Integrated approach to health monitoring: importance of multidisciplinarity in the development of methods for the collection and analysis of health data

In response to the growing threat of emerging infectious diseases, several countries have implemented epidemic intelligence activities to complement traditional surveillance systems. Epidemic intelligence is the process of early detection, verification, analysis and reporting of health events in order to recommend published health measures. In addition to indicator-based surveillance, it relies on event-based surveillance and the use of official and unofficial information sources to improve the early detection of potential health threats. Through two case studies of tool development (PADI-web and OpenESSENCE), I illustrate examples of multidisciplinary collaborations between computer scientists, epidemiologists and local health agencies to ensure the co-design of automated data collection and analysis tools in the context of online media monitoring for animal health in France (PADI-web) and enhanced electronic surveillance for human mass gatherings in the Pacific (OpenESSENCE). These developments aimed to mobilize generic and automated tools in a multidisciplinary and One Health approach to improve the early detection and alerting of health threats. Through two case studies analyzing epidemic intelligence data (spatiotemporal analysis and spread rate modelling), I highlight the integration of expert knowledge and innovative methodologies to characterize disease threats and inform risk analyses using a multidisciplinary approach. Strengthening the links between epidemic intelligence and risk analysis provides real-time data on the epidemiological situation and improves disease knowledge to adapt and target epidemic intelligence systems. an integrated approach to epidemic intelligence involving cross-border collaborations in a One Health context to promote the pooling of multidisciplinary data, expertise and tools through a common strategy. Monitoring weak signals and genetic information can help improve the timely detection of the emergence of unknown pathogens. The integration of new sources of information and innovative technologies into existing epidemic intelligence systems can form the basis for an enhanced risk detection, monitoring and assessment platform. Mathematical models can identify environmental and socio-economic drivers of disease emergence and spread to facilitate the surveillance and prediction of future outbreaks. The implementation of an integrated approach should extend beyond scientific disciplines to bring together scientists, health professionals, governmental agencies and private stakeholders to implement contextualized and collaborative actions that foster a multidisciplinary culture across the human-animal interface