Environmental Determinants of Malaria Transmission Around the Koka Reservoir in Ethiopia

Abstract

New dam construction is known to exacerbate malaria transmission in Africa as the vectors of malaria-Anopheles mosquitoes-use bodies of water as breeding sites. Precise environmental mechanisms of how reservoirs exacerbate malaria transmission are yet to be identified. Understanding of these mechanisms should lead to a better assessment of the impacts of dam construction and to new prevention strategies. Combining extensive multiyear field surveys around the Koka Reservoir in Ethiopia and rigorous model development and simulation studies, environmental mechanisms of malaria transmission around the reservoir were examined. Most comprehensive and detailed malaria transmission model, Hydrology, Entomology, and Malaria Transmission Simulator, was applied to a village adjacent to the reservoir. Significant contributions to the dynamics of malaria transmission are shaped by wind profile, marginal pools, temperature, and shoreline locations. Wind speed and wind direction influence Anopheles populations and malaria transmission during the major and secondary mosquito seasons. During the secondary mosquito season, a noticeable influence was also attributed to marginal pools. Temperature was found to play an important role, not so much in Anopheles population dynamics, but in malaria transmission dynamics. Change in shoreline locations drives malaria transmission dynamics, with closer shoreline locations to the village making malaria transmission more likely. Identified environmental mechanisms help in predicting malaria transmission seasons and in developing village relocation strategies upon dam construction to minimize the risk of malaria.

Keywords: environmental conditions; malaria transmission; water resource reservoirs.

Figure 1

Location of the field site. The Koka Reservoir is located at the center of Ethiopia, approximately 80 km southeast of Addis Ababa. The water bodies crossing the country from northeast to southwest indicates the location of the Ethiopian Rift Valley. The Koka Dam is marked with an orange triangle at the northeast of the reservoir and is connected to the Awash River. Ejersa is adjacent to the reservoir and located northwest of the reservoir.

Figure 2

Field setting in Ejersa. Our field site, Ejersa, was equipped with the Meteorological Station (MS) (red star), the Soil Moisture Station (SMS) (orange star), and the mosquito light traps (yellow circle with cross). To measure the groundwater tables in Ejersa, pressure transducers were installed in small wells created adjacent to the MS and the SMS. Light traps were placed indoors and outdoors. Light trap houses (LTHs) with indoor operations and outdoor operations were labeled with odd and even numbers, respectively. The locations of dipping pools (DPs) are indicated by blue circles, where larvae samples were taken. The locations of shorelines were estimated for the reservoir water levels of 1,591 m (light blue), 1,593 m (sky blue), and 1,595 m (dark blue), based on the topography data. The change in shoreline is relatively small (a few hundred meters) at the north of the simulation domain (red rectangle), but is significant (as large as a kilometer) at the south of the domain. A large shift in the shorelines occurs at water levels between 1,583 m and 1,595 m. The red rectangles show the 3 km‐by‐3 km simulation domains.

Figure 3

Observed environmental conditions at Ejersa. Observed (a) daily temperature [°C], (b) relative humidity [%], (c) wind speed [m/s], (d) wind direction [deg, clockwise from the north], (e) rainfall [mm/d], and (f) reservoir water levels [meter above sea level (masl)] are shown for 2012 (blue), 2013 (red), and 2014 (green).

Figure 4

Schematic of Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS). HYDREMATS is a comprehensive malaria transmission simulator, coupling hydrology, entomology, and malaria transmission modules. This village‐scale model simulates surface pools explicitly both in space and time through the use of detailed topography data and representation of hydrological processes (e.g., rainfall, evaporation, infiltration, surface runoff, and groundwater flow). It simulates four types of pools: rain‐fed pools, groundwater (GW)‐fed pools, marginal pools, and reservoir. The entomology module is an agent‐based model, which simulates the life cycle (e.g., aquatic‐stage development and adult lifespan) and behaviors (e.g., blood meals, flight, and oviposition) of individual mosquito. Development of parasites (extrinsic incubation period) and human immunity are also simulated. The locations of houses, where mosquitoes take blood meals, are also simulated in a spatially explicit manner.

Figure 5

Simulated time series of the Anopheles population in investigation of the effects of environmental factors. Based on the Ejersa model, the contributions of climatological factors and the presence of breeding sites to the Anopheles population dynamics are evaluated. The results from the Ejersa model, fixed temp model, fixed rh model, fixed wspd model, random wdir model, no RFPs model, no MGPs model, and sln‐only model are shown in black, orange, green, light blue, blue, purple, maroon, and gray, respectively.

Figure 6

Simulated relative size of the Anopheles population and of malaria infections in investigation of the effects of environmental factors. Based on the Ejersa model, the contributions of environmental factors on the size of the (a) Anopheles population and of the (b) malaria infections are evaluated. The relative size of the simulated Anopheles population and malaria infections in each model is shown with respect to that in the Ejersa model (shown as 1 in black). Colors correspond to those in Figure 5.

Figure 7

Simulated time series of the Anopheles population in investigation of the effect of shoreline locations. Two fixed sln models simulate the contributions of climatological factors by fixing the location of shoreline and removing the RFPs and the MGPs. One model used the shoreline corresponding to the reservoir water level of 1,590 masl (green) and another of 1,595 masl (blue). Remaining dynamics in Anopheles population should be explained solely by climatological factors. The result from the Ejersa model was shown in black.

Figure 8

Simulated relative size of the Anopheles population and of malaria infections in investigation of the effect of shoreline locations. Based on the Ejersa model, the contributions of environmental factors on the size of the (a) Anopheles population and of the (b) malaria infections are evaluated. The relative size of the simulated Anopheles population and malaria infections in each model is shown with respect to that in the Ejersa model (shown as 1 in black). Colors correspond to those in Figure 7.