The infection map creates a large-scale decision space and thus discrete location models are less favored under this setting. Locational optimization models often use integer programming to find an assignment of demand locations to resource locations by minimizing the sum of weighted distances, which is computationally expensive. Traditionally, spatial analysis of resource allocations is performed using locational optimization models such as the p-median model and set covering model. Therefore, spatial analysis should be used to support the resource allocation process. While the demand for medical resources is closely related to the spread of an epidemic, such complexity of the infection distribution poses great challenges to resource decision making because directly allocate resources to highly infected areas will create large disparities across the spatial region. Such spatial variability of infected cases over the region is referred to as heterogeneous infection distribution.
The number of infections is encoded by a colormap, where red and blue color represents more and less infected cases.
2, the spread of COVID-19 in the state of Pennsylvania has large spatial variability. The complexity of infection and demand distribution poses great challenges to infection modeling and resource management during an epidemic. Although these resources can be readily transported to designated areas for fighting the epidemic, the demand is increasing and varying in the space that places significant stress on the supply and allocation of medical resources. The availability of such medical resources is critical to effective epidemic control. Vaccines help develop immunity and thus reduce the risk of infection. Testing kits help identify positive cases, thereby enabling tracing protocols to slow the spread of the disease. PPEs such as facemasks and protective coveralls protect healthcare professionals and individuals from infection. The new spread tessellation algorithms are shown to have strong potentials for epidemic decision support in infection modelling and resource allocation.ĬOVID-19 infection map of the United States in a timeline.ĭue to the heterogeneity of virus spread, there are spatial variations in the demand for medical resources such as personal protective equipment (PPE), testing kits, and vaccines. Experimental results show the proposed methodology effectively tessellates the spread of infectious diseases. The proposed methodology is evaluated and validated using a COVID-19 case study of infection data in Pennsylvania. Lastly, the spread tessellation is computed to provide an estimation of resource coverages under the heterogeneous infection distribution. Next, the locations of tessellation centroids are calibrated through a gradient learning algorithm. First, spatial tessellation centroids are initialized through either greedy or cluster-centric approaches. The objective is to estimate resource locations and coverage based on the spatial analysis of heterogeneous infection distribution.
In this letter, we develop new tessellation algorithms for decision support in epidemic resource allocation and management. However, little has been done on the tessellation of infection distributions for resource management. Although these resources can be readily transported to designated areas for fighting an epidemic, the demand is increasing and varying in space that places significant stress on the supply and allocation of medical resources. Due to the heterogeneity of virus spread, there are spatial variations in the demand for medical resources such as personal protective equipment (PPE), testing kits, and vaccines. Infectious diseases such as COVID-19 have severe impacts on both economy and public health in the US and the world.
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