CovidSeniorV1, Main, Curation, bibRecord, 000709

Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach.

Identifieur interne : 000709 ( Main/Curation ); précédent : 000708; suivant : 000710

Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach.

Auteurs : Mihir Mehta [États-Unis] ; Juxihong Julaiti [États-Unis] ; Paul Griffin [États-Unis] ; Soundar Kumara [États-Unis]

Source :

JMIR public health and surveillance [ 2369-2960 ] ; 2020.

RBID : pubmed:32784193

Descripteurs français

KwdFr :
- Algorithmes (MeSH), Apprentissage machine (MeSH), Appréciation des risques (MeSH), Betacoronavirus (MeSH), Comorbidité (MeSH), Densité de population (MeSH), Humains (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Modèles statistiques (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Population urbaine (MeSH), Sujet âgé (MeSH), Surveillance de la population (méthodes), États-Unis (MeSH).
MESH :
- méthodes : Surveillance de la population.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Algorithmes, Apprentissage machine, Appréciation des risques, Betacoronavirus, Comorbidité, Densité de population, Humains, Modèles statistiques, Population urbaine, Sujet âgé, États-Unis.
Wicri :
- geographic : États-Unis.

English descriptors

KwdEn :
- Aged (MeSH), Algorithms (MeSH), Betacoronavirus (MeSH), Comorbidity (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Coronavirus Infections (virology), Humans (MeSH), Machine Learning (MeSH), Models, Statistical (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (virology), Population Density (MeSH), Population Surveillance (methods), Risk Assessment (MeSH), United States (MeSH), Urban Population (MeSH).
MESH :
- geographic : United States.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- methods : Population Surveillance.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Aged, Algorithms, Betacoronavirus, Comorbidity, Humans, Machine Learning, Models, Statistical, Population Density, Risk Assessment, Urban Population.

Abstract

BACKGROUND

The rapid spread of COVID-19 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.

OBJECTIVE

The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data.

METHODS

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. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county.

RESULTS

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.

CONCLUSIONS

The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.

DOI: 10.2196/19446
PubMed: 32784193
PubMed Central: PMC7490002

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Le document en format XML

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<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>The rapid spread of COVID-19 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.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b>
</p>
<p>The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>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. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>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.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>The developed model can be used for identification of vulnerable counties and potential data discrepancies. Limited testing facilities and delayed results introduce significant variation in reported cases, which produces a bias in the model.</p>
</div>
</front>
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<Abstract><AbstractText Label="BACKGROUND">The rapid spread of COVID-19 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.</AbstractText>
<AbstractText Label="OBJECTIVE">The aim of this study is to develop county-level prediction around near future disease movement for COVID-19 occurrences using publicly available data.</AbstractText>
<AbstractText Label="METHODS">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. Finally, these results were combined to predict the county-level risk. This risk was then used as an estimated after-five-day-vulnerability of the county.</AbstractText>
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Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach.

Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach.

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