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

Abstract

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.

Ecological Modeling of the Spatial Distribution of Wild Waterbirds to Identify the Main Areas Where Avian Influenza Viruses are Circulating in the Inner Niger Delta, Mali

“…One consequently must turn to knowledge-based approaches combining environmental and socioeconomical data to derive predictions (Smith et al, 2005). These approaches then can be used to determine high-risk areas (Cumming et al, 2008;Brochet et al, 2009), prioritize surveillance programs, and plan interventions (Clements and Pfeiffer, 2009).…”

Rift Valley Fever Virus: A Real Bioterror Threat

“…While important risk assessment and surveillance strategies for RVFV in Western countries that rely on statistical tools including landscape epidemiology and phylogeography have been suggested [84], to date no plans have been officially implemented. As described in Breiman et al 2010, RVF is a disease associated with a complex set of factors that make disease outbreaks likely including animals, mosquitoes, climate, ecology, and commercial trade [14].…”

Translating Predictions of Zoonotic Viruses for Policymakers

“…Low-and middle-income countries (LMICs) afflicted by such viral diseases may lack the resources needed for integrated pathogen surveillance systems (Perry et al 2007). However, researchers have created many predictive maps for emerging viruses that national governments could use to focus their efforts for outbreak preparedness (Clements and Pfeiffer 2009;Hay et al 2013).…”

“…Researchers also use different model types, data sets, assumptions, and parameters to generate predictions (Clements and Pfeiffer 2009;Hay et al 2013), and these factors should be considered when translating predictions for national policymakers. A common approach to spatial disease modeling is to identify areas where the virus or viral disease has occurred and then use a model to map areas similarly suitable for the virus or host.…”