the environmental factors i. The contribution of each environmental factor to the gap is illustrated in Figure S7. A large contribution from wind was found throughout a year; the contribution was especially strong around October the beginning of the major mosquito season. Figure 5 also demonstrates that the observed mosquito population dynamics cannot be reproduced in the Ejersa model without the correct representation of the wind profile.

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A large contribution from the MGPs was found around June, which accounts for the important Anopheles population dynamics during the secondary mosquito season. The magnitude of the contribution of the environmental factors to the Anopheles population is summarized in Figure 6 a, where the total number of simulated Anopheles mosquitoes are presented relative to those in the Ejersa model. The contributions of temp, rh , and RFPs were found to be small. Figures 5 and 6 a reveal the importance of the wind profile, both wspd and wdir.

Large deviations from the Ejersa model were simulated both in the fixed wspd model and in the random wdir model. The wind profile was found to be an important factor dictating the dynamics and magnitude of the Anopheles population, and hence determining the risks of malaria transmission. Similarly, the magnitude of the contribution of the environmental factors to malaria transmission is presented in Figure 6 b.

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The total number of simulated malaria infections in each model relative to that in the Ejersa model is plotted. Wspd and wdir were also found to have significant contributions to malaria transmission. Although the impact of temperature was not significant for the Anopheles population Figures 5 and 6 a , it was of significant importance for malaria transmission Figure 6 b. However, increase in temperature can significantly shorten the EIP, enhancing malaria transmission.

With the fixed temp model, temperature during the major mosquito season September—December was higher than the observational values in the Ejersa model. The combination of the large Anopheles population and the higher temperatures resulted in a large number of malaria infections. Markedly, the result suggests the importance of the combined seasonal environmental factors. Another set of two simulations was conducted fixing the locations of the shoreline and removing the RFPs and the MGPs fixed sln model.

One model used the shoreline corresponding to the reservoir water level of 1, masl close to the observed minimum and another of 1, masl close to the observed maximum. The simulated time series of the Anopheles population in the two models are presented in Figure 7 in blue and green, respectively, and compared with those from the Ejersa model black.

Because there is no variability in the presence of breeding pools, the simulated dynamics in Anopheles population should be explained solely by the climatological factors. The magnitude of the simulated Anopheles population and malaria infection is summarized in Figure 8. These models further assert that the population dynamics of Anopheles mosquitoes in Ejersa arise from both the variability in climate factors and from the change in shoreline locations gray line in Figure 5 , but not merely from the existence of a reservoir.


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The presented series of experiments elucidate a few important environmental mechanisms of malaria transmission around reservoirs. First, for Anopheles population dynamics, which of course affect the dynamics of malaria transmission, the wind conditions both wind speed and wind direction play a noticeable role.

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In addition, the heterogeneity around reservoirs, where human settlements are located on one side and reservoir on the other side, makes the population dynamics of Anopheles mosquitoes sensitive to the wind direction. This high sensitivity of malaria transmission potential to wind direction around reservoirs suggests that malaria may be mitigated if villages are located around reservoirs in places where wind conditions are unfavorable for the reproduction of Anopheles mosquitoes.

Second, the distances to the shoreline from human settlements influence the Anopheles population dynamics. With the increase in reservoir water levels, the reservoir shoreline becomes closer to the village, making mosquito reproduction and malaria transmission more likely. The increase in water levels lags rainy seasons; and hence in Ejersa, the Anopheles seasons were observed to lag the rainy seasons by a few months.

However similar these observations are, one should not confuse the distinctive mechanisms of the Anopheles population dynamics. At reservoir sites, where water is abundant throughout year, the mosquito population dynamics can be led by the shift in reservoir shorelines, which is a delayed response to the rainfall.

Pools created by rainfall are not likely to play an important role in the mosquito population dynamics where water is already not limiting. Demonstrated impact of distance between a village and a shoreline on malaria transmission provides implication that a village should be located far enough from a reservoir to prevent malaria.

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Finally, temperature was found to play an important role, not so much in Anopheles population dynamics, but in malaria transmission dynamics. This result suggests a large sensitivity to temperature of malaria transmission at the temperature range in Ejersa. It may hint that warming trend of temperature could adversely impact the regions around Ejersa.