Title : Development of rapid diagnostic tool and characterization of rift valley fever virus in Kenya
Abstract:
Rift Valley fever (RVF) is a high-priority zoonotic disease characterized by massive loss of livestock within a short time of its outbreak. The disease is endemic in sub-Saharan Africa and mainly spread via infected female Aedes and culex genera of mosquitoes. Due to climate change, the disease is likely to be introduced in areas without previous history of its outbreak. This study aimed the at development of a rapid diagnostic tool and characterization of RVF in Kenya. During this study, A calorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) for detection of Rift Valley Fever Virus had a sensitivity of 98.36% and a specificity of 96.49%. was developed and validated. The diagnostic tool was found to detect the presence of the virus within 30 minutes at a constant temperature of 65oC. the simplicity and affordability of the developed kit make it possible for it to be deployed in rural designate laboratories to be used as a point-of-care tool thereby revolutionizing the diagnosis and improving surveillance on the disease country-wide. In addition, the seroprevalence study on 615 blood samples collected in the year 2018 from Siaya, Busia and Kisumu showed that there was circulation of RVFV IgG and IgM antibodies in Siaya, Busia and Kisumu counties. Female livestock had high rate of circulation of both IgG and IgM antibodies compared to their male counterparts. Likewise, adult livestock exhibited higher positivity rate of circulation of the RVFV antibodies in relation to the young livestock sampled. The evidence of low circulation of RVFV in these counties points to a possible future outbreak of the disease in these areas as a result of paradigm shift in the weather conditions due to climate change. This calls for increased disease surveillance, proper emergency preparedness in case of RVF outbreak so that the disease is managed on time to minimize losses, by seasonal livestock vaccination to build livestock resilience. This study also sought to understand distribution patterns of the disease under climate change scenarios that led to development of the current and future outbreak prediction maps. The maps show the likelihood of having outbreaks in seventeen counties of Kenya.
Key words:
RVF, RT-LAMP, disease characterization and modelling
