A GIS analysis of the environmental variables related to Rift Valley fever outbreaks.

摘要

Rift Valley fever is a mosquito-borne disease that causes widespread febrile illness and mortality in domestic animals as well as humans (Gaff, 2007). Rift Valley fever virus was first isolated in 1931 (Daubney, 1931), and since then, outbreaks have occurred in sub-Saharan Africa, southern Africa, Egypt, Saudi Arabia, Yemen and Madagascar, proving it to be a virus able to invade ecologically diverse regions (Gaff, 2007). The potential introduction of Rift Valley fever into the United States suggests the potential for human infection and major economic disruption. It is important to understand the role environmental variables have played in historical outbreaks to further understand the disease and the possibility of translocation of the virus.;This study examines the relationship between both temperature and rainfall rates and Rift Valley fever outbreaks in Kenya, Madagascar, and South Africa. Datasets employed in the analysis are several including a long term dataset (1982-2004), short term datasets (1999-2005; 1999-2007) and datasets covering the Rift Valley fever outbreaks in Kenya (2006, 2007), Madagascar (2008, 2009), and South Africa (2008, 2009). Geographic information systems analysis, time series analysis, and statistical analyses are used to gauge the relationships among temperature, rainfall, and Rift Valley fever outbreak events.;Results of this study show a relationship between rainfall and Rift Valley fever in Kenya, but not in Madagascar or South Africa. Although a positive rainfall anomaly was found at the beginning of the Rift Valley fever outbreak in Kenya, further analysis finds above average rainfall anomalies prior to the outbreak with no Rift Valley fever activity reported. No significant differences are found among the historical temperature ranges and temperature ranges during Rift Valley fever outbreaks in Kenya, Madagascar or South Africa.;By better understanding these two important variables, disease transmission models for Rift Valley will later be able to predict future outbreaks and spread of disease. Studies about variables related to disease transmission models like this further strengthen these models, thus providing policymakers the ability to design systems to enhance preventative and control measures.