Modeling non-pharmaceutical interventions in the COVID-19 pandemic with survey-based simulations

  1. Marius Kaffai
  2. Raphael H. Heiberger

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Abstract

Governments around the globe use non-pharmaceutical interventions (NPIs) to curb the spread of coronavirus disease 2019 (COVID-19) cases. Making decisions under uncertainty, they all face the same temporal paradox: estimating the impact of NPIs before they have been implemented. Due to the limited variance of empirical cases, researchers could so far not disentangle effects of individual NPIs or their impact on different demographic groups. In this paper, we utilize large-scale agent-based simulations in combination with Susceptible-Exposed-Infectious-Recovered (SEIR) models to investigate the spread of COVID-19 for some of the most affected federal states in Germany. In contrast to other studies, we sample agents from a representative survey. Including more realistic demographic attributes that influence agents’ behavior yields accurate predictions of COVID-19 transmissions and allows us to investigate counterfactual what-if scenarios. Results show that quarantining infected people and exploiting industry-specific home office capacities are the most effective NPIs. Disentangling education-related NPIs reveals that each considered institution (kindergarten, school, university) has rather small effects on its own, yet, that combined openings would result in large increases in COVID-19 cases. Representative survey-characteristics of agents also allow us to estimate NPIs’ effects on different age groups. For instance, re-opening schools would cause comparatively few infections among the risk-group of people older than 60 years.

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  1. Version published to 10.1371/journal.pone.0259108
  2. ScreenIT

    SciScore for 10.1101/2021.04.16.21255606: (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
    We performed the calibration using the “Latin Hypercube Sampling” algorithm [45], which is implemented in the python package “Spotpy” [25].
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Like other simulation studies, our analysis has several limitations. First, our model does not contain a representation of contacts between households or agents that do no take place in one of the implemented locations, e.g., contacts with relatives or friends. Second, our model does not contain locations were people meet during spare time. However, the influence of both points is mitigated because private contacts and leisure activities were reduced to a minimum in the observed period. A further limitation is that we do not model changes of people’s behavior. For example, we do not take into account the change in caution about infection risks in daily life or the obligation to keep distance and wear masks inside buildings. Also beyond the scope of this paper is the inclusion of external influences. Instead, we simulate a closed system. After the initial infections at the beginning of a run no infections occur due to agents coming home from “outside”, e.g., commuters or tourists. As many other simulation studies, we had to estimate some parameters due to a lack of empirical data. However, we conducted a series of robustness checks for the most important of those parameters (cf. Figures 5-9 in SI). Sensitivity analyses suggest that the results are robust to changes in the parameters and, hence, to potential errors in assumed values. Despite those limitations, we provide a reproducible model based on empirical agents which can be adopted and/or extended (Python code freely avai...

    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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  3. Version published to 10.1101/2021.04.16.21255606v1 on medRxiv