Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022
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Abstract
Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all ≥80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.
Significance Statement
In many U.S. communities, the risk of exceeding local healthcare capacity during the winter and spring of 2022 remains substantial since COVID-19 hospitalizations may rise due to seasonal changes, low vaccination coverage, and the emergence of new variants of SARS-CoV-2, such as the omicron variant. Here, we provide simple and easy-to-communicate decision rules to predict whether local hospital occupancy is expected to exceed capacity within a 4- or 8-week period if no additional mitigating measures are implemented. These decision rules can serve as an alert system for local policymakers to respond proactively to mitigate future surges in the COVID-19 hospitalization and minimize risk of overwhelming local healthcare capacity.
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SciScore for 10.1101/2021.12.13.21267657: (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:Our study has a number of limitations. First, predicting the future trajectory of COVID-19 hospitalizations is challenged by various barriers, some of which are due to uncertainties in epidemic parameters and state variables (Table 2). Although our analysis accounts for these sources of uncertainties, predicting the local trajectories of …
SciScore for 10.1101/2021.12.13.21267657: (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:Our study has a number of limitations. First, predicting the future trajectory of COVID-19 hospitalizations is challenged by various barriers, some of which are due to uncertainties in epidemic parameters and state variables (Table 2). Although our analysis accounts for these sources of uncertainties, predicting the local trajectories of COVID-19 are further challenged by the unpredictability of population’s behavior and policymakers’ responses to a slowing or speeding pandemic. To minimize the impact of these unpredictable factors, we focused on short- or medium-term (4- or 8-week) predictions. Second, as the data required to develop and evaluate the decision trees considered here are not available in the real world, we had to rely on simulated trajectories to synthetize the datasets needed to train and evaluate our decision trees. As discussed before, the factors driving the COVID-19 pandemic (e.g., public health responses, population behavior and adherence to mitigating strategies, seasonal effects on the transmission of SARS-CoV-2, and vaccination coverage) have continuously changed since the beginning of the pandemic and they will most likely continue to change. Hence, predictive models trained on historical data may not perform well when employed during the upcoming winter and spring. To mitigate this issue, we used simulated trajectories, which were selected to properly match the historical data and then projected over future months, to produce the datasets needed to t...
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.
Results from scite Reference Check: We found no unreliable references.
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