Spatial Tessellation of Infectious Disease Spread for Epidemic Decision Support

Abstract

Infectious diseases such as COVID-19 have severe impacts on both economy and public health in the US and the world. 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. The availability of such medical resources is critical to effective epidemic control. 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. However, little has been done on the tessellation of infection distributions for resource management. In this letter, we develop new tessellation algorithms for decision support in epidemic resource allocation and management. The objective is to estimate resource locations and coverage based on the spatial analysis of heterogeneous infection distribution. First, spatial tessellation centroids are initialized through either greedy or clustercentric approaches. Next, the locations of tessellation centroids are calibrated through a gradient learning algorithm. Lastly, the spread tessellation is computed to provide an estimation of resource coverages under the heterogeneous infection distribution. The proposed methodology is evaluated and validated using a COVID-19 case study of infection data in Pennsylvania. Experimental results show the proposed methodology effectively tessellates the spread of infectious diseases. The new spread tessellation algorithms are shown to have strong potentials for epidemic decision support in infection modelling and resource allocation.

Consistent Healthcare Safety Recommendation System for Preventing Contagious Disease Infections in Human Crowds

“…The average observation/person and observation intervals are the variants for data validation. The methods GA-GRU (genetic algorithm-based gated recurrent unit) [25], GVT (greedy-Voronoi tessellation) [29], and PCovNet (pre-symptomatic COVID-19 detection framework) [21] were used alongside the proposed CRS-PH method in the comparative analysis.…”

“…Liu et al [29] proposed a spatial tessellation algorithm for epidemic decision support. A gradient learning algorithm is used here to locate the tessellation, reducing the latency in further processes.…”

Measurement of contagion spatial spread probability in public places: A case study on COVID-19

“…It is critical to understand the potential transmission dynamics of COVID-19 within the building environment ecosystem and the human behavior, spatial dynamics, and building operational factors that potentially promote and mitigate the spread and transmission of COVID-19 ( Dietz et al, 2020 ). In consideration of this, research has focused on the impacts of spatial variation characteristics on the spread of COVID-19, including urban density, population density, ethnicity and interactive mode ( Megahed & Ghoneim, 2020 ; Elson et al, 2021 ; Li et al, 2021 ; Liu & Yang, 2022 ; Siljander et al, 2022 ; Sun & Zhai, 2020 ) and built environment ( Fang et al, 2020 ; Nguyen et al, 2020 ; Rockloü & Sjödin, 2020; Kashem, Wilson, & Van Zandt, 2016 ).…”