Joint Bayesian modeling of time to malaria and mosquito abundance in Ethiopia

Abstract

BackgroundThis paper studies the effect of mosquito abundance and malaria incidence in the last 3 weeks, and their interaction, on the hazard of time to malaria in a previously studied cohort of children in Ethiopia.MethodsWe model the mosquito abundance and time to malaria data jointly in a Bayesian framework.ResultsWe found that the interaction of mosquito abundance and incidence plays a prominent role on malaria risk. We quantify and compare relative risks of various factors, and determine the predominant role of the interaction between incidence and mosquito abundance in describing malaria risk. Seasonal rain patterns, distance to a water source of the households, temperature and relative humidity are all significant in explaining mosquito abundance, and through this affect malaria risk.ConclusionAnalyzing jointly the time to malaria data and the mosquito abundance allows a precise comparison of factors affecting the spread of malaria. The effect of the interaction between mosquito abundances and local presence of malaria parasites has an important effect on the hazard of time to malaria, beyond abundance alone. Each additional one km away from the dam gives an average reduction of malaria relative risk of 5.7%. The importance of the interaction between abundance and incidence leads to the hypothesis that preventive intervention could advantageously target the infectious population, in addition to mosquito control, which is the typical intervention today.

Remote sensing of environmental risk factors for malaria in different geographic contexts

“…Climatic variables like temperature and precipitation in uenced malaria proportion in both landscapes. This result does not come as a surprise, as the in uences of temperature and precipitation on malaria are well established for the area [19,22,23]. Our study shows that in a geographically diverse area like the Amhara region, climatic variables can vary greatly within woredas and drive sub-woreda level malaria proportion.…”

“…These ndings are in contrast to the general expectation of relationships between malaria cases and temporary water bodies. Other studies from different parts of Ethiopia have found that woredas with high wetlands cover had high malaria incidence [24] and distance to breeding sites for samples of individual households in uences malaria risk on household level [23,67,68]. However, those previous studies have been performed at different spatial scales, and the proximity of temporary water bodies was much less important at the kebele level within he woredas that we studied.…”

“…This makes remote sensing a useful tool for studying the effect of environmental conditions on mosquito borne diseases in general [17] and malaria in particular [18][19][20][21]. In Ethiopia, such studies have established a relationship between malaria risk and remotely-sensed environmental factors, such as land surface temperature [19,22], precipitation [22,23], greenness and moisture indices [19], wetland abundance [24], and distance to water bodies [22]. However, the interaction between climatic conditions and mosquito borne disease risk can be heterogeneous across a landscape because land cover and physiography can mediate how climate and malaria risk are related [25].…”

Study on the Development Strategy of Airport Economy in Dalian

“…Dalian export-oriented economy is highlighted [5], and there is a great demand on the international freight market potentials. During the period of twelfth five, the demand of international air freight is over 300,000 tons.…”

Remote sensing of environmental risk factors for malaria in different geographic contexts

“…Thus, remote sensing is a useful tool for studying the effects of environmental conditions on mosquito borne diseases like malaria [ 24 , 32 – 35 ]. In Ethiopia, such studies have established relationships between malaria risk and remotely-sensed environmental factors, such as land surface temperature [ 33 , 36 , 37 ], precipitation [ 36 , 38 ], greenness and moisture indices [ 33 ], wetland cover [ 27 ], and distance to water bodies [ 36 ].…”