SISS-Geo: Leveraging Citizen Science to Monitor Wildlife Health Risks in Brazil

Abstract

The well-being of wildlife health involves many challenges, such as monitoring the movement of pathogens; expanding health surveillance beyond humans; collecting data and extracting information to identify and predict risks; integrating specialists from different areas to handle data, species and distinct social and environmental contexts; and, the commitment to bringing relevant information to society. In Brazil, there is still the difficulty of building a mechanism that is not impaired by its large territorial extension and its poorly integrated sectoral policies. The Brazilian Wildlife Health Information System, SISS-Geo, is a platform for collaborative monitoring that intends to overcome the challenges in wildlife health. It aims integration and participation of various segments of society, encompassing: the registration of occurrences by citizen scientists; the reliable diagnosis of pathogens from the laboratory and expert networks; and computational and mathematical challenges in analytical and predictive systems, knowledge extraction, data integration and visualization, and geographic information systems. It has been successfully applied to support decisionmaking on recent wildlife health events, such as a Yellow Fever epizooty..

Fast surveillance response and genome sequencing reveal the circulation of a new Yellow Fever Virus sublineage in 2021, in Minas Gerais, Brazil

“…Among them, worth it to mention: a) the use of smartphone applications to notify epizootics and collect the geographic coordinates of the occurrence in real time. In this sense, the SISS-Geo app, which has been progressively implemented in Brazil, speeds up the arrival of information to involved institutions and provides accurate geographic location, streamlining outbreak response (Chame et al, 2019); b) the use of messaging apps such as Whatsapp to increase the number of “watchers”. Creating Whatsapp groups with local or regional reach, composed by different members of the population (health agents, cyclists, hikers, students, rural workers, etc.)…”

“…In each municipality, meetings and lectures were organized with health agents and environmental surveillance agents, aiming to inform the teams about the importance of epizootics surveillance. The agents were trained to notify epizootics using the SISS-Geo web app (Chame et al, 2019). SISS-Geo is the abbreviation of “Sistema de Informação em Saúde Silvestre Georreferenciado” in Portuguese or “Georeferenced Wildlife Health Information System”, which is a platform that works as a public repository for collaborative surveillance of wild animals in Brazil, for the registration of animals occurrences sending pictures of dead or alive animals, and also metadata.…”

Desastres ecológicos e a saúde: plêiade de ampla magnitude e baixa percepção

“…O "efeito de diluição" que a biodiversidade promove sobre a transmissão de zoonoses, em especial, as transmitidas por vetores, talvez seja um de seus maiores benefícios aos humanos. CHAME et al, 2019). Alertas automáticos, gerados por algoritmos que avaliam fatores de risco de zoonoses pré-estabelecidos são distribuídos aos órgãos de vigilância em saúde nos três níveis de governo.…”

A survey of biodiversity informatics: Concepts, practices, and challenges

“…Automated data quality filters are used to support species identifications performed by citizen scientists. The Brazilian Wildlife Health Information System (SISS-Geo; Chame et al, 2019), for example, is a platform for collaborative monitoring that intends to overcome the challenges in wildlife health. It aims integration and participation of various segments of society, encompassing: the registration of occurrences by citizen scientists; the reliable diagnosis of pathogens from the laboratory and expert networks; and computational and mathematical challenges in analytical and predictive systems, knowledge extraction, data integration, and visualization, and geographic information systems.…”

“…Depending on the purpose of the monitoring, additional information may also be aggregated; in particular, it could be included diagnostic tests from collected animal samples, with possible indication of an ongoing epizootics. Finally, the set of acquired data, optionally coupled with extra variables (e.g., socio-environmental layers), could be used to train computational models such as alert, predictive and forecast models, and then be potentially used to extract knowledge about the subject being investigated (Chame et al, 2019).…”