Impact of multiple waves of COVID-19 on healthcare networks in the United States
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Abstract
The risk of overwhelming hospitals from multiple waves of COVID-19 is yet to be quantified. Here, we investigate the impact of different scenarios of releasing strong measures implemented around the U.S. on COVID-19 hospitalized cases and the risk of overwhelming the hospitals while considering resources at the county level. We show that multiple waves might cause an unprecedented impact on the hospitals if an increasing number of the population becomes susceptible and/or if the various protective measures are discontinued. Furthermore, we explore the ability of different mitigation strategies in providing considerable relief to hospitals. The results can help planners, policymakers, and state officials decide on additional resources required and when to return to normalcy.
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SciScore for 10.1101/2020.07.11.20151217: (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
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We assumed the number of recovered cases in each county based on the recovery rates of the U.S. due to the current limitation of these data. We evaluated the uncertainty in the hospitalized cases and fitted the disease transmission model to published data to estimate the disease transmission parameters. However, utilizing more data could …
SciScore for 10.1101/2020.07.11.20151217: (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
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:We assumed the number of recovered cases in each county based on the recovery rates of the U.S. due to the current limitation of these data. We evaluated the uncertainty in the hospitalized cases and fitted the disease transmission model to published data to estimate the disease transmission parameters. However, utilizing more data could lower the level of uncertainties in these estimates. We assumed that the population per county is constant and we neglected the impact of the relocation between states on disease spread. Furthermore, we used published data to estimate the number of staffed beds per county and the utilization of these beds and we are not accounting for the additional staffed beds and field hospitals built after the pandemic outbreak in the U.S.
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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