Abstract

MORA-AGUILERA, Gustavo et al. Web-based epidemiological surveillance systems and applications to coffee rust disease. Rev. mex. fitopatol [online]. 2021, vol.39, n.3, pp.452-492.  Epub Dec 13, 2021. ISSN 2007-8080.  https://doi.org/10.18781/r.mex.fit.2104-6.

The advancement of digital technology has made it possible to conceive automated Epidemiological Surveillance Systems (ESS) with aholistic-systemicapproach allowing effective operation, management, and processing of phytosanitary data for fast decision-making applied to regional prevention and pest management. This surveillance type focuses on plant health, overcoming the reductionist pest vision of the conventional normative surveillance. An ESS implies the precise definition of the regional framework, objectives, pest(s) in wide-sense, human/financial resources, regulatory context, support research planning, operational structure, and innovation models. These elements determine the precision, frequency, and type of sampling and monitoring and the selection of variables related to a novelepidemiological system. In contrast to the normative surveillance, a systemic ESS has descriptive and risk forecasting capabilities, including early warnings, based on spatial and temporal analyses. A web-based ESS assures a flexible-dynamic generation of reports and automated analysis. An ESS operated on web platforms, emphasizing open source software and tools, can be hosted on generic or dedicated servers for metadata storage configured with Linux / Apache technologies with 24/7 (h day-1) functional capabilities. Open source tools include MySQL / MariaDB and other systems as database managers; PHP / Node.js, and JavaScript, Ajax, HTML5 and CSS as web design base ‘back-end’ and ‘front-end’ programs, respectively. This review focuses on principles, conceptual attributes, general methodological approaches, and objectives of web-based ESS. An overview is presented with an ESS developed in Mexico for coffee plantations (Coffeaspp.), which allowed the surveillance of 19 pests, nine under quarantine status, through the generation, management, and analysis of 87.4 million climatic data and 15.7 million epidemiological records over 2013-2019.

Keywords : Hemileia vastatrix; Coffea arabica; Early warning; Prevention.