Forecasting and modeling of the COVID-19 pandemic in the USA with a timed intervention model
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Abstract
We propose a novel Timed Intervention S , P , E , I , Q , R , D model for projecting the possible futures of the COVID-19 pandemic in the USA. The proposed model introduces a series of timed interventions that can account for the influence of real time changes in government policy and social norms. We consider three separate types of interventions: (i) Protective interventions : Where population moves from susceptible to protected corresponding to mask mandates, stay-at-home orders and/or social distancing. (ii) Release interventions : Where population moves from protected to susceptible corresponding to social distancing mandates and practices being lifted by policy or pandemic fatigue. (iii) Vaccination interventions : Where population moves from susceptible, protected, and exposed to recovered (meaning immune) corresponding to the mass immunization of the U.S. Population. By treating the pandemic with timed interventions, we are able to model the pandemic extremely effectively, as well as directly predicting the course of the pandemic under differing sets of intervention schedules. We show that without prompt effective protective/vaccination interventions the pandemic will be extended significantly and result in many millions of deaths in the U.S.
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SciScore for 10.1101/2020.08.23.20180174: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For all predictions we use the following initial conditions vector, The model is implemented in an iPython notebook that can be easily altered (to account for different intervention parameters) and used to model and predict the pandemic. iPythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Resul…
SciScore for 10.1101/2020.08.23.20180174: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources For all predictions we use the following initial conditions vector, The model is implemented in an iPython notebook that can be easily altered (to account for different intervention parameters) and used to model and predict the pandemic. iPythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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