Objective： CareHPV is a human papillomavirus （HPV） DNA test for low-resource settings （LRS）. This study assesses optimum triage strategies for careHPV-positive women in LRS. Methods： A total of 2,530 Chinese wom...Objective： CareHPV is a human papillomavirus （HPV） DNA test for low-resource settings （LRS）. This study assesses optimum triage strategies for careHPV-positive women in LRS. Methods： A total of 2,530 Chinese women were concurrently screened for cervical cancer with visual inspection with acetic acid （VIA）, liquid-based cytology and HPV testing by physician- and self-collected careHPV, and physician-collected Hybrid Capture 2 （HC2）. Screen-positive women were referred to colposcopy with biopsy and endocervical curettage as necessary. HPV-positivity was defined as _〉1.0 relative light units/cutoff （RLU/CO） for both careHPV and HC2. Primary physician-HC2, physician-careHPV and self-careHPV and in sequential screening with cytology, VIA, or increased HPV test-positivity performance, stratified by age, were assessed for cervical intraepithelial neoplasia （CIN） grade 2/3 or worse （CIN2/3＋） detection. Results： The sensitivities and specificities of primary HPV testing for CIN2＋ were： 83.8%, 88.1% for physician- careHPV; 72. 1%, 88.2% for self-careHPV; and 97.1%, 86.0% for HC2. Physician-careHPV test-positive women with VIA triage had a sensitivity of 30.9% for CIN2＋ versus 80.9% with cytology triage. Self-careHPV test- positive women with VIA triage was 26.5% versus 66.2 % with cytology triage. The sensitivity of HC2 test-positive women with VIA triage was 38.2 % versus 92.6% with cytology triage. The sensitivity of physician-careHPV testing for CIN2＋ decreased from 83.8% at _〉1.0 RLU/CO to 72.1% at _〉10.00 RLU/CO, while the sensitivity of self- careHPV testing decreased from 72.1% at _〉1.0 RLU/CO to 32.4% at _〉10.00 RLU/CO; similar trends were seen with age-stratification. Conclusions： VIA and cytology triage improved specificity for CIN2/3 than no triage. Sensitivity with VIA triage was unsuitable for a mass-screening program. VIA provider training might improve this strategy. Cytology triage could be feasible where a high-quality cytology program exists. Triage of HPV test-positive women by increased test positivity cutoff adds another LRS triage option.展开更多
Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,g...Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.展开更多
An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January eFebruary 2020.Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers an...An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January eFebruary 2020.Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members,we characterize how the transmission potential varied over the course of the outbreak.Our estimate of the mean reproduction number in the confined setting reached values as high as^11,which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore(approximate range:1.1e7).Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control.Most recent estimates of Rt reached values largely below the epidemic threshold,indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.展开更多
Background The 2022-2023 mpox(monkeypox)outbreak has spread rapidly across multiple countries in the non-endemic region,mainly among men who have sex with men(MSM).In this study,we aimed to evaluate mpox's importa...Background The 2022-2023 mpox(monkeypox)outbreak has spread rapidly across multiple countries in the non-endemic region,mainly among men who have sex with men(MSM).In this study,we aimed to evaluate mpox's importation risk,border screening effectiveness and the risk of local outbreak in Chinese mainland.Methods We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11,2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide.We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement.And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men,considering different transmissibility,population immunity and population activity.Results We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively.Under the quarantine policy,15-19% of imported infections would remain undetected.Once a case of mpox is introduced into active MSM population with almost no population immunity,the risk of triggering local transmission is estimated at 42%,and would rise to>95% with over six cases.Conclusions Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COvID-19 pandemic reduced mpox importations prominently.However,the risk could be sub-stantially higher with the recovery of air-travel volume to pre-pandemic level.Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.展开更多
The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forec...The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts.Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth,which could inform the design of models of disease spread and reveal important characteristics of the transmission process.In particular,it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics.In the context of infectious disease spread,this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number.In this article,we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase(e.g.,3e5 disease generation intervals)of an infectious disease outbreak that follows near-exponential growth dynamics.Specifically,we assess the impact on:1)goodness of fit,2)bias on the growth parameter,and 3)the impact on short-term epidemic forecasts.Our findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit,improved estimates of key transmission parameters,and more realistic epidemic forecasts.展开更多
Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks.For instance,simple epidemic models relying on a small number of param...Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks.For instance,simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts.In the absence of reliable information about transmission mechanisms of emerging infectious diseases,phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease.In this article,our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014e15 Ebola epidemic in West Africa.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
Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relat...Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.展开更多
An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works.This modeling framework can character...An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works.This modeling framework can characterize complex epidemic patterns,including plateaus,epidemic resurgences,and epidemic waves characterized by multiple peaks of different sizes.In this tutorial paper,we introduce and illustrate SubEpiPredict,a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework.The toolbox can be used for model fitting,forecasting,and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score(WIS).We also provide a detailed description of these methods including the concept of the n-sub-epidemic model,constructing ensemble forecasts from the top-ranking models,etc.For the illustration of the toolbox,we utilize publicly available daily COVID-19 death data at the national level for the United States.The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences,including policymakers,and can be easily utilized by those without extensive coding and modeling backgrounds.展开更多
Background:Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data,particularly for analyzing infectious disease outbreaks.Here,we use two common methods to estimat...Background:Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data,particularly for analyzing infectious disease outbreaks.Here,we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model(GGM)calibrated to real outbreak datasets.Materials and methods:Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares(LSQ)and maximum likelihood estimation(MLE)methods.We utilize parametric bootstrapping to construct confidence intervals for parameter estimates.We compare the results including RMSE,Anscombe residual,and 95%prediction interval coverage.We also evaluate the correlation between the estimates from both methods.Results:Comparing LSQ and MLE estimates,most outbreaks have similar parameter estimates,RMSE,Anscombe,and 95%prediction interval coverage.Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase.However,for two outbreaks,there are systematic deviations in model fit to the data that explain differences in parameter estimates(e.g.,residuals represent random error rather than systematic deviation).Conclusion:Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM,provided that the model yields a good fit to the data.展开更多
Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplin...Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.展开更多
Summary What is already known about this topic?China has repeatedly contained multiple severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)outbreaks through a comprehensive set of targeted nonpharmaceutical int...Summary What is already known about this topic?China has repeatedly contained multiple severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)outbreaks through a comprehensive set of targeted nonpharmaceutical interventions(NPIs).However,the effectiveness of such NPIs has not been systematically assessed.展开更多
In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic.This report summarizes the rich discussion...In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic.This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater monitoring.Significant advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends.The role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic preparedness.Overall,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.展开更多
基金support from the Bill&Melinda Gates Foundationthe National Natural Science Foundation of China(No.81402748)Chinese Academy of Medical Sciences Initiative for Innovative Medicine(No.2017-I2M-3-005)
文摘Objective： CareHPV is a human papillomavirus （HPV） DNA test for low-resource settings （LRS）. This study assesses optimum triage strategies for careHPV-positive women in LRS. Methods： A total of 2,530 Chinese women were concurrently screened for cervical cancer with visual inspection with acetic acid （VIA）, liquid-based cytology and HPV testing by physician- and self-collected careHPV, and physician-collected Hybrid Capture 2 （HC2）. Screen-positive women were referred to colposcopy with biopsy and endocervical curettage as necessary. HPV-positivity was defined as _〉1.0 relative light units/cutoff （RLU/CO） for both careHPV and HC2. Primary physician-HC2, physician-careHPV and self-careHPV and in sequential screening with cytology, VIA, or increased HPV test-positivity performance, stratified by age, were assessed for cervical intraepithelial neoplasia （CIN） grade 2/3 or worse （CIN2/3＋） detection. Results： The sensitivities and specificities of primary HPV testing for CIN2＋ were： 83.8%, 88.1% for physician- careHPV; 72. 1%, 88.2% for self-careHPV; and 97.1%, 86.0% for HC2. Physician-careHPV test-positive women with VIA triage had a sensitivity of 30.9% for CIN2＋ versus 80.9% with cytology triage. Self-careHPV test- positive women with VIA triage was 26.5% versus 66.2 % with cytology triage. The sensitivity of HC2 test-positive women with VIA triage was 38.2 % versus 92.6% with cytology triage. The sensitivity of physician-careHPV testing for CIN2＋ decreased from 83.8% at _〉1.0 RLU/CO to 72.1% at _〉10.00 RLU/CO, while the sensitivity of self- careHPV testing decreased from 72.1% at _〉1.0 RLU/CO to 32.4% at _〉10.00 RLU/CO; similar trends were seen with age-stratification. Conclusions： VIA and cytology triage improved specificity for CIN2/3 than no triage. Sensitivity with VIA triage was unsuitable for a mass-screening program. VIA provider training might improve this strategy. Cytology triage could be feasible where a high-quality cytology program exists. Triage of HPV test-positive women by increased test positivity cutoff adds another LRS triage option.
基金Authors acknowledge financial support from the NSF grant 1610429 and the NSF grant 1414374 as part of the joint NSFNIH-USDA Ecology and Evolution of Infectious Diseases programUK BiotechnologyBiological Sciences Research Council grant BB/M008894/1 and the Division of International Epidemiology and Population Studies,National Institutes of Health.
文摘Mathematical models provide a quantitative framework with which scientists can assess hypotheses on the potential underlying mechanisms that explain patterns in observed data at different spatial and temporal scales,generate estimates of key kinetic parameters,assess the impact of interventions,optimize the impact of control strategies,and generate forecasts.We review and illustrate a simple data assimilation framework for calibrating mathematical models based on ordinary differential equation models using time series data describing the temporal progression of case counts relating,for instance,to population growth or infectious disease transmission dynamics.In contrast to Bayesian estimation approaches that always raise the question of how to set priors for the parameters,this frequentist approach relies on modeling the error structure in the data.We discuss issues related to parameter identifiability,uncertainty quantification and propagation as well as model performance and forecasts along examples based on phenomenological and mechanistic models parameterized using simulated and real datasets.
文摘An outbreak of COVID-19 developed aboard the Princess Cruises Ship during January eFebruary 2020.Using mathematical modeling and time-series incidence data describing the trajectory of the outbreak among passengers and crew members,we characterize how the transmission potential varied over the course of the outbreak.Our estimate of the mean reproduction number in the confined setting reached values as high as^11,which is higher than mean estimates reported from community-level transmission dynamics in China and Singapore(approximate range:1.1e7).Our findings suggest that Rt decreased substantially compared to values during the early phase after the Japanese government implemented an enhanced quarantine control.Most recent estimates of Rt reached values largely below the epidemic threshold,indicating that a secondary outbreak of the novel coronavirus was unlikely to occur aboard the Diamond Princess Ship.
基金This study was supported by grants from Key Program of the National Natural Science Foundation of China(82130093).
文摘Background The 2022-2023 mpox(monkeypox)outbreak has spread rapidly across multiple countries in the non-endemic region,mainly among men who have sex with men(MSM).In this study,we aimed to evaluate mpox's importation risk,border screening effectiveness and the risk of local outbreak in Chinese mainland.Methods We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11,2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide.We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement.And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men,considering different transmissibility,population immunity and population activity.Results We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively.Under the quarantine policy,15-19% of imported infections would remain undetected.Once a case of mpox is introduced into active MSM population with almost no population immunity,the risk of triggering local transmission is estimated at 42%,and would rise to>95% with over six cases.Conclusions Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COvID-19 pandemic reduced mpox importations prominently.However,the risk could be sub-stantially higher with the recovery of air-travel volume to pre-pandemic level.Mpox could emerge as a public health threat for Chinese mainland given its large MSM community.
基金The Fogarty International Center,US National Institutes of Health.GC was also supported from NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases programUK Biotechnology and Biological Sciences Research Council grant BB/M008894/1.
文摘The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts.Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth,which could inform the design of models of disease spread and reveal important characteristics of the transmission process.In particular,it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics.In the context of infectious disease spread,this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number.In this article,we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase(e.g.,3e5 disease generation intervals)of an infectious disease outbreak that follows near-exponential growth dynamics.Specifically,we assess the impact on:1)goodness of fit,2)bias on the growth parameter,and 3)the impact on short-term epidemic forecasts.Our findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit,improved estimates of key transmission parameters,and more realistic epidemic forecasts.
基金Dr.Gerardo Chowell acknowledges financial support from NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases programUK Biotechnology and Biological Sciences Research Council grant BB/M008894/1 and NSF grant 1610429.
文摘Public health officials are increasingly recognizing the need to develop disease-forecasting systems to respond to epidemic and pandemic outbreaks.For instance,simple epidemic models relying on a small number of parameters can play an important role in characterizing epidemic growth and generating short-term epidemic forecasts.In the absence of reliable information about transmission mechanisms of emerging infectious diseases,phenomenological models are useful to characterize epidemic growth patterns without the need to explicitly model transmission mechanisms and the natural history of the disease.In this article,our goal is to discuss and illustrate the role of regularization methods for estimating parameters and generating disease forecasts using the generalized Richards model in the context of the 2014e15 Ebola epidemic in West Africa.
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 Key Discipline Construction Project in Hunan Province(2008001)the National Natural Science Foundation of China and the Scientific Research Fund of Hunan Provincial Education Department(13A051)
文摘Changsha was one of the most affected areas during the 2009 A(H1N1)influenza pandemic in China.Here,we analyze the spatial–temporal dynamics of the 2009 pandemic across Changsha municipal districts,evaluate the relationship between case incidence and the local urban spatial structure and predict high-risk areas of influenza A(H1N1).We obtained epidemiological data on all cases of influenza A(H1N1)reported across municipal districts in Changsha during period May 2009–December 2010 and data on population density and basic geographic characteristics for 239 primary schools,97 middle schools,347 universities,96 malls and markets,674 business districts and 121 hospitals.Spatial–temporal K functions,proximity models and logistic regression were used to analyze the spatial distribution pattern of influenza A(H1N1)incidence and the association between influenza A(H1N1)cases and spatial risk factors and predict the infection risks.We found that the 2009 influenza A(H1N1)was driven by a transmission wave from the center of the study area to surrounding areas and reported cases increased significantly after September 2009.We also found that the distribution of influenza A(H1N1)cases was associated with population density and the presence of nearest public places,especially universities(OR=10.166).The final predictive risk map based on the multivariate logistic analysis showed high-risk areas concentrated in the center areas of the study area associated with high population density.Our findings support the identification of spatial risk factors and highrisk areas to guide the prioritization of preventive and mitigation efforts against future influenza pandemics.
基金G.C.is partially supported from NSF grants 2125246 and 2026797 and R01 GM 130900.
文摘An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works.This modeling framework can characterize complex epidemic patterns,including plateaus,epidemic resurgences,and epidemic waves characterized by multiple peaks of different sizes.In this tutorial paper,we introduce and illustrate SubEpiPredict,a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework.The toolbox can be used for model fitting,forecasting,and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score(WIS).We also provide a detailed description of these methods including the concept of the n-sub-epidemic model,constructing ensemble forecasts from the top-ranking models,etc.For the illustration of the toolbox,we utilize publicly available daily COVID-19 death data at the national level for the United States.The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences,including policymakers,and can be easily utilized by those without extensive coding and modeling backgrounds.
基金NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases programUK Biotechnology and Biological Sciences Research Council grant BB/M008894/1.
文摘Background:Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data,particularly for analyzing infectious disease outbreaks.Here,we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model(GGM)calibrated to real outbreak datasets.Materials and methods:Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares(LSQ)and maximum likelihood estimation(MLE)methods.We utilize parametric bootstrapping to construct confidence intervals for parameter estimates.We compare the results including RMSE,Anscombe residual,and 95%prediction interval coverage.We also evaluate the correlation between the estimates from both methods.Results:Comparing LSQ and MLE estimates,most outbreaks have similar parameter estimates,RMSE,Anscombe,and 95%prediction interval coverage.Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase.However,for two outbreaks,there are systematic deviations in model fit to the data that explain differences in parameter estimates(e.g.,residuals represent random error rather than systematic deviation).Conclusion:Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM,provided that the model yields a good fit to the data.
基金This work was supported by the RAPIDD program of the Science and Technology Directorate,Department of Homeland Security,and the Fogarty International Center,National Institutes of HealthNIH/NIAID[grant number U19AI089674]and the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350].CL is supported by the Fonds National de la Recherche Scientifique(F.R.S./FNRS),Brussels,Belgium.This work forms part of the outputs of the WorldPop Project(www.worldpop.org.uk)and Flowminder Foundation(www.flowminder.org).
文摘Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.
基金Supported by grants from the Key Program of the National Natural Science Foundation of China(82130093)Shanghai Municipal Science and Technology Major Project(ZD2021CY001).
文摘Summary What is already known about this topic?China has repeatedly contained multiple severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)outbreaks through a comprehensive set of targeted nonpharmaceutical interventions(NPIs).However,the effectiveness of such NPIs has not been systematically assessed.
文摘In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic.This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater monitoring.Significant advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends.The role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic preparedness.Overall,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.
