Singapore · Singapore · See the Singapore profile
Singapore's National Environment Agency runs a peer-reviewed machine-learning system that forecasts dengue cases up to three months ahead. In the 2013 outbreak it predicted a peak of 863 cases versus an observed 842, letting campaigns launch two months early.
Singapore's National Environment Agency, with NUS and NTU, built a machine-learning (LASSO regression) system forecasting weekly dengue case counts up to three months ahead, trained on 2001-2010 data and validated on 2011-2012 data across twelve weekly submodels using over 200 predictor streams.
During the severe 2013 epidemic (22,170 cases), the model forecast a week-26 peak of 863 cases against an observed peak of 842 the following week, with MAPE of 17% at one week rising to 24% at three months — outperforming benchmark SARIMA/step-down regression models (~29% MAPE at three months). The early forecast let authorities launch prevention campaigns roughly two months earlier and prioritise its ~50,000-device Gravitrap surveillance network.
Implementation detail (cost, timeline, staffing, conditions for success) is not yet available for this practice.
Where this practice's information was retrieved from, and when.
Sierra Leone
Statistics Sierra Leone and the GRID3 partnership use machine-learning building-footprint detection on satellite imagery to map population at 1-hectare resolution, …
Bangladesh
Since 2020, Google Research's machine-learning flood model — which forecasts river levels even in ungauged basins — has combined with …
Togo
To rush COVID aid to the poorest, Togo used machine learning on satellite imagery and mobile-phone data to target cash …
Algeria
Algeria's Ministry of Higher Education used an AI matching platform nationwide for the first time in 2025, placing 97.34% of …
Open full copilot Grounded in cited practices — always check the sources.