HEALTHY FUTURES

Permanent URI for this collectionhttps://hdl.handle.net/10568/27658

The full project title is "Health, environmental change and adaptive capacity; mapping, examining and anticipating future risks of water-related vector-borne diseases in eastern Africa"

Browse

Now showing 1 - 18 of 18

Correction: Modelling vaccination strategies against Rift Valley fever in livestock in Kenya
(Journal Article, 2017-01-26) Gachohi, John M.; Njenga, M.K.; Kitala, P.; Bett, Bernard K.
This corrects the article DOI: 10.1371/journal.pntd.0005049
Modelling vaccination strategies against Rift Valley fever in livestock in Kenya
(Journal Article, 2016-12-14) Gachohi, John M.; Njenga, M.K.; Kitala, P.; Bett, Bernard K.
Background The impacts of vaccination on the transmission of Rift Valley fever virus (RVFV) have not been evaluated. We have developed a RVFV transmission model comprising two hosts—cattle as a separate host and sheep and goats as one combined host (herein after referred to as sheep)—and two vectors—Aedes species (spp) and Culex spp—and used it to predict the impacts of: (1) reactive vaccination implemented at various levels of coverage at pre-determined time points, (2) targeted vaccination involving either of the two host species, and (3) a periodic vaccination implemented biannually or annually before an outbreak. Methodology/Principal Findings The model comprises coupled vector and host modules where the dynamics of vectors and hosts are described using a system of difference equations. Vector populations are structured into egg, larva, pupa and adult stages and the latter stage is further categorized into three infection categories: susceptible, exposed and infectious mosquitoes. The survival rates of the immature stages (egg, larva and pupa) are dependent on rainfall densities extracted from the Tropical Rainfall Measuring Mission (TRMM) for a Rift Valley fever (RVF) endemic site in Kenya over a period of 1827 days. The host populations are structured into four age classes comprising young, weaners, yearlings and adults and four infection categories including susceptible, exposed, infectious, and immune categories. The model reproduces the 2006/2007 RVF outbreak reported in empirical surveys in the target area and other seasonal transmission events that are perceived to occur during the wet seasons. Mass reactive vaccination strategies greatly reduce the potential for a major outbreak. The results also suggest that the effectiveness of vaccination can be enhanced by increasing the vaccination coverage, targeting vaccination on cattle given that this species plays a major role in the transmission of the virus, and using both periodic and reactive vaccination strategies. Conclusion/Significance Reactive vaccination can be effective in mitigating the impacts of RVF outbreaks but practically, it is not always possible to have this measure implemented satisfactorily due to the rapid onset and evolution of RVF epidemics. This analysis demonstrates that both periodic and reactive vaccination ought to be used strategically to effectively control the disease.
Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data
(Journal Article, 2016-05-31) Gikungu, D.; Neyole, E.; Muita, R.; Wakhungu, Judi W.; Siamba, D.; Bett, Bernard K.
Rift Valley fever (RVF) is a mosquito-borne viral zoonotic disease that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula, with heavy impact in affected countries. Outbreaks are episodic and related to climate variability, especially rainfall and flooding. Despite great strides towards better prediction of RVF epidemics, there is still no observed climate data-based warning system with sufficient lead time for appropriate response and mitigation. We present a dynamic risk model based on historical RVF outbreaks and observed meteorological data. The model uses 30-year data on rainfall, temperature, relative humidity, normalised difference vegetation index and sea surface temperature data as predictors. Our research on RVF focused on Garissa, Murang’a and Kwale counties in Kenya using a research design based on a correlational, experimental, and evaluational approach. The weather data were obtained from the Kenya Meteorological Department while the RVF data were acquired from International Livestock Research Institute, and the Department of Veterinary Services. Performance of the model was evaluated by using the first 70% of the data for calibration and the remaining 30% for validation. The assessed components of the model accurately predicted already observed RVF events. The Brier score for each of the models (ranging from 0.007 to 0.022) indicated high skill. The coefficient of determination (R2) was higher in Garissa (0.66) than in Murang’a (0.21) and Kwale (0.16). The discrepancy was attributed to data distribution differences and varying ecosystems. The model outputs should complement existing early warning systems to detect risk factors that predispose for RVF outbreaks.
A simulation model for Rift Valley fever transmission in Kenya
(Thesis, 2015-11-30) Gachohi, John M.
Rift Valley fever (RVF) is a mosquito-borne viral disease of animals and humans that occurs throughout sub-Saharan Africa, Egypt, and the Arabian Peninsula. The disease is associated with enormous burdens on human and veterinary health, socio-economics and disease management. The RVF outbreaks are preceded by an interaction of a set of conditions and events. These include both biotic and abiotic factors that interact in a complex manner and at different spatial scales. This array of factors constrains a good understanding of the epidemiology of RVF. This thesis presents a study on RVF simulation modelling to understand the epidemiology of the disease. Specifically, the study aims to: (1) determine the key processes that influence the transmission dynamics of RVF in Kenya; (2) estimate the impacts generated following a RVF outbreak; (3) assess the role of RVF herd immunity patterns in influencing the occurrence of an outbreak; and (4) evaluate RVF control strategies when implemented at different stages of RVF risk. The simulation model comprised of two hosts (cattle and sheep) and two vectors (Aedes and Culex mosquito species). The model integrated livestock host population dynamics, vector population dynamics, and vector-host transmission dynamics. Changes in the population of vectors in the model were driven by rainfall estimates obtained from the Tropical Rainfall Measuring Mission (TRMM) for Ijara Sub-county which was the study site. Simulations were implemented for 1200 days. Outputs generated by the model included: (1) incidence of RVFV infection in vectors and xxi hosts; (2) time to the peak incidence of RVFV in vectors and hosts; and (3) the duration of outbreaks. Following the predicted outbreak, further transmissions were prevented and simulations ran for five years to assess the post-outbreak evolution of host population and herd immunity dynamics. The impact of vaccinating 25%, 50% or 75% of the host population was assessed by simulating vaccinations at different stages of RVF risk borrowed from the 2006/7 pre-outbreak period and identified in a decision-support tool for prevention and control of RVF in the Greater Horn of Africa. The three different stages included (i) issuance of RVF early warning representing a lead time of 11 weeks based on the recent outbreak in 2006/7 in Kenya, (ii) onset of heavy rains with a lead time of 6 weeks, and (iii) at the outbreak onset. This study also assessed the possibility of RVF control by focusing against one host species by vaccinating 50% of cattle or sheep, 6 weeks to the outbreak. The impact of interventions was measured by estimating the area under incidence curve (AUC). Larva control was implemented at the outbreak onset by increasing basal mortality by 50% or 100% for different periods of time. The model was also used to evaluate integrated control measures, e.g. a combination of low coverage of 25% vaccination and the moderate increase in the larval mortality rate – 50% for 105 days which spanned the entire outbreak period. The model predicted elevated RVF virus (RVFV) activity during the wet seasons as well as a full-blown RVF outbreak following periods with excessive, persistent and prolonged precipitation. During the predicted full-blown outbreak, Aedes species xxii lasted for a total of 93 days with two peaks at day 29 and day 73 after the initial emergence of the adults. Culex species lasted for 157 days with a peak at 69 days after initial emergence. Rift Valley fever virus incidence peaked in Culex species at 0.36%. The hosts’ outbreak curves had a characteristic shape – RVFV activity commenced gradually ahead of the rapid amplification of the virus transmission processes due to an upsurge in Culex mosquito population. The predicted mean peak incidence of RVFV in cattle was 14%; this occurred on day 80 following initial transmissions across simulations. The predicted incidence in sheep peaked at 35% on the same day. The predicted duration of the full-blown outbreak in hosts was 100 days [range 80, 112] for both cattle and sheep. The results of the model showed that by the end of the full-blown outbreak (day 1152), cattle and sheep populations declined to an average of 76% [range 67%, 91%] and 51% [range 39%, 64%] of their pre-outbreak populations respectively, due to RVF-induced mortality. Cattle population recovered fully approximately 3-4 years (around day 1188) after the outbreak [range 85%, 109%]. At this time (after 1188 days), the sheep population was predicted at 69% [range 55%, 88%] of the pre-outbreak population. Five years after the outbreak, the populations were, on average, 102% [range 95%, 108%] and 85% [range 66%, 104%] of the pre-outbreak populations in cattle and sheep, respectively. xxiii The model predicted that by the end of the outbreak, 89% of cattle [range 80%, 96%] and 94% of sheep [range 65%, 99%] would be in the immune/recovered/removed state that is refractory to RVFV infection. Five years later in the simulation, these herd immunity levels were shown to decline to 6% [range 4%, 8%] in cattle and 0.3% [range 0.07%, 0.5%] in sheep. The rate of decline was intensely higher in sheep than cattle. The period it took for the herd immunity to decline to negligible levels closely mirrored (1) the predicted time it took for the populations to recover to pre-outbreak levels, and (2) the average inter-epidemic period in Kenya. According to the model predictions, vaccinating 25% of the host population at any stage of risk did not prevent full-blown outbreaks but was associated with marginal reductions in AUC of between 16 and 37% across the two host species. Vaccinating 50% or 75% of the host population at any stage of risk appeared to have major impacts particularly with substantial reductions in AUC of between 62 and 89% across the two host species. On targeting either of the host species, protection appeared to be species-specific, i.e., there are few benefits derived in the species that remained unvaccinated. According to the model predictions, increasing larval mortality by 50% at daily intervals from the onset of the full-blown outbreak appeared to provide a temporary protection that was lost as soon as the control was relaxed. Increasing larval mortality by 100% at daily interval was predicted to be effective only if it was sustained for more than 60 days. xxiv The thesis viewed the simulation model as a framework that could be used for predicting RVF outbreaks and understanding complex mechanisms that produce RVF outbreaks and generating hypotheses on RVF epidemiology. This thesis identified gaps in the quantification of parameters, particularly those related to transmission, and highlighted how field observational studies and small-scale transmission experiments could be used to estimate these parameters. The simulation model results seemed to agree with anecdotal evidence that suggest that herd immunity plays an important role in modifying the length of RVF inter-epidemic intervals given that the risk of an outbreak intensifies when the herd immunity is low in presence of suitable climatic indices. A better understanding of the role these patterns play in the epidemiology of RVF is critical to refine existing control strategies, for instance, in evaluation of (1) effectiveness of preventive vaccination strategies, (2) cost-effectiveness of vaccination campaigns, and (3) in the investigation of the relationship between the average inter-outbreak period, population turn-over (exit and entry rates) and population recovery patterns. The results further suggested that targeted vaccination could be effective in mitigating the impacts of RVF outbreaks. However, challenges associated with disease prediction, availability, administration and delivery of vaccines need to be addressed. The predictions also suggested that the timing of an intervention, the level of coverage and the duration of implementation are key considerations for using larvicides for xxv RVF control. Analyses on integrated control strategies such as increased larval mortality by 50% at daily intervals from the onset and lasting the entire phase of the outbreak and vaccinating 25% of the hosts were predicted to be highly effective in preventing the occurrence of a full-blown outbreak. In conclusion, the results of this model demonstrated an advance to ecological understanding of RVF transmission dynamics and provided a framework for analyzing the impacts of RVF outbreaks and its interventions. The predicted outputs will contribute greatly to the disease control policies in Kenya and elsewhere.
Rift Valley fever: Influence of herd immunity patterns on transmission dynamics
(Presentation, 2015-03-09) Gachohi, John M.; Bett, Bernard K.
Introduction Rift Valley fever virus (RVFV) transmission gets elevated following periods of excessive and persistent rainfall. The average inter-epizootic period in Kenya has been estimated to be 3.6 years (range 1–7 years). It is presumed that herd immunity plays an important role in modifying the length of these intervals given that the risk of an epidemic intensifies when herd immunity is low. The objective of this study was to evaluate the relationship between herd immunity and RVFV transmission dynamics. Materials and Methods We developed a model to simulate RVFV transmission dynamics. The model comprises 2 vectors (Aedes and Culex spp.) and 2-hosts (cattle and sheep). Vector population dynamics are driven by precipitation obtained from Tropical Rainfall Measuring Mission (TRMM) while those of hosts are parameterized based on socio-economic data obtained from empirical studies conducted in Ijara, Kenya. Simulations are implemented for the period: 1st January 2005 and 23rd June 2010 in an attempt to predict the recent 2006/7 outbreak and other seasonal transmissions that occur during wet seasons. Results The model reproduces the 2006/7 RVF outbreak and predicts a high herd immunity level at the end of that outbreak, with 90% of sheep and 72% of cattle being immune. This immunity wanes overtime, declining to 18% in sheep and 42% in cattle by the end of the simulation period (~4 years). The rate of decline is higher in sheep relative to cattle probably due to the greater population turnover associated with higher fecundity rate, off take, replacement rate and shorter lifespan. These analyses also show that seasonal/inter-annual transmissions boost herd immunity. Preventing these transmissions leads to a reduction in herd immunity levels (10.9% in sheep and 30.6% in cattle) by the end of the simulation period. These inter-annual transmissions might be responsible for sustaining herd immunity over time especially when there are no external shocks associated with droughts, migration and tribal animosities. Conclusions and Recommendations This is the first study to utilize a simulation model to demonstrate the impacts of RVF immunity on RVF transmission and it has huge potentials for use in evaluation of cost-effectiveness of vaccination campaigns.
Future disease risk and vulnerability maps
(Report, 2014) Niyonzima, T.; Kienberger, S.; Bett, Bernard K.; Namanya, D.; Murekatete, R.; Bizimana, Jean-Claude
Health and climate change in Africa
(Video, 2014-08-25) AquaTT
This documentary is the last in a series of four reportages which present climate change and water issues in relation to agriculture, ecosystems, natural hazards, health and technologies. This video focuses on the collaborative research projects HEALTHY FUTURES and QWeCI, which are funded through the European Commission’s Seventh Framework Programme. Both HEALTHY FUTURES and QWeCI focus on major vector-borne diseases such as malaria, Rift Valley Fever and schistosomiasis and are complimentary to each other, with QWeCI focusing on western Africa and HEALTHY FUTURES on eastern Africa.
Predicted changes in herd immunity levels against Rift Valley fever virus in livestock following a natural exposure
(Presentation, 2014-10-17) Gachohi, John M.; Bett, Bernard K.
Introduction Rift Valley fever virus (RVFV) transmission gets elevated following periods of excessive and persistent rainfall. The average inter-epizootic period in Kenya has been estimated to be 3.6 years (range 1–7 years). It is presumed that herd immunity plays an important role in modifying the length of these intervals given that the risk of an epidemic intensifies when herd immunity is low. The objective of this study was to evaluate the relationship between herd immunity and RVFV transmission dynamics. Materials and methods We developed a model to simulate RVFV transmission dynamics. The model comprises 2 vectors (Aedes and Culex spp.) and 2-hosts (cattle and sheep). Vector population dynamics are driven by probability functions that use precipitation obtained from Tropical Rainfall Measuring Mission (TRMM). Host related parameters are based on socio-economic data obtained from empirical studies conducted in Ijara, Kenya. Following the predicted outbreak, we prevented further transmissions and run simulations for five years to assess the evolution of herd immunity patterns. Results and discussion The model reproduces the 2006/7 RVF outbreak and predicts a high herd immunity level at the end of that outbreak (93% in cattle and 81% in sheep). Five years after the end of the outbreak, the herd immunity levels decline to an average of 5.9% [range 2.5, 7.9%] in cattle and 0.1% [range 0, 0.43%] in sheep. The period predicted by the model closely mirrors the average inter-epizootic period in Kenya. The rate of decline is higher in sheep relative to cattle probably due to the greater population turnover associated with higher fecundity rate, off take, replacement rate and shorter lifespan. Other analyses show that seasonal/inter-annual transmissions boost herd immunity. These inter-annual transmissions might be responsible for sustaining herd immunity over time especially when there are no external shocks associated with droughts, migration and tribal animosities. Conclusions This is the first study to utilize a simulation model to demonstrate the impacts of RVF immunity on RVF transmission and it has huge potentials for use in evaluation of cost-effectiveness of vaccination campaigns.
A dynamic simulation model of Rift Valley fever transmission
(Newsletter, 2014-12-30) Gachohi, John M.
Risk-based decision-support framework for prevention and control of Rift Valley fever epidemics in eastern Africa
(Report, 2014-11-20) Bett, Bernard K.; Mariner, Jeffrey C.
Review of the Rift Valley fever outputs of the HEALTHY FUTURES project and their implications for decision-making and action
(Report, 2014-10-01) International Livestock Research Institute
Mitigation of the impacts of Rift Valley fever through targeted vaccination strategies
(Poster, 2014-09-17) Gachohi, John M.; Bett, Bernard K.
The rapid evolution of Rift Valley fever (RVF) outbreaks generates exceptional challenges in its mitigation and control. A decision-support tool for prevention and control of RVF in the Greater Horn of Africa identifies a series of events that indicates increasing risk of an outbreak and matches interventions to each event. Using a 2-host (cattle and sheep) and 2-vector (Aedes and Culex species) RVF virus transmission model, we simulated the impact of vaccinating either 50% or 75% of the host population implemented over a period of 11 and 15 days respectively at different time points identified in the tool. The time points include issuance of RVF early warning representing a lead time of 11 weeks based on the recent outbreak in 2006/2007 in Kenya, onset of heavy rains with a lead time of 6 weeks, occurrence of mosquito swarms and first RVF cases in livestock at outbreak onset and laboratory RVF virus confirmation 3 weeks after outbreak onset. The impact is measured by estimating the area under incidence curve (AUC). The results show that vaccinating 50% of the host population at these time points, that is, early warning, onset of heavy rains, first RVF cases and laboratory confirmation leads to proportional reductions in AUC of 79 %, 79 %, 77% and 66% respectively in cattle and 65 %, 70 %, 42% and 1% respectively in sheep, relative to the baseline (no control) scenario. Increasing vaccination coverage to 75% during the same time points resulted in moderately higher reductions of 81 %, 91 %, 82% and 71% in cattle and 75 %, 85 %, 77% and 36% in sheep respectively. Delaying 50% vaccination by a week following the onset of outbreak resulted in reductions of 72% and 31% in cattle and sheep respectively. The results suggest that targeted vaccination can be effective in mitigating the impacts of RVF outbreaks. However, challenges associated with prediction of the outbreak, availability and delivery of vaccines need to be addressed. Impacts appear to depend on host diversity, with sheep potentially requiring more intensive vaccination coverage. If confirmed by empirical studies, these findings have important implications for the implementation of riskbased RVF interventions.
Vulnerability assessment for the eastern African region to identify hotspots
(Report, 2012-08) Notenbaert, An Maria Omer; Kienberger, S.; Bett, Bernard K.; Hagenlocher, M.; Zeil, P.; Omolo, Abisalom
The output of this task identifies vulnerable sub-regions within the five country study area that can serve as the locus of higher resolution analysis and for testing adaptation strategies.
Rift Valley fever/malaria study site analysis and major findings for Rift Valley fever and malaria transmission
(Report, 2013-08) Bett, Bernard K.; Gachohi, John M.; Mbotha, D.
This deliverable gives provisional results of the on-going analyses on RVF and malaria transmission studies in Kenya. Analyses on RVF are based on historical data on RVF outbreaks recorded in the study site between 1961 and 2007, initial outputs from the RVF dynamical model that is being developed, and data collected from participatory studies. All the analyses on malaria are based on hospital records covering the period 2006 – 2011.
Climate change and disease emergence
(Presentation, 2013-10-30) Szonyi, Barbara; Bett, Bernard K.; Grace, Delia
Coping capacity of households to Rift Valley fever case study: Ijara District, Kenya
(Conference Paper, 2013-10-15) Kiplimo, Jusper Ronoh; Waithaka, H.E.; Notenbaert, An Maria Omer; Bett, Bernard K.
East-Africa is highly vulnerable to climate variability and adverse effects of climate change. This has made it a challenge to predictors of early warning systems. Extended and above normal rainfall across arid and semi-arid Africa after a warm phase of El Niño creates conditions favourable for outbreak of Rift Valley Fever (RVF). There have been two major outbreaks in the Horn of Africa in 1997/98 and 2006/07 that had the highest mortality to both humans and livestock causing huge economic losses. RVF is a vector borne disease transmitted by a variety of mosquito species to humans and domestic animals. The disease causes abortions in domestic animals and mortality to both animals and human beings. The virus is common in sheep and cattle with secondary transmission to humans by mosquitoes and handling and consumption of infected livestock. In Kenya, the recent outbreak occurred in December 2006 to March 2007 which was confirmed by entomological and epidemiological field investigation of virus activity in areas identified at risk. The first case was reported in Garissa in early December 2006 and later antibodies were detected in blood serum from 10 humans that coincided with livestock RVF confirmations. This outbreak was the worst in history with a record death of 75 people in a span of 3 months with approximately 180 humans being affected in the whole of North Eastern. In the entire period markets remained closed for fear of further outbreak. Our study evaluates areas through an integrated geostatistical analysis where are the risk areas in Ijara, Kenya. Ijara District is one of the eleven districts that form the North Eastern Province.The site has in the past and still is the Rift Valley Fever high risk area. It lies approximately 33oE 6oN, and 43oE 5oS and is devoid of mountains. It is characterized by low undulating plains that have low-lying altitude ranging between zero and 90 metres above sea level. Utilization of remote sensing products makes it possible to capture the anomalous warming and onset of extended rainfall.This is useful to confirming episodic RVF outbreaks. Vegetation green up accompanied on precipitation has been found to be a major influence to abundance of vector populations. The vegetation green up will be determined by normalized difference vegetation index, an indicator of health of vegetation. For the ideal conditions to be created then topography and geology would be other factors to consider in space and water retention capacity respectively. All the above considered it is possible to map and utilize time series measurements to map and predict specific areas at elevated risk for RVF. The study elicits coping strategies, by way of field work, that communities have adopted to make them less exposed to Rift Valley Fever outbreaks. Coping strategies have in the past been known to influence household come community characteristics to absorb shocks that could adversely change their livelihood socially and economically. The choice of coping strategies determines the ability they have to deal with adverse climate variability and outbreaks of diseases like RVF. Then by statistical analysis evaluate which coping strategies make the communities less vulnerable to adverse climatic variability and disease outbreaks. The study also elicits which of the coping strategies adopted by the communities would require information sharing and empowerment of policy makers socio-economically.
Persistence of Rift Valley fever virus in East Africa
(Presentation, 2012-08-20) Gachohi, John M.; Hansen, F.; Bett, Bernard K.; Kitala, P.
Rift Valley fever virus (RVFv) is a mosquito-borne pathogen of livestock, wildlife and humans that causes severe outbreaks in intervals of several years. One of the open questions is how the virus persists between outbreaks. We developed a spatially-explicit, individual-based simulation model of the RVFv transmission dynamics to investigate this question. The model, is based on livestock and mosquito population dynamics. Spatial aspects are explicitly represented by a set of grid cells that represent mosquito breeding sites. A grid cell measures 500×500 m and the model considers a grid of 100×100 grid cells; the model thus operates on the regional scale of 2,500 km2. Livestock herds move between grid cells, and provide connectivity between the cells. The model is used to explore the spatio-temporal dynamics of RVFv persistence in absence of a wildlife reservoir in an east African semi-arid context. Specifically, the model assesses the importance of local virus persistence in mosquito breeding sites relative to global virus persistence mitigated by movement of hosts. Local persistence is determined by the length of time the virus remains in a mosquito breeding site once introduced. In the model, this is a function of the number of mosquitoes that emerge infected and their lifespan. Global persistence is determined by the level of connectivity between isolated grid cells. Our work gives insights into the ecological and epidemiological conditions under which RVFv persists. The implication for disease surveillance and management are discussed.
Spatial-temporal analysis of the risk of Rift Valley fever in Kenya
(Presentation, 2012-04-22) Bett, Bernard K.; Omolo, Abisalom; Hansen, F.; Notenbaert, An Maria Omer; Kemp, Stephen J.

Browse

Recent Submissions