Applying the unified models of ecology to forecast epidemics, with application to Covid-19

  1. Shelby W Loberg
  2. Clarence Lehman

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Abstract

During a burgeoning outbreak of a novel disease, public attention will ordinarily expand as the severity of the outbreak expands--as infections multiply and news reports accumulate. Such public attention will in turn reinforce tactics to control the outbreak. In classical epidemiological models, effects of such tactics can be incorporated in standard parameters of transmission, recovery, and mortality. Unfortunately, early in an outbreak those individual parameters may be poorly known, hence corresponding models can get lost in uncertainty. This makes it difficult to determine whether the disease is spreading exponentially or logistically, or along another path. Examining cases over time is also problematic, as a logistically growing infection that is leveling off appears exponential in early phases. Here we report on the most basic mechanistic, ecological model we can imagine, which can help distinguish growth that is and is not under control. This approach did a satisfactory job predicting the final outcome of the Ebola outbreak of 2014-15. The model's two parameters were computable in real time, well before the outcome was actually known. The first parameter is an intrinsic rate of increase in cumulative deaths or reported cases. The second parameter is related to the human social system and represents all tactics that combine to control the outbreak. That parameter is coupled to the number of cumulative deaths or cases. We examine the basic mechanisms operating in this model and show the predictions made during the Ebola outbreak. We also consider how this basic model is performing for the Covid-19 pandemic and highlight ecological models that align with popularly discussed concepts such as flatten-the-curve, exponential growth, and inflection points of curves.

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    SciScore for 10.1101/2020.04.07.20056754: (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

    No key resources detected.

    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.

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    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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  2. Version published to 10.1101/2020.04.07.20056754 on medRxiv