Towards real-time prediction of Rift Valley fever epidemics in Africa

Ecological and subject-level drivers of interepidemic Rift Valley fever virus exposure in humans and livestock in Northern Kenya

Nearly a century after the first reports of Rift Valley fever (RVF) were documented in Kenya, questions on the transmission dynamics of the disease remain. Specifically, data on viral maintenance in the quiescent years between epidemics is limited. We implemented a cross-sectional study in northern Kenya to determine the seroprevalence, risk factors, and ecological predictors of RVF in humans and livestock during an interepidemic period. Six hundred seventy-six human and 1,864 livestock samples were screened for anti-RVF Immunoglobulin G (IgG). Out of the 1,864 livestock samples tested for IgG, a subset of 1,103 samples was randomly selected for additional testing to detect the presence of anti-RVFV Immunoglobulin M (IgM). The anti-RVF virus (RVFV) IgG seropositivity in livestock and humans was 21.7% and 28.4%, respectively. RVFV IgM was detected in 0.4% of the livestock samples. Participation in the slaughter of livestock and age were positively associated with RVFV exposure in humans, while age was a significant factor in livestock. We detected significant interaction between rainfall and elevation's influence on livestock seropositivity, while in humans, elevation was negatively associated with RVF virus exposure. The linear increase of human and livestock exposure with age suggests an endemic transmission cycle, further corroborated by the detection of IgM antibodies in livestock.

Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector

Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and elevated land surface temperature values ~3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006-2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015-2016 period when documented outbreaks did not occur, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.

Public Health Benefits from Livestock Rift Valley Fever Control: A Simulation of Two Epidemics in Kenya

In controlling Rift Valley fever, public health sector optimises health benefits by considering cost-effective control options. We modelled cost-effectiveness of livestock RVF control from a public health perspective in Kenya. Analysis was limited to pastoral and agro-pastoral system high-risk areas, for a 10-year period incorporating two epidemics: 2006/2007 and a hypothetical one in 2014/2015. Four integrated strategies (baseline and alternatives), combined from three vaccination and two surveillance options, were compared. Baseline strategy included annual vaccination of 1.2-11% animals plus passive surveillance and monitoring of nine sentinel herds. Compared to the baseline, two alternatives assumed improved vaccination coverage. A herd dynamic RVF animal simulation model produced number of animals infected under each strategy. A second mathematical model implemented in R estimated number people who would be infected by the infected animals. The 2006/2007 RVF epidemic resulted in 3974 undiscounted, unweighted disability adjusted life years (DALYs). Improving vaccination coverage to 41-51% (2012) and 27-33% (2014) 3 years before the hypothetical 2014/2015 outbreak can avert close to 1200 DALYs. Improved vaccinations showed cost-effectiveness (CE) values of US$ 43-53 per DALY averted. The baseline practice is not cost-effective to the public health sector.

Inter-epidemic and between-season persistence of rift valley fever: vertical transmission or cryptic cycling?

Rift Valley fever (RVF) is an emerging zoonotic mosquito-borne infectious disease that has been identified as a risk for spread to other continents and can cause mass livestock mortality. In equatorial Africa, outbreaks of RVF are associated with high rainfall, when vector populations are at their highest. It is, however, unclear how RVF virus persists during the inter-epidemic periods and between seasons. Understanding inter-epidemic persistence as well as the role of vectors and hosts is paramount to creating effective management programmes for RVF control. We created a mathematical model for the spread of RVF and used the model to explore different scenarios of persistence including vertical transmission and alternate wildlife hosts, with a case study on buffalo in Kruger National Park, South Africa. Our results suggest that RVF persistence is a delicate balance between numerous species of susceptible hosts, mosquito species, vertical transmission and environmental stochasticity. Further investigations should not focus on a single species, but should instead consider a myriad of susceptible host species when seeking to understand disease dynamics.

From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives

Remote sensing techniques are becoming increasingly important for identifying mosquito habitats, investigating malaria epidemiology and assisting malaria control. Here, Simon Hay, Bob Snow and David Rogers review the development of these techniques, from aerial photographic identification of mosquito larval habitats on the local scale through to the space-based survey of malaria risk over continental areas using increasingly sophisticated airborne and satellite-sensor technology. They indicate that previous constraints to uptake are becoming less relevant and suggest how future delays in the use of remotely sensed data in malaria control might be avoided.

The spatial epidemiologic (r)evolution: a look back in time and forward to the future

Spatial epidemiology enables you to better understand diseases or ill-health processes; investigate relationships between the environment and the presence of disease; conduct disease cluster analyses; predict disease spread; evaluate control alternatives; and basically do things an epidemiologist otherwise would have been unable to do and avoid many errors that otherwise may have been committed. Recently, the discipline of spatial epidemiology has advanced substantially, owing to a combination of reasons. The introduction of the electronic computer has clearly led this advancement. Computers have facilitated the storage, management, display and analysis of data, which are critical to geographic information systems (GIS). Also, because of computers and their increased capabilities and capacities, data collection has greatly expanded and reached a new level owing in large part to the advent of geographic positioning systems (GPS). GPS enables the collection of spatial locations, which in turn present yet another attribute (location) amenable to consideration in epidemiologic studies. At the same time, spatial software has taken advantage of the evolution of computers and data, further enabling epidemiologists to perform spatial analyses that they may not have even conceived of 30 years before. Capitalizing on these now, non-binding technologic constraints, epidemiologists are more able to combine their analytic expertise with computational advances, to develop approaches, which enable them to make spatial epidemiologic methods an integral part of their toolkits. Instead of a novelty, spatial epidemiology is now more of a necessity for outbreak investigations, surveillance, hypothesis testing, and generating follow-up activities necessary to perform a complete and proper epidemiologic analysis.

Emerging viral zoonoses: frameworks for spatial and spatiotemporal risk assessment and resource planning

Spatial epidemiological tools are increasingly being applied to emerging viral zoonoses (EVZ), partly because of improving analytical methods and technologies for data capture and management, and partly because the demand is growing for more objective ways of allocating limited resources in the face of the emerging threat posed by these diseases. This review documents applications of geographical information systems (GIS), remote sensing (RS) and spatially-explicit statistical and mathematical models to epidemiological studies of EVZ.

Landscape epidemiology uses statistical associations between environmental variables and diseases to study and predict their spatial distributions. Phylogeography augments epidemiological knowledge by studying the evolution of viral genetics through space and time. Cluster detection and early warning systems assist surveillance and can permit timely interventions. Advanced statistical models can accommodate spatial dependence present in epidemiological datasets and can permit assessment of uncertainties in disease data and predictions. Mathematical models are particularly useful for testing and comparing alternative control strategies, whereas spatial decision-support systems integrate a variety of spatial epidemiological tools to facilitate widespread dissemination and interpretation of disease data. Improved spatial data collection systems and greater practical application of spatial epidemiological tools should be applied in real-world scenarios.

Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed.

Arthropods as disease vectors in a changing environment

Arthropod vectors need to acquire energy, moisture, hosts and shelter from their environment. Changing human populations and industrialization affect almost every aspect of the environment. In particular, the prospects of climatic warming, urbanization and vegetation changes have the potential to materially affect global patterns of vector-borne diseases. Global warming will enable the expansion of the geographical distributions of vectors. The population dynamics of vectors will change in response to extended seasons suitable for development followed by less severe winters. The incidence of epidemics is likely to change in response to an expected disproportionate increase in the frequency of extreme climatic events. The impact of such changes on each of the major vector-borne diseases is reviewed and projections are made on the likely global areas at risk from spread of disease vectors. Research needs are identified and response strategies are suggested in the context of the ever-increasing impact of human populations and industrial activity on the environment.

Spatial risk assessment of Rift Valley fever in Senegal

Rift Valley fever (RVF) is broadening its geographic range and is increasingly becoming a disease of global importance with potentially severe consequences for human and animal health. We conducted a spatial risk assessment of RVF in Senegal using serologic data from 16,738 animals in 211 locations. Bayesian spatial regression models were developed with interpolated seasonal rainfall, land surface temperature, distance to perennial water bodies, and time of year entered as fixed-effect variables. Average total monthly rainfall during December-February was the most important spatial predictor of risk of positive RVF serologic status. Maps derived from the models highlighted the lower Senegal River basin and the southern border regions of Senegal as high-risk areas. These risk maps are suitable for use in planning improved sentinel surveillance systems in Senegal, although further data collection is required in large areas of Senegal to better define the spatial distribution of RVF.

A Rift Valley fever atlas for Africa

Rift Valley fever (RVF) epidemics have serious consequences for human and animal health and the livestock trade. Recent epidemics have occurred in previously unaffected regions, increasing concerns that the geographical range of RVF will continue to expand. We conducted an extensive, systematic review of the literature to obtain serological data for RVF in Africa, collected between 1970 and 2000 from human, livestock and wild ungulate populations. Aims were to calculate sub-national estimates of RVF infection prevalence and to define areas where no information was available. We presented the data (aggregated at the first administrative level of countries) using a geographical information system. Data from 71 publications were used to build a spatially explicit Bayesian logistic-regression model, with spatial and non-spatial random effects, allowing us to identify clusters of high and low RVF seroprevalence, and fixed effects that described the disparate nature of the survey subjects and methods. Significant high-prevalence clusters encompassed areas that had experienced epidemics during the late 20th century and significant low-prevalence clusters were located in contiguous areas of Western and Central Africa.

Predicting St. Louis encephalitis virus epidemics: lessons from recent, and not so recent, outbreaks

St. Louis encephalitis virus was first identified as the cause of human disease in North America after a large urban epidemic in St. Louis, Missouri, during the summer of 1933. Since then, numerous outbreaks of St. Louis encephalitis have occurred throughout the continent. In south Florida, a 1990 epidemic lasted from August 1990 through January 1991 and resulted in 226 clinical cases and 11 deaths in 28 counties. This epidemic severely disrupted normal activities throughout the southern half of the state for 5 months and adversely impacted tourism in the affected region. The accurate forecasting of mosquito-borne arboviral epidemics will help minimize their impact on urban and rural population centers. Epidemic predictability would help focus control efforts and public education about epidemic risks, transmission patterns, and elements of personal protection that reduce the probability of arboviral infection. Research associated with arboviral outbreaks has provided an understanding of the strengths and weaknesses associated with epidemic prediction. The purpose of this paper is to review lessons from past arboviral epidemics and determine how these observations might aid our ability to predict and respond to future outbreaks.

Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa

This review highlights the progress and current status of remote sensing (RS) and geographical information systems (GIS) as currently applied to the problem of Plasmodium falciparum malaria in sub-Saharan Africa (SSA). The burden of P. falciparum malaria in SSA is first summarized and then contrasted with the paucity of accurate and recent information on the nature and extent of the disease. This provides perspective on both the global importance of the pathogen and the potential for contribution of RS and GIS techniques. The ecology of P. falciparum malaria and its major anopheline vectors in SSA in then outlined, to provide the epidemiological background for considering disease transmission processes and their environmental correlates. Because RS and GIS are recent techniques in epidemiology, all mosquito-borne diseases are considered in this review in order to convey the range of ideas, insights and innovation provided. To conclude, the impact of these initial studies is assessed and suggestions provided on how these advances could be best used for malaria control in an appropriate and sustainable manner, with key areas for future research highlighted.