Location: Arthropod-borne Animal Diseases Research
Project Number: 3020-32000-014-06-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2014
End Date: Jun 30, 2019
The aim of the proposed study is to define risk profiles that quantify the distribution of JEV risk in pig (feral and domestic), bird, mosquito and human populations and to explore the impact of changes in risk factors or of implementing mitigation strategies in the risk of introduction of JEV in USA. Specific objectives include: 1) perform a systematic review and meta-analysis (SR-MA) to appraise available scientific literature on risk factors and mitigation strategies for JEV control, and 2) utilize a quantitative risk assessment tool to explore risk profiles, costs and feasibility of implementation of interventions and their impact on risk factors, as well as to identify data gaps to generate further research questions. These results will be central to: identify risk profiles for introduction of JEV in USA, identify critical data gaps, in addition to enhance the development of future risk assessments and mathematical models for assessing potential patterns of disease transmission and spread.
A quantitative Monte Carlo risk assessment (QRA) simulation model will be developed using the scientific literature and expert opinion. Investigators will use simulations to incorporate input data variability and uncertainty and generate probability distributions of JEV risk of introduction in USA (e.g., frequency, prevalence, seroprevalence in humans, birds, mosquitoes or swine) based on risk factors (e.g., demographic, climate (season), spatial, ecological and environmental (region)). In addition, the impact of interventions targeting different epidemiological components of disease (agent, hosts, and environment) on JEV risk will be modeled as well as potential global climate change scenarios and shifts in mosquito vector species or virus mutation mechanisms. Data on risk factors, as well as costs and benefits of interventions applied to mitigate transmission or infection will be obtained using a systematic review (SR) of published literature and meta-analyses (MA) when appropriate data are available, as previously described (Sargeant et al., 2005). SR uses a defined search strategy to identify, assess and synthesize information, minimizing the selection bias associated with narrative reviews. SR-MA will be conducted to identify evidence needed to refine input values of model distribution parameters.