TY - JOUR
T1 - Fine-grained dengue forecasting using telephone triage services
AU - Rehman, Nabeel Abdur
AU - Kalyanaraman, Shankar
AU - Ahmad, Talal
AU - Pervaiz, Fahad
AU - Saif, Umar
AU - Subramanian, Lakshminarayanan
N1 - Publisher Copyright:
© 2016 The Authors.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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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U2 - 10.1126/sciadv.1501215
DO - 10.1126/sciadv.1501215
M3 - Article
C2 - 27419226
AN - SCOPUS:85019204092
SN - 2375-2548
VL - 2
JO - Science Advances
JF - Science Advances
IS - 7
M1 - e1501215
ER -