Abstract:
Dengue is still a major public health issue in Sri Lanka, with seasonal epidemics putting pressure
on the health system. Reliable localised early warning systems are necessary for effective
disease control and resource planning. This research suggests a machine learning model for
forecasting dengue cases monthly at the district level in Sri Lanka. The study aims to develop
an enhanced predictive framework that can accurately forecast dengue incidence, enabling more
accurate and targeted public health enhancement. The main goal is to use an improved XGBoost
model to predict dengue cases in each district and to create useful features such as past
trends and time-based patterns that help capture how the disease spreads over time. 25 individual
forecasting models were developed for each Regional Director of Health Services (RDHS)
and were subdivided using the Extreme Gradient Boosting (XGBoost) algorithm. Each of the
models has been trained on time-series data from January 2018 to May 2024. The main predictors
were district-level historical monthly dengue case counts and monthly precipitation and
average temperature from January 2018 to May 2024. Features were also created to include
temporal lags of up to six months for climate and epidemiological data, along with cyclical
time-based features to identify seasonality. The methodology integrates epidemiological data,
detailed weather parameters (rainfall and temperature) from the NASA POWER API and newly
engineered features, including extended lagged variables, advanced temporal indicators (trend
components, moving averages). Outlier capping was also applied to robust the model against
extreme values. Performance was tracked on the basis of Root Mean Squared Error (RMSE)
and R-squared (R2). An Overall Test R2 of 0.686 and an overall Test RMSE of 0.6557 were
observed across all the districts. Monaragala, Trincomalee, and Badulla exhibited high prediction
accuracies with R2 values of 0.7801, 0.7414, and 0.7349, respectively. This research shows
the potential to build strong and localised early warning systems for dengue in areas where the
disease is common. The outcomes have public health policy implications, enabling targeted
interventions to reduce the impact of dengue epidemics, and contribution to improved public
health outcomes in Sri Lanka.
