Publications
261.
Bhatt, Samir; Weiss, Daniel J; Mappin, Bonnie; Dalrymple, Ursula; Cameron, Ewan; Bisanzio, Donal; Smith, David L; Moyes, Catherine L; Tatem, Andrew J; Lynch, Michael; Fergus, Cristin A; Yukich, Joshua; Bennett, Adam; Eisele, Thomas P; Kolaczinski, Jan; Cibulskis, Richard E; Hay, Simon I; Gething, Peter W
Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017 Journal Article
In: eLife, vol. 4, pp. e09672, 2015, ISSN: 2050-084X.
Abstract | Links | BibTeX | Tags: Bayesian compartment model, ITN, LLIN, malaria
@article{10.7554/eLife.09672,
title = {Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017},
author = {Samir Bhatt and Daniel J Weiss and Bonnie Mappin and Ursula Dalrymple and Ewan Cameron and Donal Bisanzio and David L Smith and Catherine L Moyes and Andrew J Tatem and Michael Lynch and Cristin A Fergus and Joshua Yukich and Adam Bennett and Thomas P Eisele and Jan Kolaczinski and Richard E Cibulskis and Simon I Hay and Peter W Gething},
editor = {Catherine Kyobutungi},
url = {https://doi.org/10.7554/eLife.09672},
doi = {10.7554/eLife.09672},
issn = {2050-084X},
year = {2015},
date = {2015-12-01},
urldate = {2015-12-01},
journal = {eLife},
volume = {4},
pages = {e09672},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential ínefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%–26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20–28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.},
keywords = {Bayesian compartment model, ITN, LLIN, malaria},
pubstate = {published},
tppubtype = {article}
}
Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential ínefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%–26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20–28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.
262.
Reiner, Jr Robert C; Menach, Arnaud Le; Kunene, Simon; Ntshalintshali, Nyasatu; Hsiang, Michelle S; Perkins, T Alex; Greenhouse, Bryan; Tatem, Andrew J; Cohen, Justin M; Smith, David L
Mapping residual transmission for malaria elimination Journal Article
In: eLife, vol. 4, pp. e09520, 2015, ISSN: 2050-084X.
Abstract | Links | BibTeX | Tags: malaria elimination, plasmodium falciparum, spatio-temporal transmission dynamics
@article{10.7554/eLife.09520,
title = {Mapping residual transmission for malaria elimination},
author = {Jr Robert C Reiner and Arnaud Le Menach and Simon Kunene and Nyasatu Ntshalintshali and Michelle S Hsiang and T Alex Perkins and Bryan Greenhouse and Andrew J Tatem and Justin M Cohen and David L Smith},
editor = {Mark Jit},
url = {https://doi.org/10.7554/eLife.09520},
doi = {10.7554/eLife.09520},
issn = {2050-084X},
year = {2015},
date = {2015-12-01},
journal = {eLife},
volume = {4},
pages = {e09520},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections. In the last phases of malaria elimination, as universal interventions reap diminishing marginal returns, national resources must become increasingly devoted to identifying where residual transmission is occurring. The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk. Using routine national surveillance data from Swaziland (a sub-Saharan country on the verge of elimination), we estimated individual reproductive numbers. Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show ‘malariogenic potential’, a first for malaria elimination. As countries approach elimination, these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination.},
keywords = {malaria elimination, plasmodium falciparum, spatio-temporal transmission dynamics},
pubstate = {published},
tppubtype = {article}
}
Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections. In the last phases of malaria elimination, as universal interventions reap diminishing marginal returns, national resources must become increasingly devoted to identifying where residual transmission is occurring. The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk. Using routine national surveillance data from Swaziland (a sub-Saharan country on the verge of elimination), we estimated individual reproductive numbers. Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show ‘malariogenic potential’, a first for malaria elimination. As countries approach elimination, these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination.
263.
Stevens, Forrest R.; Gaughan, Andrea E.; Linard, Catherine; Tatem, Andrew J.
Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data Journal Article
In: PLOS ONE, vol. 10, no. 2, pp. 1-22, 2015.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pone.0107042,
title = {Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data},
author = {Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0107042},
doi = {10.1371/journal.pone.0107042},
year = {2015},
date = {2015-01-01},
journal = {PLOS ONE},
volume = {10},
number = {2},
pages = {1-22},
publisher = {Public Library of Science},
abstract = {High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.
264.
Alegana, V. A.; Atkinson, P. M.; Pezzulo, C.; Sorichetta, A.; Weiss, D.; Bird, T.; Erbach-Schoenberg, E.; Tatem, A. J.
Fine resolution mapping of population age-structures for health and development applications Journal Article
In: Journal of The Royal Society Interface, vol. 12, no. 105, pp. 20150073, 2015.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rsif.2015.0073,
title = {Fine resolution mapping of population age-structures for health and development applications},
author = {V. A. Alegana and P. M. Atkinson and C. Pezzulo and A. Sorichetta and D. Weiss and T. Bird and E. Erbach-Schoenberg and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2015.0073},
doi = {10.1098/rsif.2015.0073},
year = {2015},
date = {2015-01-01},
journal = {Journal of The Royal Society Interface},
volume = {12},
number = {105},
pages = {20150073},
abstract = {The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.
265.
Bharti, Nita; Lu, Xin; Bengtsson, Linus; Wetter, Erik; Tatem, Andrew J.
Remotely measuring populations during a crisis by overlaying two data sources Journal Article
In: International Health, vol. 7, no. 2, pp. 90-98, 2015, ISSN: 1876-3413.
Abstract | Links | BibTeX | Tags:
@article{10.1093/inthealth/ihv003,
title = {Remotely measuring populations during a crisis by overlaying two data sources},
author = {Nita Bharti and Xin Lu and Linus Bengtsson and Erik Wetter and Andrew J. Tatem},
url = {https://doi.org/10.1093/inthealth/ihv003},
doi = {10.1093/inthealth/ihv003},
issn = {1876-3413},
year = {2015},
date = {2015-01-01},
journal = {International Health},
volume = {7},
number = {2},
pages = {90-98},
abstract = {Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods.We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales.The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis.Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods.We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales.The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis.Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.
266.
Wesolowski, Amy; O’Meara, Wendy Prudhomme; Eagle, Nathan; Tatem, Andrew J.; Buckee, Caroline O.
Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa Journal Article
In: PLOS Computational Biology, vol. 11, no. 7, pp. 1-16, 2015.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pcbi.1004267,
title = {Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa},
author = {Amy Wesolowski and Wendy Prudhomme O’Meara and Nathan Eagle and Andrew J. Tatem and Caroline O. Buckee},
url = {https://doi.org/10.1371/journal.pcbi.1004267},
doi = {10.1371/journal.pcbi.1004267},
year = {2015},
date = {2015-01-01},
journal = {PLOS Computational Biology},
volume = {11},
number = {7},
pages = {1-16},
publisher = {Public Library of Science},
abstract = {Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.
267.
Sorichetta, Alessandro; Hornby, Graeme M.; Stevens, Forrest R.; Gaughan, Andrea E.; Linard, Catherine; Tatem, Andrew J.
Americas Datasets data
2015.
@data{DVN/PUGPVR_2015,
title = {Americas Datasets},
author = {Alessandro Sorichetta and Graeme M. Hornby and Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.7910/DVN/PUGPVR},
doi = {10.7910/DVN/PUGPVR},
year = {2015},
date = {2015-01-01},
publisher = {Harvard Dataverse},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
268.
Sedda, Luigi; Tatem, Andrew J.; Morley, David W.; Atkinson, Peter M.; Wardrop, Nicola A.; Pezzulo, Carla; Sorichetta, Alessandro; Kuleszo, Joanna; Rogers, David J.
Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa Journal Article
In: International Health, vol. 7, no. 2, pp. 99-106, 2015, ISSN: 1876-3413.
Abstract | Links | BibTeX | Tags:
@article{10.1093/inthealth/ihv005,
title = {Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa},
author = {Luigi Sedda and Andrew J. Tatem and David W. Morley and Peter M. Atkinson and Nicola A. Wardrop and Carla Pezzulo and Alessandro Sorichetta and Joanna Kuleszo and David J. Rogers},
url = {https://doi.org/10.1093/inthealth/ihv005},
doi = {10.1093/inthealth/ihv005},
issn = {1876-3413},
year = {2015},
date = {2015-01-01},
journal = {International Health},
volume = {7},
number = {2},
pages = {99-106},
abstract = {Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.
269.
Wesolowski, Amy; Metcalf, C. J. E.; Eagle, Nathan; Kombich, Janeth; Grenfell, Bryan T.; Bjørnstad, Ottar N.; Lessler, Justin; Tatem, Andrew J.; Buckee, Caroline O.
Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data Journal Article
In: Proceedings of the National Academy of Sciences, vol. 112, no. 35, pp. 11114-11119, 2015.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1073/pnas.1423542112,
title = {Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data},
author = {Amy Wesolowski and C. J. E. Metcalf and Nathan Eagle and Janeth Kombich and Bryan T. Grenfell and Ottar N. Bjørnstad and Justin Lessler and Andrew J. Tatem and Caroline O. Buckee},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1423542112},
doi = {10.1073/pnas.1423542112},
year = {2015},
date = {2015-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {112},
number = {35},
pages = {11114-11119},
abstract = {Changing patterns of human mobility can drive seasonal outbreaks of infectious diseases, but limited data about travel behavior and population flux over time have made this idea difficult to quantify. Mobile phone data provide a unique source of information about human travel. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population fluxes inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates, showing for the first time to our knowledge that mobile phone data capture epidemiologically relevant patterns of movement. Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Changing patterns of human mobility can drive seasonal outbreaks of infectious diseases, but limited data about travel behavior and population flux over time have made this idea difficult to quantify. Mobile phone data provide a unique source of information about human travel. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population fluxes inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates, showing for the first time to our knowledge that mobile phone data capture epidemiologically relevant patterns of movement. Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.
270.
Jia, Peng; Sankoh, Osman; Tatem, Andrew J.
Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network Journal Article
In: Health & Place, vol. 36, pp. 88-96, 2015, ISSN: 1353-8292.
Abstract | Links | BibTeX | Tags: Africa, Asia, Demographic surveillance sites, Health, Remote sensing
@article{JIA201588,
title = {Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network},
author = {Peng Jia and Osman Sankoh and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S1353829215001379},
doi = {https://doi.org/10.1016/j.healthplace.2015.09.009},
issn = {1353-8292},
year = {2015},
date = {2015-01-01},
journal = {Health & Place},
volume = {36},
pages = {88-96},
abstract = {The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.},
keywords = {Africa, Asia, Demographic surveillance sites, Health, Remote sensing},
pubstate = {published},
tppubtype = {article}
}
The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.
271.
Pybus, Oliver G.; Tatem, Andrew J.; Lemey, Philippe
Virus evolution and transmission in an ever more connected world Journal Article
In: Proceedings of the Royal Society B: Biological Sciences, vol. 282, no. 1821, pp. 20142878, 2015.
Abstract | Links | BibTeX | Tags:
@article{doi:10.1098/rspb.2014.2878,
title = {Virus evolution and transmission in an ever more connected world},
author = {Oliver G. Pybus and Andrew J. Tatem and Philippe Lemey},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2014.2878},
doi = {10.1098/rspb.2014.2878},
year = {2015},
date = {2015-01-01},
journal = {Proceedings of the Royal Society B: Biological Sciences},
volume = {282},
number = {1821},
pages = {20142878},
abstract = {The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.
272.
Gaughan, A. E.; Stevens, F. R.; Linard, C.; Patel, N. N.; Tatem, A. J.
Exploring nationally and regionally defined models for large area population mapping Journal Article
In: International Journal of Digital Earth, vol. 8, no. 12, pp. 989-1006, 2015.
@article{doi:10.1080/17538947.2014.965761,
title = {Exploring nationally and regionally defined models for large area population mapping},
author = {A. E. Gaughan and F. R. Stevens and C. Linard and N. N. Patel and A. J. Tatem},
url = {https://doi.org/10.1080/17538947.2014.965761},
doi = {10.1080/17538947.2014.965761},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Digital Earth},
volume = {8},
number = {12},
pages = {989-1006},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
273.
METCALF, C. J. E.; TATEM, A.; BJORNSTAD, O. N.; LESSLER, J.; O'REILLY, K.; TAKAHASHI, S.; CUTTS, F.; GRENFELL, B. T.
Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa Journal Article
In: Epidemiology and Infection, vol. 143, no. 7, pp. 1457–1466, 2015.
@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015,
title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},
author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},
doi = {10.1017/S0950268814001988},
year = {2015},
date = {2015-01-01},
journal = {Epidemiology and Infection},
volume = {143},
number = {7},
pages = {1457–1466},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
274.
Patel, Nirav N.; Angiuli, Emanuele; Gamba, Paolo; Gaughan, Andrea; Lisini, Gianni; Stevens, Forrest R.; Tatem, Andrew J.; Trianni, Giovanna
Multitemporal settlement and population mapping from Landsat using Google Earth Engine Journal Article
In: International Journal of Applied Earth Observation and Geoinformation, vol. 35, pp. 199-208, 2015, ISSN: 0303-2434.
Abstract | Links | BibTeX | Tags: Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization
@article{PATEL2015199,
title = {Multitemporal settlement and population mapping from Landsat using Google Earth Engine},
author = {Nirav N. Patel and Emanuele Angiuli and Paolo Gamba and Andrea Gaughan and Gianni Lisini and Forrest R. Stevens and Andrew J. Tatem and Giovanna Trianni},
url = {https://www.sciencedirect.com/science/article/pii/S0303243414001998},
doi = {https://doi.org/10.1016/j.jag.2014.09.005},
issn = {0303-2434},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {35},
pages = {199-208},
abstract = {As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.},
keywords = {Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization},
pubstate = {published},
tppubtype = {article}
}
As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.
275.
Mertes, C. M.; Schneider, A.; Sulla-Menashe, D.; Tatem, A. J.; Tan, B.
Detecting change in urban areas at continental scales with MODIS data Journal Article
In: Remote Sensing of Environment, vol. 158, pp. 331-347, 2015, ISSN: 0034-4257.
Abstract | Links | BibTeX | Tags: Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization
@article{MERTES2015331,
title = {Detecting change in urban areas at continental scales with MODIS data},
author = {C. M. Mertes and A. Schneider and D. Sulla-Menashe and A. J. Tatem and B. Tan},
url = {https://www.sciencedirect.com/science/article/pii/S003442571400368X},
doi = {https://doi.org/10.1016/j.rse.2014.09.023},
issn = {0034-4257},
year = {2015},
date = {2015-01-01},
journal = {Remote Sensing of Environment},
volume = {158},
pages = {331-347},
abstract = {Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.},
keywords = {Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization},
pubstate = {published},
tppubtype = {article}
}
Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.
276.
METCALF, C. J. E.; TATEM, A.; BJORNSTAD, O. N.; LESSLER, J.; O'REILLY, K.; TAKAHASHI, S.; CUTTS, F.; GRENFELL, B. T.
Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa Journal Article
In: Epidemiology and Infection, vol. 143, no. 7, pp. 1457–1466, 2015.
@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015b,
title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},
author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},
doi = {10.1017/S0950268814001988},
year = {2015},
date = {2015-01-01},
journal = {Epidemiology and Infection},
volume = {143},
number = {7},
pages = {1457–1466},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
277.
Pigott, David M; Golding, Nick; Mylne, Adrian; Huang, Zhi; Henry, Andrew J; Weiss, Daniel J; Brady, Oliver J; Kraemer, Moritz U G; Smith, David L; Moyes, Catherine L; Bhatt, Samir; Gething, Peter W; Horby, Peter W; Bogoch, Isaac I; Brownstein, John S; Mekaru, Sumiko R; Tatem, Andrew J; Khan, Kamran; Hay, Simon I
Mapping the zoonotic niche of Ebola virus disease in Africa Journal Article
In: eLife, 2014.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Mapping the zoonotic niche of Ebola virus disease in Africa},
author = {David M Pigott and Nick Golding and Adrian Mylne and Zhi Huang and Andrew J Henry and Daniel J Weiss and Oliver J Brady and Moritz U G Kraemer and David L Smith and Catherine L Moyes and Samir Bhatt and Peter W Gething and Peter W Horby and Isaac I Bogoch and John S Brownstein and Sumiko R Mekaru and Andrew J Tatem and Kamran Khan and Simon I Hay},
url = {https://doi.org/10.7554/eLife.04395},
doi = {10.7554/eLife.04395},
year = {2014},
date = {2014-09-19},
journal = {eLife},
abstract = {Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976–2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976–2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.
278.
Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G. R. William; Robinson, Timothy P.; Tatem, Andrew J.; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P.; Xiao, Xiangming; Linard, Catherine; Boeckel, Thomas P. Van; Martin, Vincent; Bhatt, Samir; Gething, Peter W.; Farrar, Jeremy J.; Hay, Simon I.; Yu, Hongjie
Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia Journal Article
In: Nature Communications, vol. 5, no. 1, pp. 4116, 2014, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@article{Gilbert2014,
title = {Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia},
author = {Marius Gilbert and Nick Golding and Hang Zhou and G. R. William Wint and Timothy P. Robinson and Andrew J. Tatem and Shengjie Lai and Sheng Zhou and Hui Jiang and Danhuai Guo and Zhi Huang and Jane P. Messina and Xiangming Xiao and Catherine Linard and Thomas P. Van Boeckel and Vincent Martin and Samir Bhatt and Peter W. Gething and Jeremy J. Farrar and Simon I. Hay and Hongjie Yu},
url = {https://doi.org/10.1038/ncomms5116},
doi = {10.1038/ncomms5116},
issn = {2041-1723},
year = {2014},
date = {2014-06-17},
journal = {Nature Communications},
volume = {5},
number = {1},
pages = {4116},
abstract = {Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.
279.
Pindolia, Deepa K.; Garcia, Andres J.; Huang, Zhuojie; Fik, Timothy; Smith, David L.; Tatem, Andrew J.
Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination Journal Article
In: Malaria Journal, vol. 13, no. 1, pp. 169, 2014, ISSN: 1475-2875.
Abstract | Links | BibTeX | Tags:
@article{Pindolia2014,
title = {Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination},
author = {Deepa K. Pindolia and Andres J. Garcia and Zhuojie Huang and Timothy Fik and David L. Smith and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-13-169},
doi = {10.1186/1475-2875-13-169},
issn = {1475-2875},
year = {2014},
date = {2014-05-03},
journal = {Malaria Journal},
volume = {13},
number = {1},
pages = {169},
abstract = {Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain `hotspots' of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain `hotspots' of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.
280.
Tatem, Andrew J.; Huang, Zhuojie; Narib, Clothilde; Kumar, Udayan; Kandula, Deepika; Pindolia, Deepa K.; Smith, David L.; Cohen, Justin M.; Graupe, Bonita; Uusiku, Petrina; Lourenço, Christopher
Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning Journal Article
In: Malaria Journal, vol. 13, no. 1, pp. 52, 2014, ISSN: 1475-2875.
Abstract | Links | BibTeX | Tags:
@article{Tatem2014b,
title = {Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning},
author = {Andrew J. Tatem and Zhuojie Huang and Clothilde Narib and Udayan Kumar and Deepika Kandula and Deepa K. Pindolia and David L. Smith and Justin M. Cohen and Bonita Graupe and Petrina Uusiku and Christopher Lourenço},
url = {https://doi.org/10.1186/1475-2875-13-52},
doi = {10.1186/1475-2875-13-52},
issn = {1475-2875},
year = {2014},
date = {2014-02-10},
journal = {Malaria Journal},
volume = {13},
number = {1},
pages = {52},
abstract = {As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.
