Forecasting the Spread of Mosquito-Borne Disease using Publicly Accessible Data: A Case Study in Chikungunya

Kathryn M. Cooper, Dhundy R. Bastola, Robin Gandhi, Dario Ghersi, Steven Hinrichs, Marsha Morien, Ann Fruhling

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)431-440
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
StatePublished - 2016

ASJC Scopus subject areas

  • General Medicine


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