The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model.

Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus. The model predicts mosquito occurrence locations with a high accuracy (Cohen's κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.

Life cycle assessment of fish and seafood processed products - A review of methodologies and new challenges.

Life cycle assessment (LCA) has been widely applied in many different sectors, but the marine products and seafood segment have received relatively little attention in the past. In recent decades, global fish production experienced sustained growth and peaked at about 179 million tonnes in 2018. Consequently, increased interest in the environmental implications of fishery products along the supply chain, namely from capture to end of life, was recently experienced by society, industry and policy-makers. This timely review aims to describe the current framework of LCA and its application to the seafood sector that mainly focused on fish extraction and processing, but it also encompassed the remaining stages. An excess of 60 studies conducted over the last decade, along with some additional publications, were comprehensively reviewed; these focused on the main LCA methodological choices, including but not limited to, functional unit, system boundaries allocation methods and environmental indicators. The review identifies key recommendations on the progression of LCA for this increasingly important sustaining seafood sector. Specifically, these recommendations include (i) the need for specific indicators for fish-related activities, (ii) the target species and their geographical origin, (iii) knowledge and technology transfer and, (iv) the application and implementation of key recommendations from LCA research that will improve the accuracy of LCA models in this sector. Furthermore, the review comprises a section addressing previous and current challenges of the seafood sector. Wastewater treatment, ghost fishing or climate change, are also the objects of discussion together with advocating support for the water-energy-food nexus as a valuable tool to minimize environmental negativities and to frame successful synergies.

Climate Variability and Malaria over West Africa.

Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

Lag effect of climatic variables on dengue burden in India.

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010-2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0-3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3-6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0-2 months lag period.

Investigating the upsurge of malaria prevalence in Zambia between 2010 and 2015: a decomposition of determinants.

BACKGROUND: Malaria is among the top causes of mortality and morbidity in Zambia. Efforts to control, prevent, and eliminate it have been intensified in the past two decades which has contributed to reductions in malaria prevalence and under-five mortality. However, there was a 21% upsurge in malaria prevalence between 2010 and 2015. Zambia is one of the only 13 countries to record an increase in malaria among 91 countries monitored by the World Health Organization in 2015. This study investigated the upsurge by decomposition of drivers of malaria. METHODS: The study used secondary data from three waves of nationally representative cross-sectional surveys on key malaria indicators conducted in 2010, 2012 and 2015. Using multivariable logistic regression, determinants of malaria prevalence were identified and then marginal effects of each determinant were derived. The marginal effects were then combined with changes in coverage rates of determinants between 2010 and 2015 to obtain the magnitude of how much each variable contributed to the change in the malaria prevalence. RESULTS: The odds ratio of malaria for those who slept under an insecticide-treated net (ITN) was 0.90 (95% CI 0.77-0.97), indoor residual spraying (IRS) was 0.66 (95% CI 0.49-0.89), urban residence was 0.23 (95% CI 0.15-0.37), standard house was 0.40 (95% CI 0.35-0.71) and age group 12-59 Months against those below 12 months was 4.04 (95% CI 2.80-5.81). Decomposition of prevalence changes by determinants showed that IRS reduced malaria prevalence by - 0.3% and ITNs by - 0.2% however, these reductions were overridden by increases in prevalence due to increases in the proportion of more at-risk children aged 12-59 months by + 2.3% and rural residents by + 2.2%. CONCLUSION: The increases in interventions, such as ITNs and IRS, were shown to have contributed to malaria reduction in 2015; however, changes in demographics such as increases in the proportion of more at risk groups among under-five children and rural residents may have overridden the impact of these interventions and resulted in an overall increase. The upsurge in malaria in 2015 compared to 2010 may not have been due to weaknesses in programme interventions but due to increases in more at-risk children and rural residents compared to 2010. The apparent increase in rural residents in the sample population may not have been a true reflection of the population structure but due to oversampling in rural areas which was not fully adjusted for. The increase in malaria prevalence may therefore have been overestimated.

Author Correction: Systematic Assessment of the Climate Sensitivity of Important Human and Domestic Animals Pathogens in Europe.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal.

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

Systematic Assessment of the Climate Sensitivity of Important Human and Domestic Animals Pathogens in Europe.

Climate change is expected to threaten human health and well-being via its effects on climate-sensitive infectious diseases, potentially changing their spatial distributions, affecting annual/seasonal cycles, or altering disease incidence and severity. Climate sensitivity of pathogens is a key indicator that diseases might respond to climate change, but the proportion of pathogens that is climate-sensitive, and their characteristics, are not known. The climate sensitivity of European human and domestic animal infectious pathogens, and the characteristics associated with sensitivity, were assessed systematically in terms of selection of pathogens and choice of literature reviewed. Sixty-three percent (N = 157) of pathogens were climate sensitive; 82% to primary drivers such as rainfall and temperature. Protozoa and helminths, vector-borne, foodborne, soilborne and waterborne transmission routes were associated with larger numbers of climate drivers. Zoonotic pathogens were more climate sensitive than human- or animal-only pathogens. Thirty-seven percent of disability-adjusted-life-years arise from human infectious diseases that are sensitive to primary climate drivers. These results help prioritize surveillance for pathogens that may respond to climate change. Although this study identifies a high degree of climate sensitivity among important pathogens, their response to climate change will be dependent on the nature of their association with climate drivers and impacts of other drivers.

Dengue burden in India: recent trends and importance of climatic parameters.

For the past ten years, the number of dengue cases has gradually increased in India. Dengue is driven by complex interactions among host, vector and virus that are influenced by climatic factors. In the present study, we focused on the extrinsic incubation period (EIP) and its variability in different climatic zones of India. The EIP was calculated by using daily and monthly mean temperatures for the states of Punjab, Haryana, Gujarat, Rajasthan and Kerala. Among the studied states, a faster/low EIP in Kerala (8-15 days at 30.8 and 23.4 °C) and a generally slower/high EIP in Punjab (5.6-96.5 days at 35 and 0 °C) were simulated with daily temperatures. EIPs were calculated for different seasons, and Kerala showed the lowest EIP during the monsoon period. In addition, a significant association between dengue cases and precipitation was also observed. The results suggest that temperature is important in virus development in different climatic regions and may be useful in understanding spatio-temporal variations in dengue risk. Climate-based disease forecasting models in India should be refined and tailored for different climatic zones, instead of use of a standard model.

Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015.

Zika, a mosquito-borne viral disease that emerged in South America in 2015, was declared a Public Health Emergency of International Concern by the WHO in February of 2016. We developed a climate-driven R0 mathematical model for the transmission risk of Zika virus (ZIKV) that explicitly includes two key mosquito vector species: Aedes aegypti and Aedes albopictus The model was parameterized and calibrated using the most up to date information from the available literature. It was then driven by observed gridded temperature and rainfall datasets for the period 1950-2015. We find that the transmission risk in South America in 2015 was the highest since 1950. This maximum is related to favoring temperature conditions that caused the simulated biting rates to be largest and mosquito mortality rates and extrinsic incubation periods to be smallest in 2015. This event followed the suspected introduction of ZIKV in Brazil in 2013. The ZIKV outbreak in Latin America has very likely been fueled by the 2015-2016 El Niño climate phenomenon affecting the region. The highest transmission risk globally is in South America and tropical countries where Ae. aegypti is abundant. Transmission risk is strongly seasonal in temperate regions where Ae. albopictus is present, with significant risk of ZIKV transmission in the southeastern states of the United States, in southern China, and to a lesser extent, over southern Europe during the boreal summer season.

Environmental change and Rift Valley fever in eastern Africa: projecting beyond HEALTHY FUTURES.

Outbreaks of Rift Valley fever (RVF), a relatively recently emerged zoonosis endemic to large parts of sub-Saharan Africa that has the potential to spread beyond the continent, have profound health and socio-economic impacts, particularly in communities where resilience is already low. Here output from a new, dynamic disease model [the Liverpool RVF (LRVF) model], driven by downscaled, bias-corrected climate change data from an ensemble of global circulation models from the Inter-Sectoral Impact Model Intercomparison Project run according to two radiative forcing scenarios [representative concentration pathway (RCP)4.5 and RCP8.5], is combined with results of a spatial assessment of social vulnerability to the disease in eastern Africa. The combined approach allowed for analyses of spatial and temporal variations in the risk of RVF to the end of the current century. Results for both scenarios highlight the high-risk of future RVF outbreaks, including in parts of eastern Africa to date unaffected by the disease. The results also highlight the risk of spread from/to countries adjacent to the study area, and possibly farther afield, and the value of considering the geography of future projections of disease risk. Based on the results, there is a clear need to remain vigilant and to invest not only in surveillance and early warning systems, but also in addressing the socio-economic factors that underpin social vulnerability in order to mitigate, effectively, future impacts.

Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty.

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

A dynamic, climate-driven model of Rift Valley fever.

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.

Modelling hotspots of the two dominant Rift Valley fever vectors (Aedes vexans and Culex poicilipes) in Barkédji, Sénégal.

BACKGROUND: Climatic and environmental variables were used successfully by using models to predict Rift Valley fever (RVF) virus outbreaks in East Africa. However, these models are not replicable in the West African context due to a likely difference of the dynamic of the virus emergence. For these reasons specific models mainly oriented to the risk mapping have been developed. Hence, the areas of high vector pressure or virus activity are commonly predicted. However, the factors impacting their occurrence are poorly investigated and still unknown. In this study, we examine the impact of climate and environmental factors on the likelihood of occurrence of the two main vectors of RVF in West Africa (Aedes vexans and Culex poicilipes) hotspots. METHODS: We used generalized linear mixed models taking into account spatial autocorrelation, in order to overcome the default threshold for areas with high mosquito abundance identified by these models. Getis' Gi*(d) index was used to define local adult mosquito abundance clusters (hotspot). RESULTS: For Culex poicilipes, a decrease of the minimum temperature promotes the occurrence of hotspots, whereas, for Aedes vexans, the likelihood of hotspot occurrence is negatively correlated with relative humidity, maximum and minimum temperatures. However, for the two vectors, proximity to ponds would increase the risk of being in an hotspot area. CONCLUSIONS: These results may be useful in the improvement of RVF monitoring and vector control management in the Barkedji area.

The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance.

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Towards seasonal forecasting of malaria in India.

BACKGROUND: Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model. METHODS: The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series. RESULTS AND DISCUSSION: The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

A quantitative prioritisation of human and domestic animal pathogens in Europe.

Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps.

Impact of climate change on global malaria distribution.

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.

Rift Valley Fever outbreaks in Mauritania and related environmental conditions.

Four large outbreaks of Rift Valley Fever (RVF) occurred in Mauritania in 1998, 2003, 2010 and 2012 which caused lots of animal and several human deaths. We investigated rainfall and vegetation conditions that might have impacted on RVF transmission over the affected regions. Our results corroborate that RVF transmission generally occurs during the months of September and October in Mauritania, similarly to Senegal. The four outbreaks were preceded by a rainless period lasting at least a week followed by heavy precipitation that took place during the second half of the rainy season. First human infections were generally reported three to five weeks later. By bridging the gap between meteorological forecasting centers and veterinary services, an early warning system might be developed in Senegal and Mauritania to warn decision makers and health services about the upcoming RVF risk.