Network Modeling and Analysis of COVID-19 Testing Strategies.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 2003-2006, 2021 11.
Article
in English
| MEDLINE | ID: covidwho-1566191
ABSTRACT
The COVID-19 preparedness plans by the Centers for Disease Control and Prevention strongly underscores the need for efficient and effective testing strategies. This, in turn, calls upon the design and development of statistical sampling and testing of COVID-19 strategies. However, the evaluation of operational details requires a detailed representation of human behaviors in epidemic simulation models. Traditional epidemic simulations are mainly based upon system dynamic models, which use differential equations to study macro-level and aggregated behaviors of population subgroups. As such, individual behaviors (e.g., personal protection, commute conditions, social patterns) can't be adequately modeled and tracked for the evaluation of health policies and action strategies. Therefore, this paper presents a network-based simulation model to optimize COVID-19 testing strategies for effective identifications of virus carriers in a spatial area. Specifically, we design a data-driven risk scoring system for statistical sampling and testing of COVID-19. This system collects real-time data from simulated networked behaviors of individuals in the spatial network to support decision-making during the virus spread process. Experimental results showed that this framework has superior performance in optimizing COVID-19 testing decisions and effectively identifying virus carriers from the population.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Epidemics
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2021
Document Type:
Article
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