title: Forecasting the Spread of Mosquito-Borne Disease using Publicly Accessible Data: A Case Study in Chikungunya.
abstract: 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.
authors.affiliation: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE, United States.
authors.forename: Kathryn M
authors.initials: KM
authors.lastname: Cooper
authors.affiliation: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE, United States.
authors.forename: Dhundy R
authors.initials: DR
authors.lastname: Bastola
authors.affiliation: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE, United States.
authors.forename: Robin
authors.initials: R
authors.lastname: Gandhi
authors.affiliation: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE, United States.
authors.forename: Dario
authors.initials: D
authors.lastname: Ghersi
authors.affiliation: Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, United States.
authors.forename: Steven
authors.initials: S
authors.lastname: Hinrichs
authors.affiliation: Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, United States.
authors.forename: Marsha
authors.initials: M
authors.lastname: Morien
authors.affiliation: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE, United States.
authors.forename: Ann
authors.initials: A
authors.lastname: Fruhling
mesh_terms: D000569:Americas / Q000453:epidemiology; D000818:Animals; D065632:Chikungunya Fever / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D004812:Epidemiologic Methods; D005544:Forecasting; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D000072138:Mosquito Vectors