On the timing of interventions to preserve hospital capacity: lessons to be learned from the Belgian SARS-CoV-2 pandemic in 2020
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Abstract
Using publicly available data on the number of new hospitalisations we use a newly developed statistical model to produce a phase portrait to monitor the epidemic allowing for assessing whether or not intervention measures are needed to keep hospital capacity under control. The phase portrait is called a cliquets’ diagram , referring to the discrete alarm phases it points to. Using this cliquets’ diagram we show that intervention measures were associated with an effective mitigation of a Summer resurgence but that too little too late was done to prevent a large autumn wave in Belgium.
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SciScore for 10.1101/2020.12.18.20248450: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations related to the proposed cliquets’ diagram. First, we relied on the daily number of new covid-19 hospitalisations which are available for Belgium through a daily hospital surge survey developed and implemented by the national public health organization Sciensano and for which hospitals provided timely input (4). This may not be available for other countries. Second, using new hospitalisations yields a more stable, …
SciScore for 10.1101/2020.12.18.20248450: (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: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are several limitations related to the proposed cliquets’ diagram. First, we relied on the daily number of new covid-19 hospitalisations which are available for Belgium through a daily hospital surge survey developed and implemented by the national public health organization Sciensano and for which hospitals provided timely input (4). This may not be available for other countries. Second, using new hospitalisations yields a more stable, but somewhat late indicator. Combining the daily ratio based on, e.g., confirmed cases, gives a lead time, which we estimated to be 7-10 days, (results not shown). We believe delays and underreporting in the number of confirmed cases doesn’t have a large impact given that changing case definitions and test saturation are only likely to occur when already in a high-impact or no-go zone. Using test positivity rates could provide a useful addition to the number of confirmed cases. Further research includes defining a phase portrait based on confirmed cases though the connection to the hospital contingency phases is less straightforward because of the age-specificity of hospitalisation rates.
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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