Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study

Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic. Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants. Methods: This report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm. Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022. Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.

The geographical dimension of genetic diversity

Stéphane Joost

In its natural framework, genetic information is embedded within a geographic context. Plants and animals are directly influenced by the specific characteristics of their surrounding environment. Therefore, spatial information is a potentially important element to be considered in trying to understand genetic resources. For many years, Geographical Information Science (GIScience) turned toward environmental modelling, generally to demonstrate how GIS basic features could be efficiently applied to fields related to the natural sciences. However, despite its current predominance in life sciences and its direct application to concerns of public society (health, food), genetics had heretofore remained outside the scope of research by the GIScience community while biologists were developing approaches based on GIS, which conducted to the elaboration of "landscape genetics". The present GIScience approach to linking genetics and geographics obviously places emphasis on geographic information, unlike most studies in this domain. This perspective is developed through an application to two case studies to assess the potential contribution of GIScience to conservation biology. The main one is provided by Econogene, a European research project aiming at promoting the sustainable conservation of genetic resources in sheep and goats. These small ruminants have considerable economic importance in marginal agrosystems in Europe. The surveying of their genetic resources makes it possible to highlight endangered breeds having high distinctiveness and priority for conservation. The second case study is complementary and supplies wild species data to demonstrate how genetic information is used to assess conservation measures applied to the endangered Scandinavian Brown Bear. Advanced molecular technologies make it possible to efficiently measure genetic information. In parallel, considerable advancements in computer science have led to the development of sophisticated GIS software and methods. The joining of molecular biology and GIScience enables novel and complementary methods of tackling some of the challenging issues related to evolutionary processes. This tentative application of diverse facets of GIScience to molecular biology addresses three distinct issues. Firstly, considering the vast quantity of information collected within the Econogene project, exploratory data analysis methods are applied to extract useful information from large spatially explicit genetic data sets. This category of GIS tools facilitates investigations to understand the geographic distribution of genetic diversity among sheep and goat breeds as well as its variation according to environmental parameters. Secondly, a review of population genetics literature having revealed weaknesses about the way spatial genetic data are generally represented on maps, the semiology of graphics is used to produce improved thematic maps representing patterns of genetic diversity. The recourse to high-performance cartography improves the interpretation of data, and facilitates the transmission of the results as well as their communication between researchers, or between researchers and the general public. Thirdly, this combination of GIScience with molecular biology mainly leads to the development of a spatial analysis method to detect signatures of natural selection within the genome. The method is adapted to a precise conception of environmental modelling, advocating the implementation of simple models in order to better grasp the functioning of natural processes, and recognizing an inescapable uncertainty. To find out these signatures, spatial analysis takes advantage of the evolution of genetics toward genomics and of the subsequent availability of large data sets generated by large genome scans. This permits to compute many simultaneous univariate logistic regression models and to identify specific regions of the genome which are selected by environmental parameters. These are identical to the ones detected by a standard approach developed in population genomics.

EPFL2006

Contribution du SIG à l'analyse des liens déchets-santé en milieu urbain dans les pays en développement

Sonwouignandé Kientga

Among the factors of health risks in urban areas, the solid and liquid wastes worsen particularly the problems of public health in developing countries. Uncontrolled solid waste disposal sites and waste water sites constitute major sources of danger for public health. Often, there is no mapped and documented inventory. The perception of danger related to infectious sources by the exposed populations is generally not well-known and even often ignored by the public institutions in their management policy of health and wastes. The same can be considered for the link between diagnosed diseases in health care services and the presence of waste disposal sites near patients' residences. The goal of this study is to determine the main factors of waste management and their consequences on health. This goal shall be reached using a GIS approach and through a case study concerning two districts in the town of Ouagadougou in Burkina Faso. In detail, the objectives are: i) to build a spatial database for waste disposal sites and public health care management and to propose a methodological approach for the elaboration of a GIS, associating social science, health and the geographical information science; ii) to identify and to analyze indicators of public health related to domestic and industrial waste; ii) to compare these indicators with the different actors' perception, according to the distance and dangers represented by the seriousness of potential danger sources; iv) to analyze if there is a relation between the actors' perception and the diseases diagnosed in health care services; v) to promote GIS in institutional structures in order to facilitate decision-making in waste management. Geographical information science has contributed to the creation of a database containing general geographical data, the disposal waste sites, the data of the actors' perception and health data. This database was used for spatial-thematic, temporal and statistical analyses. At first we have established an inventory of disposal waste sites. Hereafter we have analyzed their spatial influence. We then compared the situation to the perception of dangers and to the people's health risks, related to the distance to sources of potential danger. An analysis of the relation between the disease cases listed (counted) in health centers and the presence of waste (infectious) sources was carried out. This allowed some degree of comparison between the subjectivity of danger perception and the reality. The inventory of potential danger sources and the analysis of their influence spheres shows that 86 % (456 ha) of the study area is threatened by a danger, among which 45 % (235 ha) are considered exposed to a higher danger. The results also show that only 14 % of the population in both sectors (2'574 inhabitants of 18'978 total inhabitants) is not exposed to potential health risks. The analysis of the perceived danger according to the distance revealed that the perceived danger generally decreases proportionally to the distance. Surprisingly certain persons living near a polluted site don't consider the site as dangerous. However others, living far away from the same site, perceive it as dangerous. In these cases the subjective dimension of the perception is thus revealed. The temporal analyses of listed diseases show a correlation between the cases of malaria and the precipitation. The time gap between precipitation-related peaks and malaria cases related peaks varies from two to three months, leaving enough time to take preventive measures. We were not able to establish a link between the patient's residence and pollution sources; because the patient's geographical location is not mentioned in the health care services' registers. A modification of the registration forms has been suggested. The developed methodology and the results gathered led to the establishment of suggestions and recommendations based on the integration of GIS in waste and health management. This study has already contributed to the setup of a geo-information network composed of various actors in waste and health management.

EPFL2008

Introducing SMURF: a software system for monitoring urban functionalities

André Musy, Alexandre Repetti, Marc Soutter

Urban land-use planning and management are in constant mutation throughout the world. With sustainability as a goal, the use of participative GIS is becoming more and more in demand. Given the willingness of local authorities to test participatory models and instruments, this paper presents an original implementation of information and communication technologies to support public participation. A system for monitoring urban functionalities (SMURF) was created for supporting participatory planning and management in African cities. The software instrument consists of a geographic database and of spatial indicators, for sharing information, editing information, and evaluating city development. To collect and update data, the model begins with the gathering of the data from each stakeholder, who, in return, gets a better knowledge of the land and a planning support tool. However, the implementation of these technologies presents several challenges: create an adapted interface, elaborate a relevant set of data and indicators, manage the data, and institutionalize the instrument. After an introductory presentation of the context and use of information technologies for the management of African cities, this paper focuses on SMURF and its components. It then shows the experimental application of the software in the city of Thies, Senegal, before concluding with a more general discussion on the relationship between technology, information, knowledge, and participation.