Predictive and prescriptive analytics to strengthen infectious disease surveillance and response
| dc.contributor.author | Joy Florence Awor | |
| dc.contributor.author | Judith Anita Namaganda | |
| dc.contributor.author | Calvin Diego Rwomothio | |
| dc.date.accessioned | 2026-09-02T08:58:47Z | |
| dc.date.available | 2026-09-02T08:58:47Z | |
| dc.date.issued | 2026-06-01 | |
| dc.description | Undergraduate | |
| dc.description.abstract | Uganda continues to experience recurring outbreaks of epidemic-prone infectious diseases, driven by persistent gaps in water, sanitation, and hygiene infrastructure, rapid urbanization, population displacement, and a limited capacity for timely detection and coordinated response. Current surveillance systems remain largely reactive and descriptive, focusing on reporting confirmed cases rather than predicting and preventing outbreaks. This study develops a proactive Health Intelligence Platform designed to strengthen Uganda’s epidemic preparedness by integrating predictive and prescriptive analytics with response visualization. Using a curated cholera surveillance dataset as a proof-of-concept, the platform applies machine learning techniques to forecast potential outbreak hotspots and temporal trends. These predictive outputs are operationalized through an interactive dashboard that provides timely alerts, spatial mapping, risk identification, and decisionsupport indicators for public health officials. The system demonstrates how predictive models combined with environmental factors can enhance situational awareness and support faster, data-driven interventions at both district and national levels. The ensemble models trained on 8,702 cholera surveillance records achieved strong short-term forecasting performance: the Random Forest Regressor attained an R2 of 0.7861 for suspected case prediction and 0.6204 for confirmed case prediction, outperforming XGBoost across all evaluation metrics (MAE and MSE). These accuracy levels represent a meaningful advance over Uganda’s current surveillance infrastructure platforms such as DHIS2, IDSR, and eIDSR which remain descriptive and retrospective, providing no automated outbreak forecasting, no anomaly detection, and no environmental risk integration. The XGBoost-powered 14-day early warning module further generated forward looking case projections consistent with observed transmission trends, correctly identifying the February 2026 peak of 700 confirmed cases as the most severe anomaly in the 15-year record, with a 200% deviation above the rolling average a signal that the existing national system did not surface proactively. Overall, this project showcases a scalable, Uganda-focused Health Intelligence Platform capable of transitioning the country’s surveillance ecosystem from reactive reporting to anticipatory, data-driven epidemic preparedness. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/3607 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | Predictive and prescriptive analytics to strengthen infectious disease surveillance and response | |
| dc.type | Project Report |