“Does a respiratory virus have an ecological niche, and if so, can it be mapped?” Yes and yes

  1. Christopher R. Stephens
  2. Constantino González-Salazar
  3. Pedro Romero Martínez

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Abstract

Although the utility of Ecological Niche models (ENM) and Species Distribution models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target “species”, in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: i) dynamic, non-equilibrium “species” distributions; ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.

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  1. ScreenIT

    SciScore for 10.1101/2022.05.04.22274675: (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
    For example, using WorldClim data from a given year as habitat variables for a niche model of a species represented by collection data taken from a 150 year time period.
    WorldClim
    suggested: (WorldClim, RRID:SCR_010244)

    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:
    An apparent limitation of our work is that we have worked at a quite coarse-grained level, that of municipalities. This is a limitation of the data used, however, not the methodology. In principal, a much finer spatial resolution, at the level of “census blocks”, for instance, could be used if data was available at that resolution for the target class and habitat. In the same vein, dynamical data that represented both changes in the local environment, such as lockdowns and hospital occupation rates, or changes in behaviour, such as mask wearing compliance, cell phone data as a proxy for short-term mobility, or vaccination rates, or a host of other factors could also be included. It would be interesting for example to determine to what extent adaptations in the pathogen were potentially reflected as changes in its niche. There is also the question of the validity of the target class data as maintaining accurate counts of confirmed cases is difficult. However, this would have little to no impact on the overall conclusions of our ENM/SDMs. In summary: Our goal here was not to offer a gold-standard model for prediction of the pandemic. As mentioned, there are more predictive and directly causal factors than we have included here. However, the niche of COVID-19 is immensely complex and adaptive and the incorporation of the vast array of relevant factors that determine it is a huge challenge in data collection and integration. What we have shown is that if that data can be represen...

    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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  2. Version published to 10.1101/2022.05.04.22274675v1 on medRxiv