@article{nokey,

title = {Identifying counter-urbanisation using Facebook's user count data},

author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},

url = {https://doi.org/10.1016/j.habitatint.2024.103113},

doi = {10.1016/j.habitatint.2024.103113},

year  = {2024},

date = {2024-06-04},

journal = {Habitat International},

volume = {150},

abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Summary and prospect of early warning models and systems for infectious disease outbreaks},

author = {Lai, SJ and Feng, LZ and Leng, ZW and Lyu, X and Li, RY and Yin, L and Luo, W and Li, ZJ and Lan, YJ and Yang, WZ},

doi = {10.3760/cma.j.cn112338-20210512-00391},

issn = {0254-6450},

year  = {2021},

date = {2021-05-12},

journal = {Chinese Journal of Epidemiology},

volume = {42},

number = {8},

pages = {1330—1335},

abstract = {This paper summarizes the basic principles and models of early warning for infectious disease outbreaks, introduces the early warning systems for infectious disease based on different data sources and their applications, and discusses the application potential of big data and their analysing techniques, which have been studied and used in the prevention and control of COVID-19 pandemic, including internet inquiry, social media, mobile positioning, in the early warning of infectious diseases in order to provide reference for the establishment of an intelligent early warning mechanism and platform for infectious diseases based on multi-source big data.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{unwin2021using,

title = {Using Hawkes Processes to model imported and local malaria cases in near-elimination settings},

author = {H Juliette T Unwin and Isobel Routledge and Seth Flaxman and Marian-Andrei Rizoiu and Shengjie Lai and Justin Cohen and Daniel J Weiss and Swapnil Mishra and Samir Bhatt},

url = {https://doi.org/10.1371/journal.pcbi.1008830},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {PLoS computational biology},

volume = {17},

number = {4},

pages = {e1008830},

publisher = {Public Library of Science San Francisco, CA USA},

abstract = {Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1111/tgis.12214,

title = {Improving Large Area Population Mapping Using Geotweet Densities},

author = {Nirav N. Patel and Forrest R. Stevens and Zhuojie Huang and Andrea E. Gaughan and Iqbal Elyazar and Andrew J. Tatem},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12214},

doi = {https://doi.org/10.1111/tgis.12214},

year  = {2017},

date = {2017-01-01},

journal = {Transactions in GIS},

volume = {21},

number = {2},

pages = {317-331},

abstract = {Abstract Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}