During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.

https://www.pnas.org/content/pnas/111/45/15888.full.pdf

Dynamic population mapping using mobile phone data

Burkholderia pseudomallei, a highly pathogenic bacterium that causes melioidosis, is commonly found in soil in Southeast Asia and Northern Australia1,2. Melioidosis can be difficult to diagnose due to its diverse clinical manifestations and the inadequacy of conventional bacterial identification methods3. The bacterium is intrinsically resistant to a wide range of antimicrobials, and treatment with ineffective antimicrobials may result in case fatality rates (CFRs) exceeding 70%4,5. The importation of infected animals has, in the past, spread melioidosis to non-endemic areas6,7. The global distribution of B. pseudomallei and burden of melioidosis, however, remain poorly understood. Here, we map documented human and animal cases, and the presence of environmental B. pseudomallei, and combine this in a formal modelling framework8-10 to estimate the global burden of melioidosis. We estimate there to be 165,000 (95% credible interval 68,000-412,000) human melioidosis cases per year worldwide, of which 89,000 (36,000-227,000) die. Our estimates suggest that melioidosis is severely underreported in the 45 countries in which it is known to be endemic and that melioidosis is likely endemic in a further 34 countries which have never reported the disease. The large numbers of estimated cases and fatalities emphasise that the disease warrants renewed attention from public health officials and policy makers.

/pdf/predicted-global-distribution-of-burkholderia-pseudomallei-1of145y73x.pdf

Predicted global distribution of Burkholderia pseudomallei and burden of melioidosis

The economic and man-made resources that sustain human wellbeing are not distributed evenly across the world, but are instead heavily concentrated in cities. Poor access to opportunities and services offered by urban centres (a function of distance, transport infrastructure, and the spatial distribution of cities) is a major barrier to improved livelihoods and overall development. Advancing accessibility worldwide underpins the equity agenda of 'leaving no one behind' established by the Sustainable Development Goals of the United Nations. This has renewed international efforts to accurately measure accessibility and generate a metric that can inform the design and implementation of development policies. The only previous attempt to reliably map accessibility worldwide, which was published nearly a decade ago, predated the baseline for the Sustainable Development Goals and excluded the recent expansion in infrastructure networks, particularly in lower-resource settings. In parallel, new data sources provided by Open Street Map and Google now capture transportation networks with unprecedented detail and precision. Here we develop and validate a map that quantifies travel time to cities for 2015 at a spatial resolution of approximately one by one kilometre by integrating ten global-scale surfaces that characterize factors affecting human movement rates and 13,840 high-density urban centres within an established geospatial-modelling framework. Our results highlight disparities in accessibility relative to wealth as 50.9% of individuals living in low-income settings (concentrated in sub-Saharan Africa) reside within an hour of a city compared to 90.7% of individuals in high-income settings. By further triangulating this map against socioeconomic datasets, we demonstrate how access to urban centres stratifies the economic, educational, and health status of humanity.

/pdf/a-global-map-of-travel-time-to-cities-to-assess-inequalities-4ccpxudyi0.pdf

A global map of travel time to cities to assess inequalities in accessibility in 2015.

Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine

Name Sequence Exon 2 mG6pc S 5’-TCCCTGTCACCTGTGAG-3’ Exon 5 mG6pc AS 5’-CACAAGAAGTCTTTGTAA-3’ Exon1 mG6pcS 5’-TTACCAAGACTCCCAGGACTG-3’ Exon2 mG6pcAS 5’-GAGCTGTTGCTGTAGTAGTCG-3’ Pck1S 5’-AGCCTTTGGTCAACAACTGG-3’ Pck1AS 5’-TGCCTTCGGGGTTAGTTATG-3’ GcgR S 5’-ACCCAACTATTGCTGGTTGC-3’ GcgR AS 5’-CCATGTTGTCATTGCTGGTC-3’ Hmgcs2 S 5’-CCGTATGGGCTTCTGTTCAG-3’ Hmgcs2 AS 5’-AGCTTTGTGCGTTCCATCAG-3’ mL19S 5’-AGAAGATTGACCGCCATAT-3’ mL19AS 5’-TTCGTGCTTCCTTGGTCTTAGA-3’ CRU G6pc S 5’-TTTGCTATTTTACGTAAATCACCCT-3’  CRU G6pc AS 5’-GTACCTCAGGAAGCTGCCA-3’ CRU Pck1 S 5’-GGCCTCCCAACATTCATTAAC-3’ CRU Pck1 AS 5’-GTAGCTAGCCCTCCTCGCTTTAA-3’ GRU G6pc S 5’-CACCCCTTAGCACTGTAAGCCGTGTG-3’ GRU G6pc AS 5’-GGATTCAGTCTGTAGGTCAACCTAGCCC-3’ GRU Pck1 S 5’-TGCAGCCAGCAACATATGAA-3’

Supplementary table 1

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.

/pdf/predicting-the-risk-of-avian-influenza-a-h7n9-infection-in-3krlpxbvh9.pdf

Hui-Hui Jiang

Papers

Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia