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

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.

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

10.1101/2020.02.16.20023770 ◽

2020 ◽

Cited By ~ 5

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.

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

10.1101/2020.02.10.20021824 ◽

2020 ◽

Cited By ~ 7

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.

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The effects of the clinical symptoms pneumonia-confirmation strategy of the COVID-19 epidemic in Wuhan, China

10.21203/rs.3.rs-28619/v1 ◽

2020 ◽

Author(s):

Yanjin Wang ◽

Pei Wang ◽

Shudao Zhang ◽

Hao Pan

Keyword(s):

Turning Point ◽

Clinical Symptoms ◽

Transmission Rate ◽

Basic Reproductive Number ◽

Reproductive Number ◽

The Novel ◽

Wuhan City ◽

Start Date ◽

Rapid Spread ◽

Novel Coronavirus

Abstract Motivated by the quick control in Wuhan, China, and the rapid spread in other countries of COVID-19, we investigate the questions that what is the turning point in Wuhan by quantifying the variety of basic reproductive number after the lockdown city. The answer may help the world to control the COVID-19 epidemic. A modified SEIR model is used to study the COVID-19 epidemic in Wuhan city. Our model is calibrated by the hospitalized cases. The modeling result gives out that the means of basic reproductive numbers are 1.5517 (95% CI 1.1716-4.4283) for the period from Jan 25 to Feb 11, 2020, and 0.4738(95% CI 0.0997-0.8370) for the period from Feb 12 to Mar 10. The transmission rate fell after Feb 12, 2020 as a result of China’s COVID-19 strategy of keeping society distance and the medical support from all China, but principally because of the clinical symptoms to be used for the novel coronavirus pneumonia (NCP) confirmation in Wuhan since Feb 12, 2020. Clinical diagnosis can quicken up NCP-confirmation such that the COVID-19 patients can be isolated without delay. So the clinical symptoms pneumonia-confirmation is the turning point of the COVID-19 battle of Wuhan. The measure of clinical symptoms pneumonia-confirmation in Wuhan has delayed the growth and reduced size of the COVID-19 epidemic, decreased the peak number of the hospitalized cases by 96% in Wuhan. Our modeling also indicates that the earliest start date of COVID-19 in Wuhan may be Nov 2, 2019.

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Epidemic Situation of Novel Coronavirus Pneumonia in China mainland

10.1101/2020.02.17.20024034 ◽

2020 ◽

Cited By ~ 3

Author(s):

Youbin Liu ◽

Liming Gong ◽

Baohong Li

Keyword(s):

Prevention And Control ◽

Turning Point ◽

Hubei Province ◽

Wuhan City ◽

Novel Coronavirus ◽

China Mainland ◽

And Control

AbstractObjectiveAnalyze the occurrence of novel coronavirus pneumonia(NCP) in China mainland, explore the epidemiological rules, and evaluate the effect of prevention and control.MethodsFrom December 1, 2019 to March 4, 2020, Analysis of 80,409 confirmed cases of NCP in China mainland.ResultsFrom December 1, 2019 to March 4, 2020, a total of 80,409 cases of NCP were confirmed in China mainland, a total of 67,466 cases were confirmed in Hubei Province, a total of 49,671 cases were confirmed in Wuhan city. From December 1, 2019 to March 4, 2020, a total of 3,012 cases of NCP deaths in China mainland, the mortality was 3.75% (3012/80,409); A total of 52045 cases of cured in China mainland; The turning point of the epidemic have been reached since February 18.2020 in China mainland; The spread index of NCP gradually declined since January 27. 2020, and the extinction index of NCP rose little by little since January 29, 2020.ConclusionFrom December 1, 2019 to March 4, 2020, NCP is under control, and the trend of the epidemic will eventually disappear; The turning point of an epidemic that I’ve created is a great indicator that can calculate the turning date of an outbreak and provide a basis for scientific prevention.

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Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann function-based modeling study

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.101 ◽

2020 ◽

Vol 41 (7) ◽

pp. 841-843 ◽

Cited By ~ 5

Author(s):

Yuanyuan Gao ◽

Zuqin Zhang ◽

Wei Yao ◽

Qi Ying ◽

Cheng Long ◽

...

Keyword(s):

Confidence Interval ◽

Hubei Province ◽

Cumulative Number ◽

Wuhan City ◽

Boltzmann Function ◽

Modeling Study

AbstractThe COVID-19 outbreak is ongoing in China. Here, Boltzmann function-based analyses reveal the potential total numbers of COVID-19 deaths: 3,260 (95% confidence interval [CI], 3187–3394) in China; 110 (95% CI, 109–112) in Hubei Province; 3,174 (95% CI, 3095–3270) outside Hubei; 2,550 (95% CI, 2494–2621) in Wuhan City; and 617 (95% CI, 607–632) outside Wuhan.

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

10.21203/rs.3.rs-122945/v1 ◽

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.

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Simulating and Forecasting the Cumulative Confirmed Cases of SARS-CoV-2 in China by Boltzmann Function-based Regression Analyses

10.1101/2020.02.16.20023564 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xinmiao Fu ◽

Qi Ying ◽

Tieyong Zeng ◽

Tao Long ◽

Yan Wang

Keyword(s):

Confidence Interval ◽

Mainland China ◽

Hubei Province ◽

Atypical Pneumonia ◽

Cumulative Number ◽

Regression Analyses ◽

Wuhan City ◽

Boltzmann Function ◽

Geographic Regions ◽

Novel Coronavirus

AbstractAn ongoing outbreak of atypical pneumonia caused by the 2019 novel coronavirus (SARS-CoV-2) is hitting Wuhan City and has spread to other provinces/cities of China and overseas. It very urgent to forecast the future course of the outbreak. Here, we provide an estimate of the potential total number of confirmed cases in mainland China by applying Boltzmann-function based regression analyses. We found that the cumulative number of confirmed cases from Jan 21 to Feb 14, 2020 for mainland China, Hubei Province, Wuhan City and other provinces were all well fitted with the Boltzmann function (R2 being close to 0.999). The potential total number of confirmed cases in the above geographic regions were estimated at 95% confidence interval (CI) as 79589 (71576, 93855), 64817 (58223, 77895), 46562 (40812, 57678) and 13956 (12748, 16092), respectively. Notably, our results suggest that the number of daily new confirmed cases of SARS-CoV-2 in mainland China (including Hubei Province) will become minimal between Feb 28 and Mar 10, 2020, with 95% CI. In addition, we found that the data of cumulative confirmed cases of 2003 SARS-CoV in China and Worldwide were also well fitted to the Boltzmann function. To our knowledge this is the first study revealing that the Boltzmann function is suitable to simulate epidemics. The estimated potential total number of confirmed cases and key dates for the SARS-CoV-2 outbreak may provide certain guidance for governments, organizations and citizens to optimize preparedness and response efforts.

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Estimating the daily trend in the size of the COVID-19 infected population in Wuhan

10.21203/rs.3.rs-16657/v1 ◽

2020 ◽

Author(s):

Qiushi Lin ◽

Chong You ◽

Xiao-Hua Zhou

Keyword(s):

Confidence Interval ◽

Clinical Diagnosis ◽

Respiratory Diseases ◽

Missing Values ◽

Case Reports ◽

Current Method ◽

Hubei Province ◽

Reporting Rate ◽

Wuhan City ◽

Epidemic Size

Abstract There has been an outbreak of coronavirus disease (COVID-19) in Wuhan city, Hubei province, China since December 2019. Cases have been exported to other parts of China and many other countries. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10,940 confirmed cases outside Hubei province. In this modelling study, we first estimate the epidemic size in Wuhan from January 11, 2020, to February 13, 2020, based on the confirmed cases outside Hubei province that left Wuhan by January 23, 2020. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from January 20, 2020, to February 13, 2020. Finally, we estimate the date when the first patient was infected. We estimate the number of cases that should be reported in Wuhan by January 11, 2020, is 4,094 (95% confidence interval [CI]: 3,980 – 4,211) and 58,153 (95% CI: 56,532 – 59,811) by February 13, 2020. The reporting rate has grown rapidly from 1.41% (95% CI: 1.37% - 1.45%) on January 20, 2020, to 32.15% (95% CI: 31.26% - 33.07%) on February 11, 2020, and reaches 61.89% (95% CI: 60.17% - 63.66%) on February 13, 2020. The date of first infection is estimated as November 30, 2019. The estimated reporting rate has increased rapidly to over 60% on February 13, 2020, mainly because the inclusion of 14,031 clinically diagnosed cases in the case reports of Wuhan. This might indicate that clinical diagnosis could be a good complement to the current method of confirmation. The currently reported number of 35,991 cases as of February 13, 2020, is still far below our estimate of 58,153. There may still be a lot of unreported cases. More thorough screening of all patients with a mild or moderate symptoms of respiratory diseases should be conducted to better control the spread of COVID-19.

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

10.21203/rs.3.rs-113026/v1 ◽

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.

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