Geospatial precision simulations of community confined human interactions during SARS-CoV-2 transmission reveals bimodal intervention outcomes

  1. B Goldenbogen
  2. SO Adler
  3. O Bodeit
  4. JAH Wodke
  5. A Korman
  6. L Bonn
  7. X Escalera-Fanjul
  8. JEL Haffner
  9. M Karnetzki
  10. M Krantz
  11. I Maintz
  12. L Mallis
  13. RU Moran Torres
  14. H Prawitz
  15. PS Segelitz
  16. M Seeger
  17. R Linding
  18. E Klipp

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Abstract

Infectious disease outbreaks challenge societies by creating dynamic stochastic infection networks between human individuals in geospatial and demographical contexts. Minimizing human and socioeconomic costs of SARS-CoV-2 and future global pandemics requires data-driven and context-specific integrative modeling of detection-tracing, healthcare, and non-pharmaceutical interventions for decision-processes and reopening strategies. Traditional population-based epidemiological models cannot simulate temporal infection dynamics for individual human behavior in specific geolocations. We present an integrated geolocalized and demographically referenced spatio-temporal stochastic network- and agent-based model of COVID-19 dynamics for human encounters in real-world communities. Simulating intervention scenarios, we quantify effects of protection and identify the importance of early introduction of test-trace measures. Critically, we observe bimodality in SARS-CoV-2 infection dynamics so that the outcome of reopening can flip between good and poor outcomes stochastically. Furthermore, intervention effectiveness depends on strict execution and temporal control i.e. leaks can prevent successful outcomes. Schools are in many scenarios hubs for transmission, reopening scenarios are impacted by infection chain stochasticity and subsequent outbreaks do not always occur. This generalizable geospatial and individualized methodology is unique in precision and specificity compared to prior COVID-19 models [6, 16, 17, 19] and is applicable to scientifically guided decision processes for communities worldwide.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.03.20089235: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The ABM simulation framework was designed in an object-oriented manner, using the programming language Python version 3 [7] and the packages NumPy [8], pandas [9], GeoPandas [10], OSMnx [11].
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    The packages Matplotlib [12], seaborn [13] and the software gephi [14] were used for visualization.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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:
    Others have attempted to overcome these limitations: Already back in 2005, Ferguson et al. [18] published an agent-based model considering statistical distributions of transmission events in households, building types and alike. More recently, Gomez et al. [20] simulated infection transmission throughout Bogota representing the city’s population by 1000 agents while Lai et al. [19] created a simple model to analyze the effect of traveling between cities in China, but without realistic individual agents. Still, they all attempt to describe the infection process on population level, thus relying on a coarse-grained representation by a limited number of agents. Alternatively, Kissler et al. [6] developed an ODE model that describes increased social distancing by decreasing the R value. Ferretti et al. [16] modeled pre-symptomatic and non-diagnosed transmissions whereas Zhang et al. [17] provided valuable age specific data on contact patterns in the Chinese population. Karin et al. [21] assessed the effect of alternative working schedules. Certainly, all of those models can provide useful information for decision processes during a pandemic, however, they all focus on population dynamics and can neither consider geospatial referenced motion of agents in their communities into account, nor individually varying behavior, nor analyze infection spreading and disease progression on an individual scale. In contrast, our approach aims for precision: it explicitly considers individual ch...

    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.

    About SciScore

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  2. Version published to 10.1101/2020.05.03.20089235v2 on medRxiv
  3. Version published to 10.1101/2020.05.03.20089235v1 on medRxiv