@article{10.1093/inthealth/ihv005,

title = {Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa},

author = {Luigi Sedda and Andrew J. Tatem and David W. Morley and Peter M. Atkinson and Nicola A. Wardrop and Carla Pezzulo and Alessandro Sorichetta and Joanna Kuleszo and David J. Rogers},

url = {https://doi.org/10.1093/inthealth/ihv005},

doi = {10.1093/inthealth/ihv005},

issn = {1876-3413},

year  = {2015},

date = {2015-01-01},

journal = {International Health},

volume = {7},

number = {2},

pages = {99-106},

abstract = {Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1073/pnas.1423542112,

title = {Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data},

author = {Amy Wesolowski and C. J. E. Metcalf and Nathan Eagle and Janeth Kombich and Bryan T. Grenfell and Ottar N. Bjørnstad and Justin Lessler and Andrew J. Tatem and Caroline O. Buckee},

url = {https://www.pnas.org/doi/abs/10.1073/pnas.1423542112},

doi = {10.1073/pnas.1423542112},

year  = {2015},

date = {2015-01-01},

journal = {Proceedings of the National Academy of Sciences},

volume = {112},

number = {35},

pages = {11114-11119},

abstract = {Changing patterns of human mobility can drive seasonal outbreaks of infectious diseases, but limited data about travel behavior and population flux over time have made this idea difficult to quantify. Mobile phone data provide a unique source of information about human travel. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population fluxes inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates, showing for the first time to our knowledge that mobile phone data capture epidemiologically relevant patterns of movement. Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JIA201588,

title = {Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network},

author = {Peng Jia and Osman Sankoh and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S1353829215001379},

doi = {https://doi.org/10.1016/j.healthplace.2015.09.009},

issn = {1353-8292},

year  = {2015},

date = {2015-01-01},

journal = {Health & Place},

volume = {36},

pages = {88-96},

abstract = {The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.},

keywords = {Africa, Asia, Demographic surveillance sites, Health, Remote sensing},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1098/rspb.2014.2878,

title = {Virus evolution and transmission in an ever more connected world},

author = {Oliver G. Pybus and Andrew J. Tatem and Philippe Lemey},

url = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2014.2878},

doi = {10.1098/rspb.2014.2878},

year  = {2015},

date = {2015-01-01},

journal = {Proceedings of the Royal Society B: Biological Sciences},

volume = {282},

number = {1821},

pages = {20142878},

abstract = {The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1080/17538947.2014.965761,

title = {Exploring nationally and regionally defined models for large area population mapping},

author = {A. E. Gaughan and F. R. Stevens and C. Linard and N. N. Patel and A. J. Tatem},

url = {https://doi.org/10.1080/17538947.2014.965761},

doi = {10.1080/17538947.2014.965761},

year  = {2015},

date = {2015-01-01},

journal = {International Journal of Digital Earth},

volume = {8},

number = {12},

pages = {989-1006},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015,

title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},

author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},

doi = {10.1017/S0950268814001988},

year  = {2015},

date = {2015-01-01},

journal = {Epidemiology and Infection},

volume = {143},

number = {7},

pages = {1457–1466},

publisher = {Cambridge University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PATEL2015199,

title = {Multitemporal settlement and population mapping from Landsat using Google Earth Engine},

author = {Nirav N. Patel and Emanuele Angiuli and Paolo Gamba and Andrea Gaughan and Gianni Lisini and Forrest R. Stevens and Andrew J. Tatem and Giovanna Trianni},

url = {https://www.sciencedirect.com/science/article/pii/S0303243414001998},

doi = {https://doi.org/10.1016/j.jag.2014.09.005},

issn = {0303-2434},

year  = {2015},

date = {2015-01-01},

journal = {International Journal of Applied Earth Observation and Geoinformation},

volume = {35},

pages = {199-208},

abstract = {As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.},

keywords = {Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization},

pubstate = {published},

tppubtype = {article}

}

@article{MERTES2015331,

title = {Detecting change in urban areas at continental scales with MODIS data},

author = {C. M. Mertes and A. Schneider and D. Sulla-Menashe and A. J. Tatem and B. Tan},

url = {https://www.sciencedirect.com/science/article/pii/S003442571400368X},

doi = {https://doi.org/10.1016/j.rse.2014.09.023},

issn = {0034-4257},

year  = {2015},

date = {2015-01-01},

journal = {Remote Sensing of Environment},

volume = {158},

pages = {331-347},

abstract = {Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.},

keywords = {Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization},

pubstate = {published},

tppubtype = {article}

}

@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015b,

title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},

author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},

doi = {10.1017/S0950268814001988},

year  = {2015},

date = {2015-01-01},

journal = {Epidemiology and Infection},

volume = {143},

number = {7},

pages = {1457–1466},

publisher = {Cambridge University Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mapping the zoonotic niche of Ebola virus disease in Africa},

author = {David M Pigott and Nick Golding and Adrian Mylne and Zhi Huang and Andrew J Henry and Daniel J Weiss and Oliver J Brady and Moritz U G Kraemer and David L Smith and Catherine L Moyes and Samir Bhatt and Peter W Gething and Peter W Horby and Isaac I Bogoch and John S Brownstein and Sumiko R Mekaru and Andrew J Tatem and Kamran Khan and Simon I Hay},

url = {https://doi.org/10.7554/eLife.04395},

doi = {10.7554/eLife.04395},

year  = {2014},

date = {2014-09-19},

journal = {eLife},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gilbert2014,

title = {Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia},

author = {Marius Gilbert and Nick Golding and Hang Zhou and G. R. William Wint and Timothy P. Robinson and Andrew J. Tatem and Shengjie Lai and Sheng Zhou and Hui Jiang and Danhuai Guo and Zhi Huang and Jane P. Messina and Xiangming Xiao and Catherine Linard and Thomas P. Van Boeckel and Vincent Martin and Samir Bhatt and Peter W. Gething and Jeremy J. Farrar and Simon I. Hay and Hongjie Yu},

url = {https://doi.org/10.1038/ncomms5116},

doi = {10.1038/ncomms5116},

issn = {2041-1723},

year  = {2014},

date = {2014-06-17},

journal = {Nature Communications},

volume = {5},

number = {1},

pages = {4116},

abstract = {Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pindolia2014,

title = {Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination},

author = {Deepa K. Pindolia and Andres J. Garcia and Zhuojie Huang and Timothy Fik and David L. Smith and Andrew J. Tatem},

url = {https://doi.org/10.1186/1475-2875-13-169},

doi = {10.1186/1475-2875-13-169},

issn = {1475-2875},

year  = {2014},

date = {2014-05-03},

journal = {Malaria Journal},

volume = {13},

number = {1},

pages = {169},

abstract = {Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain `hotspots' of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2014b,

title = {Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning},

author = {Andrew J. Tatem and Zhuojie Huang and Clothilde Narib and Udayan Kumar and Deepika Kandula and Deepa K. Pindolia and David L. Smith and Justin M. Cohen and Bonita Graupe and Petrina Uusiku and Christopher Lourenço},

url = {https://doi.org/10.1186/1475-2875-13-52},

doi = {10.1186/1475-2875-13-52},

issn = {1475-2875},

year  = {2014},

date = {2014-02-10},

journal = {Malaria Journal},

volume = {13},

number = {1},

pages = {52},

abstract = {As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2014c,

title = {Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning},

author = {Andrew J. Tatem and Zhuojie Huang and Clothilde Narib and Udayan Kumar and Deepika Kandula and Deepa K. Pindolia and David L. Smith and Justin M. Cohen and Bonita Graupe and Petrina Uusiku and Christopher Lourenço},

url = {https://doi.org/10.1186/1475-2875-13-52},

doi = {10.1186/1475-2875-13-52},

issn = {1475-2875},

year  = {2014},

date = {2014-02-10},

journal = {Malaria Journal},

volume = {13},

number = {1},

pages = {52},

abstract = {As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2014,

title = {Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births},

author = {Andrew J. Tatem and James Campbell and Maria Guerra-Arias and Luc Bernis and Allisyn Moran and Zo"e Matthews},

url = {https://doi.org/10.1186/1476-072X-13-2},

doi = {10.1186/1476-072X-13-2},

issn = {1476-072X},

year  = {2014},

date = {2014-01-04},

journal = {International Journal of Health Geographics},

volume = {13},

number = {1},

pages = {2},

abstract = {The health and survival of women and their new-born babies in low income countries has been a key priority in public health since the 1990s. However, basic planning data, such as numbers of pregnancies and births, remain difficult to obtain and information is also lacking on geographic access to key services, such as facilities with skilled health workers. For maternal and newborn health and survival, planning for safer births and healthier newborns could be improved by more accurate estimations of the distributions of women of childbearing age. Moreover, subnational estimates of projected future numbers of pregnancies are needed for more effective strategies on human resources and infrastructure, while there is a need to link information on pregnancies to better information on health facilities in districts and regions so that coverage of services can be assessed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/inthealth/ihu006,

title = {Mapping population and pathogen movements},

author = {Andrew J. Tatem},

url = {https://doi.org/10.1093/inthealth/ihu006},

doi = {10.1093/inthealth/ihu006},

issn = {1876-3413},

year  = {2014},

date = {2014-01-01},

journal = {International Health},

volume = {6},

number = {1},

pages = {5-11},

abstract = {For most of human history, populations have been relatively isolated from each other, and only recently has there been extensive contact between peoples, flora and fauna from both old and new worlds. The reach, volume and speed of modern travel are unprecedented, with human mobility increasing in high income countries by over 1000-fold since 1800. This growth is putting people at risk from the emergence of new strains of familiar diseases, and from completely new diseases, while ever more cases of the movement of both disease vectors and the diseases they carry are being seen. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Equally however, we now have access to the most detailed and comprehensive datasets on human mobility and pathogen distributions ever assembled, in order to combat these threats. This short review paper provides an overview of these datasets, with a particular focus on low income regions, and covers briefly approaches used to combine them to help us understand and control some of the negative effects of population and pathogen movements.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/migration/mnu036,

title = {Modeling internal migration flows in sub-Saharan Africa using census microdata},

author = {Andres J. Garcia and Deepa K. Pindolia and Kenneth K. Lopiano and Andrew J. Tatem},

url = {https://doi.org/10.1093/migration/mnu036},

doi = {10.1093/migration/mnu036},

issn = {2049-5838},

year  = {2014},

date = {2014-01-01},

journal = {Migration Studies},

volume = {3},

number = {1},

pages = {89-110},

abstract = {Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country migration with correlations of up to 0.91, and can also predict migration in other countries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1098/rsif.2014.0642,

title = {Theory and data for simulating fine-scale human movement in an urban environment},

author = {T. Alex Perkins and Andres J. Garcia and Valerie A. Paz-Soldán and Steven T. Stoddard and Robert C. Reiner and Gonzalo Vazquez-Prokopec and Donal Bisanzio and Amy C. Morrison and Eric S. Halsey and Tadeusz J. Kochel and David L. Smith and Uriel Kitron and Thomas W. Scott and Andrew J. Tatem},

url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2014.0642},

doi = {10.1098/rsif.2014.0642},

year  = {2014},

date = {2014-01-01},

journal = {Journal of The Royal Society Interface},

volume = {11},

number = {99},

pages = {20140642},

abstract = {Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1073/pnas.1408439111,

title = {Dynamic population mapping using mobile phone data},

author = {Pierre Deville and Catherine Linard and Samuel Martin and Marius Gilbert and Forrest R. Stevens and Andrea E. Gaughan and Vincent D. Blondel and Andrew J. Tatem},

url = {https://www.pnas.org/doi/abs/10.1073/pnas.1408439111},

doi = {10.1073/pnas.1408439111},

year  = {2014},

date = {2014-01-01},

journal = {Proceedings of the National Academy of Sciences},

volume = {111},

number = {45},

pages = {15888-15893},

abstract = {Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. This study demonstrates how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. The methods outlined may be applied to estimate human population densities in low-income countries where data on population distributions may be scarce, outdated, and unreliable, or to estimate temporal variations in population density. The work highlights how facilitating access to anonymized mobile phone data might enable fast and cheap production of population maps in emergency and data-scarce situations. During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PIEL2014e80,

title = {Global migration and the changing distribution of sickle haemoglobin: a quantitative study of temporal trends between 1960 and 2000},

author = {Frédéric B Piel and Andrew J Tatem and Zhuojie Huang and Sunetra Gupta and Thomas N Williams and David J Weatherall},

url = {https://www.sciencedirect.com/science/article/pii/S2214109X13701505},

doi = {https://doi.org/10.1016/S2214-109X(13)70150-5},

issn = {2214-109X},

year  = {2014},

date = {2014-01-01},

journal = {The Lancet Global Health},

volume = {2},

number = {2},

pages = {e80-e89},

abstract = {Summary 

Background 

Changes in the geographical distribution of genetic disorders are often thought to happen slowly, especially when compared with infectious diseases. Whereas mutations, genetic drift, and natural selection take place over many generations, epidemics can spread through large populations within a few days or weeks. Nevertheless, population movements can interfere with these processes, and few studies have been done of their effect on genetic disorders. We aimed to investigate the effect of global migration on the distribution of the sickle-cell gene—the most common and clinically significant haemoglobin structural variant. 

Methods 

For each country, we extracted data from the World Bank's Global Bilateral Migration Database about international human migrations between 1960 and 2000. We combined this information with evidence-based estimates of national HbS allele frequencies, generated within a Bayesian geostatistical framework, to analyse temporal changes in the net numbers of migrants, and classified countries with an index summarising these temporal trends. 

Findings 

The number of international migrants increased from 92·6 million in 1960, to 165·2 million in 2000. The estimated global number of migrants with HbS increased from about 1·6 million in 1960, to 3·6 million in 2000. This increase was largely due to an increase in the number of migrants from countries with HbS allele frequencies higher than 10%, from 3·1 million in 1960, to 14·2 million in 2000. Additionally, the mean number of countries of origin for each destination country increased from 70 (SE 46) in 1960, to 98 (48) in 2000, showing an increasing diversity in the network of international migrations between countries. Our index of change map shows a patchy distribution of the magnitude of temporal changes, with the highest positive and negative values scattered across all continents. 

Interpretation 

Global human population movements have had a substantial effect on the distribution of the HbS gene. Population movements can create a long-term burden on health-care systems. Our findings, which emphasise countries in which migration fluxes are changing the most, should increase awareness about the global burden of haemoglobinopathies and encourage policy makers to implement specific public health interventions, such as screening programmes and genetic counselling. 

Funding 

Wellcome Trust, European Research Council, Bill & Melinda Gates Foundation, National Institute of Allergy and Infectious Diseases–National Institutes of Health, the Research and Policy for Infectious Disease Dynamics program, Fogarty International Center.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}