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      Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox’s Bazar settlement

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          Abstract

          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.

          Author summary

          The spread of infectious diseases presents many challenges to healthcare systems and infrastructures across the world. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to the dangers of disease spread.

          This study seeks to understand how COVID-19 spreads in settlements, focusing on the Cox’s Bazar refugee settlement in Bangladesh. Our model simulates the movements and interactions of each individual in the settlement, incorporating information about family structures and demographic attributes, to understand how COVID-19 might spread under various intervention strategies.

          Our analysis suggests that mask wearing in indoor locations can have a significant effect on disease spread, even when wearing reusable cotton masks, which the people in the settlement can make themselves. We also look at different ways to treat individuals who only have milder symptoms and don’t yet require hospitalisation, as well as various scenarios which might allow for the safe reopening of schools in the settlement.

          With almost 80 million forcibly displaced people in the world, we hope that this work will inspire more modeling groups to focus on these vulnerable populations, which have been traditionally under-served by such efforts, to ensure no one is left behind.

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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            Matplotlib: A 2D Graphics Environment

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              Array programming with NumPy

              Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draft
                Role: MethodologyRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                October 2021
                28 October 2021
                : 17
                : 10
                : e1009360
                Affiliations
                [1 ] United Nations Global Pulse, New York, New York, United States of America
                [2 ] Institute for Data Science, Durham University, Durham, United Kingdom
                [3 ] New York University Stern School of Business, New York, New York, United States of America
                [4 ] MIT-IBM Watson AI Lab, Cambridge, Massachusetts, United States of America
                [5 ] UNHCR Innovation, Geneva, Switzerland
                [6 ] WHO Emergency Sub-Office, Cox’s Bazar, Bangladesh
                [7 ] UK Public Health Rapid Support Team, Public Health England/London School of Hygiene and Tropical Medicine, London, United Kingdom
                [8 ] UNHCR Public Health Unit, Geneva, Switzerland
                [9 ] UNHCR Public Health Unit, Cox’s Bazar, Bangladesh
                [10 ] UNHCR Information Management Unit, Cox’s Bazar, Bangladesh
                Institute for Disease Modeling, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-7551-3423
                https://orcid.org/0000-0001-5055-9863
                https://orcid.org/0000-0003-2576-8609
                https://orcid.org/0000-0002-1022-6025
                https://orcid.org/0000-0001-5218-3185
                https://orcid.org/0000-0003-0864-5594
                https://orcid.org/0000-0003-0004-6167
                https://orcid.org/0000-0002-7995-5445
                https://orcid.org/0000-0003-4143-3833
                https://orcid.org/0000-0002-8694-2001
                Article
                PCOMPBIOL-D-21-00426
                10.1371/journal.pcbi.1009360
                8553081
                34710090
                d397feec-348e-4c4b-a0d6-3db9028d8d86
                © 2021 Aylett-Bullock et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 March 2021
                : 18 August 2021
                Page count
                Figures: 9, Tables: 1, Pages: 25
                Funding
                Funded by: Government of Sweden
                Funded by: funder-id http://dx.doi.org/10.13039/100004439, William and Flora Hewlett Foundation;
                Funded by: science and technology facilities council
                Award ID: ST/P006744/1
                Award Recipient :
                Funded by: science and technology facilities council
                Award ID: ST/P006744/1
                Award Recipient :
                Funded by: science and technologies facilities council
                Award ID: ST/P006744/1
                Award Recipient :
                Funded by: science and technology facilities council
                Award ID: ST/P006744/1
                Award Recipient :
                Funded by: UK Aid
                Funded by: funder-id http://dx.doi.org/10.13039/501100000271, Science and Technology Facilities Council;
                Award ID: ST/K00042X/1, ST/P002293/1, ST/R002371/1, ST/S002502/1, ST/R000832/1
                United Nations Global Pulse work is supported by the Government of Sweden, and the William and Flora Hewlett Foundation. J.A.B., C.C.L., A.Q.B. and A.S. are also supported by the UKRI-STFC grant number ST/P006744/1. The UK Public Health Rapid Support Team is funded by UK aid from the Department of Health and Social Care. This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital grants ST/K00042X/1, ST/P002293/1, ST/R002371/1 and ST/S002502/1, Durham University and STFC operations grant ST/R000832/1. DiRAC is part of the UK’s National e-Infrastructure. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Social Sciences
                Psychology
                Cognitive Psychology
                Learning
                Human Learning
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Learning
                Human Learning
                People and Places
                Population Groupings
                Age Groups
                Children
                People and Places
                Population Groupings
                Families
                Children
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                People and Places
                Demography
                Refugees
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Research and Analysis Methods
                Simulation and Modeling
                Biology and Life Sciences
                Developmental Biology
                Twins
                Medicine and Health Sciences
                Diagnostic Medicine
                Virus Testing
                Custom metadata
                This research has been designed and conducted following relevant data privacy and data protection principles and processes, including UNHCR data protection policies and guidelines, as well as the UN principles on Personal Data Protection and Privacy, and the UNSDG Guidance Note on Big Data for the 2030 Agenda: Ethics, Privacy and Data Protection. All data we use is derived from open source datasets and all URLs and links are provided explicitly in the manuscript and summarised in the Supporting information. However, in consultation with internal ethical and legal experts, we have decided that the processed and combined data derived from these sources and which are used as direct input to the model cannot be shared publicly. This is because the combination of the datasets increases the risk of reidentification of certain groups and individuals in the refugee settlements. Data are available from UN Global Pulse, subject to application and review (contact: caroline@ 123456unglobalpulse.org ), for researchers who meet the criteria for access to this data. Code availability: In the interest of openness and transparency, the simulation code has been released under a GPL v3 license and can be accessed from: https://github.com/UNGlobalPulse/UNGP-settlement-modelling.

                Quantitative & Systems biology
                Quantitative & Systems biology

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