Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic

Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019(COVID-19)pandemic,but studies are needed to understand their effectiveness across regions and time.Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020,we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions.We found that if these interventions had not been deployed,the cumulative number of cases could have shown a 97-fold(interquartile range 79–116)increase,as of May 31,2020.However,their effectiveness depended upon the timing,duration,and intensity of the interventions,with variations in case severity seen across populations,regions,and seasons.Additionally,before effective vaccines are widely available and herd immunity is achieved,our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.

Assessing spread risk of COVID-19 in early 2020

A novel coronavirus emerged in late 2019,named as the coronavirus disease 2019(COVID-19)by the World Health Organization(WHO).This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission.A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data.We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city,with travellers departing into neighbouring cities and other megacities in China.We estimated that there were 59,912 international air passengers,of which 834(95%uncertainty interval:478–1,349)had COVID-19 infection,with a strong correlation seen between the predicted risks of importation and the number of imported cases found.Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks,our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

Modelling changing population distributions:an example of the Kenyan Coast,1979–2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.