Fahim Tasneema Azad, Robert W. Dodge, Allen M. Varghese, Jaejin Lee, Giulia Pedrielli, K. Candan, Gerardo Chowell-Puente
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引用次数: 2
Abstract
COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such as complete lockdown, social distancing, and random testing. The key contribution of this article is twofold. First, we present a novel extended spatially informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19, that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.
期刊介绍:
ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.