The local response of El Niño events and changing disease distribution in Tanzania
Reynolds, Rachael ; Cavan, Gina ; Cresswell, Mark
Année de publication
Climate is a key determinant of a number of disease pathogen lifecycles and disease transmission processes, particularly within tropical climates such as that experienced in Tanzania. Over recent decades, climate-related diseases such as malaria, chikungunya and bacterial meningitis have shown notable changes in their spatial distribution, with instances of both re-emergence and expansion beyond previously known boundaries being recorded. The unpredicted change in disease distribution already experienced in Tanzania has placed a significant burden on health systems and available resources, and whilst a number of factors are involved, climate remains the least understood aspect within epidemiological changes. Here we examine how climate extremes – particularly El Niño events – influence key environmental and climatic elements which promote epidemiological expansion. This study investigates the baseline climatology in five of Tanzania’s varying climatological regions using the Met Office MIDAS dataset for the period 1985–1995. Its aim is to characterise the average climate and investigate the impacts of El Niño on the climatology of these regions, and to explore associated changes in disease distribution to allow identification, in the present, of changes which are anticipated to occur in the future to be put into context. The years 1997 and 2015 are used to examine the climate extremes imposed by El Niño events through statistical comparison methods. The results demonstrate that average climate conditions vary beyond previously documented observations, with each region of Tanzania responding differently to the onset of El Niño, thus potentially promoting a spatially variable disease response. These results are particularly marked for areas of greater climatic and environmental sensitivity within Tanzania. Once further understood, knowledge of this relationship could be applied to more local analysis and aid in predicting future outbreaks within Tanzania.