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Conference proceeding
Next-Generation High-Resolution Vector-Borne Disease Risk Assessment
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (Vancouver, BC, Canada, 08/27/2019–08/30/2019)
07/16/2019
Web of Science ID: WOS:000555683800106
Abstract
Vector-borne diseases cause more than 1 million deaths annually. Estimates of epidemic risk at high spatial resolutions can enable effective public health interventions. Our goal is to identify the risk of importation of such diseases into vulnerable cities at the granularity of neighborhoods. Conventional models cannot achieve such spatial resolution, especially in real-time. Besides, they lack real-time data on demographic heterogeneity, which is vital for accurate risk estimation. Social media, such as Twitter, promise data from which demographic and spatial information could be inferred in real-time. On the other hand, such data can be noisy and inaccurate. Our novel approach leverages Twitter data, using machine learning techniques at multiple spatial scales to overcome its limitations, to deliver results at the desired resolution. We validate our method against the Zika outbreak in Florida in 2016. Our main contribution lies in proposing a novel approach that uses machine learning on social media data to identify the risk of vector-borne disease importation at a sufficiently fine spatial resolution to permit effective intervention. This will lead to a new generation of epidemic risk assessment models, promising to transform public health by identifying specific locations for targeted intervention.
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Details
- Title
- Next-Generation High-Resolution Vector-Borne Disease Risk Assessment
- Publication Details
- 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
- Resource Type
- Conference proceeding
- Conference
- International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (Vancouver, BC, Canada, 08/27/2019–08/30/2019)
- Copyright
- © 2019 Advances in Social Networks Analysis and Mining (ASONAM)
- Identifiers
- WOS:000555683800106; 9781450368681/19/08; 99380178126006600
- Academic Unit
- Hal Marcus College of Science and Engineering ; Computer Science
- Language
- English