Publications
181.
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}
}
182.
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.
183.
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.
184.
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.
185.
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
186.
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.
187.
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.
188.
Patel, Nirav N.; Stevens, Forrest R.; Huang, Zhuojie; Gaughan, Andrea E.; Elyazar, Iqbal; Tatem, Andrew J.
Improving Large Area Population Mapping Using Geotweet Densities Journal Article
In: Transactions in GIS, vol. 21, no. 2, pp. 317-331, 2017.
Abstract | Links | BibTeX | Tags:
@article{https://doi.org/10.1111/tgis.12214,
title = {Improving Large Area Population Mapping Using Geotweet Densities},
author = {Nirav N. Patel and Forrest R. Stevens and Zhuojie Huang and Andrea E. Gaughan and Iqbal Elyazar and Andrew J. Tatem},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12214},
doi = {https://doi.org/10.1111/tgis.12214},
year = {2017},
date = {2017-01-01},
journal = {Transactions in GIS},
volume = {21},
number = {2},
pages = {317-331},
abstract = {Abstract Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.
189.
Buckee, Caroline O.; Tatem, Andrew J.; Metcalf, C. Jessica E.
Seasonal Population Movements and the Surveillance and Control of Infectious Diseases Journal Article
In: Trends in Parasitology, vol. 33, no. 1, pp. 10-20, 2017, ISSN: 1471-4922.
Abstract | Links | BibTeX | Tags:
@article{BUCKEE201710,
title = {Seasonal Population Movements and the Surveillance and Control of Infectious Diseases},
author = {Caroline O. Buckee and Andrew J. Tatem and C. Jessica E. Metcalf},
url = {https://www.sciencedirect.com/science/article/pii/S1471492216301891},
doi = {https://doi.org/10.1016/j.pt.2016.10.006},
issn = {1471-4922},
year = {2017},
date = {2017-01-01},
journal = {Trends in Parasitology},
volume = {33},
number = {1},
pages = {10-20},
abstract = {National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.
190.
Tatem, Andrew J; Jia, Peng; Ordanovich, Dariya; Falkner, Michael; Huang, Zhuojie; Howes, Rosalind; Hay, Simon I; Gething, Peter W; Smith, David L
The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics Journal Article
In: The Lancet Infectious Diseases, vol. 17, no. 1, pp. 98-107, 2017, ISSN: 1473-3099.
Abstract | Links | BibTeX | Tags:
@article{TATEM201798,
title = {The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics},
author = {Andrew J Tatem and Peng Jia and Dariya Ordanovich and Michael Falkner and Zhuojie Huang and Rosalind Howes and Simon I Hay and Peter W Gething and David L Smith},
url = {https://www.sciencedirect.com/science/article/pii/S1473309916303267},
doi = {https://doi.org/10.1016/S1473-3099(16)30326-7},
issn = {1473-3099},
year = {2017},
date = {2017-01-01},
journal = {The Lancet Infectious Diseases},
volume = {17},
number = {1},
pages = {98-107},
abstract = {Summary
Background
Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies.
Methods
In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations.
Findings
Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types.
Interpretation
The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance.
Funding
Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Summary
Background
Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies.
Methods
In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations.
Findings
Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types.
Interpretation
The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance.
Funding
Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.
Background
Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies.
Methods
In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations.
Findings
Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types.
Interpretation
The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance.
Funding
Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.
191.
Kraemer, Moritz U G; Faria, Nuno R; Reiner, Robert C; Golding, Nick; Nikolay, Birgit; Stasse, Stephanie; Johansson, Michael A; Salje, Henrik; Faye, Ousmane; Wint, G R William; Niedrig, Matthias; Shearer, Freya M; Hill, Sarah C; Thompson, Robin N; Bisanzio, Donal; Taveira, Nuno; Nax, Heinrich H; Pradelski, Bary S R; Nsoesie, Elaine O; Murphy, Nicholas R; Bogoch, Isaac I; Khan, Kamran; Brownstein, John S; Tatem, Andrew J; Oliveira, Tulio; Smith, David L; Sall, Amadou A; Pybus, Oliver G; Hay, Simon I; Cauchemez, Simon
Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study Journal Article
In: The Lancet Infectious Diseases, vol. 17, no. 3, pp. 330-338, 2017, ISSN: 1473-3099.
Abstract | Links | BibTeX | Tags:
@article{KRAEMER2017330,
title = {Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study},
author = {Moritz U G Kraemer and Nuno R Faria and Robert C Reiner and Nick Golding and Birgit Nikolay and Stephanie Stasse and Michael A Johansson and Henrik Salje and Ousmane Faye and G R William Wint and Matthias Niedrig and Freya M Shearer and Sarah C Hill and Robin N Thompson and Donal Bisanzio and Nuno Taveira and Heinrich H Nax and Bary S R Pradelski and Elaine O Nsoesie and Nicholas R Murphy and Isaac I Bogoch and Kamran Khan and John S Brownstein and Andrew J Tatem and Tulio Oliveira and David L Smith and Amadou A Sall and Oliver G Pybus and Simon I Hay and Simon Cauchemez},
url = {https://www.sciencedirect.com/science/article/pii/S1473309916305138},
doi = {https://doi.org/10.1016/S1473-3099(16)30513-8},
issn = {1473-3099},
year = {2017},
date = {2017-01-01},
journal = {The Lancet Infectious Diseases},
volume = {17},
number = {3},
pages = {330-338},
abstract = {Summary
Background
Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.
Methods
We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.
Findings
The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.
Interpretation
Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.
Funding
Wellcome Trust.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Summary
Background
Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.
Methods
We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.
Findings
The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.
Interpretation
Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.
Funding
Wellcome Trust.
Background
Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.
Methods
We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.
Findings
The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.
Interpretation
Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy.
Funding
Wellcome Trust.
192.
Lai, Shengjie; Wardrop, Nicola A.; Huang, Zhuojie; Bosco, Claudio; Sun, Junling; Bird, Tomas; Wesolowski, Amy; Zhou, Sheng; Zhang, Qian; Zheng, Canjun; Li, Zhongjie; Tatem, Andrew J.; Yu, Hongjie
Plasmodium falciparum malaria importation from Africa to China and its mortality: an analysis of driving factors Journal Article
In: Scientific Reports, vol. 6, no. 1, pp. 39524, 2016, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{Lai2016,
title = {Plasmodium falciparum malaria importation from Africa to China and its mortality: an analysis of driving factors},
author = {Shengjie Lai and Nicola A. Wardrop and Zhuojie Huang and Claudio Bosco and Junling Sun and Tomas Bird and Amy Wesolowski and Sheng Zhou and Qian Zhang and Canjun Zheng and Zhongjie Li and Andrew J. Tatem and Hongjie Yu},
url = {https://doi.org/10.1038/srep39524},
doi = {10.1038/srep39524},
issn = {2045-2322},
year = {2016},
date = {2016-12-21},
journal = {Scientific Reports},
volume = {6},
number = {1},
pages = {39524},
abstract = {Plasmodium falciparum malaria importation from Africa to China is rising with increasing Chinese overseas investment and international travel. Identifying networks and drivers of this phenomenon as well as the contributors to high case-fatality rate is a growing public health concern to enable efficient response. From 2011--2015, 8653thinspaceP. falciparum cases leading to 98 deaths (11.3 per 1000 cases) were imported from 41 sub-Saharan countries into China, with most cases (91.3%) occurring in labour-related Chinese travellers. Four strongly connected groupings of origin African countries with destination Chinese provinces were identified, and the number of imported cases was significantly associated with the volume of air passengers to China (Pthinspace=thinspace0.006), parasite prevalence in Africa (Pthinspace<thinspace0.001), and the amount of official development assistance from China (Pthinspace<thinspace0.001) with investment in resource extraction having the strongest relationship with parasite importation. Risk factors for deaths from imported cases were related to the capacity of malaria diagnosis and diverse socioeconomic factors. The spatial heterogeneity uncovered, principal drivers explored, and risk factors for mortality found in the rising rates of P. falciparum malaria importation to China can serve to refine malaria elimination strategies and the management of cases, and high risk groups and regions should be targeted.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Plasmodium falciparum malaria importation from Africa to China is rising with increasing Chinese overseas investment and international travel. Identifying networks and drivers of this phenomenon as well as the contributors to high case-fatality rate is a growing public health concern to enable efficient response. From 2011--2015, 8653thinspaceP. falciparum cases leading to 98 deaths (11.3 per 1000 cases) were imported from 41 sub-Saharan countries into China, with most cases (91.3%) occurring in labour-related Chinese travellers. Four strongly connected groupings of origin African countries with destination Chinese provinces were identified, and the number of imported cases was significantly associated with the volume of air passengers to China (Pthinspace=thinspace0.006), parasite prevalence in Africa (Pthinspace<thinspace0.001), and the amount of official development assistance from China (Pthinspace<thinspace0.001) with investment in resource extraction having the strongest relationship with parasite importation. Risk factors for deaths from imported cases were related to the capacity of malaria diagnosis and diverse socioeconomic factors. The spatial heterogeneity uncovered, principal drivers explored, and risk factors for mortality found in the rising rates of P. falciparum malaria importation to China can serve to refine malaria elimination strategies and the management of cases, and high risk groups and regions should be targeted.
193.
Alegana, Victor A.; Kigozi, Simon P.; Nankabirwa, Joaniter; Arinaitwe, Emmanuel; Kigozi, Ruth; Mawejje, Henry; Kilama, Maxwell; Ruktanonchai, Nick W.; Ruktanonchai, Corrine W.; Drakeley, Chris; Lindsay, Steve W.; Greenhouse, Bryan; Kamya, Moses R.; Smith, David L.; Atkinson, Peter M.; Dorsey, Grant; Tatem, Andrew J.
Spatio-temporal analysis of malaria vector density from baseline through intervention in a high transmission setting Journal Article
In: Parasites & Vectors, vol. 9, no. 1, pp. 637, 2016, ISSN: 1756-3305.
Abstract | Links | BibTeX | Tags:
@article{Alegana2016b,
title = {Spatio-temporal analysis of malaria vector density from baseline through intervention in a high transmission setting},
author = {Victor A. Alegana and Simon P. Kigozi and Joaniter Nankabirwa and Emmanuel Arinaitwe and Ruth Kigozi and Henry Mawejje and Maxwell Kilama and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Chris Drakeley and Steve W. Lindsay and Bryan Greenhouse and Moses R. Kamya and David L. Smith and Peter M. Atkinson and Grant Dorsey and Andrew J. Tatem},
url = {https://doi.org/10.1186/s13071-016-1917-3},
doi = {10.1186/s13071-016-1917-3},
issn = {1756-3305},
year = {2016},
date = {2016-12-12},
journal = {Parasites & Vectors},
volume = {9},
number = {1},
pages = {637},
abstract = {An increase in effective malaria control since 2000 has contributed to a decline in global malaria morbidity and mortality. Knowing when and how existing interventions could be combined to maximise their impact on malaria vectors can provide valuable information for national malaria control programs in different malaria endemic settings. Here, we assess the effect of indoor residual spraying on malaria vector densities in a high malaria endemic setting in eastern Uganda as part of a cohort study where the use of long-lasting insecticidal nets (LLINs) was high.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An increase in effective malaria control since 2000 has contributed to a decline in global malaria morbidity and mortality. Knowing when and how existing interventions could be combined to maximise their impact on malaria vectors can provide valuable information for national malaria control programs in different malaria endemic settings. Here, we assess the effect of indoor residual spraying on malaria vector densities in a high malaria endemic setting in eastern Uganda as part of a cohort study where the use of long-lasting insecticidal nets (LLINs) was high.
194.
Erbach-Schoenberg, Elisabeth; Alegana, Victor A.; Sorichetta, Alessandro; Linard, Catherine; Lourenço, Christoper; Ruktanonchai, Nick W.; Graupe, Bonita; Bird, Tomas J.; Pezzulo, Carla; Wesolowski, Amy; Tatem, Andrew J.
Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates Journal Article
In: Population Health Metrics, vol. 14, no. 1, pp. 35, 2016, ISSN: 1478-7954.
Abstract | Links | BibTeX | Tags:
@article{zuErbach-Schoenberg2016,
title = {Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates},
author = {Elisabeth Erbach-Schoenberg and Victor A. Alegana and Alessandro Sorichetta and Catherine Linard and Christoper Lourenço and Nick W. Ruktanonchai and Bonita Graupe and Tomas J. Bird and Carla Pezzulo and Amy Wesolowski and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12963-016-0106-0},
doi = {10.1186/s12963-016-0106-0},
issn = {1478-7954},
year = {2016},
date = {2016-10-12},
journal = {Population Health Metrics},
volume = {14},
number = {1},
pages = {35},
abstract = {Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.
195.
Bharti, Nita; Djibo, Ali; Tatem, Andrew J.; Grenfell, Bryan T.; Ferrari, Matthew J.
Measuring populations to improve vaccination coverage Journal Article
In: Scientific Reports, vol. 6, no. 1, pp. 34541, 2016, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{Bharti2016,
title = {Measuring populations to improve vaccination coverage},
author = {Nita Bharti and Ali Djibo and Andrew J. Tatem and Bryan T. Grenfell and Matthew J. Ferrari},
url = {https://doi.org/10.1038/srep34541},
doi = {10.1038/srep34541},
issn = {2045-2322},
year = {2016},
date = {2016-10-05},
journal = {Scientific Reports},
volume = {6},
number = {1},
pages = {34541},
abstract = {In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.
196.
Lu, Xin; Wrathall, David J.; Sundsøy, Pål Roe; Nadiruzzaman, Md.; Wetter, Erik; Iqbal, Asif; Qureshi, Taimur; Tatem, Andrew J.; Canright, Geoffrey S.; Engø-Monsen, Kenth; Bengtsson, Linus
In: Climatic Change, vol. 138, no. 3, pp. 505-519, 2016, ISSN: 1573-1480.
Abstract | Links | BibTeX | Tags:
@article{Lu2016,
title = {Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen},
author = {Xin Lu and David J. Wrathall and Pål Roe Sundsøy and Md. Nadiruzzaman and Erik Wetter and Asif Iqbal and Taimur Qureshi and Andrew J. Tatem and Geoffrey S. Canright and Kenth Engø-Monsen and Linus Bengtsson},
url = {https://doi.org/10.1007/s10584-016-1753-7},
doi = {10.1007/s10584-016-1753-7},
issn = {1573-1480},
year = {2016},
date = {2016-10-01},
journal = {Climatic Change},
volume = {138},
number = {3},
pages = {505-519},
abstract = {Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm's landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people's preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm's landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people's preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.
197.
Armstrong, Corinne E.; Martínez-Álvarez, Melisa; Singh, Neha S.; John, Theopista; Afnan-Holmes, Hoviyeh; Grundy, Chris; Ruktanochai, Corrine W.; Borghi, Josephine; Magoma, Moke; Msemo, Georgina; Matthews, Zoe; Mtei, Gemini; Lawn, Joy E.
Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs? Journal Article
In: BMC Public Health, vol. 16, no. 2, pp. 795, 2016, ISSN: 1471-2458.
Abstract | Links | BibTeX | Tags:
@article{Armstrong2016,
title = {Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs?},
author = {Corinne E. Armstrong and Melisa Martínez-Álvarez and Neha S. Singh and Theopista John and Hoviyeh Afnan-Holmes and Chris Grundy and Corrine W. Ruktanochai and Josephine Borghi and Moke Magoma and Georgina Msemo and Zoe Matthews and Gemini Mtei and Joy E. Lawn},
url = {https://doi.org/10.1186/s12889-016-3404-3},
doi = {10.1186/s12889-016-3404-3},
issn = {1471-2458},
year = {2016},
date = {2016-09-12},
journal = {BMC Public Health},
volume = {16},
number = {2},
pages = {795},
abstract = {Tanzania achieved the Millennium Development Goal for child survival, yet made insufficient progress for maternal and neonatal survival and stillbirths, due to low coverage and quality of services for care at birth, with rural women left behind. Our study aimed to evaluate Tanzania's subnational (regional-level) variations for rural care at birth outcomes, i.e., rural women giving birth in a facility and by Caesarean section (C-section), and associations with health systems inputs (financing, health workforce, facilities, and commodities), outputs (readiness and quality of care) and context (education and GDP).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Tanzania achieved the Millennium Development Goal for child survival, yet made insufficient progress for maternal and neonatal survival and stillbirths, due to low coverage and quality of services for care at birth, with rural women left behind. Our study aimed to evaluate Tanzania's subnational (regional-level) variations for rural care at birth outcomes, i.e., rural women giving birth in a facility and by Caesarean section (C-section), and associations with health systems inputs (financing, health workforce, facilities, and commodities), outputs (readiness and quality of care) and context (education and GDP).
198.
Neal, Sarah; Ruktanonchai, Corrine; Chandra-Mouli, Venkatraman; Matthews, Zo"e; Tatem, Andrew J.
In: Reproductive Health, vol. 13, no. 1, pp. 98, 2016, ISSN: 1742-4755.
Abstract | Links | BibTeX | Tags:
@article{Neal2016,
title = {Mapping adolescent first births within three east African countries using data from Demographic and Health Surveys: exploring geospatial methods to inform policy},
author = {Sarah Neal and Corrine Ruktanonchai and Venkatraman Chandra-Mouli and Zo"e Matthews and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12978-016-0205-1},
doi = {10.1186/s12978-016-0205-1},
issn = {1742-4755},
year = {2016},
date = {2016-08-23},
journal = {Reproductive Health},
volume = {13},
number = {1},
pages = {98},
abstract = {Early adolescent pregnancy presents a major barrier to the health and wellbeing of young women and their children. Previous studies suggest geographic heterogeneity in adolescent births, with clear ``hot spots'' experiencing very high prevalence of teenage pregnancy. As the reduction of adolescent pregnancy is a priority in many countries, further detailed information of the geographical areas where they most commonly occur is of value to national and district level policy makers. The aim of this study is to develop a comprehensive assessment of the geographical distribution of adolescent first births in Uganda, Kenya and Tanzania using Demographic and Household (DHS) data using descriptive, spatial analysis and spatial modelling methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Early adolescent pregnancy presents a major barrier to the health and wellbeing of young women and their children. Previous studies suggest geographic heterogeneity in adolescent births, with clear ``hot spots'' experiencing very high prevalence of teenage pregnancy. As the reduction of adolescent pregnancy is a priority in many countries, further detailed information of the geographical areas where they most commonly occur is of value to national and district level policy makers. The aim of this study is to develop a comprehensive assessment of the geographical distribution of adolescent first births in Uganda, Kenya and Tanzania using Demographic and Household (DHS) data using descriptive, spatial analysis and spatial modelling methods.
199.
Early, Regan; Bradley, Bethany A.; Dukes, Jeffrey S.; Lawler, Joshua J.; Olden, Julian D.; Blumenthal, Dana M.; Gonzalez, Patrick; Grosholz, Edwin D.; Ibañez, Ines; Miller, Luke P.; Sorte, Cascade J. B.; Tatem, Andrew J.
Global threats from invasive alien species in the twenty-first century and national response capacities Journal Article
In: Nature Communications, vol. 7, no. 1, pp. 12485, 2016, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@article{Early2016,
title = {Global threats from invasive alien species in the twenty-first century and national response capacities},
author = {Regan Early and Bethany A. Bradley and Jeffrey S. Dukes and Joshua J. Lawler and Julian D. Olden and Dana M. Blumenthal and Patrick Gonzalez and Edwin D. Grosholz and Ines Ibañez and Luke P. Miller and Cascade J. B. Sorte and Andrew J. Tatem},
url = {https://doi.org/10.1038/ncomms12485},
doi = {10.1038/ncomms12485},
issn = {2041-1723},
year = {2016},
date = {2016-08-23},
journal = {Nature Communications},
volume = {7},
number = {1},
pages = {12485},
abstract = {Invasive alien species (IAS) threaten human livelihoods and biodiversity globally. Increasing globalization facilitates IAS arrival, and environmental changes, including climate change, facilitate IAS establishment. Here we provide the first global, spatial analysis of the terrestrial threat from IAS in light of twenty-first century globalization and environmental change, and evaluate national capacities to prevent and manage species invasions. We find that one-sixth of the global land surface is highly vulnerable to invasion, including substantial areas in developing economies and biodiversity hotspots. The dominant invasion vectors differ between high-income countries (imports, particularly of plants and pets) and low-income countries (air travel). Uniting data on the causes of introduction and establishment can improve early-warning and eradication schemes. Most countries have limited capacity to act against invasions. In particular, we reveal a clear need for proactive invasion strategies in areas with high poverty levels, high biodiversity and low historical levels of invasion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Invasive alien species (IAS) threaten human livelihoods and biodiversity globally. Increasing globalization facilitates IAS arrival, and environmental changes, including climate change, facilitate IAS establishment. Here we provide the first global, spatial analysis of the terrestrial threat from IAS in light of twenty-first century globalization and environmental change, and evaluate national capacities to prevent and manage species invasions. We find that one-sixth of the global land surface is highly vulnerable to invasion, including substantial areas in developing economies and biodiversity hotspots. The dominant invasion vectors differ between high-income countries (imports, particularly of plants and pets) and low-income countries (air travel). Uniting data on the causes of introduction and establishment can improve early-warning and eradication schemes. Most countries have limited capacity to act against invasions. In particular, we reveal a clear need for proactive invasion strategies in areas with high poverty levels, high biodiversity and low historical levels of invasion.
200.
Sorichetta, Alessandro; Bird, Tom J.; Ruktanonchai, Nick W.; Erbach-Schoenberg, Elisabeth; Pezzulo, Carla; Tejedor, Natalia; Waldock, Ian C.; Sadler, Jason D.; Garcia, Andres J.; Sedda, Luigi; Tatem, Andrew J.
Mapping internal connectivity through human migration in malaria endemic countries Journal Article
In: Scientific Data, vol. 3, no. 1, pp. 160066, 2016, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{Sorichetta2016,
title = {Mapping internal connectivity through human migration in malaria endemic countries},
author = {Alessandro Sorichetta and Tom J. Bird and Nick W. Ruktanonchai and Elisabeth Erbach-Schoenberg and Carla Pezzulo and Natalia Tejedor and Ian C. Waldock and Jason D. Sadler and Andres J. Garcia and Luigi Sedda and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2016.66},
doi = {10.1038/sdata.2016.66},
issn = {2052-4463},
year = {2016},
date = {2016-08-16},
journal = {Scientific Data},
volume = {3},
number = {1},
pages = {160066},
abstract = {Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.
