Prediction and preparedness against outbreaks with devastating economic impact
This project aims at improving the capacity to predict and be prepared against the impact of sudden disease outbreaks that not only cause direct suffering, but also leads to severe negative impact on economy, exaggerating poverty to already deprived communities. Rift Valley Fever (RVF), a neglected, mosquito-borne viral disease, will be used as a model for these types of outbreaks, which are strongly influenced by environmental and climatic changes.
This project aims at improving the capacity to predict, prevent and mitigate the impact of sudden outbreaks with severe negative impact on economy in already deprived communities. Rift Valley fever will be used as a model for outbreaks that are strongly influenced by environmental and climatic changes. A high-resolution prediction model will be developed that enables specific counter measures specifically directed to the affected areas and stakeholders. The model will be built on translational research including sustainable relationship between environment and biological life and cultural and equality factors and will deploy a multidisciplinary team of specialists to set up a strategy towards prevention and control. The project emphasizes the essential role of the policy maker in the development of the needed infrastructure.
Rosemary Sang, Associate professor, PhD, icipe (International centre of insect physiology and ecology), Kenya Rees Mbabu Murithi, Dr, DVM, Ministry of Livestock Development, Kenya Jacqueline Kasiiti Lichoti-Orengo, Dr, DVM, Ministry of Livestock Development, Kenya Per Sandström, Dr, PhD, SLU, Umeå Hippolyte Affognon Djosse', Dr, PhD, icipe (International centre of insect physiology and ecology), Kenya
The targeted research area at the Horn of Africa has high climatic variability, which leads to recurrent droughts and floods. This region is overwhelmingly inhabited by nomadic pastoral communities who make use of this harsh environment, keeping sheep, goats, camels and cattle. The high mobility of livestock and the temporary character of the nomadic settlements make it necessary to collect data not only from sentinel herds located on fixed positions, but most importantly, from the tracks of the nomadic herds in order to create a high resolution prediction model. To reach the aim, Geographic Information Systems (GIS), remote sensing (RS) and Global positioning system (GPS) will be used as tools together with studies of cultural and equality factors and analysis of samples from mosquitoes, animals and humans. This information will be combined and analyzed at a high resolution to generate information towards the development of a modified prediction model. Translational research is crucial to be able to counteract these types of devastating outbreaks and is the best strategy towards the prevention and control. The project emphasizes the essential role of the policy maker in the development of the needed infrastructure. Generally, the results of this study will improve existing early warning systems and decision support tools. These high-resolution early warning systems are extremely relevant for poor communities that do not have access to substitute supplies.