Abstract:
Assessment and Modelling of Malaria outbreak
(the case study of Arba Minch area)
The impact of climate and other environmental changes on population health poses
radical challenges to scientists. A fundamental characteristic of this topic was the
persistent combination of complexity and uncertainty. This thesis seeks to identify the
nature and scope of the problem, and to explore the conceptual and methodological
approaches to studying these qualitative and quantitative relationships, modelling their
future realization, providing estimates of health impacts, and communicating the
attendant uncertainties. Climate has been established as an important determinant in the
distribution of vectors and pathogens. The purpose of this case study was to seek out
relationships between the global and local climatic variables, which currently best
describe malaria outbreak. So, climate data, malaria morbidity data and different oceanic
and atmospheric indices were used to explore the temporal and spatial climatic pattern in
terms of the annual and seasonal outlook with respect to increasing/decreasing trend
regarding malaria prevalence. The correlation analyses between seasonal rainfall of Arba
Minch area and climatic parameters such as local and global oceanic-atmospheric indices
showed significant relationship with seasonal rainfall of the area in addition to ENSO.
Forecasting of March to May (MAM) and September to November (SON) seasons using
climatic parameters was also possible with reasonable skill of at the area. The stepwise
multiple linear regression method was used to develop the forecasting model. In addition,
the analyses done using SYSTAT 8 . 0 software in the development of malaria outbreak
predictive model on which stepwise multiple linear regressions was employed to
screening potential predictors. The malaria outbreak model was used to show the
predictability of malaria incidence before occurrence with certain skill at Arba Minch and
showed a factor of 12.5% in extent that climate has to force malaria. Therefore, user
tailored seasonal climate early warning information is very critical for malaria outbreak over the study area.
