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Fine-grained dengue forecasting using telephone triage services.
Copyright policy )Fine-grained dengue forecasting using telephone triage services.
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.
Calling patterns on a health hotline can accurately forecast dengue cases, 2 to 3 weeks ahead of time, at a subcity level.
- State University of New York at Potsdam United States
- Information Technology University Pakistan
- Department of Computer Science and Engineering University of Washington United States
- Courant Institute of Computer Science New York University United States
- University of Mary United States
Microsoft Academic Graph classification: medicine.medical_specialty Computer security computer.software_genre Phone Call volume Medicine Government Disease surveillance business.industry Hotline Public health medicine.disease Triage Software deployment Medical emergency business computer
disease forecasting, Hotlines, Humans, Community Health Services, Research Articles, Multidisciplinary, SciAdv r-articles, Awareness, dengue, Hospitals, Telephone, Public Health, disease surveillance, Triage, Forecasting, Research Article
disease forecasting, Hotlines, Humans, Community Health Services, Research Articles, Multidisciplinary, SciAdv r-articles, Awareness, dengue, Hospitals, Telephone, Public Health, disease surveillance, Triage, Forecasting, Research Article
Microsoft Academic Graph classification: medicine.medical_specialty Computer security computer.software_genre Phone Call volume Medicine Government Disease surveillance business.industry Hotline Public health medicine.disease Triage Software deployment Medical emergency business computer
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6 Centers for Disease Control and Prevention, Dengue symptoms and treatment; www.cdc.gov/dengue/symptoms/ [accessed September 1, 2015].
7 Ferguson N. M., Cummings D. A. T., Cauchemez S., Fraser C., Riley S., Meeyai A., Iamsirithaworn S., Burke D. S., Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005).16079797 [OpenAIRE] [PubMed]
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10 Yih W. K., Teates K. S., Abrams A., Kleinman K., Kulldorff M., Pinner R., Harmon R., Wang S., Platt R., Telephone triage service data for detection of influenza-like illness. PLOS One 4, e5260 (2009).19381342 [OpenAIRE] [PubMed]
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).25 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).25 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!- State University of New York at Potsdam United States
- Information Technology University Pakistan
- Department of Computer Science and Engineering University of Washington United States
- Courant Institute of Computer Science New York University United States
- University of Mary United States
- University of Washington United States
- Computer Science and Engineering School of Engineering and Applied Sciences Harvard University United States
- Abu Dhabi University United Arab Emirates
- Courant Institut of Mathematics Department of Computer Science New York University United States
- Courant Institute of Mathematical Sciences United States
- Courant Institute of Mathematical Sciences New York University United States
- Courant Institut of Mathematics New York University United States
- New York University United States
- Washington State University United States
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.
Calling patterns on a health hotline can accurately forecast dengue cases, 2 to 3 weeks ahead of time, at a subcity level.