Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
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Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.
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Disease surveillance and outbreak forecasting systems can be built from web data and specially-designed apps, in addition to traditional surveys.
“Disease surveillance and outbreak forecasting systems can be built from web data and specially-designed apps, in addition to traditional surveys [394,467,627].”
From Tackling climate change with machine learning (2023) · cites this paper as [394]
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Self-citation
- Authors
- Johansson, M. A., Reich, N. G., Hota, A., Brownstein, J. S., & Santillana, M.
- Affiliations
- Centers for Disease Control and Prevention (US)
- University of Massachusetts Amherst (US)
- Boston Children's Hospital (US)
- Corresponding author
- Johansson, Michael A.
- Santillana, Mauricio
- Venue
- Scientific Reports
- Published
- 2016-09-26
- Volume
- 6
- Issue
- 1
- Pages
- 33707-33707
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5036038
- OpenAlex
- W2525587339
- Cited by
- 120
- References
- 60
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 11 · 2023: 5 · 2022: 11 · 2021: 10 · 2020: 25 · 2019: 15 · 2018: 12 · 2017: 8 · 2016: 1
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion, cc-by)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (U54GM088558)
- National Institute of Allergy and Infectious Diseases (R01AI102939, R21AI115173, AI102939)
- U.S. National Library of Medicine (R01LM010812)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mosquito-borne diseases and control
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- dengue, infectious disease forecasting, seasonal autoregressive model, dengue incidence, forecast evaluation, autocorrelation, out-of-sample validation, Mexico
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