@article{nokey,

title = {Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories},

author = {Ge, Yong and Zhang, Wen-Bin and Wu, Xilin and Ruktanonchai, Corrine W and Liu, Haiyan and Wang, Jianghao and Song, Yongze and Liu, Mengxiao and Yan, Wei and Yang, Juan and Cleary, Eimear and Qader, Sarchil H. and Atuhaire, Fatumah and Ruktanonchai, Nick W. and Tatem, Andrew J. and Lai Shengjie },

doi = {10.1038/s41467-022-30897-1},

year  = {2022},

date = {2022-06-03},

urldate = {2022-06-03},

journal = {Nature Communications},

volume = {13},

issue = {3106},

abstract = {Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.},

keywords = {covid-19, Europe, NPIs, vaccination},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling},

author = {Palacios-Lopez, Daniela and Esch, Thomas and MacManus, Kytt and Marconcini, Mattia and Sorichetta, Alessandro and Yetman, Greg and Zeidler, Julian and Dech, Stefan and Tatem, Andrew J. and and Reinartz, Peter},

doi = {10.3390/rs14020325},

issn = {2072-4292},

year  = {2022},

date = {2022-01-20},

urldate = {2022-01-20},

journal = {Remote Sensing},

volume = {14},

number = {2},

abstract = {Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.},

keywords = {Europe, Population, Random forest},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data},

author = {Shepherd, Harry ER and Atherden, Florence S and Chan, Ho Man Theophilus and Loveridge, Alexandra and Tatem, Andrew J},

doi = {10.1186/s12942-021-00299-5},

year  = {2021},

date = {2021-12-04},

urldate = {2021-12-04},

journal = {International Journal of Health Geographics},

volume = {20},

number = {46},

abstract = {Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility.



Methods

Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021.



Results

We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination.



Conclusions

While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.},

keywords = {covid-19, Europe, Mobility, NPIs, United Kingdom},

pubstate = {published},

tppubtype = {article}

}