Advancing epidemic intelligence

Evaluating Senegal’s mpox surveillance system and readiness for AI-driven predictive modelling

Datos Bibliográficos

ID22066683
AutoresSylvain L B Faye (Cheikh Anta Diop University), Fatoumata Binetou Diongue (0000-0003-0063-6933, Cheikh Anta Diop University), Abdourakhmane Ndao (Ministère de la Santé et de l'Hygiène Publique), Boly Diop (0000-0001-5950-2628, Ministère de la Santé et de l'Hygiène Publique), Georgette H C Sow (FIT Consulting (Italy)), Ndiaye Dia (Computer Algorithms for Medicine), Fallou Diakhate (Computer Algorithms for Medicine), Tidiane Gadiaga (0000-0001-8718-9683, Ministère de la Santé et de l'Hygiène Publique), Pape Samba Dieye (Ministère de la Santé et de l'Hygiène Publique), Oumou Kalsom D Gueye (Ministère de la Santé et de l'Hygiène Publique), Yoro Sall (Ministère de la Santé et de l'Hygiène Publique), Ibrahima Seck (0000-0002-9706-5913, Cheikh Anta Diop University), Youssou Bamar Gueye (Ministère de la Santé et de l'Hygiène Publique), Aminata Massaly (Hospital for Tropical Diseases), Moussa Seydi (0000-0002-0977-1085, Hospital for Tropical Diseases), Ibrahima Sy (0000-0002-3266-1001, Ministère de la Santé et de l'Hygiène Publique)
Año2026
Volumen14
Páginas1742888-1742888
Fecha de publicación2026-02-03
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1742888
PMID41710307
OpenAlexW7127097765
IdiomaEN
Referencias citadas65

Introduction: Mpox has re-emerged as a public health issue in West Africa, underscoring the need for robust surveillance systems that can detect outbreaks and facilitate effective responses. This study evaluates Senegal's mpox surveillance system, focusing on performance, data quality, governance, and potential for Artificial Intelligence-powered, predictive epidemic intelligence. It reviews trends and system operations while exploring AI and modeling to improve early warnings. Methodology: A descriptive, exploratory approach combined quantitative and qualitative data from various sources. A retrospective review of mpox cases from January 2024 to October 2025 utilized DHIS2 Tracker to analyze geographical, temporal, and demographic patterns, as well as reporting delays and biases. Data-quality checks and stakeholder interviews provided insights into system performance, intersectoral coordination, and preparedness for advanced analytics. Results: By late October 2025, Senegal had reported seven mpox cases, all in Dakar, primarily affecting young, mobile populations, with a higher incidence among children and working-age adults. Transmission followed population movement along the Dakar-Thiès-Diourbel corridor, showing how urban density and mobility influence spread. The surveillance system improved reporting, geolocation, and follow-up, supported by One Health coordination and digital health infrastructure. Challenges include underreporting in rural areas, uneven coverage, limited real-time analytics, and gaps in data interoperability and responsible AI regulation. The AI4MPOX-SN initiative offers an opportunity to enhance epidemic intelligence by integrating human-animal-environment data, using AI for anomaly detection and predictive modeling to inform interventions. Conclusion: To develop predictive epidemic intelligence in Senegal, it's vital to involve local stakeholders, promote transparency, build workforce capacity, and establish safeguards for the ethical use of data. Combining technology, participatory governance, and institutional strengthening will enable Senegal to transition from reactive detection to proactive surveillance, positioning it as a regional leader in health security in West Africa

Citizen journalism · Participatory action research · Public health surveillance · Risk assessment · Workforce · Workforce development · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Zoonotic diseases and public health