Applications of ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Rift Valley Fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino/Southern Oscillation weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. Occurrence of RVF data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: land use, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to March 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.77 indicating that the model predicted well. The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Introduction Rift valley fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. Materials and Methods In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. RVF occurrence data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: landuse, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to march 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. Results The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.74 indicating that the model predicted well. Conclusion and Recommendations The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Distribution and abundance of key vectors of Rift Valley fever and other arboviruses in two ecologically distinct counties in Kenya

Background Rift Valley fever (RVF) is a mosquito-borne viral zoonosis of ruminants and humans that causes outbreaks in Africa and the Arabian Peninsula with significant public health and economic consequences. Humans become infected through mosquito bites and contact with infected livestock. The virus is maintained between outbreaks through vertically infected eggs of the primary vectors of Aedes species which emerge following rains with extensive flooding. Infected female mosquitoes initiate transmission among nearby animals, which amplifies virus, thereby infecting more mosquitoes and moving the virus beyond the initial point of emergence. With each successive outbreak, RVF has been found to expand its geographic distribution to new areas, possibly driven by available vectors. The aim of the present study was to determine if RVF virus (RVFV) transmission risk in two different ecological zones in Kenya could be assessed by looking at the species composition, abundance and distribution of key primary and secondary vector species and the level of virus activity. Methodology Mosquitoes were trapped during short and long rainy seasons in 2014 and 2015 using CO2 baited CDC light traps in two counties which differ in RVF epidemic risk levels(high risk Tana-River and low risk Isiolo),cryo-preserved in liquid nitrogen, transported to the laboratory, and identified to species. Mosquito pools were analyzed for virus infection using cell culture screening and molecular analysis. Findings Over 69,000 mosquitoes were sampled and identified as 40 different species belonging to 6 genera (Aedes, Anopheles, Mansonia, Culex, Aedeomyia, Coquillettidia). The presence and abundance of Aedes mcintoshi and Aedes ochraceus, the primary mosquito vectors associated with RVFV transmission in outbreaks, varied significantly between Tana-River and Isiolo. Ae. mcintoshi was abundant in Tana-River and Isiolo but notably, Aedes ochraceus found in relatively high numbers in Tana-River (n = 1,290), was totally absent in all Isiolo sites. Fourteen virus isolates including Sindbis, Bunyamwera, and West Nile fever viruses were isolated mostly from Ae. mcintoshi sampled in Tana-River. RVFV was not detected in any of the mosquitoes. Conclusion This study presents the geographic distribution and abundance of arbovirus vectors in two Kenyan counties, which may assist with risk assessment for mosquito borne diseases

Characterizing movement patterns of nomadic pastoralists and their exposure to rift valley fever in Kenya

The role of animal movement in spreading infectious diseases is highly recognized by various legislations and institutions such as the World Organisation for Animal Health and the International Animal Health Code. The increased interactions at the nexus of human-animal-ecosystem interface have seen an unprecedented introduction and reintroduction of new zoonotic diseases with high socio-economic impacts such as the COVID-19 pandemic. Rift Valley fever (RVF) is a zoonotic disease that affects both humans and animals and is transmitted by Aedes mosquitoes or through contact with the body fluids of infected animals. This study seeks to characterize movement patterns of pastoralist and how this movement behaviour increases their susceptibility to RVF virus exposure. We levarage on a rapidly growing field of movement ecology to monitor five herds collared from 2013 - 2015 in an RVF endemic semi-arid region in Kenya. The herds were also sampled for RVF antibodies to assess their exposure to RVF virus during the rainy seasons. adehabitatLT package in R was used to analyze the trajectory data whereas the first passage time (FPT) analysis was used to measure the area utilized in grazing. Sedentary herds grazed within 15km radius while migrating herds presented restricted space use patterns during the dry seasons and transient movement during the start and end of the rainy season. Furthermore, RVF virus antibodies were generally low for sedentary herds whereas the migrating herds recorded high levels during their transition periods. This study can be used to identify RVF risk zones for timely and targeted management strategies

Location of the study transect over an elevation gradient on the south-eastern slope of Mount Kilimanjaro, in Moshi district, Tanzania.

<p>The transect delimited in pink is approximately 11 km-long and 2 km-wide with a total surface area of around 22.2 km².</p

Distribution of establishment risk index (ERI), generation index (GI) and activity index (AI) of <i>A</i>. <i>thunbergii</i> on Kilimanjaro elevation transect calculated from the temperature dependent-development models under current (2013) and future (2055) temperature conditions.

<p>(A) ERI, (B) GI and (C) AI under current temperature; (D) ERI, (E) GI and (F) AI under future temperature projections; (G) ERI, (H) GI and (I) AI absolute difference between future and current temperature conditions.</p

Change in risk indices for <i>A</i>. <i>thunbergii</i> populations of Kilimanjaro transect between current (2013) and future (2055) temperature conditions, plotted against elevations.

<p>(A) Establishment risk index (ERI), (B) generation index (GI), (C) activity index (AI). * = <i>P</i> < 0.05, ** = <i>P</i> < 0.001, *** = <i>P</i> < 0.0001.</p

Distribution of <i>Antestiopsis thunbergii</i> populations (mean density per tree) over the elevation transect on Kilimanjaro, for the different evaluation periods.

<p>(A) June 2014, cool dry season, (B) October 2014, short rainy season, (C) January 2015, warm dry season, (D) June 2015, cool dry season.</p

Change in minimum and maximum temperatures (mean ± SD) between current (2013) and future (2055) climatic conditions on selected locations along the Kilimanjaro transect.

<p>The current temperatures were recorded using iButtons Hygrochron data loggers in the selected locations and future temperatures obtained from AFRICLIM 3.0 climatic projections of RCP 4.5 scenario.</p