@article{Alegana2016,

title = {Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence},

author = {Victor A. Alegana and Peter M. Atkinson and Christopher Lourenço and Nick W. Ruktanonchai and Claudio Bosco and Elisabeth zu Erbach-Schoenberg and Bradley Didier and Deepa Pindolia and Arnaud Le Menach and Stark Katokele and Petrina Uusiku and Andrew J. Tatem},

url = {https://doi.org/10.1038/srep29628},

doi = {10.1038/srep29628},

issn = {2045-2322},

year  = {2016},

date = {2016-07-13},

journal = {Scientific Reports},

volume = {6},

number = {1},

pages = {29628},

abstract = {The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ruktanonchai2016,

title = {Census-derived migration data as a tool for informing malaria elimination policy},

author = {Nick W. Ruktanonchai and Darlene Bhavnani and Alessandro Sorichetta and Linus Bengtsson and Keith H. Carter and Roberto C. Córdoba and Arnaud Le Menach and Xin Lu and Erik Wetter and Elisabeth Erbach-Schoenberg and Andrew J. Tatem},

url = {https://doi.org/10.1186/s12936-016-1315-5},

doi = {10.1186/s12936-016-1315-5},

issn = {1475-2875},

year  = {2016},

date = {2016-05-11},

journal = {Malaria Journal},

volume = {15},

number = {1},

pages = {273},

abstract = {Numerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.7554/eLife.15272,

title = {Mapping global environmental suitability for Zika virus},

author = {Jane P Messina and Moritz UG Kraemer and Oliver J Brady and David M Pigott and Freya M Shearer and Daniel J Weiss and Nick Golding and Corrine W Ruktanonchai and Peter W Gething and Emily Cohn and John S Brownstein and Kamran Khan and Andrew J Tatem and Thomas Jaenisch and Christopher JL Murray and Fatima Marinho and Thomas W Scott and Simon I Hay},

editor = {Mark Jit},

url = {https://doi.org/10.7554/eLife.15272},

doi = {10.7554/eLife.15272},

issn = {2050-084X},

year  = {2016},

date = {2016-04-01},

journal = {eLife},

volume = {5},

pages = {e15272},

publisher = {eLife Sciences Publications, Ltd},

abstract = {Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas.},

keywords = {disease mapping, vector-borne disease, Zika virus},

pubstate = {published},

tppubtype = {article}

}

@article{Zhao2016,

title = {Exploring the spatiotemporal drivers of malaria elimination in Europe},

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

url = {https://doi.org/10.1186/s12936-016-1175-z},

doi = {10.1186/s12936-016-1175-z},

issn = {1475-2875},

year  = {2016},

date = {2016-03-04},

journal = {Malaria Journal},

volume = {15},

number = {1},

pages = {122},

abstract = {Europe once had widespread malaria, but today it is free from endemic transmission. Changing land use, agricultural practices, housing quality, urbanization, climate change, and improved healthcare are among the many factors thought to have played a role in the declines of malaria seen, but their effects and relative contributions have rarely been quantified.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gaughan2016,

title = {Spatiotemporal patterns of population in mainland China, 1990 to 2010},

author = {Andrea E. Gaughan and Forrest R. Stevens and Zhuojie Huang and Jeremiah J. Nieves and Alessandro Sorichetta and Shengjie Lai and Xinyue Ye and Catherine Linard and Graeme M. Hornby and Simon I. Hay and Hongjie Yu and Andrew J. Tatem},

url = {https://doi.org/10.1038/sdata.2016.5},

doi = {10.1038/sdata.2016.5},

issn = {2052-4463},

year  = {2016},

date = {2016-02-16},

journal = {Scientific Data},

volume = {3},

number = {1},

pages = {160005},

abstract = {According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from textasciitilde18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (textasciitilde100thinspacem) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LU20161,

title = {Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh},

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 Tatem and Geoffrey Canright and Kenth Engø-Monsen and Linus Bengtsson},

url = {https://www.sciencedirect.com/science/article/pii/S0959378016300140},

doi = {https://doi.org/10.1016/j.gloenvcha.2016.02.002},

issn = {0959-3780},

year  = {2016},

date = {2016-01-01},

journal = {Global Environmental Change},

volume = {38},

pages = {1-7},

abstract = {Climate change is likely to drive migration from environmentally stressed areas. However quantifying short and long-term movements across large areas is challenging due to difficulties in the collection of highly spatially and temporally resolved human mobility data. In this study we use two datasets of individual mobility trajectories from six million de-identified mobile phone users in Bangladesh over three months and two years respectively. Using data collected during Cyclone Mahasen, which struck Bangladesh in May 2013, we show first how analyses based on mobile network data can describe important short-term features (hours–weeks) of human mobility during and after extreme weather events, which are extremely hard to quantify using standard survey based research. We then demonstrate how mobile data for the first time allow us to study the relationship between fundamental parameters of migration patterns on a national scale. We concurrently quantify incidence, direction, duration and seasonality of migration episodes in Bangladesh. While we show that changes in the incidence of migration episodes are highly correlated with changes in the duration of migration episodes, the correlation between in- and out-migration between areas is unexpectedly weak. The methodological framework described here provides an important addition to current methods in studies of human migration and climate change.},

keywords = {Adaptation, Bangladesh, Climate change, Disaster, Migration, Mobile data},

pubstate = {published},

tppubtype = {article}

}

@article{UTAZI2016161,

title = {A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks},

author = {C. Edson Utazi and Sujit K. Sahu and Peter M. Atkinson and Natalia Tejedor and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S2211675316300240},

doi = {https://doi.org/10.1016/j.spasta.2016.05.006},

issn = {2211-6753},

year  = {2016},

date = {2016-01-01},

journal = {Spatial Statistics},

volume = {17},

pages = {161-178},

abstract = {Health and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network.},

keywords = {Bayesian inference, BIC, Central clustering, Finite Gaussian mixture model, Gibbs sampling, Predictive clustering},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pone.0162006,

title = {Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries},

author = {Corrine W. Ruktanonchai and Nick W. Ruktanonchai and Andrea Nove and Sofia Lopes and Carla Pezzulo and Claudio Bosco and Victor A. Alegana and Clara R. Burgert and Rogers Ayiko and Andrew SEK Charles and Nkurunziza Lambert and Esther Msechu and Esther Kathini and Zoë Matthews and Andrew J. Tatem},

url = {https://doi.org/10.1371/journal.pone.0162006},

doi = {10.1371/journal.pone.0162006},

year  = {2016},

date = {2016-01-01},

journal = {PLOS ONE},

volume = {11},

number = {8},

pages = {1-17},

publisher = {Public Library of Science},

abstract = {Background Geographic accessibility to health facilities represents a fundamental barrier to utilisation of maternal and newborn health (MNH) services, driving historically hidden spatial pockets of localized inequalities. Here, we examine utilisation of MNH care as an emergent property of accessibility, highlighting high-resolution spatial heterogeneity and sub-national inequalities in receiving care before, during, and after delivery throughout five East African countries. Methods We calculated a geographic inaccessibility score to the nearest health facility at 300 x 300 m using a dataset of 9,314 facilities throughout Burundi, Kenya, Rwanda, Tanzania and Uganda. Using Demographic and Health Surveys data, we utilised hierarchical mixed effects logistic regression to examine the odds of: 1) skilled birth attendance, 2) receiving 4+ antenatal care visits at time of delivery, and 3) receiving a postnatal health check-up within 48 hours of delivery. We applied model results onto the accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. Results Across all outcomes, decreasing wealth and education levels were associated with lower odds of obtaining MNH care. Increasing geographic inaccessibility scores were associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance. Specifically, for each increase in the inaccessibility score to the nearest health facility, the odds of having skilled birth attendance at delivery was reduced by over 75% (0.24; CI: 0.19–0.3), while the odds of receiving antenatal care decreased by nearly 25% (0.74; CI: 0.61–0.89) and 40% for obtaining postnatal care (0.58; CI: 0.45–0.75). Conclusions Overall, these results suggest decreasing accessibility to the nearest health facility significantly deterred utilisation of all maternal health care services. These results demonstrate how spatial approaches can inform policy efforts and promote evidence-based decision-making, and are particularly pertinent as the world shifts into the Sustainable Goals Development era, where sub-national applications will become increasingly useful in identifying and reducing persistent inequalities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1098/rsif.2015.1101,

title = {Introduction of rubella-containing-vaccine to Madagascar: implications for roll-out and local elimination},

author = {Amy Wesolowski and Keitly Mensah and Cara E. Brook and Miora Andrianjafimasy and Amy Winter and Caroline O. Buckee and Richter Razafindratsimandresy and Andrew J. Tatem and Jean-Michel Heraud and C. Jessica E. Metcalf},

url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2015.1101},

doi = {10.1098/rsif.2015.1101},

year  = {2016},

date = {2016-01-01},

journal = {Journal of The Royal Society Interface},

volume = {13},

number = {117},

pages = {20151101},

abstract = {Few countries in Africa currently include rubella-containing vaccination (RCV) in their immunization schedule. The Global Alliance for Vaccines Initiative (GAVI) recently opened a funding window that has motivated more widespread roll-out of RCV. As countries plan RCV introductions, an understanding of the existing burden, spatial patterns of vaccine coverage, and the impact of patterns of local extinction and reintroduction for rubella will be critical to developing effective programmes. As one of the first countries proposing RCV introduction in part with GAVI funding, Madagascar provides a powerful and timely case study. We analyse serological data from measles surveillance systems to characterize the epidemiology of rubella in Madagascar. Combining these results with data on measles vaccination delivery, we develop an age-structured model to simulate rubella vaccination scenarios and evaluate the dynamics of rubella and the burden of congenital rubella syndrome (CRS) across Madagascar. We additionally evaluate the drivers of spatial heterogeneity in age of infection to identify focal locations where vaccine surveillance should be strengthened and where challenges to successful vaccination introduction are expected. Our analyses indicate that characteristics of rubella in Madagascar are in line with global observations, with an average age of infection near 7 years, and an impact of frequent local extinction with reintroductions causing localized epidemics. Modelling results indicate that introduction of RCV into the routine programme alone may initially decrease rubella incidence but then result in cumulative increases in the burden of CRS in some regions (and transient increases in this burden in many regions). Deployment of RCV with regular supplementary campaigns will mitigate these outcomes. Results suggest that introduction of RCV offers a potential for elimination of rubella in Madagascar, but also emphasize both that targeted vaccination is likely to be a lynchpin of this success, and the public health vigilance that this introduction will require.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pcbi.1004846,

title = {Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data},

author = {Nick W. Ruktanonchai and Patrick DeLeenheer and Andrew J. Tatem and Victor A. Alegana and T. Trevor Caughlin and Elisabeth Erbach-Schoenberg and Christopher Lourenço and Corrine W. Ruktanonchai and David L. Smith},

url = {https://doi.org/10.1371/journal.pcbi.1004846},

doi = {10.1371/journal.pcbi.1004846},

year  = {2016},

date = {2016-01-01},

journal = {PLOS Computational Biology},

volume = {12},

number = {4},

pages = {1-19},

publisher = {Public Library of Science},

abstract = {Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1016/j.ijgo.2015.11.004,

title = {Spatial distribution of emergency obstetric and newborn care services in Ghana: Using the evidence to plan interventions},

author = {Samuel Bosomprah and Andrew J. Tatem and Winfred Dotse-Gborgbortsi and Patrick Aboagye and Zoe Matthews},

url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1016/j.ijgo.2015.11.004},

doi = {https://doi.org/10.1016/j.ijgo.2015.11.004},

year  = {2016},

date = {2016-01-01},

journal = {International Journal of Gynecology & Obstetrics},

volume = {132},

number = {1},

pages = {130-134},

abstract = {Abstract Objective To provide clear policy directions for gaps in the provision of signal function services and sub-regions requiring priority attention using data from the 2010 Ghana Emergency Obstetric and Newborn Care (EmONC) survey. Methods Using 2010 survey data, the fraction of facilities with only one or two signal functions missing was calculated for each facility type and EmONC designation. Thematic maps were used to provide insight into inequities in service provision. Results Of 1159 maternity facilities, 89 provided all the necessary basic or comprehensive EmONC signal functions 3 months prior to the 2010 survey. Only 21% of facility-based births were in fully functioning EmONC facilities, but an additional 30% occurred in facilities missing one or two basic signal functions—most often assisted vaginal delivery and removal of retained products. Tackling these missing signal functions would extend births taking place in fully functioning facilities to over 50%. Subnational analyses based on estimated total pregnancies in each district revealed a pattern of inequity in service provision across the country. Conclusion Upgrading facilities missing only one or two signal functions will allow Ghana to meet international standards for availability of EmONC services. Reducing maternal deaths will require high national priority given to addressing inequities in the distribution of EmONC services.},

keywords = {Emergency obstetric and newborn care (EmONC), Geospatial analysis, Ghana, Maternal mortality, Needs assessment, Signal functions},

pubstate = {published},

tppubtype = {article}

}

@article{Sedda2015,

title = {A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997--2010},

author = {Luigi Sedda and Qiuyin Qi and Andrew J. Tatem},

url = {https://doi.org/10.1186/s12936-015-1024-5},

doi = {10.1186/s12936-015-1024-5},

issn = {1475-2875},

year  = {2015},

date = {2015-12-16},

journal = {Malaria Journal},

volume = {14},

number = {1},

pages = {500},

abstract = {The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}