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| 1 | Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020显示文摘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. | Kenji Mizumoto Gerardo Chowell | 2020 | Infectious Disease Modelling2020，5，1： | 14 |
| 2 | Fitting dynamic models to epidemic outbreaks with quantified uncertainty:A primer for parameter uncertainty,identifiability,and forecasts显示文摘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. | Gerardo Chowell | 2017 | Infectious Disease Modelling2017，2，3： | 8 |
| 3 | Is it growing exponentially fast? -Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics显示文摘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. | Gerardo Chowell Cecile Viboud | 2016 | Infectious Disease Modelling2016，1，1： | 4 |
| 4 | A primer on stable parameter estimation and forecasting in epidemiology by a problem-oriented regularized least squares algorithm显示文摘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. | Alexandra Smirnova Gerardo Chowell | 2017 | Infectious Disease Modelling2017，2，2： | 2 |
| 5 | Severe respiratory disease concurrent with the circulation of H1N1 influenza显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | N Engl J Med2009，361，7： | 1 |
| 6 | Severe respiratory disease concurrent with the circulation of H1N1 influenza显示文摘 | Chowell G Bertozzi S M Colchero M A | 2009 | N EnglJ Meal2009，361，7： | 1 |
| 7 | Severe respiratory disease concurrent with the circulation of H1N1 influenza显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | N Engl J Med2009，361，7： | 1 |
| 8 | Urban structure and the risk of influenza A(H1N1) outbreaks in municipal districts显示文摘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. | Hong Xiao Xiaoling Lin Gerardo Chowell Cunrui Huang Lidong Gao Biyun Chen Zheng Wang Liang Zhou Xinguang He Haining Liu Xixing Zhang Huisuo Yang | 2014 | Chinese Science Bulletin2014，59，5： | 1 |
| 9 | Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions 显示文摘 | Chowell G Ammon CE Hengartner NW | 2006 | J Theor Biol2006，241，2： | 1 |
| 10 | Severe respiratory disease concurrent with the circulation of H1N1 influenza显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | N Engl J Med2009，361，7： | 1 |
| 11 | Severe respiratory disease concurrent with the circulation of H1N1 Influenza显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | N Engl J Med2009，361，7： | 1 |
| 12 | Seasonal influenza in the United States, France and Australia: transmission and prospects for control 显示文摘 | Chowell G Miller MA Viboud C | 2007 | Epidemiol Infect2007，136，6： | 1 |
| 13 | HLA基因特征影响免疫检查点抑制剂疗效显示文摘PD-1单抗为代表的免疫检查点抑制剂(immune checkpoint blockades,ICB)在肿瘤免疫治疗领域取得了重大突破,但是其总体有效率只有30%左右,且有部分患者在经过治疗后出现了爆发性进展。因此,寻找ICB疗效预测标志物已经成为重要的研究热点。以往大多数研究主要关注于肿瘤免疫表型、肠道微生物等外因,对于先天性基因特征内因的研究,尚不清楚。 | 虞淦军 CHOWELL D MORRIS L G T GRIGG C M | 2018 | 中国肿瘤生物治疗杂志2018，25，1： | 1 |
| 14 | Early transmission dynamics of COVID-19 in a southern hemisphere setting:Lima-Peru:February 29^(th)-March 30^(th),2020显示文摘The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months.The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th,2020.Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th,2020,the daily number of new COVID-19 cases continues to accumulate in this country.We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima,Peru.We estimated the reproduction number,R,during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th,2020.We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place.Prior to the implementation of the social distancing measures in Lima,the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter,p,estimated at 0.96(95%CI:0.87,1.0)and the reproduction number at 2.3(95%CI:2.0,2.5).Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus,with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government.While the interventions appear to have slowed the transmission rate in Lima,the number of new COVID-19 cases continue to accumulate,highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region. | César V.Munayco Amna Tariq Richard Rothenberg Gabriela G.Soto-Cabezas Mary F.Reyes Andree Valle Leonardo Rojas-Mezarina César Cabezas Manuel Loayza Gerardo Chowell 无 | 2020 | Infectious Disease Modelling2020，5，1： | 1 |
| 15 | Severe respiratory disease concurrent with the circulation of H1N1 influenza 显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | New Engl J Med2009，361，7： | 1 |
| 16 | SARS outbreaks in Ontario, Hong Kong and Singapore: the role of diagnosis and isolation as a control显示文摘 | Chowell G Fenimore P W Castillo-Garsow M A | 2003 | Journal of Theoretical Biology2003，224，1： | 1 |
| 17 | Severe Respiratory disease concurrent with the circulation of H1N1 Influenza显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | N Engl J Med2009，361，7： | 1 |
| 18 | Comparative estimation of the reproduction number for pandemic influenza from daily case notification data显示文摘 | CHOWELL G NISHIURA H BETrENCOURT L M A | 2007 | Journal of the Royal Society Interface2007，4，12： | 1 |
| 19 | A practical method to target indi- viduals for outbreak detection and control 显示文摘 | Chowell G Viboud C | 2013 | BMC Med2013，11，： | 1 |
| 20 | Severe respira- tory disease concurrent with the circulation of HIN1 influenza 显示文摘 | Chowell G Bertozzi SM Colchero MA | 2009 | NEnslJ Med2009，361，： | 1 |
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