Association mining based approach to analyze COVID-19 response and case growth in the United States.
Sci Rep
; 11(1): 18635, 2021 09 20.
Article
in English
| MEDLINE | ID: covidwho-1428895
ABSTRACT
Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Data Mining
/
COVID-19
Type of study:
Observational study
/
Reviews
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-96912-5
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