Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Johansson, Michael A., Reich, Nicholas G., Hota, Aditi, Brownstein, John S., Santillana, Mauricio·2016·Scientific Reports
DOI 10.1038/srep33707

Cited by (118)

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  10. Étude du potentiel des biomarqueurs sérologiques pour évaluer le risque de transmission de la dengue dans le nord-est de la Thaïlande
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  11. Lagged and non-linear effects of Aedes surveillance indices on dengue incidence in Malaysia: evidence from sentinel-site data
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  12. Machine learning reveals temperature as a key predictor of dengue risk across Thailand’s provinces: A 20-year analysis
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  13. Geographic expansion, not viral intensification, leads to human dengue outbreaks in Mexico: a 40-year integrated remote sensing and machine learning analysis
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  14. Tactical vs. strategic: An adaptable framework for horizon-dependent dengue forecasting using data-driven approaches with serotype and climate covariates in Bangladesh
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  15. Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil
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  16. A Hybrid Statistical and Probabilistic Model for Forecasting Uncertain Events
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  17. Spatio-Temporal DeepONets with Graph Neural Networks for Probabilistic Dengue Forecasting in Honduras
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  18. A nationwide ecological time-series analysis found no stable temporal or predictive relationship between dengue and COVID-19 surveillance records in Mexico from 2020 to 2025
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