United Nations Global Pulse, New York, New York, United States of America.
Institute for Data Science, Durham University, Durham, United Kingdom.
PLoS Comput Biol. 2021 Oct 28;17(10):e1009360. doi: 10.1371/journal.pcbi.1009360. eCollection 2021 Oct.
The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world's most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox's Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.
传染病的传播，如 COVID-19，给全球各地的医疗系统和基础设施带来了许多挑战，加剧了不平等现象，使世界上最脆弱的人群受到的影响最大。考虑到难民和境内流离失所者（IDP）定居点的密度和可用基础设施，它们特别容易受到疾病传播的影响。在本文中，我们提出了一种基于代理的建模方法，用于模拟在各种非药物干预策略下，难民和 IDP 定居点中疾病的传播。该模型基于 6 月开源框架，以实地观察和以往文献中获得的地理、人口统计学、合并症、物理基础设施和其他参数的数据为依据。该方法的开发和测试主要集中在孟加拉国的考克斯巴扎尔难民营，尽管我们的模型旨在推广到其他非正式环境。我们的研究结果表明，鼓励轻度至重度有症状的患者在家中进行自我隔离，而不是将所有阳性病例隔离在专门建造的隔离和治疗中心，不会增加二次感染的风险，这意味着这些中心可以为 COVID-19 最严重的病例提供医院支持。其次，我们发现，在所有室内公共区域佩戴口罩可以有效地抑制病毒传播，即使口罩的效果和佩戴率都很低。最后，我们对在各种缓解策略下重新开放定居点的学习中心的效果进行了建模。例如，在教室中佩戴口罩、将出勤率减半以实现身体距离、以及更好的通风等措施，几乎可以完全减轻因重新开放学习中心而可能导致的感染风险。这些建模工作正在被纳入决策过程，为未来的规划提供信息，应该在类似的地理区域进行进一步的演练，以帮助保护那些最脆弱的人群。
