Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia

Abstract

BackgroundThe impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.MethodsThis study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January–April), early transition (May–June), and wet (July–September) seasons with malaria incidence in the early peak (May–July) and late peak (September–December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).ResultsWe found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.ConclusionsThese findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

Spatial, Temporal, and Spatiotemporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone From 1 July 2013- 30 June 2018, Northwest Ethiopia, 2019.

“…Temperature was statistically significant and positively associated to count of malaria incidence in this study. The finding of this study is in line with previous studies of Jimma town(3), and Amhara region (38). Again it is supported by a study done in Yunnan Province, China which showed that annual average temperature was positively associated with the malaria incidence rate, according to the GWR model (39).…”

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

“…In this study, five remote sensing based environmental variables-precipitation (TRMM_3B43_7), NDVI (MOD13C25), EVI (MOD13C25), nighttime land surface temperature (nLST) (MYD11C3), and daytime land surface temperature (dLST) (MYD11C3)-from TRMM and MODIS satellites were used to understand temporal patterns and the associations of remotely sensed variables with monthly dengue fever cases in Chitwan district of Nepal. These variables have been used for analysis in many previous studies [20,26].…”

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia

“…Malaria transmission in Ethiopia is seasonal and depends on favorable climatic and ecological factors for the growth of mosquito populations and the transmission of malaria parasites. In the Amhara Region, malaria cases typically peak at the end of the rainy season between September and December, and in some areas also have a smaller peak at the beginning of the rainy season in May and June [3,4]. Understanding how environmental factors trigger these seasonal outbreaks provides the basis for malaria early warning systems that can forecast changes in malaria transmission risk based on climate variability [5][6][7].…”