Publications
41.
Shan, Xuzheng; Lai, Shengjie; Liao, Hongxiu; Li, Zhongjie; Lan, Yajia; Yang, Weizhong
The epidemic potential of avian influenza A (H7N9) virus in humans in mainland China: A two-stage risk analysis Journal Article
In: PLOS ONE, vol. 14, no. 4, pp. 1-14, 2019.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pone.0215857,
title = {The epidemic potential of avian influenza A (H7N9) virus in humans in mainland China: A two-stage risk analysis},
author = {Xuzheng Shan and Shengjie Lai and Hongxiu Liao and Zhongjie Li and Yajia Lan and Weizhong Yang},
url = {https://doi.org/10.1371/journal.pone.0215857},
doi = {10.1371/journal.pone.0215857},
year = {2019},
date = {2019-01-01},
journal = {PLOS ONE},
volume = {14},
number = {4},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Background From 2013 to 2017, more than one thousand avian influenza A (H7N9) confirmed cases with hundreds of deaths were reported in mainland China. To identify priorities for epidemic prevention and control, a risk assessing framework for subnational variations is needed to define the epidemic potential of A (H7N9). Methods We established a consolidated two-stage framework that outlined the potential epidemic of H7N9 in humans: The Stage 1, index-case potential, used a Boosted Regression Trees model to assess population at risk due to spillover from poultry; the Stage 2, epidemic potential, synthesized the variables upon a framework of the Index for Risk Management to measure epidemic potential based on the probability of hazards and exposure, the vulnerability and coping capacity. Results Provinces in southern and eastern China, especially Jiangsu, Zhejiang, Guangzhou, have high index-case potential of human infected with A (H7N9), while northern coastal provinces and municipalities with low morbidity, i.e. Tianjin and Liaoning, have an increasing risk of A (H7N9) infection. Provinces in central China are likely to have high potential of epidemic due to the high vulnerability and the lack of coping capacity. Conclusions This study provides a unified risk assessment of A (H7N9) to detect the two-stage heterogeneity of epidemic potential among different provinces in mainland China, allowing proactively evaluate health preparedness at subnational levels to improve surveillance, diagnostic capabilities, and health promotion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background From 2013 to 2017, more than one thousand avian influenza A (H7N9) confirmed cases with hundreds of deaths were reported in mainland China. To identify priorities for epidemic prevention and control, a risk assessing framework for subnational variations is needed to define the epidemic potential of A (H7N9). Methods We established a consolidated two-stage framework that outlined the potential epidemic of H7N9 in humans: The Stage 1, index-case potential, used a Boosted Regression Trees model to assess population at risk due to spillover from poultry; the Stage 2, epidemic potential, synthesized the variables upon a framework of the Index for Risk Management to measure epidemic potential based on the probability of hazards and exposure, the vulnerability and coping capacity. Results Provinces in southern and eastern China, especially Jiangsu, Zhejiang, Guangzhou, have high index-case potential of human infected with A (H7N9), while northern coastal provinces and municipalities with low morbidity, i.e. Tianjin and Liaoning, have an increasing risk of A (H7N9) infection. Provinces in central China are likely to have high potential of epidemic due to the high vulnerability and the lack of coping capacity. Conclusions This study provides a unified risk assessment of A (H7N9) to detect the two-stage heterogeneity of epidemic potential among different provinces in mainland China, allowing proactively evaluate health preparedness at subnational levels to improve surveillance, diagnostic capabilities, and health promotion.
42.
Lai, Shengjie; Farnham, Andrea; Ruktanonchai, Nick W; Tatem, Andrew J
Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine Journal Article
In: Journal of Travel Medicine, vol. 26, no. 3, 2019, ISSN: 1708-8305, (taz019).
Abstract | Links | BibTeX | Tags:
@article{10.1093/jtm/taz019,
title = {Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine},
author = {Shengjie Lai and Andrea Farnham and Nick W Ruktanonchai and Andrew J Tatem},
url = {https://doi.org/10.1093/jtm/taz019},
doi = {10.1093/jtm/taz019},
issn = {1708-8305},
year = {2019},
date = {2019-01-01},
journal = {Journal of Travel Medicine},
volume = {26},
number = {3},
abstract = {The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data.Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel.Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.},
note = {taz019},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data.Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel.Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.
43.
Lai, Shengjie; Johansson, Michael A.; Yin, Wenwu; Wardrop, Nicola A.; Panhuis, Willem G.; Wesolowski, Amy; Kraemer, Moritz U. G.; Bogoch, Isaac I.; Kain, Dylain; Findlater, Aidan; Choisy, Marc; Huang, Zhuojie; Mu, Di; Li, Yu; He, Yangni; Chen, Qiulan; Yang, Juan; Khan, Kamran; Tatem, Andrew J.; Yu, Hongjie
Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015 Journal Article
In: PLOS Neglected Tropical Diseases, vol. 12, no. 11, pp. 1-16, 2018.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pntd.0006743,
title = {Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015},
author = {Shengjie Lai and Michael A. Johansson and Wenwu Yin and Nicola A. Wardrop and Willem G. Panhuis and Amy Wesolowski and Moritz U. G. Kraemer and Isaac I. Bogoch and Dylain Kain and Aidan Findlater and Marc Choisy and Zhuojie Huang and Di Mu and Yu Li and Yangni He and Qiulan Chen and Juan Yang and Kamran Khan and Andrew J. Tatem and Hongjie Yu},
url = {https://doi.org/10.1371/journal.pntd.0006743},
doi = {10.1371/journal.pntd.0006743},
year = {2018},
date = {2018-01-01},
journal = {PLOS Neglected Tropical Diseases},
volume = {12},
number = {11},
pages = {1-16},
publisher = {Public Library of Science},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
44.
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.
45.
Gaughan, Andrea E.; Stevens, Forrest R.; Huang, Zhuojie; Nieves, Jeremiah J.; Sorichetta, Alessandro; Lai, Shengjie; Ye, Xinyue; Linard, Catherine; Hornby, Graeme M.; Hay, Simon I.; Yu, Hongjie; Tatem, Andrew J.
Spatiotemporal patterns of population in mainland China, 1990 to 2010 Journal Article
In: Scientific Data, vol. 3, no. 1, pp. 160005, 2016, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
46.
Gilbert, Marius; Golding, Nick; Zhou, Hang; Wint, G. R. William; Robinson, Timothy P.; Tatem, Andrew J.; Lai, Shengjie; Zhou, Sheng; Jiang, Hui; Guo, Danhuai; Huang, Zhi; Messina, Jane P.; Xiao, Xiangming; Linard, Catherine; Boeckel, Thomas P. Van; Martin, Vincent; Bhatt, Samir; Gething, Peter W.; Farrar, Jeremy J.; Hay, Simon I.; Yu, Hongjie
Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia Journal Article
In: Nature Communications, vol. 5, no. 1, pp. 4116, 2014, ISSN: 2041-1723.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
