Publications
221.
Utazi, C Edson; Sahu, Sujit K; Atkinson, Peter M; Tejedor-Garavito, Natalia; Lloyd, Christopher T; Tatem, Andrew J
Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa Journal Article
In: BMJ Global Health, vol. 3, no. 2, 2018.
Abstract | Links | BibTeX | Tags:
@article{Utazie000611,
title = {Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa},
author = {C Edson Utazi and Sujit K Sahu and Peter M Atkinson and Natalia Tejedor-Garavito and Christopher T Lloyd and Andrew J Tatem},
url = {https://gh.bmj.com/content/3/2/e000611},
doi = {10.1136/bmjgh-2017-000611},
year = {2018},
date = {2018-01-01},
journal = {BMJ Global Health},
volume = {3},
number = {2},
publisher = {BMJ Specialist Journals},
abstract = {A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.
222.
Juran, Sabrina; Broer, P. Niclas; Klug, Stefanie J.; Snow, Rachel C.; Okiro, Emelda A.; Ouma, Paul O.; Snow, Robert W.; Tatem, Andrew J.; Meara, John G.; Alegana, Victor A.
Geospatial mapping of access to timely essential surgery in sub-Saharan Africa Journal Article
In: BMJ Global Health, vol. 3, no. 4, 2018.
Abstract | Links | BibTeX | Tags:
@article{Jurane000875,
title = {Geospatial mapping of access to timely essential surgery in sub-Saharan Africa},
author = {Sabrina Juran and P. Niclas Broer and Stefanie J. Klug and Rachel C. Snow and Emelda A. Okiro and Paul O. Ouma and Robert W. Snow and Andrew J. Tatem and John G. Meara and Victor A. Alegana},
url = {https://gh.bmj.com/content/3/4/e000875},
doi = {10.1136/bmjgh-2018-000875},
year = {2018},
date = {2018-01-01},
journal = {BMJ Global Health},
volume = {3},
number = {4},
publisher = {BMJ Specialist Journals},
abstract = {Introduction Despite an estimated one-third of the global burden of disease being surgical, only limited estimates of accessibility to surgical treatment in sub-Saharan Africa exist and these remain spatially undefined. Geographical metrics of access to major hospitals were estimated based on travel time. Estimates were then used to assess need for surgery at country level.Methods Major district and regional hospitals were assumed to have capability to perform bellwether procedures. Geographical locations of hospitals in relation to the population in the 47 sub-Saharan countries were combined with spatial ancillary data on roads, elevation, land use or land cover to estimate travel-time metrics of 30 min, 1 hour and 2 hours. Hospital catchment was defined as population residing in areas less than 2 hours of travel time to the next major hospital. Travel-time metrics were combined with fine-scale population maps to define burden of surgery at hospital catchment level.Results Overall, the majority of the population (92.5%) in sub-Saharan Africa reside in areas within 2 hours of a major hospital catchment defined based on spatially defined travel times. The burden of surgery in all-age population was 257.8 million to 294.7 million people and was highest in high-population density countries and lowest in sparsely populated or smaller countries. The estimated burden in children <15 years was 115.3 million to 131.8 million and had similar spatial distribution to the all-age pattern.Conclusion The study provides an assessment of accessibility and burden of surgical disease in sub-Saharan Africa. Yet given the optimistic assumption of adequare surgical capability of major hospitals, the true burden of surgical disease is expected to be much greater. In-depth health facility assessments are needed to define infrastructure, personnel and medicine supply for delivering timely and safe affordable surgery to further inform the analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Introduction Despite an estimated one-third of the global burden of disease being surgical, only limited estimates of accessibility to surgical treatment in sub-Saharan Africa exist and these remain spatially undefined. Geographical metrics of access to major hospitals were estimated based on travel time. Estimates were then used to assess need for surgery at country level.Methods Major district and regional hospitals were assumed to have capability to perform bellwether procedures. Geographical locations of hospitals in relation to the population in the 47 sub-Saharan countries were combined with spatial ancillary data on roads, elevation, land use or land cover to estimate travel-time metrics of 30 min, 1 hour and 2 hours. Hospital catchment was defined as population residing in areas less than 2 hours of travel time to the next major hospital. Travel-time metrics were combined with fine-scale population maps to define burden of surgery at hospital catchment level.Results Overall, the majority of the population (92.5%) in sub-Saharan Africa reside in areas within 2 hours of a major hospital catchment defined based on spatially defined travel times. The burden of surgery in all-age population was 257.8 million to 294.7 million people and was highest in high-population density countries and lowest in sparsely populated or smaller countries. The estimated burden in children <15 years was 115.3 million to 131.8 million and had similar spatial distribution to the all-age pattern.Conclusion The study provides an assessment of accessibility and burden of surgical disease in sub-Saharan Africa. Yet given the optimistic assumption of adequare surgical capability of major hospitals, the true burden of surgical disease is expected to be much greater. In-depth health facility assessments are needed to define infrastructure, personnel and medicine supply for delivering timely and safe affordable surgery to further inform the analysis.
223.
Weber, Eric M.; Seaman, Vincent Y.; Stewart, Robert N.; Bird, Tomas J.; Tatem, Andrew J.; McKee, Jacob J.; Bhaduri, Budhendra L.; Moehl, Jessica J.; Reith, Andrew E.
Census-independent population mapping in northern Nigeria Journal Article
In: Remote Sensing of Environment, vol. 204, pp. 786-798, 2018, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: Demographics, Nigeria, Polio, Population, Settlement mapping
@article{WEBER2018786,
title = {Census-independent population mapping in northern Nigeria},
author = {Eric M. Weber and Vincent Y. Seaman and Robert N. Stewart and Tomas J. Bird and Andrew J. Tatem and Jacob J. McKee and Budhendra L. Bhaduri and Jessica J. Moehl and Andrew E. Reith},
url = {https://www.sciencedirect.com/science/article/pii/S0034425717304364},
doi = {https://doi.org/10.1016/j.rse.2017.09.024},
issn = {0034-4257},
year = {2018},
date = {2018-01-01},
journal = {Remote Sensing of Environment},
volume = {204},
pages = {786-798},
abstract = {Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.},
keywords = {Demographics, Nigeria, Polio, Population, Settlement mapping},
pubstate = {published},
tppubtype = {article}
}
Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.
224.
Wesolowski, Amy; Erbach-Schoenberg, Elisabeth; Tatem, Andrew J.; Lourenço, Christopher; Viboud, Cecile; Charu, Vivek; Eagle, Nathan; Engø-Monsen, Kenth; Qureshi, Taimur; Buckee, Caroline O.; Metcalf, C. J. E.
Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics Journal Article
In: Nature Communications, vol. 8, no. 1, pp. 2069, 2017, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@article{Wesolowski2017,
title = {Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics},
author = {Amy Wesolowski and Elisabeth Erbach-Schoenberg and Andrew J. Tatem and Christopher Lourenço and Cecile Viboud and Vivek Charu and Nathan Eagle and Kenth Engø-Monsen and Taimur Qureshi and Caroline O. Buckee and C. J. E. Metcalf},
url = {https://doi.org/10.1038/s41467-017-02064-4},
doi = {10.1038/s41467-017-02064-4},
issn = {2041-1723},
year = {2017},
date = {2017-12-12},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {2069},
abstract = {Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.
225.
Tejedor‐Garavito, Natalia; Dlamini, Nomcebo; Pindolia, Deepa; Soble, Adam; Ruktanonchai, Nick W.; Alegana, Victor; Menach, Arnaud Le; Ntshalintshali, Nyasatu; Dlamini, Bongani; Smith, David L.; Tatem, Andrew J.; Kunene, Simon
Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010--2014 Journal Article
In: Malaria Journal, vol. 16, no. 1, pp. 359, 2017, ISSN: 1475-2875.
Abstract | Links | BibTeX | Tags:
@article{Tejedor‐Garavito2017,
title = {Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010--2014},
author = {Natalia Tejedor‐Garavito and Nomcebo Dlamini and Deepa Pindolia and Adam Soble and Nick W. Ruktanonchai and Victor Alegana and Arnaud Le Menach and Nyasatu Ntshalintshali and Bongani Dlamini and David L. Smith and Andrew J. Tatem and Simon Kunene},
url = {https://doi.org/10.1186/s12936-017-2004-8},
doi = {10.1186/s12936-017-2004-8},
issn = {1475-2875},
year = {2017},
date = {2017-09-08},
journal = {Malaria Journal},
volume = {16},
number = {1},
pages = {359},
abstract = {As Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.
226.
Pezzulo, Carla; Hornby, Graeme M.; Sorichetta, Alessandro; Gaughan, Andrea E.; Linard, Catherine; Bird, Tomas J.; Kerr, David; Lloyd, Christopher T.; Tatem, Andrew J.
Sub-national mapping of population pyramids and dependency ratios in Africa and Asia Journal Article
In: Scientific Data, vol. 4, no. 1, pp. 170089, 2017, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Pezzulo2017,
title = {Sub-national mapping of population pyramids and dependency ratios in Africa and Asia},
author = {Carla Pezzulo and Graeme M. Hornby and Alessandro Sorichetta and Andrea E. Gaughan and Catherine Linard and Tomas J. Bird and David Kerr and Christopher T. Lloyd and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.89},
doi = {10.1038/sdata.2017.89},
issn = {2052-4463},
year = {2017},
date = {2017-07-19},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170089},
abstract = {The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.
227.
Thomson, Dana R.; Stevens, Forrest R.; Ruktanonchai, Nick W.; Tatem, Andrew J.; Castro, Marcia C.
GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data Journal Article
In: International Journal of Health Geographics, vol. 16, no. 1, pp. 25, 2017, ISSN: 1476-072X.
Abstract | Links | BibTeX | Tags:
@article{Thomson2017,
title = {GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data},
author = {Dana R. Thomson and Forrest R. Stevens and Nick W. Ruktanonchai and Andrew J. Tatem and Marcia C. Castro},
url = {https://doi.org/10.1186/s12942-017-0098-4},
doi = {10.1186/s12942-017-0098-4},
issn = {1476-072X},
year = {2017},
date = {2017-07-19},
journal = {International Journal of Health Geographics},
volume = {16},
number = {1},
pages = {25},
abstract = {Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample ``seed'' cells with probability proportionate to estimated population size, then ``grows'' PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample ``seed'' cells with probability proportionate to estimated population size, then ``grows'' PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.
228.
Takahashi, Saki; Metcalf, C. Jessica E.; Ferrari, Matthew J.; Tatem, Andrew J.; Lessler, Justin
The geography of measles vaccination in the African Great Lakes region Journal Article
In: Nature Communications, vol. 8, no. 1, pp. 15585, 2017, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@article{Takahashi2017,
title = {The geography of measles vaccination in the African Great Lakes region},
author = {Saki Takahashi and C. Jessica E. Metcalf and Matthew J. Ferrari and Andrew J. Tatem and Justin Lessler},
url = {https://doi.org/10.1038/ncomms15585},
doi = {10.1038/ncomms15585},
issn = {2041-1723},
year = {2017},
date = {2017-05-25},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {15585},
abstract = {Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.
229.
Takahashi, Saki; Metcalf, C. Jessica E.; Ferrari, Matthew J.; Tatem, Andrew J.; Lessler, Justin
The geography of measles vaccination in the African Great Lakes region Journal Article
In: Nature Communications, vol. 8, no. 1, pp. 15585, 2017, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@article{Takahashi2017b,
title = {The geography of measles vaccination in the African Great Lakes region},
author = {Saki Takahashi and C. Jessica E. Metcalf and Matthew J. Ferrari and Andrew J. Tatem and Justin Lessler},
url = {https://doi.org/10.1038/ncomms15585},
doi = {10.1038/ncomms15585},
issn = {2041-1723},
year = {2017},
date = {2017-05-25},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {15585},
abstract = {Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.
230.
Alegana, Victor A.; Wright, Jim; Pezzulo, Carla; Tatem, Andrew J.; Atkinson, Peter M.
Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model Journal Article
In: BMC Medical Research Methodology, vol. 17, no. 1, pp. 67, 2017, ISSN: 1471-2288.
Abstract | Links | BibTeX | Tags:
@article{Alegana2017,
title = {Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model},
author = {Victor A. Alegana and Jim Wright and Carla Pezzulo and Andrew J. Tatem and Peter M. Atkinson},
url = {https://doi.org/10.1186/s12874-017-0346-0},
doi = {10.1186/s12874-017-0346-0},
issn = {1471-2288},
year = {2017},
date = {2017-04-20},
journal = {BMC Medical Research Methodology},
volume = {17},
number = {1},
pages = {67},
abstract = {Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).
231.
Dudas, Gytis; Carvalho, Luiz Max; Bedford, Trevor; Tatem, Andrew J.; Baele, Guy; Faria, Nuno R.; Park, Daniel J.; Ladner, Jason T.; Arias, Armando; Asogun, Danny; Bielejec, Filip; Caddy, Sarah L.; Cotten, Matthew; DÁmbrozio, Jonathan; Dellicour, Simon; Caro, Antonino Di; Diclaro, Joseph W.; Duraffour, Sophie; Elmore, Michael J.; Fakoli, Lawrence S.; Faye, Ousmane; Gilbert, Merle L.; Gevao, Sahr M.; Gire, Stephen; Gladden-Young, Adrianne; Gnirke, Andreas; Goba, Augustine; Grant, Donald S.; Haagmans, Bart L.; Hiscox, Julian A.; Jah, Umaru; Kugelman, Jeffrey R.; Liu, Di; Lu, Jia; Malboeuf, Christine M.; Mate, Suzanne; Matthews, David A.; Matranga, Christian B.; Meredith, Luke W.; Qu, James; Quick, Joshua; Pas, Suzan D.; Phan, My V. T.; Pollakis, Georgios; Reusken, Chantal B.; Sanchez-Lockhart, Mariano; Schaffner, Stephen F.; Schieffelin, John S.; Sealfon, Rachel S.; Simon-Loriere, Etienne; Smits, Saskia L.; Stoecker, Kilian; Thorne, Lucy; Tobin, Ekaete Alice; Vandi, Mohamed A.; Watson, Simon J.; West, Kendra; Whitmer, Shannon; Wiley, Michael R.; Winnicki, Sarah M.; Wohl, Shirlee; Wölfel, Roman; Yozwiak, Nathan L.; Andersen, Kristian G.; Blyden, Sylvia O.; Bolay, Fatorma; Carroll, Miles W.; Dahn, Bernice; Diallo, Boubacar; Formenty, Pierre; Fraser, Christophe; Gao, George F.; Garry, Robert F.; Goodfellow, Ian; Günther, Stephan; Happi, Christian T.; Holmes, Edward C.; Kargbo, Brima; Ke"ita, Sakoba; Kellam, Paul; Koopmans, Marion P. G.; Kuhn, Jens H.; Loman, Nicholas J.; Magassouba, N'Faly; Naidoo, Dhamari; Nichol, Stuart T.; Nyenswah, Tolbert; Palacios, Gustavo; Pybus, Oliver G.; Sabeti, Pardis C.; Sall, Amadou; Ströher, Ute; Wurie, Isatta; Suchard, Marc A.; Lemey, Philippe; Rambaut, Andrew
Virus genomes reveal factors that spread and sustained the Ebola epidemic Journal Article
In: Nature, vol. 544, no. 7650, pp. 309-315, 2017, ISSN: 1476-4687.
Abstract | Links | BibTeX | Tags:
@article{Dudas2017,
title = {Virus genomes reveal factors that spread and sustained the Ebola epidemic},
author = {Gytis Dudas and Luiz Max Carvalho and Trevor Bedford and Andrew J. Tatem and Guy Baele and Nuno R. Faria and Daniel J. Park and Jason T. Ladner and Armando Arias and Danny Asogun and Filip Bielejec and Sarah L. Caddy and Matthew Cotten and Jonathan DÁmbrozio and Simon Dellicour and Antonino Di Caro and Joseph W. Diclaro and Sophie Duraffour and Michael J. Elmore and Lawrence S. Fakoli and Ousmane Faye and Merle L. Gilbert and Sahr M. Gevao and Stephen Gire and Adrianne Gladden-Young and Andreas Gnirke and Augustine Goba and Donald S. Grant and Bart L. Haagmans and Julian A. Hiscox and Umaru Jah and Jeffrey R. Kugelman and Di Liu and Jia Lu and Christine M. Malboeuf and Suzanne Mate and David A. Matthews and Christian B. Matranga and Luke W. Meredith and James Qu and Joshua Quick and Suzan D. Pas and My V. T. Phan and Georgios Pollakis and Chantal B. Reusken and Mariano Sanchez-Lockhart and Stephen F. Schaffner and John S. Schieffelin and Rachel S. Sealfon and Etienne Simon-Loriere and Saskia L. Smits and Kilian Stoecker and Lucy Thorne and Ekaete Alice Tobin and Mohamed A. Vandi and Simon J. Watson and Kendra West and Shannon Whitmer and Michael R. Wiley and Sarah M. Winnicki and Shirlee Wohl and Roman Wölfel and Nathan L. Yozwiak and Kristian G. Andersen and Sylvia O. Blyden and Fatorma Bolay and Miles W. Carroll and Bernice Dahn and Boubacar Diallo and Pierre Formenty and Christophe Fraser and George F. Gao and Robert F. Garry and Ian Goodfellow and Stephan Günther and Christian T. Happi and Edward C. Holmes and Brima Kargbo and Sakoba Ke"ita and Paul Kellam and Marion P. G. Koopmans and Jens H. Kuhn and Nicholas J. Loman and N'Faly Magassouba and Dhamari Naidoo and Stuart T. Nichol and Tolbert Nyenswah and Gustavo Palacios and Oliver G. Pybus and Pardis C. Sabeti and Amadou Sall and Ute Ströher and Isatta Wurie and Marc A. Suchard and Philippe Lemey and Andrew Rambaut},
url = {https://doi.org/10.1038/nature22040},
doi = {10.1038/nature22040},
issn = {1476-4687},
year = {2017},
date = {2017-04-01},
journal = {Nature},
volume = {544},
number = {7650},
pages = {309-315},
abstract = {The 2013--2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic `gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The 2013--2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic `gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.
232.
Tatem, Andrew J.
WorldPop, open data for spatial demography Journal Article
In: Scientific Data, vol. 4, no. 1, pp. 170004, 2017, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Tatem2017,
title = {WorldPop, open data for spatial demography},
author = {Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.4},
doi = {10.1038/sdata.2017.4},
issn = {2052-4463},
year = {2017},
date = {2017-01-31},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170004},
abstract = {High resolution, contemporary data on human population distributions, their characteristics and changes over time are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. WorldPop aims to meet these needs through the provision of detailed and open access spatial demographic datasets built using transparent approaches. The Scientific Data WorldPop collection brings together descriptor papers on these datasets and is introduced here.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High resolution, contemporary data on human population distributions, their characteristics and changes over time are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. WorldPop aims to meet these needs through the provision of detailed and open access spatial demographic datasets built using transparent approaches. The Scientific Data WorldPop collection brings together descriptor papers on these datasets and is introduced here.
233.
Lloyd, Christopher T.; Sorichetta, Alessandro; Tatem, Andrew J.
High resolution global gridded data for use in population studies Journal Article
In: Scientific Data, vol. 4, no. 1, pp. 170001, 2017, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Lloyd2017,
title = {High resolution global gridded data for use in population studies},
author = {Christopher T. Lloyd and Alessandro Sorichetta and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.1},
doi = {10.1038/sdata.2017.1},
issn = {2052-4463},
year = {2017},
date = {2017-01-31},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170001},
abstract = {Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.
234.
Linard, Catherine; Kabaria, Caroline W.; Gilbert, Marius; Tatem, Andrew J.; Gaughan, Andrea E.; Stevens, Forrest R.; Sorichetta, Alessandro; Noor, Abdisalan M.; Snow, Robert W.
Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009 Journal Article
In: International Journal of Digital Earth, vol. 10, no. 10, pp. 1017-1029, 2017, (PMID: 29098016).
@article{doi:10.1080/17538947.2016.1275829,
title = {Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009},
author = {Catherine Linard and Caroline W. Kabaria and Marius Gilbert and Andrew J. Tatem and Andrea E. Gaughan and Forrest R. Stevens and Alessandro Sorichetta and Abdisalan M. Noor and Robert W. Snow},
url = {https://doi.org/10.1080/17538947.2016.1275829},
doi = {10.1080/17538947.2016.1275829},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Digital Earth},
volume = {10},
number = {10},
pages = {1017-1029},
publisher = {Taylor & Francis},
note = {PMID: 29098016},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
235.
Steele, Jessica E.; Sundsøy, Pål Roe; Pezzulo, Carla; Alegana, Victor A.; Bird, Tomas J.; Blumenstock, Joshua; Bjelland, Johannes; Engø-Monsen, Kenth; Montjoye, Yves-Alexandre; Iqbal, Asif M.; Hadiuzzaman, Khandakar N.; Lu, Xin; Wetter, Erik; Tatem, Andrew J.; Bengtsson, Linus
Mapping poverty using mobile phone and satellite data Journal Article
In: Journal of The Royal Society Interface, vol. 14, no. 127, pp. 20160690, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2016.0690,
title = {Mapping poverty using mobile phone and satellite data},
author = {Jessica E. Steele and Pål Roe Sundsøy and Carla Pezzulo and Victor A. Alegana and Tomas J. Bird and Joshua Blumenstock and Johannes Bjelland and Kenth Engø-Monsen and Yves-Alexandre Montjoye and Asif M. Iqbal and Khandakar N. Hadiuzzaman and Xin Lu and Erik Wetter and Andrew J. Tatem and Linus Bengtsson},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0690},
doi = {10.1098/rsif.2016.0690},
year = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {127},
pages = {20160690},
abstract = {Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.
236.
Bosco, C.; Alegana, V.; Bird, T.; Pezzulo, C.; Bengtsson, L.; Sorichetta, A.; Steele, J.; Hornby, G.; Ruktanonchai, C.; Ruktanonchai, N.; Wetter, E.; Tatem, A. J.
Exploring the high-resolution mapping of gender-disaggregated development indicators Journal Article
In: Journal of The Royal Society Interface, vol. 14, no. 129, pp. 20160825, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2016.0825,
title = {Exploring the high-resolution mapping of gender-disaggregated development indicators},
author = {C. Bosco and V. Alegana and T. Bird and C. Pezzulo and L. Bengtsson and A. Sorichetta and J. Steele and G. Hornby and C. Ruktanonchai and N. Ruktanonchai and E. Wetter and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0825},
doi = {10.1098/rsif.2016.0825},
year = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {129},
pages = {20160825},
abstract = {Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.
237.
Nieves, Jeremiah J.; Stevens, Forrest R.; Gaughan, Andrea E.; Linard, Catherine; Sorichetta, Alessandro; Hornby, Graeme; Patel, Nirav N.; Tatem, Andrew J.
Examining the correlates and drivers of human population distributions across low- and middle-income countries Journal Article
In: Journal of The Royal Society Interface, vol. 14, no. 137, pp. 20170401, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2017.0401,
title = {Examining the correlates and drivers of human population distributions across low- and middle-income countries},
author = {Jeremiah J. Nieves and Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Alessandro Sorichetta and Graeme Hornby and Nirav N. Patel and Andrew J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2017.0401},
doi = {10.1098/rsif.2017.0401},
year = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {137},
pages = {20170401},
abstract = {Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low- and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, they are generally remarkably consistent, pointing to universal drivers of human population distribution. Here, we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low- and middle-income regions of the world.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low- and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, they are generally remarkably consistent, pointing to universal drivers of human population distribution. Here, we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low- and middle-income regions of the world.
238.
Tusting, Lucy S.; Bottomley, Christian; Gibson, Harry; Kleinschmidt, Immo; Tatem, Andrew J.; Lindsay, Steve W.; Gething, Peter W.
Housing Improvements and Malaria Risk in Sub-Saharan Africa: A Multi-Country Analysis of Survey Data Journal Article
In: PLOS Medicine, vol. 14, no. 2, pp. 1-15, 2017.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pmed.1002234,
title = {Housing Improvements and Malaria Risk in Sub-Saharan Africa: A Multi-Country Analysis of Survey Data},
author = {Lucy S. Tusting and Christian Bottomley and Harry Gibson and Immo Kleinschmidt and Andrew J. Tatem and Steve W. Lindsay and Peter W. Gething},
url = {https://doi.org/10.1371/journal.pmed.1002234},
doi = {10.1371/journal.pmed.1002234},
year = {2017},
date = {2017-01-01},
journal = {PLOS Medicine},
volume = {14},
number = {2},
pages = {1-15},
publisher = {Public Library of Science},
abstract = {Background Improvements to housing may contribute to malaria control and elimination by reducing house entry by malaria vectors and thus exposure to biting. We tested the hypothesis that the odds of malaria infection are lower in modern, improved housing compared to traditional housing in sub-Saharan Africa (SSA). Methods and Findings We analysed 15 Demographic and Health Surveys (DHS) and 14 Malaria Indicator Surveys (MIS) conducted in 21 countries in SSA between 2008 and 2015 that measured malaria infection by microscopy or rapid diagnostic test (RDT). DHS/MIS surveys record whether houses are built with finished materials (e.g., metal) or rudimentary materials (e.g., thatch). This information was used to develop a binary housing quality variable where houses built using finished wall, roof, and floor materials were classified as “modern”, and all other houses were classified as “traditional”. Conditional logistic regression was used to determine the association between housing quality and prevalence of malaria infection in children aged 0–5 y, adjusting for age, gender, insecticide-treated net (ITN) use, indoor residual spraying, household wealth, and geographic cluster. Individual survey odds ratios (ORs) were combined to determine a summary OR using a random effects meta-analysis. Of 284,532 total children surveyed, 139,318 were tested for malaria infection using microscopy (n = 131,652) or RDT (n = 138,540). Within individual surveys, malaria prevalence measured by microscopy ranged from 0.4% (Madagascar 2011) to 45.5% (Burkina Faso 2010) among children living in modern houses and from 0.4% (The Gambia 2013) to 70.6% (Burkina Faso 2010) in traditional houses, and malaria prevalence measured by RDT ranged from 0.3% (Senegal 2013–2014) to 61.2% (Burkina Faso 2010) in modern houses and from 1.5% (The Gambia 2013) to 79.8% (Burkina Faso 2010) in traditional houses. Across all surveys, modern housing was associated with a 9% to 14% reduction in the odds of malaria infection (microscopy: adjusted OR 0.91, 95% CI 0.85–0.97},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background Improvements to housing may contribute to malaria control and elimination by reducing house entry by malaria vectors and thus exposure to biting. We tested the hypothesis that the odds of malaria infection are lower in modern, improved housing compared to traditional housing in sub-Saharan Africa (SSA). Methods and Findings We analysed 15 Demographic and Health Surveys (DHS) and 14 Malaria Indicator Surveys (MIS) conducted in 21 countries in SSA between 2008 and 2015 that measured malaria infection by microscopy or rapid diagnostic test (RDT). DHS/MIS surveys record whether houses are built with finished materials (e.g., metal) or rudimentary materials (e.g., thatch). This information was used to develop a binary housing quality variable where houses built using finished wall, roof, and floor materials were classified as “modern”, and all other houses were classified as “traditional”. Conditional logistic regression was used to determine the association between housing quality and prevalence of malaria infection in children aged 0–5 y, adjusting for age, gender, insecticide-treated net (ITN) use, indoor residual spraying, household wealth, and geographic cluster. Individual survey odds ratios (ORs) were combined to determine a summary OR using a random effects meta-analysis. Of 284,532 total children surveyed, 139,318 were tested for malaria infection using microscopy (n = 131,652) or RDT (n = 138,540). Within individual surveys, malaria prevalence measured by microscopy ranged from 0.4% (Madagascar 2011) to 45.5% (Burkina Faso 2010) among children living in modern houses and from 0.4% (The Gambia 2013) to 70.6% (Burkina Faso 2010) in traditional houses, and malaria prevalence measured by RDT ranged from 0.3% (Senegal 2013–2014) to 61.2% (Burkina Faso 2010) in modern houses and from 1.5% (The Gambia 2013) to 79.8% (Burkina Faso 2010) in traditional houses. Across all surveys, modern housing was associated with a 9% to 14% reduction in the odds of malaria infection (microscopy: adjusted OR 0.91, 95% CI 0.85–0.97
239.
Head, Michael G; Goss, Sian; Gelister, Yann; Alegana, Victor; Brown, Rebecca J; Clarke, Stuart C; Fitchett, Joseph R A; Atun, Rifat; Scott, J Anthony G; Newell, Marie-Louise; Padmadas, Sabu S; Tatem, Andrew J
Global funding trends for malaria research in sub-Saharan Africa: a systematic analysis Journal Article
In: The Lancet Global Health, vol. 5, no. 8, pp. e772-e781, 2017, ISSN: 2214-109X.
Abstract | Links | BibTeX | Tags:
@article{HEAD2017e772,
title = {Global funding trends for malaria research in sub-Saharan Africa: a systematic analysis},
author = {Michael G Head and Sian Goss and Yann Gelister and Victor Alegana and Rebecca J Brown and Stuart C Clarke and Joseph R A Fitchett and Rifat Atun and J Anthony G Scott and Marie-Louise Newell and Sabu S Padmadas and Andrew J Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X17302450},
doi = {https://doi.org/10.1016/S2214-109X(17)30245-0},
issn = {2214-109X},
year = {2017},
date = {2017-01-01},
journal = {The Lancet Global Health},
volume = {5},
number = {8},
pages = {e772-e781},
abstract = {Summary
Background
Total domestic and international funding for malaria is inadequate to achieve WHO global targets in burden reduction by 2030. We describe the trends of investments in malaria-related research in sub-Saharan Africa and compare investment with national disease burden to identify areas of funding strength and potentially neglected populations. We also considered funding for malaria control.
Methods
Research funding data related to malaria for 1997–2013 were sourced from existing datasets, from 13 major public and philanthropic global health funders, and from funding databases. Investments (reported in US$) were considered by geographical area and compared with data on parasite prevalence and populations at risk in sub-Saharan Africa. 45 sub-Saharan African countries were ranked by amount of research funding received.
Findings
We found 333 research awards totalling US$814·4 million. Public health research covered $308·1 million (37·8%) and clinical trials covered $275·2 million (33·8%). Tanzania ($107·8 million [13·2%]), Uganda ($97·9 million [12·0%]), and Kenya ($92·9 million [11·4%]) received the highest sum of research investment and the most research awards. Malawi, Tanzania, and Uganda remained highly ranked after adjusting for national gross domestic product. Countries with a reasonably high malaria burden that received little research investment or funding for malaria control included Central African Republic (ranked 40th) and Sierra Leone (ranked 35th). Congo (Brazzaville) and Guinea had reasonably high malaria mortality, yet Congo (Brazzaville) ranked 38th and Guinea ranked 25th, thus receiving little investment.
Interpretation
Some countries receive reasonably large investments in malaria-related research (Tanzania, Kenya, Uganda), whereas others receive little or no investments (Sierra Leone, Central African Republic). Research investments are typically highest in countries where funding for malaria control is also high. Investment strategies should consider more equitable research and operational investments across countries to include currently neglected and susceptible populations.
Funding
Royal Society of Tropical Medicine and Hygiene and Bill & Melinda Gates Foundation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Summary
Background
Total domestic and international funding for malaria is inadequate to achieve WHO global targets in burden reduction by 2030. We describe the trends of investments in malaria-related research in sub-Saharan Africa and compare investment with national disease burden to identify areas of funding strength and potentially neglected populations. We also considered funding for malaria control.
Methods
Research funding data related to malaria for 1997–2013 were sourced from existing datasets, from 13 major public and philanthropic global health funders, and from funding databases. Investments (reported in US$) were considered by geographical area and compared with data on parasite prevalence and populations at risk in sub-Saharan Africa. 45 sub-Saharan African countries were ranked by amount of research funding received.
Findings
We found 333 research awards totalling US$814·4 million. Public health research covered $308·1 million (37·8%) and clinical trials covered $275·2 million (33·8%). Tanzania ($107·8 million [13·2%]), Uganda ($97·9 million [12·0%]), and Kenya ($92·9 million [11·4%]) received the highest sum of research investment and the most research awards. Malawi, Tanzania, and Uganda remained highly ranked after adjusting for national gross domestic product. Countries with a reasonably high malaria burden that received little research investment or funding for malaria control included Central African Republic (ranked 40th) and Sierra Leone (ranked 35th). Congo (Brazzaville) and Guinea had reasonably high malaria mortality, yet Congo (Brazzaville) ranked 38th and Guinea ranked 25th, thus receiving little investment.
Interpretation
Some countries receive reasonably large investments in malaria-related research (Tanzania, Kenya, Uganda), whereas others receive little or no investments (Sierra Leone, Central African Republic). Research investments are typically highest in countries where funding for malaria control is also high. Investment strategies should consider more equitable research and operational investments across countries to include currently neglected and susceptible populations.
Funding
Royal Society of Tropical Medicine and Hygiene and Bill & Melinda Gates Foundation.
Background
Total domestic and international funding for malaria is inadequate to achieve WHO global targets in burden reduction by 2030. We describe the trends of investments in malaria-related research in sub-Saharan Africa and compare investment with national disease burden to identify areas of funding strength and potentially neglected populations. We also considered funding for malaria control.
Methods
Research funding data related to malaria for 1997–2013 were sourced from existing datasets, from 13 major public and philanthropic global health funders, and from funding databases. Investments (reported in US$) were considered by geographical area and compared with data on parasite prevalence and populations at risk in sub-Saharan Africa. 45 sub-Saharan African countries were ranked by amount of research funding received.
Findings
We found 333 research awards totalling US$814·4 million. Public health research covered $308·1 million (37·8%) and clinical trials covered $275·2 million (33·8%). Tanzania ($107·8 million [13·2%]), Uganda ($97·9 million [12·0%]), and Kenya ($92·9 million [11·4%]) received the highest sum of research investment and the most research awards. Malawi, Tanzania, and Uganda remained highly ranked after adjusting for national gross domestic product. Countries with a reasonably high malaria burden that received little research investment or funding for malaria control included Central African Republic (ranked 40th) and Sierra Leone (ranked 35th). Congo (Brazzaville) and Guinea had reasonably high malaria mortality, yet Congo (Brazzaville) ranked 38th and Guinea ranked 25th, thus receiving little investment.
Interpretation
Some countries receive reasonably large investments in malaria-related research (Tanzania, Kenya, Uganda), whereas others receive little or no investments (Sierra Leone, Central African Republic). Research investments are typically highest in countries where funding for malaria control is also high. Investment strategies should consider more equitable research and operational investments across countries to include currently neglected and susceptible populations.
Funding
Royal Society of Tropical Medicine and Hygiene and Bill & Melinda Gates Foundation.
240.
Metcalf, C. Jessica E.; Walter, Katharine S.; Wesolowski, Amy; Buckee, Caroline O.; Shevliakova, Elena; Tatem, Andrew J.; Boos, William R.; Weinberger, Daniel M.; Pitzer, Virginia E.
Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead Journal Article
In: Proceedings of the Royal Society B: Biological Sciences, vol. 284, no. 1860, pp. 20170901, 2017.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rspb.2017.0901,
title = {Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead},
author = {C. Jessica E. Metcalf and Katharine S. Walter and Amy Wesolowski and Caroline O. Buckee and Elena Shevliakova and Andrew J. Tatem and William R. Boos and Daniel M. Weinberger and Virginia E. Pitzer},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2017.0901},
doi = {10.1098/rspb.2017.0901},
year = {2017},
date = {2017-01-01},
journal = {Proceedings of the Royal Society B: Biological Sciences},
volume = {284},
number = {1860},
pages = {20170901},
abstract = {Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.
