TYPE Original Research PUBLISHED 19 January 2024 DOI 10.3389/fpubh.2024.1323618 OPEN ACCESS EDITED BY Manel Ben M’Hadheb, University of Monastir, Tunisia REVIEWED BY Victoria Pando-Robles, National Institute of Public Health, Mexico Badu Sarkodie, Ghana Health Service, Ghana *CORRESPONDENCE Laith Hussain-Alkhateeb laith.hussain@gu.se RECEIVED 18 October 2023 ACCEPTED 08 January 2024 PUBLISHED 19 January 2024 CITATION Schlesinger M, Prieto Alvarado FE, Borb ´ on Ramos ME, Sewe MO, Merle CS, Kroeger A and Hussain-Alkhateeb L (2024) Enabling countries to manage outbreaks: statistical, operational, and contextual analysis of the early warning and response system (EWARS-csd) for dengue outbreaks. Front. Public Health 12:1323618. doi: 10.3389/fpubh.2024.1323618 COPYRIGHT © 2024 Schlesinger, Prieto Alvarado, Borb ´ on Ramos, Sewe, Merle, Kroeger and Hussain-Alkhateeb. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Enabling countries to manage outbreaks: statistical, operational, and contextual analysis of the early warning and response system (EWARS-csd) for dengue outbreaks Mikaela Schlesinger 1 , Franklyn Edwin Prieto Alvarado 2 , Milena Edith Borb ´ on Ramos 2 , Maquins Odhiambo Sewe 3 , Corinne Simone Merle 4 , Axel Kroeger 5 and Laith Hussain-Alkhateeb 1,6 * 1 Global Health Research Group, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden, 2 Directorate of Surveillance and Risk Analysis in Public Health, Instituto Nacional de Salud (INS) de Colombia, Bogota, Colombia, 3 Department of Public Health and Clinical Medicine, Epidemiology and Global Health, Umeå University, Umeå, Sweden, 4 Special Program for Research and Training in Tropical Diseases (TDR-WHO), World Health Organization, Geneva, Switzerland, 5 Freiburg University, Center for Medicine, and Society (ZMG)/Institute of Infection Prevention, Freiburg, Germany, 6 Population Health Research Section, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard - Health Affairs, Riyadh, Saudi Arabia Introduction: Dengue is currently the fastest-spreading mosquito-borne viral illness in the world, with over half of the world’s population living in areas at risk of dengue. As dengue continues to spread and become more of a health burden, it is essential to have tools that can predict when and where outbreaks might occur to better prepare vector control operations and communities’ responses. One such predictive tool, the Early Warning and Response System for climate-sensitive diseases (EWARS-csd), primarily uses climatic data to alert health systems of outbreaks weeks before they occur. EWARS-csd uses the robust Distribution Lag Non-linear Model in combination with the INLA Bayesian regression framework to predict outbreaks, utilizing historical data. This study seeks to validate the tool’s performance in two states of Colombia, evaluating how well the tool performed in 11 municipalities of varying dengue endemicity levels. Methods: The validation study used retrospective data with alarm indicators (mean temperature and rain sum) and an outbreak indicator (weekly hospitalizations) from 11 municipalities spanning two states in Colombia from 2015 to 2020. Calibrations of different variables were performed to find the optimal sensitivity and positive predictive value for each municipality. Results: The study demonstrated that the tool produced overall reliable early outbreak alarms. The median of the most optimal calibration for each municipality was very high: sensitivity (97%), specificity (94%), positive predictive value (75%), and negative predictive value (99%; 95% CI). Discussion: The tool worked well across all population sizes and all endemicity levels but had slightly poorer results in the highly endemic municipality at predicting non-outbreak weeks. Migration and/or socioeconomic status are factors that might Frontiers in Public Health 01 frontiersin.org