Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps....Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.展开更多
During the late 18th and early 19th centuries,a series of conflicts erupted in the Caribbean,leading to the spread of yellow fever to North America and Europe.This yellow fever epidemic was aggravated by war,migration...During the late 18th and early 19th centuries,a series of conflicts erupted in the Caribbean,leading to the spread of yellow fever to North America and Europe.This yellow fever epidemic was aggravated by war,migration,trade,and other human behaviors,resulting in a decadelong transatlantic pandemic.Groups of physicians in Europe and the United States established a transatlantic network focused on epidemic prevention,to investigate the pathology,causes,and treatments of yellow fever Subsequently,some consular officers were also concerned about the yellow fever epidemic,which led to the expansion of this network.The formation and expansion of the transatlantic knowledge network profoundly demonstrated the spirit of transnationalism and promotes progress in international public health.It sets a precedent for international health cooperation.However,this network was dominated by the so-called"white elite",with European and American countries holding the knowledge hegemony,it had a clear racist and colonialism feature.展开更多
Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models.The recently introduced synthetic network model,which is an ...Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models.The recently introduced synthetic network model,which is an SIRS type model based on a non-linear transmission rate,effectively decouples the underlying population network structure from the epidemiological parameters of disease,and has been shown to produce superior fits to multi-wave epidemics.However,inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual.An alternate source of data that can inform the network alone has the potential to improve overall modeling results.Aggregate cell phone mobility data,which record daily numbers of visits to points of interest,provide a proxy for the number of contacts that people establish during their visits.In this paper,we link the contact rate from an epidemic model to the total number of contacts formed in the population.Inferring the latter from Google Community Mobility Reports data,we develop an integrated epidemic model whose transmission adapts to population mobility.This model is illustrated on the first four waves of the COVID-19 pandemic.展开更多
For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread netw...For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions,the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions.Three typical spatial information parameters including working unit/address,onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed.Furthermore,by the methods of spatial-temporal statistical analysis and network characteristic analysis,spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored,and spatial autocorrelation/heterogeneity,spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed.The results show that(1)The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces,but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong.And the control measurement should focus on the early and interim progress of SARS breakout.(2)The inner output cases had significant positive autocorrelative characteristics in the whole studied region,and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer.(3)The downtown districts were main high-risk hotspots of SARS epidemic in Beijing,the northwest suburban districts/counties were secondary high-risk hotspots,and northeast suburban areas were relatively safe.(4)The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity.The suburban Tongzhou and Changping districts were the underlying high-risk regions,and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow.The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic,and provide a more effective theoretical basis for emergency/control measurements and decision-making.展开更多
基金Supported by the National Natural Science Foundation of China(61374180,61373136,61304169)the Research Foundation for Humanities and Social Sciences of Ministry of Education,China(12YJAZH120)+1 种基金the Six Projects Sponsoring Talent Summits of Jiangsu Province,China(RLD201212)the Natural Science Foundation of Anhui Province(1608085MF127)
文摘Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.
基金research result of the major research project of the Humanities and Social Sciences Key Research Base of the Ministry of Education:"Infectious Diseases and the Foundation of Early Epidemic Prevention and public health system in the United States"(Project No.:22JJD770038).
文摘During the late 18th and early 19th centuries,a series of conflicts erupted in the Caribbean,leading to the spread of yellow fever to North America and Europe.This yellow fever epidemic was aggravated by war,migration,trade,and other human behaviors,resulting in a decadelong transatlantic pandemic.Groups of physicians in Europe and the United States established a transatlantic network focused on epidemic prevention,to investigate the pathology,causes,and treatments of yellow fever Subsequently,some consular officers were also concerned about the yellow fever epidemic,which led to the expansion of this network.The formation and expansion of the transatlantic knowledge network profoundly demonstrated the spirit of transnationalism and promotes progress in international public health.It sets a precedent for international health cooperation.However,this network was dominated by the so-called"white elite",with European and American countries holding the knowledge hegemony,it had a clear racist and colonialism feature.
基金support from Research Manitoba,as part of its COVID-19 Research Fund.
文摘Advances in modeling the spread of infectious diseases have allowed modellers to relax the homogeneous mixing assumption of traditional compartmental models.The recently introduced synthetic network model,which is an SIRS type model based on a non-linear transmission rate,effectively decouples the underlying population network structure from the epidemiological parameters of disease,and has been shown to produce superior fits to multi-wave epidemics.However,inference from case counts alone is generally problematic due to the partial unidentifiability between probability of person to person transmission and the average number of contacts per individual.An alternate source of data that can inform the network alone has the potential to improve overall modeling results.Aggregate cell phone mobility data,which record daily numbers of visits to points of interest,provide a proxy for the number of contacts that people establish during their visits.In this paper,we link the contact rate from an epidemic model to the total number of contacts formed in the population.Inferring the latter from Google Community Mobility Reports data,we develop an integrated epidemic model whose transmission adapts to population mobility.This model is illustrated on the first four waves of the COVID-19 pandemic.
基金supported by National Natural Science Foundation of China(Grant Nos. 40871181 and 41101369)Key Knowledge Innovative Program of Chinese Academy of Sciences (Grant No. KZCX2-EW-318)+2 种基金Jiangxi Provincial Natural Science Foundation (Grant No. 20114BAB215024)Natural Science Youth Foundation of Jiangxi Provincial Office of Education (Grant No. GJJ11073)Open Foundation of Key Laboratory of Poyang Lake Wetland and Watershed Research,Ministry of Education (Grant No.PK2010001)
文摘For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions,the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions.Three typical spatial information parameters including working unit/address,onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed.Furthermore,by the methods of spatial-temporal statistical analysis and network characteristic analysis,spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored,and spatial autocorrelation/heterogeneity,spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed.The results show that(1)The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces,but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong.And the control measurement should focus on the early and interim progress of SARS breakout.(2)The inner output cases had significant positive autocorrelative characteristics in the whole studied region,and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer.(3)The downtown districts were main high-risk hotspots of SARS epidemic in Beijing,the northwest suburban districts/counties were secondary high-risk hotspots,and northeast suburban areas were relatively safe.(4)The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity.The suburban Tongzhou and Changping districts were the underlying high-risk regions,and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow.The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic,and provide a more effective theoretical basis for emergency/control measurements and decision-making.
