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10.2196/19446 http://scihub22266oqcxt.onion/10.2196/19446 32784193!7490002!32784193     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JMIR+Public+Health+Surveill 2020 ; 6 (3): e19446 Nephropedia Template TP {{tp|p=32784193|t=2020. Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach |pdf=|usr=}}{{32784193}} gab.com Text Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach JO: JMIR Public Health Surveill http://www.kidney.de/pget.php?&tw=1&pmid=32784193 #FOAvip 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. Twit Text FOAVip Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach ????JMIR Public Health Surveill http://www.kidney.de/pget.php?&tw=1&pmid=32784193 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32784193 #FOAvip English Wikipedia <ref>{{Cite pmid|32784193}}</ref> Early Stage Machine Learning-Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach #MMPMID32784193 Mehta M; Julaiti J; Griffin P; Kumara S JMIR Public Health Surveill 2020[Sep]; 6 (3): e19446 PMID32784193show ga 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. |*Models, Statistical[MESH] |*Pandemics/prevention & control[MESH] |Aged[MESH] |Algorithms[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Comorbidity[MESH] |Coronavirus Infections/*epidemiology/prevention & control/virology[MESH] |Humans[MESH] |Machine Learning[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/virology[MESH] |Population Density[MESH] |Population Surveillance/*methods[MESH] |Risk Assessment[MESH] |SARS-CoV-2[MESH] |United States[MESH]Early Stage Machine Learning-Based Prediction of US County Vulnerabili(epmc).pdf DeepDyve Pubget Overpricing