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Huijser L, Paszkowski A, de Ruiter M, Tiggeloven T. From erosion to epidemics: Understanding the overlapping vulnerability of hydrogeomorphic hotspots, malaria affliction, and poverty in Nigeria. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172245. [PMID: 38604368 DOI: 10.1016/j.scitotenv.2024.172245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/15/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Hydrogeomorphic changes, encompassing erosion, waterlogging, and siltation, disproportionately threaten impoverished rural communities. Yet, they are often marginalized in discussions of disasters. This oversight is especially concerning as vulnerable households with limited healthcare access are most susceptible to related diseases and displacement. However, our current understanding of how these risks intersect remains limited. We explore the complex relationships between hydrogeomorphic hazards, malaria incidence, and poverty in Nigeria. Through spatial analyses we expand traditional boundaries, incorporating factors such as healthcare access, migration patterns, dam locations, demographics, and wealth disparities into a unified framework. Our findings reveal a stark reality: most residents in hydrogeomorphic hotspots live in poverty (earnings per person ≤$1.25/day), face elevated malaria risks (80 % in malaria hotspots), reside near dams (59 %), and struggle with limited healthcare access. Moreover, exposure to hydrogeomorphic hotspots could double by 2080, affecting an estimated 5.8 million Nigerians. This forecast underscores the urgent need for increased support and targeted interventions to protect those living in poverty within these hazardous regions. In shedding light on these dynamics, we expose and emphasise the pressing urgency of the risks borne by the most vulnerable populations residing in these regions-communities often characterised by limited wealth and resilience.
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Affiliation(s)
- Lise Huijser
- Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Marleen de Ruiter
- Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Timothy Tiggeloven
- Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
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2
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Xu A. Spatial Patterns and Determinants of Inter-county Migration in California: A Multilevel Gravity Model Approach. POPULATION RESEARCH AND POLICY REVIEW 2023; 42:40. [PMID: 37128246 PMCID: PMC10132804 DOI: 10.1007/s11113-023-09782-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/22/2023] [Indexed: 05/03/2023]
Abstract
Understanding migration patterns and their determinants is crucial for population estimation and resource allocation for policymakers. Utilizing residential mobility data collected by the Department of Motor Vehicles, this present study provides a spatiotemporal analysis of inter-county migration in California for the period 2014-2021. We use multilevel gravity models to address the hierarchical nature of migration data and the effects of migration flows sharing common origins, destinations, and regions, providing a substantively complete examination of push and pull forces affecting migration. Our findings show that populous counties in Southern California and the San Francisco Bay Area represent the largest origins and destinations, despite a systemic decline in intra-state migration. Migration is strongly associated with population size, geographic proximity (i.e., distance and contiguity), job availability, and industrial composition similarity between origins and destinations. Our findings also highlight the contribution of shared origins, destinations, and regions in explaining the systematic variation of migration flows. Counties vary more in the number of migrants they attract than the number they send. The purposed multilevel modeling approach is useful in identifying place-specific influences on migration and in improving estimation accuracy. Supplementary Information The online version contains supplementary material available at 10.1007/s11113-023-09782-2.
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Affiliation(s)
- Anqi Xu
- Demographic Research Unit, California Department of Finance, Sacramento, CA USA
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3
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Kluge L, Levermann A, Schewe J. Radiation model for migration with directional preferences. Phys Rev E 2022; 106:064138. [PMID: 36671094 DOI: 10.1103/physreve.106.064138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
The radiation model is a parameter-free model of human mobility that has been applied primarily for short-distance moves, such as commuting. When applied to migration, it underestimates the number of long-range moves, such as between different US states. Here we show that it additionally suffers from a conceptual inconsistency that can have substantial numerical effects on long-distance moves. We propose a modification of the radiation model that introduces a dependence on the angle between any two alternative potential destinations, accounting for the possibility that migrants may have preferences about the approximate direction of their move. We demonstrate that this modification mitigates the conceptual inconsistency and improves the model fit to observational migration data, without introducing any fitting parameters.
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Affiliation(s)
- Lucas Kluge
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
| | - Anders Levermann
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
- Lamont-Doherty Earth Observatory, Columbia University, New York, New York 10964-1000, USA
| | - Jacob Schewe
- Potsdam Insitute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
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4
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Kluge L, Schewe J. Evaluation and extension of the radiation model for internal migration. Phys Rev E 2021; 104:054311. [PMID: 34942836 DOI: 10.1103/physreve.104.054311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 11/11/2021] [Indexed: 11/07/2022]
Abstract
Human migration is often studied using gravity models. These models, however, have known limitations, including analytic inconsistencies and a dependence on empirical data to calibrate multiple parameters for the region of interest. Overcoming these limitations, the radiation model has been proposed as an alternative, universal approach to predicting different forms of human mobility, but has not been adopted for studying migration. Here we show, using data on within-country migration from the USA and Mexico, that the radiation model systematically underpredicts long-range moves, while the traditional gravity model performs well for large distances. The universal opportunity model, an extension of the radiation model, shows an improved fit of long-range moves compared to the original radiation model, but at the cost of introducing two additional parameters. We propose a more parsimonious extension of the radiation model that introduces a single parameter. We demonstrate that it fits the data over the full distance spectrum and also-unlike the universal opportunity model-preserves the analytical property of the original radiation model of being equivalent to a gravity model in the limit of a uniform population distribution.
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Affiliation(s)
- Lucas Kluge
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany and Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Strasse 24/25, 14476 Potsdam, Germany
| | - Jacob Schewe
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, D-14412 Potsdam, Germany
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5
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. Epidemics 2021; 38:100534. [PMID: 34915300 PMCID: PMC8641444 DOI: 10.1016/j.epidem.2021.100534] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/29/2021] [Accepted: 12/02/2021] [Indexed: 12/24/2022] Open
Abstract
For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar; Department of Mathematics, University of Fianarantsoa, Madagascar; Department of Integrative Biology, University of Wisconsin-Madison, WI, USA.
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | - Antso Hasina Raherinandrasana
- Surveillance Unit, Ministry of Health of Madagascar, Madagascar; Faculty of Medicine, University of Antananarivo, Madagascar
| | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA; Mahaliana Labs SARL, Antananarivo, Madagascar
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6
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Meredith HR, Giles JR, Perez-Saez J, Mande T, Rinaldo A, Mutembo S, Kabalo EN, Makungo K, Buckee CO, Tatem AJ, Metcalf CJE, Wesolowski A. Characterizing human mobility patterns in rural settings of sub-Saharan Africa. eLife 2021; 10:e68441. [PMID: 34533456 PMCID: PMC8448534 DOI: 10.7554/elife.68441] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/21/2021] [Indexed: 11/27/2022] Open
Abstract
Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.
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Affiliation(s)
- Hannah R Meredith
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - John R Giles
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Javier Perez-Saez
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Théophile Mande
- Bureau d'Etudes Scientifiques et Techniques - Eau, Energie, Environnement (BEST-3E), Ouagadougou, Burkina Faso
| | - Andrea Rinaldo
- Dipartimento di Ingegneria Civile Edile ed Ambientale, Università di Padova, Padova, Italy
- Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon Mutembo
- Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
- Macha Research Trust, Choma, Zambia
| | - Elliot N Kabalo
- Zambia Information and Communications Technology Authority, Lusaka, Zambia
| | | | - Caroline O Buckee
- Department of Epidemiology and the Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology and the Princeton School of Public and International Affairs, Princeton University, Princeton, United States
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
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7
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Rathinam F, Khatua S, Siddiqui Z, Malik M, Duggal P, Watson S, Vollenweider X. Using big data for evaluating development outcomes: A systematic map. CAMPBELL SYSTEMATIC REVIEWS 2021; 17:e1149. [PMID: 37051451 PMCID: PMC8354555 DOI: 10.1002/cl2.1149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
BACKGROUND Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs. OBJECTIVES Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts. SEARCH METHODS A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist. SELECTION CRITERIA The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes. DATA COLLECTION AND ANALYSIS Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only. MAIN RESULTS The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs. AUTHORS' CONCLUSIONS This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.
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8
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Ramiadantsoa T, Metcalf CJE, Raherinandrasana AH, Randrianarisoa S, Rice BL, Wesolowski A, Randriatsarafara FM, Rasambainarivo F. Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.07.30.21261392. [PMID: 34373863 PMCID: PMC8351785 DOI: 10.1101/2021.07.30.21261392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.
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Affiliation(s)
- Tanjona Ramiadantsoa
- Department of Life Science, University of Fianarantsoa, Madagascar
- Department of Mathematics, University of Fianarantsoa, Madagascar
- Department of Integrative Biology, University of Wisconsin-Madison, WI, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, NJ, USA
| | | | | | - Benjamin L Rice
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Fidisoa Rasambainarivo
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Mahaliana Labs SARL, Antananarivo, Madagascar
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9
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Exploring Connections—Environmental Change, Food Security and Violence as Drivers of Migration—A Critical Review of Research. SUSTAINABILITY 2020. [DOI: 10.3390/su12145702] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Migration, whether triggered by single events, such as violent conflict, or by long term pressures related to environmental change or food insecurity is altering sustainable development in societies. Although there is a large amount of literature, there is a gap for consolidating frameworks of migration-related to the interaction and correlation between drivers. We review scientific papers and research reports about three categories of drivers: Environmental Change (EC), Food Security (FS), and Violent Conflict (VC). First, we organize the literature to understand the explanations of the three drivers on migration individually, as well as the interactions among each other. Secondly, we analyse the literature produced regarding Colombia, Myanmar, and Tanzania; countries with different combinations of the driving factors for migration. Although we find that many correlations are explained in the literature, migration is mostly driven by structural vulnerabilities and unsustainable development paths in places that have a low resilience capacity to cope with risk. For example, food insecurity, as a product of environmental changes (droughts and floods), is seen as a mediating factor detonating violent conflict and migration in vulnerable populations. The paper contributes to the literature about multi-driven migration, presenting an overview of the way in which different driver combinations trigger migration. This is important for determining the best governance mechanisms and policy responses that tackle forced migration and improve the resilience of vulnerable communities as well as sustainable development.
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10
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Modeling human migration across spatial scales in Colombia. PLoS One 2020; 15:e0232702. [PMID: 32379787 PMCID: PMC7205305 DOI: 10.1371/journal.pone.0232702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 04/20/2020] [Indexed: 12/03/2022] Open
Abstract
Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.
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11
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Colombi D, Poletto C, Nakouné E, Bourhy H, Colizza V. Long-range movements coupled with heterogeneous incubation period sustain dog rabies at the national scale in Africa. PLoS Negl Trop Dis 2020; 14:e0008317. [PMID: 32453756 PMCID: PMC7274467 DOI: 10.1371/journal.pntd.0008317] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 06/05/2020] [Accepted: 04/22/2020] [Indexed: 12/25/2022] Open
Abstract
Dog-transmitted rabies is responsible for more than 98% of human cases worldwide, remaining a persistent problem in developing countries. Mass vaccination targets predominantly major cities, often compromising disease control due to re-introductions. Previous work suggested that areas neighboring cities may behave as the source of these re-introductions. To evaluate this hypothesis, we introduce a spatially explicit metapopulation model for rabies diffusion in Central African Republic. Calibrated on epidemiological data for the capital city, Bangui, the model predicts that long-range movements are essential for continuous re-introductions of rabies-exposed dogs across settlements, eased by the large fluctuations of the incubation period. Bangui's neighborhood, instead, would not be enough to self-sustain the epidemic, contrary to previous expectations. Our findings suggest that restricting long-range travels may be very efficient in limiting rabies persistence in a large and fragmented dog population. Our framework can be applied to other geographical contexts where dog rabies is endemic.
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Affiliation(s)
- Davide Colombi
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Paris, France
- Computational Epidemiology Laboratory, Institute for Scientific Interchange (ISI), Turin, Italy
- Physics Department and INFN, University of Turin, Turin, Italy
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Paris, France
| | | | - Hervé Bourhy
- Institut Pasteur, Unit Lyssavirus Epidemiology and Neuropathology, WHO Collaborating Center for Reference and Research on Rabies, Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Paris, France
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12
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Dotse-Gborgbortsi W, Dwomoh D, Alegana V, Hill A, Tatem AJ, Wright J. The influence of distance and quality on utilisation of birthing services at health facilities in Eastern Region, Ghana. BMJ Glob Health 2020; 4:e002020. [PMID: 32154031 PMCID: PMC7044703 DOI: 10.1136/bmjgh-2019-002020] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/20/2019] [Accepted: 01/09/2020] [Indexed: 11/03/2022] Open
Abstract
Objectives Skilled birth attendance is the single most important intervention to reduce maternal mortality. However, studies have not used routinely collected health service birth data at named health facilities to understand the influence of distance and quality of care on childbirth service utilisation. Thus, this paper aims to quantify the influence of distance and quality of healthcare on utilisation of birthing services using routine health data in Eastern Region, Ghana. Methods We used a spatial interaction model (a model that predicts movement from one place to another) drawing on routine birth data, emergency obstetric care surveys, gridded estimates of number of pregnancies and health facility location. We compared travel distances by sociodemographic characteristics and mapped movement patterns. Results A kilometre increase in distance significantly reduced the prevalence rate of the number of women giving birth in health facilities by 6.7%. Although quality care increased the number of women giving birth in health facilities, its association was insignificant. Women travelled further than expected to give birth at facilities, on average journeying 4.7 km beyond the nearest facility with a recorded birth. Women in rural areas travelled 4 km more than urban women to reach a hospital. We also observed that 56% of women bypassed the nearest hospital to their community. Conclusion This analysis provides substantial opportunities for health planners and managers to understand further patterns of skilled birth service utilisation, and demonstrates the value of routine health data. Also, it provides evidence-based information for improving maternal health service provision by targeting specific communities and health facilities.
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Affiliation(s)
- Winfred Dotse-Gborgbortsi
- School of Geography and Environmental Science, University of Southampton, Southampton, UK.,WorldPop Research Group, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Duah Dwomoh
- Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Legon, Accra, Ghana
| | - Victor Alegana
- School of Geography and Environmental Science, University of Southampton, Southampton, UK.,WorldPop Research Group, School of Geography and Environmental Science, University of Southampton, Southampton, UK.,Population Health Unit, Kenya Medical Research Institute - Wellcome Trust Research Programme P.O. Box 43640-00100, Nairobi, Kenya.,Faculty of Science and Technology, Lancaster University, Lancaster, UK
| | - Allan Hill
- Social Statistics and Demography, University of Southampton, Southampton, UK
| | - Andrew J Tatem
- School of Geography and Environmental Science, University of Southampton, Southampton, UK.,WorldPop Research Group, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Jim Wright
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
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13
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Banougnin BH. Examining internal migration effects on short versus long interbirth intervals in Cotonou, Benin Republic. BMC Pregnancy Childbirth 2019; 19:375. [PMID: 31646982 PMCID: PMC6813098 DOI: 10.1186/s12884-019-2529-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 09/23/2019] [Indexed: 11/10/2022] Open
Abstract
Background The literature on migration-fertility relationship uses various measures of fertility, such as fertility rates, actual fertility and family size preferences. This study introduces a different measure—interbirth intervals over women’s reproductive years—to examine how internal migration is associated with short interbirth intervals (less than 24 months) and long interbirth intervals (greater than 60 months) in Cotonou, the largest city of Benin Republic. Methods The paper uses primary data on 2852 live births to 1659 women aged 15–49 years from the 2018 Fertility and Migration Survey in Cotonou. Competing-risks models were fitted for the analysis. Results Nineteen percent of live births were of short interbirth intervals and 16% were of long interbirth intervals. The prevalence of short interbirth intervals was higher among migrants who spent less than 5 years in Cotonou (29%) than among non-migrants (19%) and earlier migrants (18%). Non-migrants had the highest proportion of long interbirth intervals (19%). Within the first 5 years following the migration to Cotonou, migrants had higher subhazard ratio (SHR) of short interbirth intervals (SHR: 1.71, 95% CI: 1.33–2.21) and lower SHR of long interbirth intervals (SHR: 0.64, 95% CI: 0.47–0.87) than non-migrants. This association holds after controlling for socioeconomic characteristics—but with a slightly reduced gap between migrants who spent less than 5 years in Cotonou and non-migrants. Afterwards and irrespective of women’s socioeconomic backgrounds, migrants who spent 5 or more years in Cotonou and non-migrants had similar risks of short and long interbirth intervals. Finally, from 5 years of stay in Cotonou, migrants for reasons other than school or job were less likely to experience short interbirth intervals (SHR: 0.65, 95% CI: 0.46–0.98 for migrants who spent 5–10 years in Cotonou, and SHR: 0.74, 95% CI: 0.54–1.02 for migrants who spent more than 10 years in Cotonou) than non-migrants. Conclusion Family planning programmes should mainly target migrants in the early years after their arrival in Cotonou. Moreover, non-migrants need to be sensitised on the adverse health outcomes of long interbirth intervals.
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Affiliation(s)
- Boladé Hamed Banougnin
- Panafrican University, Life and Earth Sciences Institute (Including Health and Agriculture), University of Ibadan, PMB 5017, GPO, Ibadan, Nigeria. .,Ecole Nationale de la Statistique, de la Planification et de la Démographie, Université de Parakou, Route de l'Okpara, B.P. 123, Parakou, Bénin.
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14
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DM-LIMGA: Dual Migration Localized Island Model Genetic Algorithm—a better diversity preserver island model. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00253-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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15
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Lai S, zu Erbach-Schoenberg E, Pezzulo C, Ruktanonchai NW, Sorichetta A, Steele J, Li T, Dooley CA, Tatem AJ. Exploring the use of mobile phone data for national migration statistics. PALGRAVE COMMUNICATIONS 2019; 5:34. [PMID: 31579302 PMCID: PMC6774788 DOI: 10.1057/s41599-019-0242-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/01/2019] [Indexed: 05/22/2023]
Abstract
Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.
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Affiliation(s)
- Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, 130 Dongan Road, Shanghai 200032, China
- Correspondence and requests for materials should be addressed to A.J.T () or S.L. ()
| | - Elisabeth zu Erbach-Schoenberg
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Carla Pezzulo
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Nick W Ruktanonchai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Alessandro Sorichetta
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Jessica Steele
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
| | - Tracey Li
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Claire A Dooley
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
| | - Andrew J Tatem
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Flowminder Foundation, SE-113 55 Stockholm, Sweden
- Correspondence and requests for materials should be addressed to A.J.T () or S.L. ()
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16
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Xu H, Vorderstrasse AA, McConnell ES, Dupre ME, Østbye T, Wu B. Migration and cognitive function: a conceptual framework for Global Health Research. Glob Health Res Policy 2018; 3:34. [PMID: 30519639 PMCID: PMC6267896 DOI: 10.1186/s41256-018-0088-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/01/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Migration is a fundamental demographic process that has been observed globally. It is suggested that migration is an issue of global health importance that can have an immediate and lasting impact on an individual's health and well-being. There is now an increasing body of evidence linking migration with cognitive function in older adults. In this paper, we synthesized the current evidence to develop a general conceptual framework to understand the factors contributing to the association between migration and cognitive function. METHODS A comprehensive review of the literature was conducted on the associations between migration and cognition among middle-aged and older adults. RESULTS Five potential mechanisms were identified from the literature: 1) socioeconomic status-including education, occupation, and income; 2) psychosocial factors-including social networks, social support, social stressors, and discrimination; 3) behavioral factors-including smoking, drinking, and health service utilization; 4) physical and psychological health status-including chronic conditions, physical function, and depression; and 5) environmental factors-including both physical and social environment. Several underlying factors were also identified-including early-life conditions, gender, and genetic factors. CONCLUSIONS The factors linking migration and cognitive function are multidimensional and complex. This conceptual framework highlights potential implications for global health policies and planning on healthy aging and migrant health. Additional studies are needed to further examine these mechanisms to extend and refine our general conceptual framework.
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Affiliation(s)
- Hanzhang Xu
- 1School of Nursing, Duke University, Durham, NC USA
- 2Department of Community and Family Medicine, Duke University, Durham, NC USA
| | | | - Eleanor S McConnell
- 1School of Nursing, Duke University, Durham, NC USA
- 4Geriatric Research, Education and Clinical Center, Durham Department of Veterans Affairs Healthcare System, Durham, NC USA
| | - Matthew E Dupre
- 5Department of Population Health Sciences, Duke University, Durham, NC USA
- 6Duke Clinical Research Institute, Duke University, Durham, NC USA
- 7Department of Sociology, Duke University, Durham, NC USA
| | - Truls Østbye
- 1School of Nursing, Duke University, Durham, NC USA
- 2Department of Community and Family Medicine, Duke University, Durham, NC USA
- 6Duke Clinical Research Institute, Duke University, Durham, NC USA
- 8Duke Global Health Institute, Duke University, Durham, NC USA
| | - Bei Wu
- 3New York University Rory Meyers College of Nursing, New York, NY USA
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17
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Nawrotzki RJ, DeWaard J. Putting trapped populations into place: Climate change and inter-district migration flows in Zambia. REGIONAL ENVIRONMENTAL CHANGE 2018; 18:533-546. [PMID: 29456454 PMCID: PMC5810408 DOI: 10.1007/s10113-017-1224-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Research shows that the association between adverse climate conditions and human migration is heterogeneous. One reason for this heterogeneity is the differential vulnerability of populations to climate change. This includes highly vulnerable, "trapped" populations that are too poor to migrate given deep and persistent poverty, the financial costs of migrating, and the erosion of already fragile economic livelihoods under climate change. Another reason for this heterogeneity is the differential vulnerability of places. However, despite the growing list of studies showing that the climate-migration relationship clearly varies across places, there is surprisingly little research on the characteristics of places themselves that trap, or immobilize, populations. Accordingly, we provide the first account of the "holding power" of places in the association between adverse climate conditions and migration flows among 55 districts in Zambia in 2000 and 2010. Methodologically, we combine high resolution climate information with aggregated census micro data to estimate gravity models of inter-district migration flows. Results reveal that the association between adverse climate conditions and migration is positive only for wealthy migrant-sending districts. In contrast, poor districts are characterized by climate-related immobility. Yet, our findings show that access to migrant networks enables climate related mobility in the poorest districts, suggesting a viable pathway to overcome mobility constraints. Planners and policy makers need to recognize the holding power of places that can trap populations and develop programs to support in situ adaptation and to facilitate migration to avoid humanitarian emergencies.
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Affiliation(s)
- Raphael J Nawrotzki
- University of Minnesota, Minnesota Population Center, 225 19th Avenue South, 50 Willey Hall, Minneapolis, MN 55455, U.S.A
| | - Jack DeWaard
- University of Minnesota, Department of Sociology, Minnesota Population Center, Institute on the Environment, 267 19th Avenue South, 909 Social Science Tower, Minneapolis, MN 55455, U.S.A
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18
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Leyk S, Runfola D, Nawrotzki RJ, Hunter LM, Riosmena F. Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data. POPULATION, SPACE AND PLACE 2017; 23:e2047. [PMID: 29170619 PMCID: PMC5695688 DOI: 10.1002/psp.2047] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on "environmental migration" in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005-2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.
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Affiliation(s)
- Stefan Leyk
- Department of Geography and CU Population Center (Institute of Behavioral Science), University of Colorado, Boulder, Boulder, CO, USA
| | - Dan Runfola
- Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA, USA
| | | | - Lori M. Hunter
- Department of Sociology and CU Population Center (Institute of Behavioral Science), University of Colorado, Boulder, Boulder, CO, USA
| | - Fernando Riosmena
- Department of Geography and CU Population Center (Institute of Behavioral Science), University of Colorado, Boulder, Boulder, CO, USA
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19
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Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis. Sci Rep 2017; 7:489. [PMID: 28352101 PMCID: PMC5428445 DOI: 10.1038/s41598-017-00493-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 02/27/2017] [Indexed: 11/09/2022] Open
Abstract
Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.
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20
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Sorichetta A, Bird TJ, Ruktanonchai NW, zu Erbach-Schoenberg E, Pezzulo C, Tejedor N, Waldock IC, Sadler JD, Garcia AJ, Sedda L, Tatem AJ. Mapping internal connectivity through human migration in malaria endemic countries. Sci Data 2016; 3:160066. [PMID: 27529469 PMCID: PMC5127488 DOI: 10.1038/sdata.2016.66] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 07/01/2016] [Indexed: 12/22/2022] Open
Abstract
Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.
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Affiliation(s)
- Alessandro Sorichetta
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
- Institute for Life Sciences, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
| | - Tom J. Bird
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
| | - Nick W. Ruktanonchai
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
| | - Elisabeth zu Erbach-Schoenberg
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
| | - Carla Pezzulo
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
| | - Natalia Tejedor
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
- GeoData, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
| | - Ian C. Waldock
- GeoData, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
| | - Jason D. Sadler
- GeoData, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
| | - Andres J. Garcia
- Bill and Melinda Gates Foundation, 440 5th Ave N., Seattle, Washington 98109, USA
| | - Luigi Sedda
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster LA1 4YG, UK
| | - Andrew J. Tatem
- WorldPop, Geography and Environment, University of Southampton, Highfield Campus, Southampton SO17 1BJ, UK
- Flowminder Foundation, Roslagsgatan 17, Stockholm SE-11355, Sweden
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Ruktanonchai NW, Bhavnani D, Sorichetta A, Bengtsson L, Carter KH, Córdoba RC, Le Menach A, Lu X, Wetter E, zu Erbach-Schoenberg E, Tatem AJ. Census-derived migration data as a tool for informing malaria elimination policy. Malar J 2016; 15:273. [PMID: 27169470 PMCID: PMC4864939 DOI: 10.1186/s12936-016-1315-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 04/27/2016] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Numerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale. METHODS Movement patterns exhibited in census-based migration data are compared with patterns exhibited in a mobile phone data set from Haiti to quantify how well migration data predict short-term movement patterns. Because short-term movement data were unavailable for Mesoamerica, a logistic regression model fit to migration data from three countries in Mesoamerica is used to predict flows of infected people between subnational administrative units throughout the region. RESULTS Population flows predicted using census-based migration data correlated strongly with mobile phone-derived movements when used as a measure of relative connectivity. Relative population flows are therefore predicted using census data across Mesoamerica, informing the areas that are likely exporters and importers of infected people. Relative population flows are used to identify community structure, useful for coordinating interventions and elimination efforts to minimize importation risk. Finally, the ability of census microdata inform future intervention planning is discussed in a country-specific setting using Costa Rica as an example. CONCLUSIONS These results show long-term migration data can effectively predict the relative flows of infected people to direct malaria elimination policy, a particularly relevant result because migration data are generally easier to obtain than short-term movement data such as mobile phone records. Further, predicted relative flows highlight policy-relevant population dynamics, such as major exporters across the region, and Nicaragua and Costa Rica's strong connection by movement of infected people, suggesting close coordination of their elimination efforts. Country-specific applications are discussed as well, such as predicting areas at relatively high risk of importation, which could inform surveillance and treatment strategies.
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Affiliation(s)
- Nick W. Ruktanonchai
- />WorldPop, Geography and Environment, University of Southampton, Southampton, SO17 1BJ UK
- />Flowminder Foundation, Stockholm, Sweden
| | | | - Alessandro Sorichetta
- />WorldPop, Geography and Environment, University of Southampton, Southampton, SO17 1BJ UK
- />Flowminder Foundation, Stockholm, Sweden
| | - Linus Bengtsson
- />WorldPop, Geography and Environment, University of Southampton, Southampton, SO17 1BJ UK
- />Flowminder Foundation, Stockholm, Sweden
- />Karolinska Institute, Stockholm, Sweden
| | - Keith H. Carter
- />Pan American Health Organization/World Health Organization, Washington, DC USA
| | - Roberto C. Córdoba
- />Department of Health Surveillance, Costa Rica Ministry of Health, San Jose, Costa Rica
| | | | - Xin Lu
- />Flowminder Foundation, Stockholm, Sweden
- />Karolinska Institute, Stockholm, Sweden
| | - Erik Wetter
- />Flowminder Foundation, Stockholm, Sweden
- />Stockholm School of Economics, Stockholm, Sweden
| | - Elisabeth zu Erbach-Schoenberg
- />WorldPop, Geography and Environment, University of Southampton, Southampton, SO17 1BJ UK
- />Flowminder Foundation, Stockholm, Sweden
| | - Andrew J. Tatem
- />WorldPop, Geography and Environment, University of Southampton, Southampton, SO17 1BJ UK
- />Flowminder Foundation, Stockholm, Sweden
- />Fogarty International Center, National Institutes of Health, Bethesda, MD USA
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Jia P, Sankoh O, Tatem AJ. Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network. Health Place 2015; 36:88-96. [DOI: 10.1016/j.healthplace.2015.09.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 09/18/2015] [Accepted: 09/27/2015] [Indexed: 01/20/2023]
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23
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Takahashi S, Metcalf CJE, Ferrari MJ, Moss WJ, Truelove SA, Tatem AJ, Grenfell BT, Lessler J. Reduced vaccination and the risk of measles and other childhood infections post-Ebola. Science 2015; 347:1240-2. [PMID: 25766232 DOI: 10.1126/science.aaa3438] [Citation(s) in RCA: 139] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
The Ebola epidemic in West Africa has caused substantial morbidity and mortality. The outbreak has also disrupted health care services, including childhood vaccinations, creating a second public health crisis. We project that after 6 to 18 months of disruptions, a large connected cluster of children unvaccinated for measles will accumulate across Guinea, Liberia, and Sierra Leone. This pool of susceptibility increases the expected size of a regional measles outbreak from 127,000 to 227,000 cases after 18 months, resulting in 2000 to 16,000 additional deaths (comparable to the numbers of Ebola deaths reported thus far). There is a clear path to avoiding outbreaks of childhood vaccine-preventable diseases once the threat of Ebola begins to recede: an aggressive regional vaccination campaign aimed at age groups left unprotected because of health care disruptions.
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Affiliation(s)
- Saki Takahashi
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | - C Jessica E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA
| | - Matthew J Ferrari
- Centre for Infectious Disease Dynamics, Pennsylvania State University, State College, PA 16801, USA
| | - William J Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Shaun A Truelove
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Southampton SO17 1BJ, UK. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA. Flowminder Foundation, 17177 Stockholm, Sweden
| | - Bryan T Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss DJ, Brady OJ, Kraemer MUG, Smith DL, Moyes CL, Bhatt S, Gething PW, Horby PW, Bogoch II, Brownstein JS, Mekaru SR, Tatem AJ, Khan K, Hay SI. Mapping the zoonotic niche of Ebola virus disease in Africa. eLife 2014; 3:e04395. [PMID: 25201877 PMCID: PMC4166725 DOI: 10.7554/elife.04395] [Citation(s) in RCA: 243] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 08/31/2014] [Indexed: 11/17/2022] Open
Abstract
Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976-2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.
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Affiliation(s)
- David M Pigott
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Nick Golding
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Adrian Mylne
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Zhi Huang
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Andrew J Henry
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Oliver J Brady
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Moritz UG Kraemer
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - David L Smith
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, United States
| | - Catherine L Moyes
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Samir Bhatt
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Peter W Gething
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Peter W Horby
- Epidemic Diseases Research Group, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Isaac I Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
- Divisions of Internal Medicine and Infectious Diseases, University Health Network, Toronto, Toronto, Canada
| | - John S Brownstein
- Department of Pediatrics, Harvard Medical School, Boston, United States
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States
| | - Sumiko R Mekaru
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States
| | - Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Flowminder Foundation, Stockholm, Sweden
| | - Kamran Khan
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Simon I Hay
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethseda, United States
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Pindolia DK, Garcia AJ, Huang Z, Smith DL, Alegana VA, Noor AM, Snow RW, Tatem AJ. The demographics of human and malaria movement and migration patterns in East Africa. Malar J 2013; 12:397. [PMID: 24191976 PMCID: PMC3829999 DOI: 10.1186/1475-2875-12-397] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 10/24/2013] [Indexed: 11/28/2022] Open
Abstract
Introduction The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations. Methods National population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined. Results Patterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20–30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10–20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use. Conclusion Census and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.
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Affiliation(s)
- Deepa K Pindolia
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
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