TY - JOUR
T1 - Forecasting the Spread of Mosquito-Borne Disease using Publicly Accessible Data
T2 - A Case Study in Chikungunya
AU - Cooper, Kathryn M.
AU - Bastola, Dhundy R.
AU - Gandhi, Robin
AU - Ghersi, Dario
AU - Hinrichs, Steven
AU - Morien, Marsha
AU - Fruhling, Ann
PY - 2016
Y1 - 2016
N2 - Mosquito-borne diseases account for multiple public health challenges in our modern world. The international health community has seen a number of mosquito-borne diseases come to the forefront in recent years, including West Nile virus, Chikungunya virus, and currently, Zika virus. Predicting the spread of mosquito-borne disease can aid early decision support for when and how to employ public health interventions within a community; however, accurate and fast predictions, months into the future, are difficult to achieve in urgent scenarios, particularly when little information is known about infection rates. New sources of information including social media have been proposed to accelerate the development of predictive models of disease progression. In this research, we adapted a previously described model for the spread of mosquito-borne disease using open intelligence sources. The novel implementation of a mixed-model for mosquito-borne disease was capable of being executed in minimal runtime. The results indicate that this model yields fast and relevant results with acceptable margins of error.
AB - Mosquito-borne diseases account for multiple public health challenges in our modern world. The international health community has seen a number of mosquito-borne diseases come to the forefront in recent years, including West Nile virus, Chikungunya virus, and currently, Zika virus. Predicting the spread of mosquito-borne disease can aid early decision support for when and how to employ public health interventions within a community; however, accurate and fast predictions, months into the future, are difficult to achieve in urgent scenarios, particularly when little information is known about infection rates. New sources of information including social media have been proposed to accelerate the development of predictive models of disease progression. In this research, we adapted a previously described model for the spread of mosquito-borne disease using open intelligence sources. The novel implementation of a mixed-model for mosquito-borne disease was capable of being executed in minimal runtime. The results indicate that this model yields fast and relevant results with acceptable margins of error.
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M3 - Article
C2 - 28269838
AN - SCOPUS:85027967854
SN - 1559-4076
VL - 2016
SP - 431
EP - 440
JO - AMIA ... Annual Symposium proceedings. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings. AMIA Symposium
ER -