Modeling effect of climate variability on malaria in Ethiopia

Abstract

Abstract
Background: Temperature in Ethiopia has increased at about 0.2°C per decade. This coupled with global evidences on relationship between weather and disease outcome suggest that climate variability facilitates and exacerbates the transmission of several infectious diseases. Despite wide recognition of the impact of climate variability on health, there is scanty information on climate variability and its implication on specific disease outcome in Ethiopia. Statistical methods are available for studying the relationship between climate variability and disease outcome but use of such methods to forecast future disease burden has not been widely considered.
Objective: The study aims to model climate variability and its impact on burden of malaria.
Methods: Twenty one year weather data, from National Metrology Agency of Ethiopia (NMAE) and 11 years Malaria prevalence data, from Federal Ministry of Health (FMoH) was used in the analysis. Box plot, time series plot, time series based models (ARIMA with different parameters and smoothing methods) and poison regression were employed to identify pattern of climate variability over a period of 21 years; determine vulnerability of disease to climate change and forecast future burden of the disease. Data were organized by region and analyzed using SPSS and findings are presented by region.
Results: The result shows that average maximum and minimum temperatures and total annual rainfall are characterized by high inter-annual variability for all regions during the last 21 years. Minimum temperature was associated with high malaria prevalence in Tigray (p=0.01), Gambella (p=0.01), Dire Dawa (p=0.025) and Afar regions (p=0.03). Conversely maximum temperature was associated with high malaria prevalence in SNNP (p=0.05), Oromia (p=0.01), Benishangul-Gumuz (p=0.01), Amhara (p=0.01), and Afar regions (p=0.01). Malaria prevalence, projected until 2020, showed increasing trend over years for all regions indicating that climate change exacerbate malaria cases if no intervention is in place.
Conclusion: Effect of climate variability is felt on malaria cases through changing magnitude and seasonality of rainfall and temperature. Forecasts of standardized malaria cases showed wide confidence interval and increasing trend in the coming five years for all regions and require intervention in the years to come Poison regression is useful to study relationship between weather and disease prevalence, while selection of appropriate time series model is important to forecast future disease burden. In view of this, it is recommended to choose appropriate model parameters to obtain accurate disease burden forecasts. [Ethiop. J. Health Dev. 2015;29(3):183-196]
Key words: Modeling climate variability, Health, Malaria, Ethiopia