Probabilistic forecasting of monthly dengue cases using epidemiological and climate signals: A BiLSTM-Negative Binomial Model versus Mechanistic and Count-Model Baselines

Reliable short-term forecasts enable urban health systems to anticipate dengue surges and allocate resources effectively. We assembled monthly dengue case counts for Freetown, Sierra Leone (2015–2024), and compared four probabilistic model families under a leakage-safe, rolling-origin evaluation at 1–3-month horizons: a negative binomial generalized linear model (NB-GLM), a negative binomial INGARCH model (INGARCH-NB), a mechanistic renewal model with negative binomial observations (Renewal-NB), and a bidirectional long short-term memory network with a negative binomial output (BiLSTM-NB). All models used the same seasonal harmonics and autoregressive lags; “light” climate inputs (rainfall, temperature, and relative humidity) were restricted to lag-1 covariates to reflect real-time availability. We evaluated probabilistic performance using mean log score (primary), empirical coverage, and median widths of 50% and 90% predictive intervals, calibration diagnostics based on the probability integral transform, and Diebold-Mariano tests with Newey-West standard errors. For the main comparison, we evaluated models on a strictly matched set of common issue-target pairs within each horizon (n = 32 per horizon). On this aligned set, INGARCH-NB achieved the best mean log score at all horizons, indicating the strongest overall distributional accuracy. BiLSTM-NB remained competitive and provided more conservative upper-tail uncertainty at longer horizons (e.g., 90% interval coverage of 100% at h = 3), at the cost of wider intervals. NB-GLM variants produced the sharpest intervals but were substantially undercovered, indicating overconfidence, while renewal-based forecasts attained nominal coverage largely through uncertainty inflation that degraded sharpness and log score. In a leakage-safe light-climate ablation, adding lag-1 climate covariates yielded small, statistically non-significant gains for NB-GLM and did not improve renewal forecasts. Overall, the results support a horizon-aware toolkit for operational dengue forecasting: INGARCH-NB as a strong default when distributional accuracy is prioritized, complemented by calibrated deep learning (BiLSTM-NB) when conservative tail reliability is preferred. The aligned indices, per-issue forecasts, and code provide a transparent baseline for future work in similar urban settings.Author summary: We conducted this study to help public health teams in Freetown, Sierra Leone, plan clinical capacity and vector control using reliable short-term dengue forecasts. Many forecasting approaches exist, but they are rarely compared under the same leakage-safe conditions. We assembled monthly dengue case data (2015–2024) and built consistent seasonal and autoregressive predictors for all models, using only a lightweight set of lagged climate inputs feasible in real time. We compared four model families: a negative binomial generalized linear model, an INGARCH count model, a mechanistic renewal model, and a bidirectional LSTM with a negative binomial output. Using an expanding-window, rolling-origin evaluation at 1- to 3-month horizons, we assessed probabilistic accuracy with proper scoring rules, predictive interval coverage, probability integral transform (PIT) histograms, and Diebold–Mariano tests on aligned targets. No single method dominated across all horizons: parsimonious count models performed best at 1–2 months, while a calibrated BiLSTM excelled at the 3-month horizon and provided reliable uncertainty estimates. These findings support a horizon-specific toolkit for operational dengue forecasting in similar urban settings.