Work

An index-based system for early alerts of potential zoonotic disease outbreaks

Year: 2026

Type: article

Abstract: The Information System on Wildlife Health (SISS-Geo) is a free platform designed for the real-time collection of georeferenced data on wildlife health and environmental conditions via mobile devices. It serves as a collaborative tool, enabling health professionals, researchers, environmental managers, and the general public to report information about wildlife health occurrences directly within the system. Since its launch in 2014, SISS-Geo has been successfully applied in supporting decision-making during significant wildlife health events in Brazil. In this paper, we introduce a dynamic alert system based on a Multi-Attribute Zoonotic Alert Index (Z-Alert) to enhance disease outbreak detection and response in wildlife. The proposed approach improves the current alert system of the SISS-Geo platform by integrating clustering techniques with multi-objective optimization. The clustering stage groups spatiotemporally related SISS-Geo records, including observations of animals reported as alive, dead, or sick, allowing the identification of patterns relevant for epidemiological surveillance. Each identified cluster is assigned a numerical alert index, the Z-Alert index, reflecting its level of attention, based on optimally weighted attributes such as the percentage of dead animals, temporal interval, and geographical spread. The system also offers customization alert thresholds, allowing health managers to adapt the alert system to their specific needs. To validate the model, we used historical Yellow Fever case data from Brazil's Ministry of Health. By supporting surveillance teams in prioritizing prevention and investigation actions, the system enhances the capacity of public health authorities to respond effectively through supporting risk assessment, optimizing resource allocation, and promoting cost-effective strategies for outbreak control.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0356739

Authors: Jaqueline S. Angelo, Livia Abdala, Douglas A. Augusto, Marcia Chame, Eduardo Krempser

Institution: Fundação Oswaldo Cruz

Language: English

Topic: Data-Driven Disease Surveillance

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,382