Application of SEIR Model to Predict Covid-19’s Early Stage in Hubei Province

2020 ◽  
Author(s):  
Tianmeng Huang
Keyword(s):  
Early Stage ◽  
Hubei Province ◽  
Seir Model

scholarly journals Epidemic analysis of COVID-19 in China by dynamical modeling

2020 ◽  
Author(s):  
Liangrong Peng ◽  
Wuyue Yang ◽  
Dongyan Zhang ◽  
Changjing Zhuge ◽  
Liu Hong
Keyword(s):  
National Health ◽  
Inflection Point ◽  
Doubling Time ◽  
Early Stage ◽  
Hubei Province ◽  
Dynamical Modeling ◽  
Seir Model ◽  
The Public ◽  
Public Data ◽  
Novel Coronavirus

The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.

scholarly journals A modified SEIR model to predict the behavior of the early stage in coronavirus and coronavirus-like outbreaks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wilfredo Angulo ◽  
José M. Ramírez ◽  
Dany De Cecchis ◽  
Juan Primera ◽  
Henry Pacheco ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Risk Perceptions ◽  
Transmission Rate ◽  
Early Stage ◽  
Individual Risk ◽  
Seir Model ◽  
Proposed Model ◽  
Probable Site ◽  
Adjusted Model ◽  
The Impact

AbstractCOVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than $$4\%$$ 4 % . The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.

scholarly journals The descriptive epidemiology of coronavirus disease 2019 during the epidemic period in Lu'an, China: achieving limited community transmission using proactive response strategies

2020 ◽  
Vol 148 ◽  
Author(s):  
Wei Qin ◽  
Jie Sun ◽  
Pengpeng Xu ◽  
Tianqi Gong ◽  
Xiude Li ◽  
...  
Keyword(s):  
Early Stage ◽  
Asymptomatic Carrier ◽  
Hubei Province ◽  
Initial Symptom ◽  
Asymptomatic Carriers ◽  
Epidemic Period ◽  
Imported Cases ◽  
Community Transmission ◽  
The Mean ◽  
The Impact

Abstract Hubei province in China has had the most confirmed coronavirus disease 2019 (COVID-19) cases and has reported sustained transmission of the disease. Although Lu'an city is adjacent to Hubei province, its community transmission was blocked at the early stage, and the impact of the epidemic was limited. Therefore, we summarised the overall characteristics of the entire epidemic course in Lu'an to help cities with a few imported cases better contain the epidemic. A total of 69 confirmed COVID-19 cases and 11 asymptomatic carriers were identified in Lu'an during the epidemic from 12 January to 21 February 2020. Fifty-two (65.0%) cases were male, and the median age was 40 years. On admission, 56.5% of cases had a fever as the initial symptom, and pneumonia was present in 89.9% of cases. The mean serial interval and the mean duration of hospitalisation were 6.5 days (95% CI: 4.8–8.2) and 18.2 days (95% CI: 16.8–19.5), respectively. A total of 16 clusters involving 60 cases (17 first-generation cases and 43 secondary cases) were reported during the epidemic. We observed that only 18.9% (7/37) index cases resulted in community transmission during the epidemic in Lu'an, indicating that the scale of the epidemic was limited to a low level in Lu'an city. An asymptomatic carrier caused the largest cluster, involving 13 cases. Spread of COVID-19 by asymptomatic carriers represents an enormous challenge for countries responding to the pandemic.

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

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Qiu-Shi Lin ◽  
Tao-Jun Hu ◽  
Xiao-Hua Zhou
Keyword(s):  
Confidence Interval ◽  
Missing Values ◽  
Early Stage ◽  
Hubei Province ◽  
Reporting Rate ◽  
Infected Case ◽  
Epidemic Size ◽  
Proposed Model ◽  
Global Health Problem ◽  
Municipal Health

Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. 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. Methods In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. 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 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan. Results We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [CI]: 3139–3321) and 51 273 (95% CI: 49 844–52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5–1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0–40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4–73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8–100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019. Conclusions In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

scholarly journals Estimating the real-time case fatality rate of COVID-19 using Poisson mixtures model

2020 ◽  
Author(s):  
Paul H. Lee
Keyword(s):  
Real Time ◽  
Case Fatality Rate ◽  
Early Stage ◽  
Case Fatality ◽  
Hubei Province ◽  
Fatality Rate ◽  
The Real ◽  
Poisson Mixtures

ABSTRACTWe proposed using Poisson mixtures model that utilized data of deaths, recoveries, and total confirmed cases in each day since the outbreak. We demonstrated that our CFR estimates for Hubei Province and other parts of China were superior to the simple CFR estimators in the early stage of COVID-19 outbreak.

scholarly journals The spatial transmission of SARS-CoV-2 in China under the prevention and control measures at the early outbreak

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Jianli Liu ◽  
Yuan Zhou ◽  
Chuanyu Ye ◽  
Guangming Zhang ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Prevention And Control ◽  
Reproduction Number ◽  
Early Stage ◽  
Chinese Mainland ◽  
Hubei Province ◽  
Control Measures ◽  
Provincial Level ◽  
Prevention And Control Measures ◽  
And Control

Abstract Background Since severe acute respiratory syndrome coronavirus, 2 (SARS-CoV-2) was firstly reported in Wuhan City, China in December 2019, Novel Coronavirus Disease 2019 (COVID-19) that is caused by SARS-CoV-2 is predominantly spread from person-to-person on worldwide scales. Now, COVID-19 is a non-traditional and major public health issue the world is facing, and the outbreak is a global pandemic. The strict prevention and control measures have mitigated the spread of SARS-CoV-2 and shown positive changes with important progress in China. But prevention and control tasks remain arduous for the world. The objective of this study is to discuss the difference of spatial transmission characteristics of COVID-19 in China at the early outbreak stage with resolute efforts. Simultaneously, the COVID-19 trend of China at the early time was described from the statistical perspective using a mathematical model to evaluate the effectiveness of the prevention and control measures. Methods In this study, the accumulated number of confirmed cases publicly reported by the National Health Committee of the People’s Republic of China (CNHC) from January 20 to February 11, 2020, were grouped into three partly overlapping regions: Chinese mainland including Hubei province, Hubei province alone, and the other 30 provincial-level regions on Chinese mainland excluding Hubei province, respectively. A generalized-growth model (GGM) was used to estimate the basic reproduction number to evaluate the transmissibility in different spatial locations. The prevention and control of COVID-19 in the early stage were analyzed based on the number of new cases of confirmed infections daily reported. Results Results indicated that the accumulated number of confirmed cases reported from January 20 to February 11, 2020, is well described by the GGM model with a larger correlation coefficient than 0.99. When the accumulated number of confirmed cases is well fitted by an exponential function, the basic reproduction number of COVID-19 of the 31 provincial-level regions on the Chinese mainland, Hubei province, and the other 30 provincial-level regions on the Chinese mainland excluding Hubei province, is 2.68, 6.46 and 2.18, respectively. The consecutive decline of the new confirmed cases indicated that the prevention and control measures taken by the Chinese government have contained the spread of SARS-CoV-2 in a short period. Conclusions The estimated basic reproduction number thorough GGM model can reflect the spatial difference of SARS-CoV-2 transmission in China at the early stage. The strict prevention and control measures of SARS-CoV-2 taken at the early outbreak can effectively reduce the new confirmed cases outside Hubei and have mitigated the spread and yielded positive results since February 2, 2020. The research results indicated that the outbreak of COVID-19 in China was sustaining localized at the early outbreak stage and has been gradually curbed by China’s resolute efforts.

scholarly journals Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19

2020 ◽  
Author(s):  
Huiwen Wang ◽  
Yanwen Zhang ◽  
Shan Lu ◽  
Shanshan Wang
Keyword(s):  
Early Stage ◽  
Turning Points ◽  
Mainland China ◽  
Hubei Province ◽  
Reasonable Assumption ◽  
Government Control ◽  
Whole Process ◽  
Number Of Patients ◽  
The Government ◽  
Zero Points

AbstractBackgroundThe outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. In the early stage of the outbreak, the most important question concerns some meaningful milepost moments, including (1) the time when the number of daily confirmed cases decreases, (2) the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), (3) the time when the number of daily confirmed cases becomes zero, and (4) the time when the number of patients treated in hospital is zero, which indicates the end of the epidemic. Intuitively, the former two can be regarded as two important turning points which indicate the alleviation of epidemic to some extent, while the latter two as two “zero” points, respectively. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at a early stage of the outbreak.MethodTo address it, in this paper, we propose a flexible framework incorporating the effectiveness of the government control to forecast the whole process of a new unknown infectious disease in its early-outbreak. Specially, we first establish the iconic indicators to characterize the extent of epidemic spread, yielding four periods of the whole process corresponding to the four meaningful milepost moments: two turning points and two “zero” points. Then we develop the tracking and forecasting procedure with mild and reasonable assumption. Finally we apply it to analyze and evaluate the COVID-19 using the public available data for mainland China beyond Hubei Province from the China Centers for Disease Control (CDC) during the period of Jan 29th, 2020, to Feb 29th, 2020, which shows the effectiveness of the proposed procedure.ResultsResults show that our model can clearly outline the development of the epidemic at a very early stage. The first prediction results on Jan 29th reveal that the first and second milepost moments for mainland China beyond Hubei Province would appear on Jan 31st and Feb 14th respectively, which are only one day and three days behind the real world situations. Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-late March, and the number of patients treated in the hospital will become zero between mid-March and mid-April in mainland China beyond Hubei Province. The framework proposed in this paper can help people get a general understanding of the epidemic trends in counties where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.

scholarly journals Global COVID-19 fatality analysis reveals Hubei-like countries potentially with severe outbreaks

2020 ◽  
Author(s):  
Boyan Lv ◽  
Zhongyan Li ◽  
Yajuan Chen ◽  
Cheng Long ◽  
Xinmiao Fu
Keyword(s):  
South Korea ◽  
Early Stage ◽  
Hubei Province ◽  
The Usa

1. CFR in Iran in the early stage of the outbreak is the highest among all the countries 2. CFRs in the USA and Italy are similar to that in Hubei Province in the early stage of the outbreak. 3. CFRs in South Korea are similar to that outside Hubei (in China), indicating less severe outbreaks therein. 4. Our findings highlight the potential severity of outbreaks globally, particular in the USA.

scholarly journals Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Liang Wang ◽  
Xavier Didelot ◽  
Jing Yang ◽  
Gary Wong ◽  
Yi Shi ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Bayesian Inference ◽  
Early Stage ◽  
Hubei Province ◽  
Epidemiological Model ◽  
Dispersion Parameter ◽  
Emerging Viruses ◽  
Genome Sequences ◽  
Offspring Distribution ◽  
Transmission Chain

Abstract Coronavirus disease 2019 (COVID-19) was first identified in late 2019 in Wuhan, Hubei Province, China and spread globally in months, sparking worldwide concern. However, it is unclear whether super-spreading events occurred during the early outbreak phase, as has been observed for other emerging viruses. Here, we analyse 208 publicly available SARS-CoV-2 genome sequences collected during the early outbreak phase. We combine phylogenetic analysis with Bayesian inference under an epidemiological model to trace person-to-person transmission. The dispersion parameter of the offspring distribution in the inferred transmission chain was estimated to be 0.23 (95% CI: 0.13–0.38), indicating there are individuals who directly infected a disproportionately large number of people. Our results showed that super-spreading events played an important role in the early stage of the COVID-19 outbreak.

scholarly journals Prediction of the Epidemic of COVID-19 Based on Quarantined Surveillance in China

2020 ◽  
Author(s):  
Rui Li ◽  
Wenliang Lu ◽  
Xifei Yang ◽  
Peihua Feng ◽  
Ozarina Muqimova ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Incubation Period ◽  
Real World ◽  
Epidemiological Data ◽  
Hubei Province ◽  
Chinese Government ◽  
Peak Time ◽  
Newly Diagnosed ◽  
Seir Model ◽  
Design And Methods

AbstractBackgroud and ObjectiveTo predict the epidemic of COVID-19 based on quarantined surveillance from real world in China by modified SEIR model different from the previous simply mathematical model.Design and MethodsWe forecasted the epidemic of COVID-19 based on current clinical and epidemiological data and built a modified SEIR model to consider both the infectivity during incubation period and the influence on the epidemic from strict quarantined measures.ResultsThe peak time of the curve for the infected newly diagnosed as COVID-19 should substantially present on Feb. 5, 2020 (in non-Hubei areas) and Feb. 19, 2020 (in Hubei). It is estimated that the peak of the curve of the cumulative confirmed cases will appear in non-Hubei areas on Mar. 3, 2020 and in Hubei province on Mar. 10, 2020, and the total number of the patients diagnosed as COVID-19 is 18,000 in non-Hubei areas and 78,000-96,000 in Hubei. The Chinese COVID-19 epidemic can be completetly controlled in May, 2020.ConclusionsCOVID-19 is only a local outbreak in Hubei Province, China. It can be probably avoided the pandemic of global SARS-CoV-2 cases rise with the great efforts by Chinese government and its people.

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