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...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.展开更多
Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore ...Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.展开更多
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 geog...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.展开更多
Emerging infectious diseases have been frequently observed.The occurrence of infectious diseases is highly uncertain because of the unpredictability of pathogen,the complexity of occurrence time and site,and the chara...Emerging infectious diseases have been frequently observed.The occurrence of infectious diseases is highly uncertain because of the unpredictability of pathogen,the complexity of occurrence time and site,and the characteristics of transmission.Early detection and immediate implementation of effective interventions within a reasonable time are the keys to preventing pandemics.[1]However,due to the insufficiency of the traditional surveillance and early warning,which relied heavily on passive reporting by healthcare institutions,[2]the early warning time lags behind the risk factor identification,syndrome differentiation(pneumonia of unknown cause),and suspected case confirmation of the infectious disease at the gateway of clustered cases.This paper aimed to face the challenges mentioned above and propose a universal epidemic intelligence model to establish a novel infectious disease surveillance and early warning system based on the trinity of surveillance and detection,risk assessment,and early warning.展开更多
Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have in...Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.展开更多
Brucellosis is one of the most common zoonotic diseases,caused by species of the genus Brucella,that affects domestic and farm livestock and a wide range of wild mammals(1-2).Endemic areas are primarily located in the...Brucellosis is one of the most common zoonotic diseases,caused by species of the genus Brucella,that affects domestic and farm livestock and a wide range of wild mammals(1-2).Endemic areas are primarily located in the low-and middle-income countries across the Mediterranean region,the Arabian Peninsula,Africa,Asia,and Central and South America,with major regional differences(3-5).The highest prevalence in animals was observed in countries of the Middle East and sub-Saharan Africa,China。展开更多
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 ...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.展开更多
Background:A local coronavirus disease 2019(COVID-19)case confirmed on June 11,2020 triggered an outbreak in Beijing,China after 56 consecutive days without a newly confirmed case.Non-pharmaceutical interventions(NPIs...Background:A local coronavirus disease 2019(COVID-19)case confirmed on June 11,2020 triggered an outbreak in Beijing,China after 56 consecutive days without a newly confirmed case.Non-pharmaceutical interventions(NPIs)were used to contain the source in Xinfadi(XFD)market.To rapidly control the outbreak,both traditional and newly introduced NPIs in eluding large-scale management of high-risk populations and expanded severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)PCR-based screening in the general population were conducted in Beijing.We aimed to assess the effectiveness of the response to the COVID-19 outbreak in Beijing's XFD market and inform future response efforts of resurgence across regions.展开更多
Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area...Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.展开更多
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...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.展开更多
Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)transmission.There has been scarce information to understand the future...Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)transmission.There has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal patterns.Methods:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs.We considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal influenza.For the United Kingdom,data were collected for England only.Results:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying regions.The rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza season.Further,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.Conclusions:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)pandemic.In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza viruses.Seasonal influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing positive,dropped from 11.8%to 2.0%in 2020–2021 influenza season,compared to the past 5 years(5).The long-period of low-exposure to influenza viruses adds great uncertainty on preparedness for the incoming 2021–2022 influenza season.Influenza vaccination is one of the most effective measures in seasonal influenza prevention and control,but with only a few influenza viruses circulating,it could be difficult to determine the targeted strains for vaccination.In this context,it is of primary importance to identify alternative mitigation measures for the incoming 2021–2022 influenza season,the first season after long periods of virtually no influenza outbreaks worldwide.Using data from China,the United Kingdom,and the United States,we forecasted the influenza activity in the incoming 2021–2022 influenza season under hypothetical scenarios without non-pharmaceutical interventions(NPIs)and with different assumptions on mask-wearing and mobility levels.展开更多
基金supported by grants from the National Natural Science Foundation of China(82041023 and 81773546)the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(2020-I2M-1-001)+2 种基金the Chinese Academy of Medical Sciences Fund for Influenza Pandemic Response and Public Health Emergency System(2021P062QG008)the Bill&Melinda Gates Foundation(2021P057QG006)the Special Fund for Health Development Research of Beijing(2021-1G-3013)。
文摘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.
基金supported by grants from the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(2021I2M-1-044)。
文摘Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.
基金supported by the grants from the Bill&Melinda Gates Foundation(Grant Nos.:INV-024911 and OPP1134076)the European Union Horizon 2020(Grant No.:MOOD 874850)+8 种基金the National Natural Science Fund of China(Grant Nos.:81773498,71771213 and 91846301)National Science and Technology Major Project of China(Grant No.:2016ZX10004222-009)Program of Shanghai Academic/Technology Research Leader(Grant No.:18XD1400300)Hunan Science and Technology Plan Project(Grant Nos.:2017RS3040 and 2018JJ1034)supported by funding from the Bill&Melinda Gates Foundation(Grant Nos.:OPP1106427,OPP1032350,OPP1134076,and OPP1094793)the Clinton Health Access Initiative,the UK Department for International Development(DFID)and the Wellcome Trust(Grant Nos.:106866/Z/15/Z and 204613/Z/16/Z)supported by funding from the National Natural Science Fund for Distinguished Young Scholars of China(Grant No.:81525023)Program of Shanghai Academic/Technology Research Leader(Grant No.:18XD1400300)the United States National Institutes of Health(Comprehensive International Program for Research on AIDS grant U19 AI51915).
文摘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.
基金CAMS Innovation Fund for Medical Sciences(No.2021-I2M-1-044)Natural Science Foundation of China(No.82320108018)
文摘Emerging infectious diseases have been frequently observed.The occurrence of infectious diseases is highly uncertain because of the unpredictability of pathogen,the complexity of occurrence time and site,and the characteristics of transmission.Early detection and immediate implementation of effective interventions within a reasonable time are the keys to preventing pandemics.[1]However,due to the insufficiency of the traditional surveillance and early warning,which relied heavily on passive reporting by healthcare institutions,[2]the early warning time lags behind the risk factor identification,syndrome differentiation(pneumonia of unknown cause),and suspected case confirmation of the infectious disease at the gateway of clustered cases.This paper aimed to face the challenges mentioned above and propose a universal epidemic intelligence model to establish a novel infectious disease surveillance and early warning system based on the trinity of surveillance and detection,risk assessment,and early warning.
基金The Special Fund for Health Development Research of Beijing(2021-1G-3013)the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(2021-I2M-1-044)the Bill&Melinda Gates Foundation(INV-024911).
文摘Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.
基金National Science and Technology Major Project of China(2018ZX10713001-001)The National Natural Science Fund of China(81773498)National Science and Technology Major Project of China(2016ZX10004222-009).
文摘Brucellosis is one of the most common zoonotic diseases,caused by species of the genus Brucella,that affects domestic and farm livestock and a wide range of wild mammals(1-2).Endemic areas are primarily located in the low-and middle-income countries across the Mediterranean region,the Arabian Peninsula,Africa,Asia,and Central and South America,with major regional differences(3-5).The highest prevalence in animals was observed in countries of the Middle East and sub-Saharan Africa,China。
基金This work was supported by the Bill and Melinda Gates Foundation[OPP1134076,OPP1106427,OPP1032350,OPP1094793]National Institute of Allergy and Infectious Diseases[U19AI089674]Wellcome Trust[106866/Z/15/Z].
文摘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.
文摘Background:A local coronavirus disease 2019(COVID-19)case confirmed on June 11,2020 triggered an outbreak in Beijing,China after 56 consecutive days without a newly confirmed case.Non-pharmaceutical interventions(NPIs)were used to contain the source in Xinfadi(XFD)market.To rapidly control the outbreak,both traditional and newly introduced NPIs in eluding large-scale management of high-risk populations and expanded severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)PCR-based screening in the general population were conducted in Beijing.We aimed to assess the effectiveness of the response to the COVID-19 outbreak in Beijing's XFD market and inform future response efforts of resurgence across regions.
基金FRS,AEG,JNN,AK,and AS are funded by the Bill&Melinda Gates Foundation(OPP1134076)AJT is supported by funding from U.S.National Institutes of Health/National Institute of Allergy and Infectious Diseases(U19AI089674)+1 种基金the Bill&Melinda Gates Foundation(OPP1106427,OPP1032350,OPP1134076)the Clinton Health Access Initiative,National Institutes of Health,and a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z).
文摘Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.
基金supported by the Belgian Science Policy(BELSPO)under the Research programme for Earth Obser-vation“STEREO III”[grant number SR/00/304]AJT is supported by a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z)+4 种基金AJT,AS,AEG and FRS are supported by funding from the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350][grant number OPP1134076]supported by the Well-come Trust,UK as an intermediate fellow[grant number 095127]RWS is supported by the Wellcome Trust as Prin-cipal Research Fellow[grant number 103602]that also supported CWK.CWK is also grateful to the KEMRI Wellcome Trust Overseas Programme Strategic Award[grant number 084538]for additional support.
文摘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.
基金Supported by the grants from National Natural Science Fund of China(No.82041023,No.81773546)the Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(2020-I2M-1-001)+1 种基金the Chinese Academy of Medical Sciences Fund for Influenza Pandemic Response and Public Health Emergency System(2021P062QG008)and the Bill&Melinda Gates Foundation(2021P057QG006).
文摘Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)transmission.There has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal patterns.Methods:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs.We considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal influenza.For the United Kingdom,data were collected for England only.Results:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying regions.The rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza season.Further,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.Conclusions:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)pandemic.In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza viruses.Seasonal influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing positive,dropped from 11.8%to 2.0%in 2020–2021 influenza season,compared to the past 5 years(5).The long-period of low-exposure to influenza viruses adds great uncertainty on preparedness for the incoming 2021–2022 influenza season.Influenza vaccination is one of the most effective measures in seasonal influenza prevention and control,but with only a few influenza viruses circulating,it could be difficult to determine the targeted strains for vaccination.In this context,it is of primary importance to identify alternative mitigation measures for the incoming 2021–2022 influenza season,the first season after long periods of virtually no influenza outbreaks worldwide.Using data from China,the United Kingdom,and the United States,we forecasted the influenza activity in the incoming 2021–2022 influenza season under hypothetical scenarios without non-pharmaceutical interventions(NPIs)and with different assumptions on mask-wearing and mobility levels.
