# Advancing epidemic intelligence: evaluating Senegal’s mpox surveillance system and readiness for AI-driven predictive modelling

## TL;DR

This study evaluates Senegal's mpox surveillance system and explores how AI can improve early outbreak detection and response.

## Contribution

The paper introduces a framework for AI-driven predictive modeling tailored to Senegal's public health infrastructure.

## Key findings

- Mpox cases in Senegal were concentrated in urban areas, affecting young and mobile populations.
- The surveillance system showed improved reporting but faced challenges in rural coverage and real-time analytics.
- The AI4MPOX-SN initiative aims to integrate human-animal-environment data for better epidemic intelligence.

## Abstract

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.

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.

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.

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.