@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}

}

@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{Gilbert2014,

title = {Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia},

author = {Marius Gilbert and Nick Golding and Hang Zhou and G. R. William Wint and Timothy P. Robinson and Andrew J. Tatem and Shengjie Lai and Sheng Zhou and Hui Jiang and Danhuai Guo and Zhi Huang and Jane P. Messina and Xiangming Xiao and Catherine Linard and Thomas P. Van Boeckel and Vincent Martin and Samir Bhatt and Peter W. Gething and Jeremy J. Farrar and Simon I. Hay and Hongjie Yu},

url = {https://doi.org/10.1038/ncomms5116},

doi = {10.1038/ncomms5116},

issn = {2041-1723},

year  = {2014},

date = {2014-06-17},

journal = {Nature Communications},

volume = {5},

number = {1},

pages = {4116},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}