ArticleScience advances2016
Fine-grained dengue forecasting using telephone triage services.
Article in Science advances, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Integrated Surveillance Strategies and Clinical Characteristics of Arboviral Diseases in a Pediatric Cohort in Merida, Mexico, 2021-2023.The American journal of tropical medicine and hygiene · 2026Article
- World Dengue Day: A call for action.PLoS neglected tropical diseases · 2022Article
- Neighborhood level chronic respiratory disease prevalence estimation using search query data.PloS one · 2021Article
- Imagining a lean and agile digital health ecosystem - a measure of pandemic responsiveness.Journal of global health · 2020Review
- Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interactions of human movements between urban regions.PLoS neglected tropical diseases · 2020Article
- Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.PLoS neglected tropical diseases · 2020Article
- Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.PLoS neglected tropical diseases · 2020Article
- Quantifying the localized relationship between vector containment activities and dengue incidence in a real-world setting: A spatial and time series modelling analysis based on geo-located data from Pakistan.PLoS neglected tropical diseases · 2020Article
- Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data.PLoS computational biology · 2019Article
- A dynamic neural network model for predicting risk of Zika in real time.BMC medicine · 2019Article
- Nonannual seasonality of influenza-like illness in a tropical urban setting.Influenza and other respiratory viruses · 2018Article
- Neighbourhood level real-time forecasting of dengue cases in tropical urban Singapore.BMC medicine · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. We present results from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a subcity level (correlation of up to 0.93). Our system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from our system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. Our work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline.
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