@article{Linard2012,

title = {Large-scale spatial population databases in infectious disease research},

author = {Catherine Linard and Andrew J. Tatem},

url = {https://doi.org/10.1186/1476-072X-11-7},

doi = {10.1186/1476-072X-11-7},

issn = {1476-072X},

year  = {2012},

date = {2012-03-20},

journal = {International Journal of Health Geographics},

volume = {11},

number = {1},

pages = {7},

abstract = {Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1126/science.1229045,

title = {Optimizing Investments in Malaria Treatment and Diagnosis},

author = {Justin M. Cohen and Aaron M. Woolsey and Oliver J. Sabot and Peter W. Gething and Andrew J. Tatem and Bruno Moonen},

url = {https://www.science.org/doi/abs/10.1126/science.1229045},

doi = {10.1126/science.1229045},

year  = {2012},

date = {2012-01-01},

journal = {Science},

volume = {338},

number = {6107},

pages = {612-614},

abstract = {Better targeting of antimalarials to people who need them will maximize the impact of interventions in the private sector. The Roll Back Malaria (RBM) Partnership has set an ambitious target of achieving near zero deaths from malaria by 2015 (1). Scale-up of insecticide-treated nets, indoor residual spraying of insecticide, and increased access to treatment with artemisinin-based combination therapies (ACTs) over the past decade have led to reductions in malaria incidence of more than 50% in 43 countries, including 8 in Africa (2). However, as an estimated 655,000 malaria deaths still occurred in 2010 (2), with the great majority in sub-Saharan Africa, substantial challenges remain.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1126/science.1223467,

title = {Quantifying the Impact of Human Mobility on Malaria},

author = {Amy Wesolowski and Nathan Eagle and Andrew J. Tatem and David L. Smith and Abdisalan M. Noor and Robert W. Snow and Caroline O. Buckee},

url = {https://www.science.org/doi/abs/10.1126/science.1223467},

doi = {10.1126/science.1223467},

year  = {2012},

date = {2012-01-01},

journal = {Science},

volume = {338},

number = {6104},

pages = {267-270},

abstract = {An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital. Geographical information in mobile phone records for 15 million Kenyans is linked to malaria prevalence estimates. Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pone.0031743,

title = {Population Distribution, Settlement Patterns and Accessibility across Africa in 2010},

author = {Catherine Linard and Marius Gilbert and Robert W. Snow and Abdisalan M. Noor and Andrew J. Tatem},

url = {https://doi.org/10.1371/journal.pone.0031743},

doi = {10.1371/journal.pone.0031743},

year  = {2012},

date = {2012-01-01},

journal = {PLOS ONE},

volume = {7},

number = {2},

pages = {1-8},

publisher = {Public Library of Science},

abstract = {The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Linard2011,

title = {Assessing the use of global land cover data for guiding large area population distribution modelling},

author = {Catherine Linard and Marius Gilbert and Andrew J. Tatem},

url = {https://doi.org/10.1007/s10708-010-9364-8},

doi = {10.1007/s10708-010-9364-8},

issn = {1572-9893},

year  = {2011},

date = {2011-10-01},

journal = {GeoJournal},

volume = {76},

number = {5},

pages = {525-538},

abstract = {Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2011b,

title = {Population mapping of poor countries},

author = {Andrew Tatem and Catherine Linard},

url = {https://doi.org/10.1038/474036d},

doi = {10.1038/474036d},

issn = {1476-4687},

year  = {2011},

date = {2011-06-01},

journal = {Nature},

volume = {474},

number = {7349},

pages = {36-36},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2011,

title = {The effects of spatial population dataset choice on estimates of population at risk of disease},

author = {Andrew J. Tatem and Nicholas Campiz and Peter W. Gething and Robert W. Snow and Catherine Linard},

url = {https://doi.org/10.1186/1478-7954-9-4},

doi = {10.1186/1478-7954-9-4},

issn = {1478-7954},

year  = {2011},

date = {2011-02-07},

journal = {Population Health Metrics},

volume = {9},

number = {1},

pages = {4},

abstract = {The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bharti2011explaining,

title = {Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery},

author = {Nita Bharti and Andrew J Tatem and Matthew J Ferrari and Rebecca F Grais and Ali Djibo and Bryan T Grenfell},

url = {https://doi.org/10.1126/science.1210554},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

journal = {Science},

volume = {334},

number = {6061},

pages = {1424–1427},

publisher = {American Association for the Advancement of Science},

abstract = {Measles epidemics in West Africa cause a significant proportion of vaccine-preventable childhood mortality. Epidemics are strongly seasonal, but the drivers of these fluctuations are poorly understood, which limits the predictability of outbreaks and the dynamic response to immunization. We show that measles seasonality can be explained by spatiotemporal changes in population density, which we measure by quantifying anthropogenic light from satellite imagery. We find that measles transmission and population density are highly correlated for three cities in Niger. With dynamic epidemic models, we demonstrate that measures of population density are essential for predicting epidemic progression at the city level and improving intervention strategies. In addition to epidemiological applications, the ability to measure fine-scale changes in population density has implications for public health, crisis management, and economic development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Linard2010,

title = {A high resolution spatial population database of Somalia for disease risk mapping},

author = {Catherine Linard and Victor A. Alegana and Abdisalan M. Noor and Robert W. Snow and Andrew J. Tatem},

url = {https://doi.org/10.1186/1476-072X-9-45},

doi = {10.1186/1476-072X-9-45},

issn = {1476-072X},

year  = {2010},

date = {2010-09-14},

journal = {International Journal of Health Geographics},

volume = {9},

number = {1},

pages = {45},

abstract = {Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tatem2010ranking,

title = {Ranking of elimination feasibility between malaria-endemic countries},

author = {Andrew J Tatem and David L Smith and Peter W Gething and Caroline W Kabaria and Robert W Snow and Simon I Hay},

doi = {10.1016/S0140-6736(10)61301-3},

year  = {2010},

date = {2010-01-01},

urldate = {2010-01-01},

journal = {The Lancet},

volume = {376},

number = {9752},

pages = {1579–1591},

publisher = {Elsevier},

abstract = {Experience gained from the Global Malaria Eradication Program (1955–72) identified a set of shared technical and operational factors that enabled some countries to successfully eliminate malaria. Spatial data for these factors were assembled for all malaria-endemic countries and combined to provide an objective, relative ranking of countries by technical, operational, and combined elimination feasibility. The analysis was done separately for Plasmodium falciparum and Plasmodium vivax, and the limitations of the approach were discussed. The relative rankings suggested that malaria elimination would be most feasible in countries in the Americas and Asia, and least feasible in countries in central and west Africa. The results differed when feasibility was measured by technical or operational factors, highlighting the different types of challenge faced by each country. The results are not intended to be prescriptive, predictive, or to provide absolute assessments of feasibility, but they do show that spatial information is available to facilitate evidence-based assessments of the relative feasibility of malaria elimination by country that can be rapidly updated.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1073/pnas.1002971107,

title = {International population movements and regional \textit{Plasmodium falciparum} malaria elimination strategies},

author = {Andrew J. Tatem and David L. Smith},

url = {https://www.pnas.org/doi/abs/10.1073/pnas.1002971107},

doi = {10.1073/pnas.1002971107},

year  = {2010},

date = {2010-01-01},

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

volume = {107},

number = {27},

pages = {12222-12227},

abstract = {Calls for the eradication of malaria require the development of global and regional strategies based on a strong and consistent evidence base. Evidence from the previous global malaria eradication program and more recent transborder control campaigns have shown the importance of accounting for human movement in introducing infections to areas targeted for elimination. Here, census-based migration data were analyzed with network analysis tools, Plasmodium falciparum malaria transmission maps, and global population databases to map globally communities of countries linked by relatively high levels of infection movements. The likely principal sources and destinations of imported cases in each region were also mapped. Results indicate that certain groups of countries, such as those in West Africa and central Asia are much more strongly connected by relatively high levels of population and infection movement than others. In contrast, countries such as Ethiopia and Myanmar display significantly greater isolation in terms of likely infection movements in and out. The mapping here of both communities of countries linked by likely higher levels of infection movement, and “natural” migration boundaries that display reduced movement of people and infections between regions has practical utility. These maps can inform the design of malaria elimination strategies by identifying regional communities of countries afforded protection from recolonization by surrounding regions of reduced migration. For more isolated countries, a nationally focused control or elimination program is likely to stand a better chance of success than those receiving high levels of visitors and migrants from high-transmission regions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2008,

title = {Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity},

author = {Andrew J. Tatem and Carlos A. Guerra and Caroline W. Kabaria and Abdisalan M. Noor and Simon I. Hay},

url = {https://doi.org/10.1186/1475-2875-7-218},

doi = {10.1186/1475-2875-7-218},

issn = {1475-2875},

year  = {2008},

date = {2008-10-27},

journal = {Malaria Journal},

volume = {7},

number = {1},

pages = {218},

abstract = {The efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (Pf PR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pone.0001298,

title = {High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa},

author = {Andrew J. Tatem and Abdisalan M. Noor and Craig Hagen and Antonio Di Gregorio and Simon I. Hay},

url = {https://doi.org/10.1371/journal.pone.0001298},

doi = {10.1371/journal.pone.0001298},

year  = {2007},

date = {2007-01-01},

journal = {PLOS ONE},

volume = {2},

number = {12},

pages = {1-8},

publisher = {Public Library of Science},

abstract = {BackgroundBetween 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.Methodology/Principal FindingsWe examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.ConclusionsWe find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/j.1365-3156.2005.01487.x,

title = {The accuracy of human population maps for public health application},

author = {S. I. Hay and A. M. Noor and A. Nelson and A. J. Tatem},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-3156.2005.01487.x},

doi = {https://doi.org/10.1111/j.1365-3156.2005.01487.x},

year  = {2005},

date = {2005-01-01},

journal = {Tropical Medicine & International Health},

volume = {10},

number = {10},

pages = {1073-1086},

abstract = {Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.},

keywords = {areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation},

pubstate = {published},

tppubtype = {article}

}

@article{TATEM200587,

title = {Assessing the accuracy of satellite derived global and national urban maps in Kenya},

author = {A. J. Tatem and A. M. Noor and S. I. Hay},

url = {https://www.sciencedirect.com/science/article/pii/S0034425705000702},

doi = {https://doi.org/10.1016/j.rse.2005.02.001},

issn = {0034-4257},

year  = {2005},

date = {2005-01-01},

journal = {Remote Sensing of Environment},

volume = {96},

number = {1},

pages = {87-97},

abstract = {Ninety percent of projected global urbanization will be concentrated in low income countries. This will have considerable environmental, economic and public health implications for those populations. Objective and efficient methods of delineating urban extent are a cross-sectoral need complicated by a diversity of urban definition rubrics world-wide. Large-area maps of urban extents are becoming increasingly available in the public domain, as are a wide-range of medium spatial resolution satellite imagery. Here we describe the extension of a methodology based on Landsat ETM and Radarsat imagery to the production of a human settlement map of Kenya. This map was then compared with five satellite imagery-derived, global maps of urban extent at Kenya national-level, against an expert opinion coverage for accuracy assessment. The results showed the map produced using medium spatial resolution satellite imagery was of comparable accuracy to the expert opinion coverage. The five global urban maps exhibited a range of inaccuracies, emphasising that care should be taken with use of these maps at national and sub-national scale.},

keywords = {Accuracy assessment, Urban area mapping, Urbanization},

pubstate = {published},

tppubtype = {article}

}

@article{Tatem2004,

title = {Measuring urbanization pattern and extent for malaria research: A review of remote sensing approaches},

author = {A. J. Tatem and S. I. Hay},

url = {https://doi.org/10.1093/jurban/jth124},

doi = {10.1093/jurban/jth124},

issn = {1468-2869},

year  = {2004},

date = {2004-09-01},

journal = {Journal of Urban Health},

volume = {81},

number = {3},

pages = {363-376},

abstract = {Within the next 30 years, the proportion of urban dwellers will rise from under half to two thirds of the world's population. Such a shift will entail massive public health consequences, and most of this urban transition will occur in low-income regions of the world. Urban populations face very different health risks compared to those in rural areas, particularly in terms of malaria. To target effective and relevant public health interventions, the need for clear, consistent definitions of what determines urban areas and urban communities is paramount. Decision makers are increasingly seeing remote sensing as a cost-effective solution to monitoring urbanization at a range of spatial scales. This review focuses on the progress made within the field of remote sensing on mapping, monitoring, and modeling urban environments and examines existing challenges, drawbacks, and future prospects. We conclude by exploring' some of the particular relevance of these issues to malaria and note that they are of more general relevance to all those interested in urban public health.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{TATEM200442,

title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},

author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},

url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},

doi = {https://doi.org/10.1016/j.rse.2004.06.014},

issn = {0034-4257},

year  = {2004},

date = {2004-01-01},

journal = {Remote Sensing of Environment},

volume = {93},

number = {1},

pages = {42-52},

abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},

keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},

pubstate = {published},

tppubtype = {article}

}