Towards An Integrated Geospatial Pandemic Response System

The recent COVID-19 pandemic has shown that rapid information integration is of high importance to control the spatial and temporal spread of unknown deadly diseases across different scales, from local sources of origin to the entire globe. Risk and probability maps can provide a good picture of the spatial and temporal distribution of COVID-19 across local, regional, national and continental areas and can help to control spreads, not overwhelming health emergency infrastructures and allowing for controlled return to our daily lives. First results of the CON-VINCE study shows that only 2 % of the population has been in contact with the virus. Due to low, daily infection rates, the Luxembourgish Government is reopening schools, parts of the economy and societal life. But a second infection wave is likely to occur. Therefore, further testing and surveys are currently implemented through the COVID-19 TaskForce, building on interdisciplinary probabilistic scenarios and the Weizmann COVID-19 pilot study for self reporting of COVID-19 cases to get a better idea about the spatio-temporal distribution across the country. This is of high importance to allow controlled reopenings and a good functioning of logistics chains. The TIGER project will strongly contribute to the COVID-19 TaskForce's WPs 06, 07 and 13 with a replicable, reproducible and scalable probabilistic approach, implemented into an interoperable geospatial web platform which can be reused by, linked to or integrated into current and future initiatives on infectuous disease spread and control. We propose an interoperable geospatial disease response system, built upon the existing iGuess® software technology platform developed at LIST to: 1) integrate existing high resolution geospatial information on infrastructure, population vulnerability, self reporting, publicly released and TaskForce COVID-19 data; 2) provide tools to analyse and forecast spatio-temporal disease patterns and to optimise spatial sampling to get a better picture on spatial coverage of infected cases and disease spread; 3) provide easy access to tools and generated information for experts and decision makers through secure interoperable web services. We suggest to use geostatistical spatio-temporal methods, such as space-time point-to-area (aggregation) and area-to-point (disaggregation) simulation and Log-Gaussian Cox based simulations to analyse and estimate current and near future status of the pandemic situations. We will use established OGC web service standards which enable the integration of distributed geospatial data from geoportals, OpenStreetMap, social media etc. with COVID-19 observation data, and provide an automated spatio-temporal mapping and prediction system to retrieve optimal information for crisis response support for public health across the entire country. The TIGER project has strong impacts and innovation potential due to its integrated approach combining distributed data, methods and tools to provide spatio-temporal evidence for multiple experts, researchers and stakeholders, to take improved decision for lock-down measures and a controlled reopening of societal life. The platform will provide easy secured access to different experts and researchers within the TaskForce and thus can provide integrated views across different WPs and expert teams. Results can be easily shared through interoperable web services to the different expert teams and the government in forms of maps, tables, graphs and summary statistics at different aggregation levels. Such a replicable, reproducible and scalable response system will help to react more rapid in future outbreaks of infectious diseases and can provide better coordination with neighbouring countries as if well established data standards for sharing are used and further adapted to pandemic outbreaks.