Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study

  1. Tim Litwin
  2. Jens Timmer
  3. Mathias Berger
  4. Andreas Wahl-Kordon
  5. Matthias J. Müller
  6. Clemens Kreutz

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Abstract

Background

Surveillance testing within healthcare facilities provides an opportunity to prevent severe outbreaks of coronavirus disease 2019 (COVID-19). However, the quantitative impact of different available surveillance strategies and their potential to decrease the frequency of outbreaks are not well-understood.

Methods

We establish an individual-based model representative of a mental health hospital yielding generalizable results. Attributes and features of this facility were derived from a prototypical hospital, which provides psychiatric, psychosomatic and psychotherapeutic treatment. We estimate the relative reduction of outbreak probability for three test strategies (entry test, once-weekly test and twice-weekly test) relative to a symptom-based baseline strategy. Based on our findings, we propose determinants of successful surveillance measures.

Results

Entry Testing reduced the outbreak probability by 26%, additionally testing once or twice weekly reduced the outbreak probability by 49% or 67% respectively. We found that fast diagnostic test results and adequate compliance of the clinic population are mandatory for conducting effective surveillance. The robustness of these results towards uncertainties is demonstrated via comprehensive sensitivity analyses.

Conclusions

We conclude that active testing in mental health hospitals and similar facilities considerably reduces the number of COVID-19 outbreaks compared to symptom-based surveillance only.

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  1. Version published to 10.1186/s12879-022-07075-1
  2. ScreenIT

    SciScore for 10.1101/2021.05.03.21255976: (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
    SentencesResources
    Performance of the PoC test used in the model is based on the Panbio™ COVID-19 antigen rapid test (Abbott) [29] as employed in the factual hospital.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Another important structural limitation is the assumption of constant sensitivity of diagnostic tests during the course of disease. To account for this, in the future, the course of disease could be defined based on a viral load profile across time which is proportional to infectiousness as well as the sensitivity of the diagnostic test [24]. Current evidence suggests that PoC antigen tests can indeed perform well to detect relevant levels of viral load [29], highlighting the difficulty in establishing PoC test performance when compared to PCR tests as a reference standard [5]. In order to allow for a fair comparison between surveillance based on PCR tests and PoC antigen tests different detection threshold levels for viral load have to be included in the model. However, this does not affect our conclusion that short test-to-result delays are crucial for effective surveillance. COVID-19 surveillance in hospitals with long treatment duration and long-term care facilities provides a unique opportunity to create a safe environment for a vulnerable population. In this context, adequate assessment of the gain of various mitigation strategies is sparse but urgently needed to establish standards for practical implementation of strategies. In order to complement the existing literature, we quantified the effect of various surveillance strategies on the probability of occurring viral outbreaks. We demonstrated that implementing these strategies is practically feasible. We found that i...

    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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  3. Version published to 10.1101/2021.05.03.21255976v1 on medRxiv