Download RESEARCH 2

Spatio-temporal predictive modelling of Rift Valley Fever in response to climate change
in Garissa, Kenya
Rift Valley Fever (RVF) is a mosquito-borne viral disease that has a significant public threat
to humans and livestock in Kenya. Major cyclic epidemics have occurred in 1997-1998 and
2006-2007 in Garissa, Kenya closely associated with El Nino Phenomenon. This study
proposes to assess adaptation and mitigation strategies in pastoral livestock production
systems and investigate the application of spatio-temporal model-based approaches in future
prediction and burden analysis of Rift Valley Fever in relation to climate change stress
factors. The research evaluates the current situation of RVF in Garissa by describing
epidemiological risk factors, spatiotemporal distribution, impact and economic burden,
predictive warning systems and control strategies. Using participatory epidemiology
information on indigenous knowledge, attitude and practices in RVF management in response
to climate change will be analysed. Disease burden estimates (morbidity, mortality,
interventional costs) related RVF outbreaks will be used in estimation of economic losses due
to changing climatic conditions. Predictive models based on existing incidence data and
climatic parameters will be explored for increased preparedness to disease outbreaks. The
study aims to provide empirical evidence on the vulnerability of livestock systems to improve
livelihood resilience by quantification of the temporal and spatial patterns of climate risk for
spread of RVF. The findings would improve our understanding of the geographical
distribution of RVF and encourage wider application of these methods in study of other
animal diseases. The research output will aid vulnerable communities to make choices and
take actions that lead to sustainable livelihoods in the face of climate change.
Prof. S. G. Kiama, BVM, MSC, PHD
Dr. Gerald Muchemi
Dr. Bett
Dr. Thumbi Mwangi
Dr. Mark Nanyingi (Phd STUDENT)
Supported by Colorado State University