scholarly journals Distribution of the 2019-nCoV Epidemic and Correlation with Population Emigration from Wuhan, China

2020 ◽  
Author(s):  
Zeliang Chen ◽  
Qi Zhang ◽  
Yi Lu ◽  
Zhongmin Guo ◽  
Xi Zhang ◽  
...  
Keyword(s):  
Hot Spot ◽  
Development Program ◽  
Hubei Province ◽  
Control Measures ◽  
Wuhan City ◽  
Spring Festival ◽  
Control And Prevention ◽  
Infection Source ◽  
Highly Correlated ◽  
Case Number

AbstractBACKGROUNDSThe ongoing new coronavirus (2019-nCoV) pneumonia outbreak is spreading in China and has not reached its peak. Five millions of people had emigrated from Wuhan before the city lockdown, which potentially represent a source of virus spreaders. Case distribution and its correlation with population emigration from Wuhan in early epidemic are of great importance for early warning and prevention of future outbreak.METHODSThe officially reported cases of 2019-nCoV pneumonia were collected as of January 30, 2020. Time and location information of these cases were extracted analyzed with ArcGIS and WinBUGS. Population migration data of Wuhan City and Hubei province were extracted from Baidu Qianxi and analyzed for their correlation with case number.FINDINGSThe 2019-nCoV pneumonia cases were predominantly distributed in Hubei and other provinces of South China. Hot spot provinces included Sichuan and Yunnan provinces that are adjacent to Hubei. While Wuhan city has the highest number of cases, the time risk is relatively stable. Numbers of cases in some cities are relatively low, but the time risks are continuously rising. The case numbers of different provinces and cities of Hubei province were highly correlated with the emigrated populations from Wuhan. Lockdown of 19 cities of Hubei province, and implementation of nationwide control measures efficiently prevented the exponential growth of case number.INTERPRETATIONPopulation emigrated from Wuhan was the main infection source for other cities and provinces. Some cities with low case number but were in rapid increase. Due to the upcoming Spring Festival return transport wave, understanding of the trends of risks in different regions is of great significance for preparedness for both individuals and institutions.FUNDINGSNational Key Research and Development Program of China, National Major Project for Control and Prevention of Infectious Disease in China, State Key Program of National Natural Science of China.

scholarly journals Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study

2020 ◽  
Vol 17 (5) ◽  
pp. 1679 ◽  
Author(s):  
Changyu Fan ◽  
Linping Liu ◽  
Wei Guo ◽  
Anuo Yang ◽  
Chenchen Ye ◽  
...  
Keyword(s):  
Nuclear Family ◽  
Population Based ◽  
Hubei Province ◽  
Social Characteristics ◽  
Doing Business ◽  
Spring Festival ◽  
Quasi Experimental ◽  
Highly Correlated ◽  
Novel Coronavirus ◽  
And Control

After the 2019 novel coronavirus (2019-nCoV) outbreak, we estimated the distribution and scale of more than 5 million migrants residing in Wuhan after they returned to their hometown communities in Hubei Province or other provinces at the end of 2019 by using the data from the 2013–2018 China Migrants Dynamic Survey (CMDS). We found that the distribution of Wuhan’s migrants is centred in Hubei Province (approximately 75%) at a provincial level, gradually decreasing in the surrounding provinces in layers, with obvious spatial characteristics of circle layers and echelons. The scale of Wuhan’s migrants, whose origins in Hubei Province give rise to a gradient reduction from east to west within the province, and account for 66% of Wuhan’s total migrants, are from the surrounding prefectural-level cities of Wuhan. The distribution comprises 94 districts and counties in Hubei Province, and the cumulative percentage of the top 30 districts and counties exceeds 80%. Wuhan’s migrants have a large proportion of middle-aged and high-risk individuals. Their social characteristics include nuclear family migration (84%), migration with families of 3–4 members (71%), a rural household registration (85%), and working or doing business (84%) as the main reason for migration. Using a quasi-experimental analysis framework, we found that the size of Wuhan’s migrants was highly correlated with the daily number of confirmed cases. Furthermore, we compared the epidemic situation in different regions and found that the number of confirmed cases in some provinces and cities in Hubei Province may be underestimated, while the epidemic situation in some regions has increased rapidly. The results are conducive to monitoring the epidemic prevention and control in various regions.

scholarly journals Estimation of local novel coronavirus (COVID-19) cases in Wuhan, China from off-site reported cases and population flow data from different sources

2020 ◽  
Author(s):  
Zian Zhuang ◽  
Peihua Cao ◽  
Shi Zhao ◽  
Yijun Lou ◽  
Shu Yang ◽  
...  
Keyword(s):  
Likelihood Estimation ◽  
Hubei Province ◽  
Flow Data ◽  
Daily Newspaper ◽  
Wuhan City ◽  
Spring Festival ◽  
Log Likelihood ◽  
Novel Coronavirus ◽  
Different Sources

AbstractBackgroundsIn December 2019, a novel coronavirus (COVID-19) pneumonia hit Wuhan, Hubei Province, China and spread to the rest of China and overseas. The emergence of this virus coincided with the Spring Festival Travel Rush in China. It is possible to estimate total number of cases of COVID-19 in Wuhan, by 23 January 2020, given the cases reported in other cities and population flow data between cities.MethodsWe built a model to estimate the total number of cases in Wuhan by 23 January 2020, based on the number of cases detected outside Wuhan city in China, with the assumption that if the same screening effort used in other cities applied in Wuhan. We employed population flow data from different sources between Wuhan and other cities/regions by 23 January 2020. The number of total cases was determined by the maximum log likelihood estimation.FindingsFrom overall cities/regions data, we predicted 1326 (95% CI: 1177, 1484), 1151 (95% CI: 1018, 1292) and 5277 (95% CI: 4732, 5859) as total cases in Wuhan by 23 January 2020, based on different source of data from Changjiang Daily newspaper, Tencent, and Baidu. From separate cities/regions data, we estimated 1059 (95% CI: 918, 1209), 5214 (95% CI: 4659, 5808) as total cases in Wuhan in Wuhan by 23 January 2020, based on different sources of population flow data from Tencent and Baidu.ConclusionSources of population follow data and methods impact the estimates of local cases in Wuhan before city lock down.

scholarly journals Study on SARS-CoV-2 transmission and the effects of control measures in China

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242649
Author(s):  
Bo Zhang ◽  
Hongwei Zhou ◽  
Fang Zhou
Keyword(s):  
Confidence Interval ◽  
Turning Point ◽  
Estimation Method ◽  
Hubei Province ◽  
Control Measures ◽  
Reproductive Number ◽  
Wuhan City ◽  
Epidemic Area ◽  
Stage 1 ◽  
The City

Objective To reconstruct the transmission trajectory of SARS-CoV-2 and analyze the effects of control measures in China. Methods Python 3.7.1 was used to write a SEIR class to model the epidemic procedure and proportional estimation method to estimate the initial true infected number. The epidemic area in China was divided into three parts, Wuhan city, Hubei province (except Wuhan) and China (except Hubei) based on the different transmission pattern. A testing capacity limitation factor for medical resources was imposed to model the number of infected but not quarantined individuals. Baidu migration data were used to assess the number of infected individuals who migrated from Wuhan to other areas. Results Basic reproduction number, R0, was 3.6 before the city was lockdown on Jan 23, 2020. The actual infected number the model predicted was 4508 in Wuhan before Jan 23, 2020. By January 22 2020, it was estimated that 1764 infected cases migrated from Wuhan to other cities in Hubei province. Effective reproductive number, R, gradually decreased from 3.6 (Wuhan), 3.4 (Hubei except Wuhan,) and 3.3 (China except Hubei) in stage 1 (from Dec 08, 2019 to Jan 22, 2020) to 0.67 (Wuhan), 0.59 (Hubei except Wuhan) and 0.63 (China except Hubei) respectively. Especially after January 23, 2020 when Wuhan City was closed, the infected number showed a turning point in Wuhan. By early April, there would be 42073 (95% confidence interval, 41673 to 42475), 21342 (95% confidence interval, 21057 to 21629) and 13384 (95% confidence interval, 13158 to 13612) infected cases in Wuhan, Hubei (except Wuhan) and China (except Hubei), respectively. Conclusion A series of control measures in China have effectively prevented the spread of COVID-19, and the epidemic should be under control in early April with very few new cases occasionally reported.

scholarly journals Modeling and Reviewing Study to the COVID-19 Epidemic in PR China

2020 ◽  
Author(s):  
Mingzhao Wang ◽  
Juanying Xie ◽  
Shengquan Xu
Keyword(s):  
Prevention And Control ◽  
Hubei Province ◽  
Control Measures ◽  
Transmission Model ◽  
Chinese Government ◽  
Wuhan City ◽  
The Mainland Of China ◽  
Prevention And Control Measures ◽  
And Control ◽  
Model Fits

Abstract Background: COVID-19 epidemic has been widely spread all over the world. During it appears in China, Chinese government quickly put forward and implement prevention and control measures to keep its spread within limits. This study aims to investigate the impacts of the prevention and control measures in controlling COVID-19 epidemic in China, so as to give a clue to control its spread in the world. Methods: We establish a two-stage dynamics transmission model with "lockdown of Wuhan city" as the time line. The first stage is the SEIR derived model that considers the contagious of the exposed. It simulates the COVID-19 epidemic in Hubei Province before "lockdown of Wuhan city". The second stage is a novel transmission dynamics model named SEIRQH. It takes into account the influence on the COVID-19 epidemic from the series of measures such as travel restriction, contact tracing, centralized treatment, the asymptomatic infected patients, hospitalized patients and so on. It simulates the COVID-19 epidemic in China after "lockdown of Wuhan city". The least square method is used to estimate the parameters of SEIR derived model and the proposed SEIRQH model based on the collected epidemic data of COVID-19 from Hubei Province and the mainland of China. Results: The SEIR derived model fits the actual data in Hubei Province before "lockdown of Wuhan city". The basic reproduction number of COVID-19 epidemic in Hubei Province is 3.2035 before "lockdown of Wuhan city". The SEIRQH model fits the number of the hospitalized persons of COVID-19 in Hubei Province and the mainland of China perfectly. The control reproductive number are 0.11428 and 0.09796 in Hubei Province and the mainland of China, respectively. The prevention and control measures taken by Chinese government play the significant role against the COVID-19 spread in China. Conclusions: Our two-stage dynamics transmission model simulates the COVID-19 in China, especially our SEIRQH model fits the actual data very well. The prevention and control measures implemented by Chinese government are effective in preventing the wide spread of COVID-19 epidemic in China. These measures give the reference to World Health Organization and other countries in controlling COVID-19 epidemic.

scholarly journals Study on SARS-COV-2 transmission and the effects of control measures in China

2020 ◽  
Author(s):  
Bo Zhang ◽  
Hongwei Zhou ◽  
Fang Zhou
Keyword(s):  
Early Stage ◽  
Back Propagation ◽  
Hubei Province ◽  
Control Measures ◽  
Reproductive Number ◽  
Wuhan City ◽  
Stage 4 ◽  
Epidemic Area ◽  
Data Source ◽  
Stage 1

ObjectiveTo reconstruct the transmission trajectory of SARS-COV-2 and analyze the effects of control measures in China.MethodsPython 3.7.1 was used to write a SEIR class to model the epidemic procedure and a back propagation class to estimate the initial true infected number. The epidemic area in China was divided into three parts, Wuhan city, Hubei province (except Wuhan) and China (except Hubei) based on the different transmission pattern. A limitation factor for the medical resource was imposed to model the infected but not quarantined. Credible data source from Baidu Qianxi were used to assess the number of infected cases migrated from Wuhan to other areas.ResultsBasic reproduction number, R0, was 3.6 in the very early stage. The true infected number was 4508 in our model in Wuhan before January 22, 2020. By January 22 2020, it was estimated that 1764 infected cases migrated from Wuhan to other cities in Hubei province. Effective reproductive number, R, gradually decreased from 3.6 (Wuhan, stage 1), 3.4 (Hubei except Wuhan, stage 1) and 3.3 (China except Hubei, stage 1) to 0.67 (Wuhan, stage 4), 0.83 (Hubei except Wuhan, stage 2) and 0.63 (China except Hubei, stage 2), respectively. Especially after January 23, 2020 when Wuhan City was closed, the infected number showed a turning point in Wuhan. By early April, there would be 42073, 21342 and 13384 infected cases in Wuhan, Hubei (except Wuhan) and China (except Hubei) respectively, and there would be 2179, 633 and 107 death in Wuhan, Hubei (except Wuhan) and China (except Hubei) respectively.ConclusionA series of control measures in China have effectively prevented the spread of COVID-19, and the epidemic will end in early April.

scholarly journals Reductions of Migrant Population Reduces the Number of COVID-19 Epidemic: A Case Study in China

2020 ◽  
Author(s):  
Lizhen Han ◽  
Jinzhu Jia
Keyword(s):  
Population Density ◽  
Generalized Additive Model ◽  
Hot Spot ◽  
Additive Model ◽  
Hubei Province ◽  
Control Measures ◽  
Cumulative Number ◽  
Migrant Population ◽  
High Prevalence ◽  
The Impact

Abstract Background: The novel coronavirus disease (COVID-19) broke out worldwide in 2020. The purpose of this paper was to find out the impact of migrant population on the epidemic, aiming to provide data support and suggestions for control measures in various epidemic areas. Methods: Generalized additive model was utilized to model the relationship between migrant population and the cumulative number of confirmed cases of COVID-19. The difference of spatial distribution was analyzed through spatial autocorrelation and hot spot analysis. Results: Generalized additive model demonstrated that the cumulative number of confirmed cases was positively correlated with migration index and population density. The predictive results showed that if no travel restrictions are imposed on the migrant population as usual, the total cumulative number of confirmed cases of COVID-19 would have reached 27 483 (95% CI: 16 074, 48 097; the actual number was 23 177). The increase in one city (Jian) would be 577.23% (95% CI: 322.73%, 972.73%) compared to the real confirmed cases of COVID-19. The average increase in 73 cities was 85.53% (95% CI: 19.53%, 189.81%). Among the migration destinations, the number of cases in cities of Hubei province, Chongqing and Beijing was relatively high, and there were large-scale high-prevalence clusters in eastern Hubei province. Meanwhile, without restrictions on migration, the high prevalence areas in Hubei province and its surrounding areas will be further expanded. Conclusions: The reduced population mobility and population density can greatly slow down the spread of the epidemic. All epidemic areas should suspend the transportation between cities, comprehensively and strictly control the population travel and decrease the population density, so as to reduce the spread of COVID-19.

scholarly journals Modeling and Reviewing Study to the COVID-19 Epidemic in PR China

2020 ◽  
Author(s):  
Mingzhao Wang ◽  
Juanying Xie ◽  
Shengquan Xu
Keyword(s):  
Prevention And Control ◽  
Hubei Province ◽  
Control Measures ◽  
Transmission Model ◽  
Chinese Government ◽  
Wuhan City ◽  
The Mainland Of China ◽  
Prevention And Control Measures ◽  
And Control ◽  
Model Fits

Abstract Background: COVID-19 epidemic has been widely spread all over the world. During it appears in China, Chinese government quickly put forward and implement prevention and control measures to keep its spread within limits. This study aims to investigate the impacts of the prevention and control measures in controlling COVID-19 epidemic in China, so as to give a clue to control its spread in the world. Methods: We establish a two-stage dynamics transmission model with "lockdown of Wuhan city" as the time line. The first stage is the SEIR derived model that considers the contagious of the exposed. It simulates the COVID-19 epidemic in Hubei Province before "lockdown of Wuhan city". The second stage is a novel transmission dynamics model named SEIRQH. It takes into account the influence on the COVID-19 epidemic from the series of measures such as travel restriction, contact tracing, centralized treatment, the asymptomatic infected patients, hospitalized patients and so on. It simulates the COVID-19 epidemic in China after "lockdown of Wuhan city". The least square method is used to estimate the parameters of SEIR derived model and the proposed SEIRQH model based on the collected epidemic data of COVID-19 from Hubei Province and the mainland of China. Results: The SEIR derived model fits the actual data in Hubei province before "lockdown of Wuhan city". The basic reproduction number of COVID-19 epidemic in Hubei Province is 3.2035 before "lockdown of Wuhan city". The SEIRQH model fits the number of the hospitalized persons of COVID-19 in Hubei Province and the mainland of China perfectly. The control reproductive number are 0.11428 and 0.09796 in Hubei Province and the mainland of China, respectively. The prevention and control measures taken by Chinese government play the significant role against the COVID-19 spread in China. Conclusions: Our two-stage dynamics transmission model simulates the COVID-19 in China, especially our SEIRQH model fits the actual data very well. The prevention and control measures implemented by Chinese government are effective in preventing the wide spread of COVID-19 epidemic in China. These measures give the reference to World Health Organization and other countries in controlling COVID-19 epidemic.

scholarly journals Effects of management of infection source of echinococcosis in Linzhi, Tibet Autonomous Region of China

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ying Wang ◽  
Bing-Cheng Ma ◽  
Li-Ying Wang ◽  
Gongsang Quzhen ◽  
Hua-Sheng Pang
Keyword(s):  
Infection Rate ◽  
Domestic Dog ◽  
Enzyme Linked Immunosorbent Assay ◽  
Fecal Samples ◽  
Autonomous Region ◽  
Prevention Campaign ◽  
Tibet Autonomous Region ◽  
Control And Prevention ◽  
Infection Source ◽  
Dog Infection

Abstract Background Echinococcosis is highly endemic in western and northern China. Tibet Autonomous Region (TAR) is the most serious prevalent area. Linzhi is located in southeastern part of TAR. Dogs are the primary infection source for the transmission of echinococcosis to humans. A control and prevention campaign based on dog management has been implemented in the past three years. This study aims to evaluate the effects of dog management on the infection rate of dogs. Methods Data of dog population, registration and de-worming of seven counties/district in Linzhi between 2017 and 2019 were obtained from the annual prevention and control report. Domestic dog fecal samples were collected from each endemic town of seven counties/district in Linzhi in 2019 to determine the infection of domestic dogs using coproantigen enzyme-linked immunosorbent assay (ELISA). Data analysis was processed using SPSS statistics to compare dog infection rate between 2016 and 2019 by chi-square test, and maps were mapped using ArcGIS. Results In Linzhi, domestic dog population has decreased from 17 407 in 2017 to 12 663 in 2019, while the registration rate has increased from 75.9% in 2017 to 98.6% in 2019. Similarly, stray dog population has decreased from 14 336 in 2017 to 11 837 in 2019, while sheltered rate has increased from 84.6% in 2017 to 96.6% in 2019. Dog de-worming frequency has increased from 4 times per annum in 2017 to 12 times in 2019, indicating that approximately every dog was dewormed monthly. A total of 2715 dog fecal samples were collected for coproantigen ELISA assay. The dog infection rate was 2.8% (77/2715) in 2019, which was significantly lower than 7.3% (45/618) in 2016 (P < 0.05). Conclusions Increased dog registration, decreased dog population, and increased dog de-worming frequency contributed to significantly decrease the dog infection rate in Linzhi. Control and prevention campaign based on dog management could significantly decrease dog infection with Echinococcus spp. in echinococcosis endemic areas.

scholarly journals COVID-19 in South Korea: Proper timing for Easing Mask mandates after COVID-19 Vaccination

2021 ◽  
pp. 1-9
Author(s):  
Yun-Jung Kang
Keyword(s):  
South Korea ◽  
Disease Control ◽  
Prevention And Control ◽  
Control Measures ◽  
Social Distancing ◽  
Proper Timing ◽  
Control And Prevention ◽  
Prevention And Control Measures ◽  
And Control

Abstract As of 25 July 2021, the Korea Disease Control and Prevention Agency reported 1,422 new COVID-19 cases, 188,848 total cases, and 2.073 total deaths (1.10% fatality rates). Since the first SARS-CoV-2 case was reported, efforts to find a treatment and vaccine against COVID-19 have been widespread. Four vaccines are on the WHO’s emergency use listing and are approved of their usage; BNT162b2, mRNA-1273, AZD1222, and Ad26.COV2.S. Vaccines against SARS-CoV-2 need at least 14 days to achieve effectiveness. Thus, people should abide by prevention and control measures, including wearing masks, washing hands, and social distancing. However, a lot of new cases were reported after vaccinations, as many people did not follow the prevention control measures before the end of the 14 days period. There is no doubt we need to break free from mask mandates. But let us not decide the timing in haste. Even if the mask mandates are eased, they should be changed depending on the number of reported cases, vaccinations, as well as prevention and control measures on how circumstances are changing under the influence of mutant coronavirus.

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