Metapopulation dynamics of SARS-CoV-2 transmission in a small-scale Amazonian society.

IF 7.8 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
PLoS Biology Pub Date : 2023-08-22 eCollection Date: 2023-08-01 DOI:10.1371/journal.pbio.3002108
Thomas S Kraft, Edmond Seabright, Sarah Alami, Samuel M Jenness, Paul Hooper, Bret Beheim, Helen Davis, Daniel K Cummings, Daniel Eid Rodriguez, Maguin Gutierrez Cayuba, Emily Miner, Xavier de Lamballerie, Lucia Inchauste, Stéphane Priet, Benjamin C Trumble, Jonathan Stieglitz, Hillard Kaplan, Michael D Gurven
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引用次数: 0

Abstract

The severity of infectious disease outbreaks is governed by patterns of human contact, which vary by geography, social organization, mobility, access to technology and healthcare, economic development, and culture. Whereas globalized societies and urban centers exhibit characteristics that can heighten vulnerability to pandemics, small-scale subsistence societies occupying remote, rural areas may be buffered. Accordingly, voluntary collective isolation has been proposed as one strategy to mitigate the impacts of COVID-19 and other pandemics on small-scale Indigenous populations with minimal access to healthcare infrastructure. To assess the vulnerability of such populations and the viability of interventions such as voluntary collective isolation, we simulate and analyze the dynamics of SARS-CoV-2 infection among Amazonian forager-horticulturalists in Bolivia using a stochastic network metapopulation model parameterized with high-resolution empirical data on population structure, mobility, and contact networks. Our model suggests that relative isolation offers little protection at the population level (expected approximately 80% cumulative incidence), and more remote communities are not conferred protection via greater distance from outside sources of infection, due to common features of small-scale societies that promote rapid disease transmission such as high rates of travel and dense social networks. Neighborhood density, central household location in villages, and household size greatly increase the individual risk of infection. Simulated interventions further demonstrate that without implausibly high levels of centralized control, collective isolation is unlikely to be effective, especially if it is difficult to restrict visitation between communities as well as travel to outside areas. Finally, comparison of model results to empirical COVID-19 outcomes measured via seroassay suggest that our theoretical model is successful at predicting outbreak severity at both the population and community levels. Taken together, these findings suggest that the social organization and relative isolation from urban centers of many rural Indigenous communities offer little protection from pandemics and that standard control measures, including vaccination, are required to counteract effects of tight-knit social structures characteristic of small-scale populations.

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小规模亚马逊社会中严重急性呼吸系统综合征冠状病毒2型传播的元种群动态。
传染病爆发的严重程度取决于人类接触的模式,这些模式因地理位置、社会组织、流动性、获得技术和医疗保健的机会、经济发展和文化而异。尽管全球化社会和城市中心表现出的特征可能会加剧对流行病的脆弱性,但占据偏远农村地区的小规模自给社会可能会得到缓冲。因此,已提议将自愿集体隔离作为一项战略,以减轻新冠肺炎和其他流行病对小规模土著人口的影响,使其获得医疗保健基础设施的机会最少。为了评估这些群体的脆弱性和自愿集体隔离等干预措施的可行性,我们使用随机网络集合种群模型模拟和分析了玻利维亚亚马逊觅食园艺师感染严重急性呼吸系统综合征冠状病毒2型的动态,该模型以人口结构、流动性和接触网络的高分辨率经验数据为参数。我们的模型表明,相对隔离在人口水平上几乎没有提供保护(预计累计发病率约为80%),而且更偏远的社区没有通过与外部感染源的更大距离获得保护,由于促进疾病快速传播的小规模社会的共同特征,如高旅行率和密集的社交网络。社区密度、村庄的中心家庭位置和家庭规模大大增加了个人感染的风险。模拟干预进一步表明,如果没有令人难以置信的高水平集中控制,集体隔离就不太可能有效,尤其是在难以限制社区之间的探视以及前往外部地区的情况下。最后,模型结果与通过血清测定测量的新冠肺炎实证结果的比较表明,我们的理论模型在预测人口和社区层面的疫情严重程度方面是成功的。总之,这些发现表明,许多农村土著社区的社会组织和与城市中心的相对隔离对流行病几乎没有保护作用,需要包括疫苗接种在内的标准控制措施来抵消小规模人口特征的紧密社会结构的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS Biology
PLoS Biology 生物-生化与分子生物学
CiteScore
14.40
自引率
2.00%
发文量
359
审稿时长
3 months
期刊介绍: PLOS Biology is an open-access, peer-reviewed general biology journal published by PLOS, a nonprofit organization of scientists and physicians dedicated to making the world's scientific and medical literature freely accessible. The journal publishes new articles online weekly, with issues compiled and published monthly. ISSN Numbers: eISSN: 1545-7885 ISSN: 1544-9173
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