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10.1016/j.seps.2021.101091 http://scihub22266oqcxt.onion/10.1016/j.seps.2021.101091 34121777!8184360!34121777     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34121777&cmd=llinks): Failed to open stream: HTTP request failed! 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Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan |pdf=|usr=}}{{34121777}} gab.com Text Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan JO: Socioecon Plann Sci http://www.kidney.de/pget.php?&tw=1&pmid=34121777 #FOAvip AIMS: We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. METHODS: A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. RESULTS: The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. CONCLUSIONS: We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications. Twit Text FOAVip Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan ????Socioecon Plann Sci http://www.kidney.de/pget.php?&tw=1&pmid=34121777 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34121777 #FOAvip English Wikipedia <ref>{{Cite pmid|34121777}}</ref> Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan #MMPMID34121777 Huang C; Wang M; Rafaqat W; Shabbir S; Lian L; Zhang J; Lo S; Song W Socioecon Plann Sci 2022[Mar]; 80 (ä): 101091 PMID34121777show ga AIMS: We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. METHODS: A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. RESULTS: The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. CONCLUSIONS: We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications. äData-driven test strategy for COVID-19 using machine learning: A study(epmc).pdf DeepDyve Pubget Overpricing