The use of spatiotemporal analytical tools to inform decisions and policy in One Health scenarios

Abstract

The use of spatiotemporal analytical tools to generate risk maps and risk scores that facilitate early detection of health and environmental threats is increasingly popular in many countries and international organizations around the world. The traditional approach of spatial epidemiology focuses on mapping and conducting tests for detection of spatial aggregation of cases, referred to as “clusters”, to determine visual and geographical relational clues, and then ecologic approaches to recognize etiologic signs of disease distribution in relation to explanatory factors. The advances in spatial epidemiology are focused on the application of spatiotemporal findings to inform mitigation measures, use of big data to improve the validity and reliability of case-data based analyses, and eventually to provide risk estimates in a timely manner to support decision and policy in preventive and control measures, while supporting the improvement of existing data collection processes. This study provided a framework for choosing spatiotemporal analytical tools, summarizing the features of tools commonly used in spatial analysis, and discussing their potential use when informing decisions related to One Health scenarios. To this end, three case studies addressing endemic conditions affecting ecosystem health, animal health, and public health in Minnesota were compared. A risk score; an estimate/characterization of the disease spread, and suggestions on risk zones were introduced, using spatiotemporal analytical tools, addressing aquatic invasive species in Minnesota waters, Johne’s disease in dairy cattle, and Anthrax, affecting wildlife, livestock, and humans, respectively. The One Health concept promotes a collaborative approach, through effective communication and cooperation across disciplines and sectors, to solve complex problems that intersect animal, human and environmental health. An essential component in the process is understanding the stakeholder perspectives of the problem. Therefore, the comparison between the case studies focused on the lessons learned through the researcher-stakeholder interactions and identification of the opportunities and challenges in the process. Overall, the work presented through this dissertation, serves as precedent for establishing a protocol of “good practices” when promoting the use of spatiotemporal analytical tools to inform the implementation of scientifically driven risk management and policy solutions to One Health scenarios.