Total: 126
Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria
Scientific Data volume 5, Article number: 180256 (2018) .
Author(s): Nita Bharti & Andrew J. Tatem.
Type: method. Year: 2018
DOI: 10.1038/sdata.2018.256.

Abstract: Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000–2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.
Link to paper

Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015.
PLoS Negl Trop Dis 12(11): e0006743..
Author(s): Shengjie Lai, Michael A. Johansson, Wenwu Yin, Nicola A. Wardrop, Willem G. van Panhuis, Amy Wesolowski, Moritz U. G. Kraemer, Isaac I. Bogoch, Dylain Kain, Aidan Findlater, Marc Choisy, Zhuojie Huang, Di Mu, Yu Li, Yangni He, Qiulan Chen, Juan Yang, Kamran Khan , Andrew J. Tatem , Hongjie Yu.
Type: method. Year: 2018
DOI: 10.1371/journal.pntd.0006743.

Abstract: Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005–15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.
Link to paper

A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping
Statistical Methods in Medical Research 0(0) 1–16.
Author(s): CE Utazi, J Thorley, VA Alegana, MJ Ferrari, K Nilsen, S Takahashi, CJE Metcalf, J Lessler and AJ Tatem.
Type: method. Year: 2018
DOI: 10.1177/0962280218797362.

Abstract: The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.
Link to paper

Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis.
The Lancet Global Health, 26 January 2018.
Author(s): Ouma Paul O, Maina Joseph, Thuranira Pamela N, Macharia Peter M, Alegana Victor A, English Mike, Okiro Emelda A, Snow Robert W.
Type: application. Year: 2018
DOI: 10.1016/S2214-109X(17)30488-6.

Abstract: Timely access to emergency care can substantially reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, no Pan-African database of where hospitals are located exists; therefore, we aimed to complete a geocoded inventory of hospital services in Africa in relation to how populations might access these services in 2015, with focus on women of child bearing age.
Link to paper