Background: Rift Valley fever (RVF) is an emerging vector-borne zoonotic disease that represents a threat to human health, animal health, and livestock production, particularly in Africa. The epidemiology of RVF is not well understood, so that forecasting RVF outbreaks and carrying out efficient and timely control measures remains a challenge. Various epidemiological modeling tools have been used to increase knowledge on RVF epidemiology and to inform disease management policies. Aim: This narrative review gives an overview of modeling tools used to date to measure or model RVF risk in animals, and presents how they have contributed to increasing our understanding of RVF occurrence or informed RVF surveillance and control strategies. Methodology: Systematic literature searches were performed in PubMed and ISI Web of Knowledge. Additional research work was identified from other sources. Results: Literature was scarce. Research work was highly heterogeneous in methodology, level of complexity, geographic scale of approach, and geographical area of study. Gaps in knowledge and data were frequent, and uncertainty was not always explored. Spatial approaches were the most commonly utilized techniques and have been used at both local and continental scales, the latter leading to the implementation of an early warning system. Three articles using dynamic transmission models explored the potential of RVF endemicity. Risk factor studies identified water-related environmental risk factors associated with RVF occurrence in domestic livestock. Risk assessments identified importation of infected animals, contaminated products, or infected vectors as key risk pathways for the introduction of RVF virus into disease-free areas. Conclusions: Enhanced outbreak prediction and control and increased knowledge on RVF epidemiology would benefit from additional field data, continued development, and refinement of modeling techniques for exploring plausible disease transmission mechanisms and the impact of intervention strategies.

Significance: Rift Valley fever (RVF) is an emerging, zoonotic hemorrhagic fever, affecting mainly livestock and humans in Africa. Despite its growing global concern, the impact of control measures on epidemic dynamics using empirical data has not been assessed. By combining a unique RVF epidemic dataset covering both livestock and human data in a closed ecosystem (Mayotte island) with a dynamic model, we estimate viral transmission potential among livestock, and from livestock to humans. We also quantify the impact of vaccination in decreasing the epidemic size. We demonstrate that reactive livestock vaccination is key. We present a reference case study for RVF and illustrate the value of a One Health quantitative approach to surveillance and control of zoonotic infectious diseases.

Rift Valley fever (RVF) is a zoonotic arboviral disease that is a threat to human health, animal health and production, mainly in Sub-Saharan Africa. RVF virus dynamics have been poorly studied due to data scarcity. On the island of Mayotte in the Indian Ocean, off the Southeastern African coast, RVF has been present since at least 2004. Several retrospective and prospective serological surveys in livestock have been conducted over eleven years (2004–15). These data are collated and presented here. Temporal patterns of seroprevalence were plotted against time, as well as age-stratified seroprevalence. Results suggest that RVF was already present in 2004–07. An epidemic occurred between 2008 and 2010, with IgG and IgM peak annual prevalences of 36% in 2008–09 (N = 142, n = 51, 95% CI [17–55]) and 41% (N = 96, n = 39, 95% CI [25–56]), respectively. The virus seems to be circulating at a low level since 2011, causing few new infections. In 2015, about 95% of the livestock population was susceptible (IgG annual prevalence was 6% (N = 584, n = 29, 95% CI [3–10])). Monthly rainfall varied a lot (2–540mm), whilst average temperature remained high with little variation (about 25–30°C). This large dataset collected on an insular territory for more than 10 years, suggesting a past epidemic and a current inter-epidemic period, represents a unique opportunity to study RVF dynamics. Further data collection and modelling work may be used to test different scenarios of animal imports and rainfall pattern that could explain the observed epidemiological pattern and estimate the likelihood of a potential re-emergence. Rift Valley fever (RVF) is a viral zoonotic disease mainly present in Sub-Saharan Africa, transmitted by mosquitoes and primarily affecting livestock (cattle, sheep and goats). The epidemiology of the disease is not fully known, mainly because of data scarcity. In Mayotte, an island close to Madagascar, RVF has been present since at least 2004, and retrospective and prospective surveys have been conducted to collect data from livestock over 11 years. Our work uses this 2004–15 dataset to describe the past and current epidemiology of RVF on the island. Results suggest that the disease was endemic between 2004–07. An epidemic occurred between 2008 and 2010. Since 2011, the virus appears to be circulating at a low-level causing few new infections. Modelling work may be used to explain the observed epidemiological pattern, and estimate the likelihood of a potential re-emergence.

Rift Valley fever (RVF) is a zoonotic and vector-borne disease, mainly present in Africa, which represents a threat to human health, animal health and production. South Africa has experienced three major RVF epidemics (1950–51, 1973–75 and 2008–11). Due to data scarcity, no previous study has quantified risk factors associated with RVF epidemics in animals in South Africa. Using the 2008–11 epidemic datasets, a retrospective longitudinal study was conducted to identify and quantify spatial and temporal environmental factors associated with RVF incidence. Cox regressions with a Besag model to account for the spatial effects were fitted to the data. Coefficients were estimated by Bayesian inference using integrated nested Laplace approximation. An increase in vegetation density was the most important risk factor until 2010. In 2010, increased temperature was the major risk factor. In 2011, after the large 2010 epidemic wave, these associations were reversed, potentially confounded by immunity in animals, probably resulting from earlier infection and vaccination. Both vegetation density and temperature should be considered together in the development of risk management strategies. However, the crucial need for improved access to data on population at risk, animal movements and vaccine use is highlighted to improve model predictions.

Rift Valley fever (RVF) is a zoonotic arbovirosis for which the primary hosts are domestic livestock (cattle, sheep and goats). RVF was first described in South Africa in 1950–1951. Mechanisms for short and long distance transmission have been hypothesised, but there is little supporting evidence. Here we describe RVF occurrence and spatial distribution in South Africa in 2008–11, and investigate the presence of a contagious process in order to generate hypotheses on the different mechanisms of transmission. A total of 658 cases were extracted from World Animal Health Information Database. Descriptive statistics, epidemic curves and maps were produced. The space-time K-function was used to test for evidence of space-time interaction. Five RVF outbreak waves (one in 2008, two in 2009, one in 2010 and one in 2011) of varying duration, location and size were reported. About 70% of cases (n = 471) occurred in 2010, when the epidemic was almost country-wide. No strong evidence of space-time interaction was found for 2008 or the second wave in 2009. In the first wave of 2009, a significant space-time interaction was detected for up to one month and over 40 km. In 2010 and 2011 a significant intense, short and localised space-time interaction (up to 3 days and 15 km) was detected, followed by one of lower intensity (up to 2 weeks and 35 to 90 km). The description of the spatiotemporal patterns of RVF in South Africa between 2008 and 2011 supports the hypothesis that during an epidemic, disease spread may be supported by factors other than active vector dispersal. Limitations of under-reporting and space-time K-function properties are discussed. Further spatial analyses and data are required to explain factors and mechanisms driving RVF spread. The factors explaining Rift Valley fever (RVF) spread in domestic livestock during an epidemic are attributed to short and long distance mechanisms, including active vector dispersal, passive vector dispersal and movements of infectious animals. However, because of data scarcity, quantifying and disentangling these mechanisms remains challenging. Here, we generate hypotheses on the possible mechanisms involved in RVF spread in South Africa between 2008 and 2011. We use descriptive statistics and estimate the space-time K-function to explore the presence of space-time interactions, being interpreted as an indicator of an underlying transmission process. Our results confirm the presence of an intense, short, initial transmission process that could be attributed to active vector dispersal; but also highlight the presence of another transmission mechanism of a lower intensity and over further distances that could be explained by the movements of infectious animals, passive vector dispersal or emergence of other foci. Further data collection and modelling tools are required to confirm these hypotheses.

Bats are natural reservoirs of the largest proportion of viral zoonoses among mammals, thus understanding the conditions for pathogen persistence in bats is essential to reduce human risk. Focusing on the European Bat Lyssavirus subtype 1 (EBLV-1), causing rabies disease, we develop a data-driven spatially explicit metapopulation model to investigate EBLV-1 persistence inMyotis myotisandMiniopterus schreibersiibat species in Catalonia. We find that persistence relies on host spatial structure through the migratory nature ofM. schreibersii, on cross-species mixing withM. myotis, and on survival of infected animals followed by temporary immunity. The virus would not persist in the single colony ofM. myotis. Our study provides for the first time epidemiological estimates for EBLV-1 progression inM. schreibersii. Our approach can be readily adapted to other zoonoses of public health concern where long-range migration and habitat sharing may play an important role.