Spatial Tessellation of Infectious Disease Spread for Epidemic Decision Support.
IEEE Robot Autom Lett
; 7(1): 626-633, 2022 Jan.
Article
in English
| MEDLINE | ID: covidwho-1583802
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 cluster-centric 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Vaccines
Language:
English
Journal:
IEEE Robot Autom Lett
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS