Résumé
Introduction/Background:The consequences of climate and environmental changes on health are now obvious to communities, institutions and researchers alike. The impact of these changes must now be considered in an operational way in health management, in order to anticipate their effects, prevent them or mitigate them where possible. In practice, there is very little routine real-time use of space observation data in the public health sector, despite the increasing availability of space data. Indeed, space observation technologies have been constantly evolving since the 1970s, and now offer a wide range of data at different spatial, temporal and radiometric resolutions. More recently, access to data acquired by satellite has greatly improved, with free, massive data and easier processing. This offers the possibility of supporting health surveillance at various scales, which will be explored in this presentation using different examples from South-East Asia.Main Aim/Purpose:This presentation aims at raising the issue of integrating environmental and climate change indicators into health monitoring, by presenting practical tools and case studies that are operational in Southeast Asia. It will provide an update on the needs for further operational implementation that will benefit health monitoring.Methodology and Findings:The presentation will focus firstly on the development of a web platform that aims at modeling suitable climate and environmental conditions for leptospirosis through Earth observation, over the agglomeration of Yangon, Myanmar. Leptospirosis is a bacterial zoonosis that remains rarely diagnosed in Southeast Asia despite a high morbidity as shown in several active investigations. It is strongly associated to water and seasons with epidemics following heavy rainfall and flooding episodes. In the frame of the ECOMORE 2 Project (coordinated by Institut Pasteur and funded by the French Agency for Development - AFD), the locations of leptospirosis confirmed cases (vs non-leptospirosis controls) included in 2019 and 2020 were analyzed retrospectively. Time series of vegetation, water, and moisture indices from Sentinel-2 satellite imagery (available at 10 meters spatial resolution, every 5 days, from the European Space Agency, Copernicus Program) were produced to describe the dynamics of the environment around the locations of residence. This process relies on the use of the Sen2Chain processing chain developed in Python and open (https://framagit.org/espace-dev/sen2chain). The most relevant indices were used to build a spatiotemporal prediction model of positive vs negative locations. This model was spatialized on homogeneous landscape units from the point of view of land use, and describing the whole study area. The acquisition of Sentinel-2 images, their processing and the modelling were then automated as soon as a new image is available (every 5 days). An online platform, named LeptoYangon (https://leptoyangon.geohealthresearch.org/), was developed with R and R-Shiny to display this dynamic mapping of suitable environments and inform the epidemiologists and physicians of the study, in the frame of the ClimHealth project (funded by CNES and accredited by the Space Climate Observatory International Initiative). This fully automated tool allows retrospective consultation at any date since the first Sentinel-2 image was available in March 2016 (over 7 years). By clicking on the map, the user can select a landscape unit and view the temporal dynamics of the risk for that unit (i.e. whether the risk is increasing and decreasing). The user can also view the vegetation, water and moisture indicators to get an idea of the environmental data more specifically. This platform was designed to be used by epidemiologists and physicians to visualize the most at-risk areas and those where the risk is increasing, in order to raise physicians' awareness of leptospirosis (often confused with other fevers).Discussion:Implementing this tool in other territories is facing 1) methodological challenges regarding the volume of satellite data to be processed and 2) the need for detailed knowledge of the ecology of leptospirosis and exposure factors to adapt the models in different contexts. However, this already operational tool opens the way to the development of climate and environmental monitoring systems to increase the vigilance of healthcare workers and populations to the risk of leptospirosis. This also shows the relevance of developing specific tools for other diseases associated to climate and environment. At a country or regional scale, it is mainly meteorological variations and climatic anomalies that are relevant to the surveillance of certain diseases, such as dengue fever. The presentation will finally review the development of a national early warning system in Cambodia based on the acquisition of such climatic data.