scholarly journals Modeling the Impact of Climate Change on the Dynamics of Rift Valley Fever

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Saul C. Mpeshe ◽  
Livingstone S. Luboobi ◽  
Yaw Nkansah-Gyekye
Keyword(s):  
Climate Change ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Model Parameters ◽  
Climatic Data ◽  
Elasticity Analysis ◽  
Expected Number ◽  
Valley Fever ◽  
Temperature And Precipitation ◽  
The Impact

A deterministic SEIR model of rift valley fever (RVF) with climate change parameters was considered to compute the basic reproduction numberℛ0and investigate the impact of temperature and precipitation onℛ0. To study the effect of model parameters toℛ0, sensitivity and elasticity analysis ofℛ0were performed. When temperature and precipitation effects are not considered,ℛ0is more sensitive to the expected number of infectedAedesspp. due to one infected livestock and more elastic to the expected number of infected livestock due to one infectedAedesspp. When climatic data are used,ℛ0is found to be more sensitive and elastic to the expected number of infected eggs laid byAedesspp. via transovarial transmission, followed by the expected number of infected livestock due to one infectedAedesspp. and the expected number of infectedAedesspp. due to one infected livestock for both regions Arusha and Dodoma. These results call for attention to parameters regarding incubation period, the adequate contact rate ofAedesspp. and livestock, the infective periods of livestock andAedesspp., and the vertical transmission inAedesspecies.

scholarly journals Estimation of Rift Valley fever virus spillover to humans during the Mayotte 2018–2019 epidemic

2020 ◽  
Vol 117 (39) ◽  
pp. 24567-24574 ◽  
Author(s):  
Raphaëlle Métras ◽  
W. John Edmunds ◽  
Chouanibou Youssouffi ◽  
Laure Dommergues ◽  
Guillaume Fournié ◽  
...  
Keyword(s):  
Rift Valley ◽  
Direct Contact ◽  
Rift Valley Fever ◽  
Rift Valley Fever Virus ◽  
Animal Health ◽  
Hemorrhagic Fever ◽  
Control Measures ◽  
Similar Reduction ◽  
Valley Fever ◽  
The Impact

Rift Valley fever (RVF) is an emerging, zoonotic, arboviral hemorrhagic fever threatening livestock and humans mainly in Africa. RVF is of global concern, having expanded its geographical range over the last decades. The impact of control measures on epidemic dynamics using empirical data has not been assessed. Here, we fitted a mathematical model to seroprevalence livestock and human RVF case data from the 2018–2019 epidemic in Mayotte to estimate viral transmission among livestock, and spillover from livestock to humans through both direct contact and vector-mediated routes. Model simulations were used to assess the impact of vaccination on reducing the epidemic size. The rate of spillover by direct contact was about twice as high as vector transmission. Assuming 30% of the population were farmers, each transmission route contributed to 45% and 55% of the number of human infections, respectively. Reactive vaccination immunizing 20% of the livestock population reduced the number of human cases by 30%. Vaccinating 1 mo later required using 50% more vaccine doses for a similar reduction. Vaccinating only farmers required 10 times as more vaccine doses for a similar reduction in human cases. Finally, with 52.0% (95% credible interval [CrI] [42.9–59.4]) of livestock immune at the end of the epidemic wave, viral reemergence in the next rainy season (2019–2020) is unlikely. Coordinated human and animal health surveillance, and timely livestock vaccination appear to be key to controlling RVF in this setting. We furthermore demonstrate the value of a One Health quantitative approach to surveillance and control of zoonotic infectious diseases.

scholarly journals Drivers of Rift Valley fever virus persistence and the impact of control measures in a spatially heterogeneous landscape: the case of the Comoros archipelago, 2004–2015

2021 ◽  
Author(s):  
Warren S. D. Tennant ◽  
Eric Cardinale ◽  
Catherine Cêtre-Sossah ◽  
Youssouf Moutroifi ◽  
Gilles Le Godais ◽  
...  
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Rift Valley Fever Virus ◽  
Virus Transmission ◽  
Control Measures ◽  
Virus Persistence ◽  
Valley Fever ◽  
Grande Comore ◽  
The Many ◽  
The Impact

AbstractRift Valley fever (RVF) is one of the many zoonotic arboviral haemorrhagic fevers present in Africa. The ability of the pathogen to persist in multiple geographically distinct regions has raised concerns about its potential for spread to and persistence within currently disease-free areas. However, the mechanisms for which RVF virus persistence occurs at both local and broader geographical scales have yet to be fully understood and rigorously quantified. Here, we developed a mathematical metapopulation model describing RVF virus transmission in livestock across the four islands of the Comoros archipelago and fitted this model in a Bayesian framework to surveillance data conducted in livestock across those islands between 2004 and 2015. In doing so, we estimated the importance of island-specific environmental factors and animal movements between those islands on the persistence of RVF virus in the archipelago, and we further tested the impact of different control scenarios on reducing disease burden. We demonstrated that the archipelago network was able to sustain viral transmission over 10 years after assuming only one introduction event during early 2007. Movement restrictions were only useful to control the disease in Anjouan and Mayotte, as Grande Comore and Mohéli were able to self-sustain RVF viral persistence, probably due to local environmental conditions that are more favourable for vectors. We also evidenced that repeated outbreaks during 2004-2020 may have gone under-detected by local surveillance in Grande Comore and Mohéli. Strengthened longterm and coordinated surveillance would enable the detection of viral re-emergence and evaluation of different relevant vaccination programmes.

scholarly journals Estimation of Rift Valley fever virus spillover to humans during the Mayotte 2018-2019 epidemic

2020 ◽  
Author(s):  
Raphaëlle Métras ◽  
W John Edmunds ◽  
Chouanibou Youssouffi ◽  
Laure Dommergues ◽  
Guillaume Fournié ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Disease Risk ◽  
Rift Valley Fever Virus ◽  
Animal Health ◽  
Control Measures ◽  
Real World Data ◽  
Valley Fever ◽  
The Impact

AbstractRift Valley fever (RVF) is an emerging, zoonotic, arboviral haemorrhagic fever threatening livestock and humans mainly in Africa. RVF is of global concern, having expanded its geographical range over the last decades. The impact of control measures on epidemic dynamics using empirical data has not been assessed. Here, we combined seroprevalence livestock and human RVF case data from the 2018-2019 epidemic in Mayotte, with a dynamic mathematical model. Using a Bayesian inference framework, we estimated viral transmission potential amongst livestock, and spillover from livestock to humans, through both direct contact and vector-mediated routes. Model simulations were used to assess the impact of vaccination on reducing the human epidemic size. Reactive vaccination immunising 20% of the livestock population reduced the number of human cases by 30%. To achieve a similar impact, delaying the vaccination by one month required using 50% more vaccine doses, and vaccinating only humans required 20 times as more as the number of doses for livestock. Finally, with 53.92% (95%CrI [44.76-61.29]) of livestock estimated to be immune at the end of the epidemic wave, viral re-emergence in the next rainy season (2019-2020) was unlikely. We present the first mathematical model for RVF fitted to real-world data to estimate virus transmission parameters, and able to inform potential control programmes. Human and animal health surveillance, and timely livestock vaccination appear to be key in reducing disease risk in humans. We furthermore demonstrate the value of a One Health quantitative approach to surveillance and control of zoonotic infectious diseases.

scholarly journals Modelling the persistence and control of Rift Valley fever virus in a spatially heterogeneous landscape

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Warren S. D. Tennant ◽  
Eric Cardinale ◽  
Catherine Cêtre-Sossah ◽  
Youssouf Moutroifi ◽  
Gilles Le Godais ◽  
...  
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Animal Movement ◽  
Rift Valley Fever Virus ◽  
Virus Transmission ◽  
Control Measures ◽  
Environmental Drivers ◽  
Animal Movements ◽  
Valley Fever ◽  
The Impact

AbstractThe persistence mechanisms of Rift Valley fever (RVF), a zoonotic arboviral haemorrhagic fever, at both local and broader geographical scales have yet to be fully understood and rigorously quantified. We developed a mathematical metapopulation model describing RVF virus transmission in livestock across the four islands of the Comoros archipelago, accounting for island-specific environments and inter-island animal movements. By fitting our model in a Bayesian framework to 2004–2015 surveillance data, we estimated the importance of environmental drivers and animal movements on disease persistence, and tested the impact of different control scenarios on reducing disease burden throughout the archipelago. Here we report that (i) the archipelago network was able to sustain viral transmission in the absence of explicit disease introduction events after early 2007, (ii) repeated outbreaks during 2004–2020 may have gone under-detected by local surveillance, and (iii) co-ordinated within-island control measures are more effective than between-island animal movement restrictions.

scholarly journals A dynamic, climate-driven model of Rift Valley fever

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Anne E. Jones ◽  
Cyril Caminade ◽  
Andrew P. Morse
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Climatic Factors ◽  
Model Development ◽  
Eastern Africa ◽  
Climate Data ◽  
Valley Fever ◽  
Model Driven ◽  
Spatio Temporal ◽  
The Impact

Outbreaks of Rift Valley fever (RVF) in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF) model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

scholarly journals Present and Future Ecological Niche Modeling of Rift Valley fever in East Africa in Response to Climate Change

2021 ◽  
Author(s):  
Caroline Muema ◽  
Boniface K. Ngarega ◽  
Elishiba Muturi ◽  
Hongping Wei ◽  
Hang Yang
Keyword(s):  
Climate Change ◽  
East Africa ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Ecological Niche Modeling ◽  
Climate Scenarios ◽  
African Countries ◽  
Valley Fever ◽  
Maxent Model ◽  
And Control

Rift Valley fever (RVF) has been linked with recurrent outbreaks among humans and livestock in several parts of the globe. Predicting RVF's habitat suitability under different climate scenarios offers vital information for developing informed management schemes. The present study evaluated the probable impacts of climate change on the distribution of RVF disease in East Africa (E. A.), using the maximum entropy (MaxEnt) model and the disease outbreak cases. Considering the potential of the spread of the disease in the East Africa region, we utilized two representative concentration pathways (RCP 4.5 and RCP 8.5) climate scenarios in the 2050s and 2070s (average for 2041-2060, and 2061-2080), respectively. All models had satisfactory AUC values of more than 0.809, which are considered excellent. Jackknife tests revealed that Bio4 (temperature seasonality), land use, and population density were the main factors influencing RVF distribution in the region. From the risk maps generated, we infer that, without regulations, this disease might establish itself across more extensive areas in the region, including most of Rwanda and Burundi. The ongoing trade between East African countries and changing climates could intensify RVF spread into new geographic extents with suitable habitats for the important zoonosis. The predicted suitable areas for RVF in eastern Kenya, southern Tanzania, and Somalia overlaps to a large extent where cattle keeping and pastoralism are highly practiced, thereby signifying the urgency to manage and control the disease. This work validates RVF outbreak cases' effectiveness to map the disease's distribution, thus contributing to enhanced ecological modeling and improved disease tracking and control efforts in East Africa.

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