Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-Co...

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Veröffentlicht in:	PloS one 2021-11, Vol.16 (11), p.e0259538-e0259538, Article 0259538
Hauptverfasser:	Price, Bradley S., Khodaverdi, Maryam, Halasz, Adam, Hendricks, Brian, Kimble, Wesley, Smith, Gordon S., Hodder, Sally L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Biology and life sciences Computer and Information Sciences Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 Testing - statistics & numerical data Disease transmission Epidemiology Evaluation Forecasting - methods Human resources Humans Incidence Infections Learning algorithms Long short-term memory Machine Learning Medicine and Health Sciences Methods Models, Statistical Multidisciplinary Sciences Pandemics People and places Physical Sciences Population (statistical) Population number Population statistics Predictive Value of Tests Public health Random variables Research and Analysis Methods Rural areas Rural Population Science & Technology Science & Technology - Other Topics Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Short term memory Software Standard deviation Trends West Virginia - epidemiology
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