The impact of natural disasters on the spread of COVID-19: a geospatial, agent-based epidemiology model

  1. Maximillian Van Wyk de Vries
  2. Lekaashree Rambabu

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Abstract

Background

Natural disasters and infectious diseases result in widespread disruption to human health and livelihood. At the scale of a global pandemic, the co-occurrence of natural disasters is inevitable. However, the impact of natural disasters on the spread of COVID-19 has not been extensively evaluated through epidemiological modelling.

Methods

We create an agent-based epidemiology model based on COVID-19 clinical, epidemiological, and geographic data. We first model 35 scenarios with varying natural disaster timing and duration for a COVID-19 outbreak in a theoretical region. We then evaluate the potential effect of an eruption of Vesuvius volcano on the spread of COVID-19 in Campania, Italy.

Results

In a majority of cases, the occurrence of a natural disaster increases the number of disease related fatalities. For a natural disaster fifty days after infection onset, the median increase in fatalities is 2, 59, and 180% for a 2, 14, and 31-day long natural disaster respectively, when compared to the no natural disaster scenario. For the Campania case, the median increase in fatalities is 1.1 and 2.4 additional fatalities per 100,000 for eruptions on day 1 and 100 respectively, and 60.0 additional fatalities per 100,000 for an eruption close to the peak in infections (day 50).

Conclusion

Our results show that the occurrence of a natural disaster in most cases leads to an increase in infection related fatalities, with wide variance in possible outcomes depending on the timing of the natural disaster relative to the peak in infections and the duration of the natural disaster.

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  1. Version published to 10.1186/s12976-021-00151-0
  2. ScreenIT

    SciScore for 10.1101/2020.09.12.20193433: (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 conduct 100 different runs for each scenario, seeding the Mercenne Twister with system time to ensure independent runs.
    Mercenne Twister
    suggested: None

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.09.12.20193433 on medRxiv