Stephens Christopher R, González-Salazar Constantino, Romero-Martínez Pedro
C3-Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.
Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.
Trop Med Infect Dis. 2023 Mar 17;8(3):178. doi: 10.3390/tropicalmed8030178.
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.
尽管生态位模型（ENM）和物种分布模型（SDM）在许多生态应用中的效用已得到证明，但其对诸如新冠病毒（SARS-CoV-2）等流行病或大流行病建模的适用性却受到了质疑。在本文中，与这一观点相反，我们表明可以创建能够描述大流行病在空间和时间上演变的ENM和SDM。作为一个说明性的用例，我们创建了用于预测2020年至2021年墨西哥新冠确诊病例的模型，将其视为我们的目标“物种”，结果表明这些模型在空间和时间上都具有预测性。为了实现这一点，我们扩展了最近开发的用于生态位建模的贝叶斯框架，以纳入：（i）动态、非平衡的“物种”分布；（ii）更广泛的一组栖息地变量，包括行为、社会经济和社会人口变量以及标准气候变量；（iii）针对不同物种特征的不同模型及相关生态位，展示了通过存在 - 缺失数据推导的生态位与从丰度数据推导的生态位如何不同。我们表明，在整个大流行期间，与病例丰度最高的那些地方相关的生态位一直高度保守，而与病例存在相关的推断生态位则一直在变化。最后，我们通过表明行为和社会因素比气候更具预测性，且进一步表明后者被前者混淆，展示了如何推断因果链并识别混杂因素。
