Article

Probabilistic Forecasting of Monthly Dengue Cases Using Epidemiological and Climate Signals: A BiLSTM–Naive Bayes Model Versus Mechanistic and Count-Model Baselines

2025-10-22

Abstract excerpt

Reliable short-term forecasts can help urban health systems anticipate dengue surges and allocate resources. We assembled monthly dengue case counts for Freetown, Sierra Leone (2015–2025), and compared four probabilistic model families under a leakage-safe, rolling-origin protocol at 1–3-month horizons: a negative-binomial generalized linear model (NB-GLM), a negative-binomial INGARCH, a mechanistic renewal model...

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Literature Corpus work: 044dbc1b-51f4-5c4b-8dc4-df4935beca1e
DOI: 10.1101/2025.10.20.25338419

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