ABSTRACT

Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach   [open pdf - 1MB]

From the Abstract: "The rapid spread of COVID-19 [coronavirus disease 2019] means that government and health services providers have little time to plan and design effective response policies. It is therefore important to quickly provide accurate predictions of how vulnerable geographic regions such as counties are to the spread of this virus. [...] The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data. [...] We estimated county-level COVID-19 occurrences for the period March 14 to 31, 2020, based on data fused from multiple publicly available sources inclusive of health statistics, demographics, and geographical features. We developed a three-stage model using XGBoost, a machine learning algorithm, to quantify the probability of COVID-19 occurrence and estimate the number of potential occurrences for unaffected counties. [...] The model predictions showed a sensitivity over 71% and specificity over 94% for models built using data from March 14 to 31, 2020. We found that population, population density, percentage of people aged >70 years, and prevalence of comorbidities play an important role in predicting COVID-19 occurrences. We observed a positive association at the county level between urbanicity and vulnerability to COVID-19." The original publication of this article can be found here: [http://publichealth.jmir.org/2020/3/e19446/].

Author:
Publisher:
Date:
2020-09-11
Series:
Copyright:
Mihir Mehta, Juxihong Julaiti, Paul Griffin, Soundar Kumara. Posted here with permission. Document is under a Creative Commons license and requires proper attribution and noncommercial use to be shared: [https://creativecommons.org/licenses/by/4.0/].
Retrieved From:
JMIR Publications: https://publichealth.jmir.org/
Format:
pdf
Media Type:
application/pdf
Source:
JMIR Public Health and Surveillance (2020), v.6 issue 3
URL:
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