Project Number: 3022-32000-024-006-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 15, 2021
End Date: Sep 14, 2024
Rift Valley fever virus (RVFV) is included on all of the major animal and public health agencies lists of infectious threats that have epidemic potential and would have devastating effects on either animal or human health. RVFV is a mosquito transmitted bunyavirus that can cause severe infections in both animals and humans. In naïve herds, RVFV leads to near 100% mortality of young livestock and high rates of abortion in pregnant animals, which leads to devastating economic consequences in the agricultural sector. Exposure to raw meat and milk are also potential risk factors that have been qualified through extensive metanalysis. Yet, there is very little field data on the infectious nature of raw meat and milk underscoring a major gap regarding worldwide RVFV emergence prevention and control. The collective expertise of this cooperative group and the in-country infrastructure allows an unparalleled “One Health” opportunity to assess the infectivity of milk in a high-risk country. RVFV is a highly adaptable pathogen that can circulate undetected, hiding between outbreaks when awareness in the human and animal health system is low. This underscores a major gap in risk mitigation as animal products, including raw milk, are highly mobile within and between countries. The outcomes of this project will provide crucial evidence to protect naïve populations in Kenya, better control outbreaks in high-risk areas, and give naïve countries, like the USA. The objective is to assess the risk RVFV transmission to animals and humans through consumption of raw milk.
Historically, RVFV has been associated with periodic disease outbreaks due to climatic events such as heavy rainfall, and RVFV has received the most attention and funding during this outbreak period. However, epidemiological and veterinary surveillance studies conducted during the inter-epidemic period have documented low levels of annual transmission. This low-level transmission provides the perfect opportunity for RVFV to spread to new areas at low levels that do not trigger immediate public health responses. Aims and their respective approaches are outlined below: 1. Develop appropriated diagnostic assays to detect presence of RVFV in milk. We will explore the usage of both rt-PCR and ELISA to document active infection and previous exposure, respectively in raw milk samples. Detecting active infections in animal provides the opportunity to construct direct epidemiologic links. However, the viremic period for livestock is short which makes detection in the acute phase difficult between large-scale outbreaks. Thus, serology studies can provide an opportunity to explore spatial and temporal transmission and infer a degree of risk to the food chain. 2. Perform an epidemiological assessment of RVFV in unpasteurized milk and explore the potential for leveraging the milk vendor system in Kenya to optimize the cost effectiveness of RVFV surveillance using milk. The proposed sampling framework of milk relies on a local system in Kenya where fresh raw milk is collected from milk vendors from several regions surrounding a large urban center. The milk is pooled and enters the value chain unpasteurized. By sampling from the pooled urban samples, we have the ability to assess potential cryptic transmission of RVFV over a larger area. With each sample collected in the urban areas, a brief survey on origins, time of milking, and storage conditions will be recorded. The pooling strategy of sampling will be informed by the diagnostic assay development in Aim 1. Samples will be collected during the long and short rainy season and also around religious holidays where a large number of livestock are mobilized for consumption. 3. Compare genetic sequences of interepidemic infections in animal products to animal and human cases from previous outbreaks to explore pathways of viral maintenance. All PCR positive samples will then be individually sequenced using equipment that is already available. Sequences will be correlated with animal, human, and vector sequences from the last large-scale outbreak in Kenya. These sequence data will be analyzed using a bioinformatic workflow to assess viral genetic population variation in between and potentially during RVFV outbreaks.