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Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data
Acm Transactions on Spatial Algorithms and Systems ; 8(2):30, 2022.
Article in English | English Web of Science | ID: covidwho-1883315
ABSTRACT
As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim at developing risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this article, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.
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Full text: Available Collection: Databases of international organizations Database: English Web of Science Type of study: Prognostic study Language: English Journal: Acm Transactions on Spatial Algorithms and Systems Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: English Web of Science Type of study: Prognostic study Language: English Journal: Acm Transactions on Spatial Algorithms and Systems Year: 2022 Document Type: Article