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10.1371/journal.pone.0250110 http://scihub22266oqcxt.onion/10.1371/journal.pone.0250110 33852642!8046206!33852642     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (4): e0250110 Nephropedia Template TP {{tp|p=33852642|t=2021. COVID-19: Short term prediction model using daily incidence data |pdf=|usr=}}{{33852642}} gab.com Text COVID-19: Short term prediction model using daily incidence data JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33852642 #FOAvip BACKGROUND: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. METHODS: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. RESULTS: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. CONCLUSION: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning. Twit Text FOAVip COVID-19: Short term prediction model using daily incidence data ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33852642 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33852642 #FOAvip English Wikipedia <ref>{{Cite pmid|33852642}}</ref> COVID-19: Short term prediction model using daily incidence data #MMPMID33852642 Zhao H; Merchant NN; McNulty A; Radcliff TA; Cote MJ; Fischer RSB; Sang H; Ory MG PLoS One 2021[]; 16 (4): e0250110 PMID33852642show ga BACKGROUND: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. METHODS: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. RESULTS: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. CONCLUSION: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning. |Basic Reproduction Number[MESH] |COVID-19/*epidemiology/transmission[MESH] |Disease Transmission, Infectious/prevention & control/statistics & numerical data[MESH] |Forecasting[MESH] |Humans[MESH] |Incidence[MESH] |Models, Statistical[MESH] |Pandemics[MESH] |Public Health[MESH] |SARS-CoV-2/isolation & purification[MESH]COVID-19: Short term prediction model using daily incidence data (epmc).pdf DeepDyve Pubget Overpricing