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

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 333 ◽  
Author(s):  
Huiwen Wang ◽  
Yanwen Zhang ◽  
Shan Lu ◽  
Shanshan Wang
Keyword(s):  
Early Stage ◽  
Mainland China ◽  
Hubei Province ◽  
Reasonable Assumption ◽  
Government Control ◽  
Whole Process ◽  
Number Of Patients ◽  
Precise Prediction ◽  
The Government ◽  
Novel Coronavirus

Background: The 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 the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To 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. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. 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. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early 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. Conclusions: 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 Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 333
Author(s):  
Huiwen Wang ◽  
Yanwen Zhang ◽  
Shan Lu ◽  
Shanshan Wang
Keyword(s):  
Early Stage ◽  
Mainland China ◽  
Hubei Province ◽  
Government Control ◽  
The Public ◽  
Whole Process ◽  
Number Of Patients ◽  
Precise Prediction ◽  
The Government ◽  
Novel Coronavirus

Background: The 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 the time when the number of daily confirmed cases decreases, the time when the number of daily confirmed cases becomes smaller than that of the daily removed (recovered and death), and the time when the number of daily confirmed cases and patients treated in hospital, which can be called “active cases”, becomes zero. Unfortunately, it is extremely difficult to make right and precise prediction due to the limited amount of available data at the early stage of the outbreak. To 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. Methods: We first establish the iconic indicators to characterize the extent of epidemic spread. Then we develop the tracking and forecasting procedure with mild and reasonable assumptions. Finally we apply it to analyze and evaluate the COVID-19 outbreak 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. Results: Forecasting results indicate that the number of newly confirmed cases will become zero in the mid-early 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. Conclusions: The framework proposed in this paper can help people get a general understanding of the epidemic trends in countries where COVID-19 are raging as well as any other outbreaks of new and unknown infectious diseases in the future.

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 Re-evaluation of Nucleic Acid Retested Positive Cases in the Recovered COVID-19 Patients: Report from a Designated Transfer Hospital in Chongqing, China

2020 ◽  
Author(s):  
Yan Chen ◽  
Weizhi Bai ◽  
Bin Liu ◽  
Jian Huang ◽  
Irakoze Laurent ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Medical Center ◽  
Mainland China ◽  
Hubei Province ◽  
Policy Formulation ◽  
Impact Factors ◽  
Number Of Patients ◽  
Discharged Patients ◽  
Novel Coronavirus

Abstract Since the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, Hubei province, China, 1 the epidemic has spread rapidly in Hubei province and other regions in China. A total of 80,552 patients confirmed with COVID-19 have been reported in the main land China up to March 5th, 2020. 2 They included a huge number of patients discharged from hospital. A total of 53726 cases have met the discharge criteria (one of the criteria includes two continued negative result of nucleic acid test with repeated interval period of at least 1day or 24 hours.) in mainland China up to March 5th, 2020. Previous studies have paid more attention to the epidemic situation of COVID-19 and patient's diagnosis and treatment.Closely attention should be paid to the discharged patients. Surprising, previous follow-up reported that some patients nucleic acid retest result was positive again after discharge. 3 Impact factors should be further investigated. Since the first confirmed case was diagnosed in our hospital (Chongqing Emergency Medical Center, the designated transfer hospital) on February 4th, we have confirmed a total of 17 cases. All the patients infected with the novel coronavirus have been transferred to a designated hospital in Southwest China's Chongqing by ambulance with an inbuilt negative-pressure chamber. 4In the follow-up of these patients, all patients accepted RT-PCR tests again after having discharged from designated hospital 3 days later. Four of them showed recurrence of positive results after few days of discharge. Thus, we reported these cases aiming to provide information on policy formulation and modification of discharge plans.

scholarly journals Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Maria Litvinova ◽  
Wei Wang ◽  
Yan Wang ◽  
Xiaowei Deng ◽  
...  
Keyword(s):  
Age Groups ◽  
Mainland China ◽  
The Elderly ◽  
Hubei Province ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Serial Interval ◽  
The Mean ◽  
Novel Coronavirus ◽  
Local Transmission

AbstractBackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.ResultsThe median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January.ConclusionOur findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

scholarly journals Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations

2020 ◽  
Vol 17 (19) ◽  
pp. 7080 ◽  
Author(s):  
Abdelrahman E. E. Eltoukhy ◽  
Ibrahim Abdelfadeel Shaban ◽  
Felix T. S. Chan ◽  
Mohammad A. M. Abdel-Aal
Keyword(s):  
Data Analytics ◽  
Historical Data ◽  
External Factors ◽  
Number Of Patients ◽  
Future Prediction ◽  
The Government ◽  
Using Data ◽  
Novel Coronavirus ◽  
Improved Accuracy ◽  
Nonlinear Autoregressive

The outbreak of the 2019 novel coronavirus disease (COVID-19) has adversely affected many countries in the world. The unexpected large number of COVID-19 cases has disrupted the healthcare system in many countries and resulted in a shortage of bed spaces in the hospitals. Consequently, predicting the number of COVID-19 cases is imperative for governments to take appropriate actions. The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. In the literature, most of the existing prediction methods focus only on the historical data and overlook most of the external factors. Hence, the number of COVID-19 cases is inaccurately predicted. Therefore, the main objective of this study is to simultaneously consider historical data and the external factors. This can be accomplished by adopting data analytics, which include developing a nonlinear autoregressive exogenous input (NARX) neural network-based algorithm. The viability and superiority of the developed algorithm are demonstrated by conducting experiments using data collected for top five affected countries in each continent. The results show an improved accuracy when compared with existing methods. Moreover, the experiments are extended to make future prediction for the number of patients afflicted with COVID-19 during the period from August 2020 until September 2020. By using such predictions, both the government and people in the affected countries can take appropriate measures to resume pre-epidemic activities.

scholarly journals Deficiencies in Planning Interventional Trial Registration of COVID-19 in China

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaodan Li ◽  
Shengzhao Zhang ◽  
Yiling Zhou ◽  
Ying Liu ◽  
Youlian Zhou ◽  
...  
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Mainland China ◽  
Trial Registration ◽  
Inclusion Criteria ◽  
Clinical Trial Registry ◽  
Number Of Patients ◽  
Size Index ◽  
Novel Coronavirus ◽  
Clinical Trials Registry

Background: The coronavirus disease 2019 (COVID-19) pandemic has affected the world since late 2019. The efforts to control the spread of the virus need to be supported by credible evidence. Therefore, we analyzed the rationality of the timeline and geographic distribution of COVID-19 trial registration in mainland China.Methods: We searched the Chinese Clinical Trial Registry (ChiCTR, http://www.chictr.org.cn/) and International Clinical Trials Registry Platform (ICTRP, https://www.who.int/ictrp/en/) using keywords including novel coronavirus, coronavirus pneumonia, 2019-nCoV, COVID-19, and SARS-COV-2 from 1 December 2019 to 27 April 2020 and included interventional randomized and non-randomized trials including patients with confirmed cases of COVID-19 in mainland China. The registered trials were reviewed, and data were independently extracted by two reviewers based on the inclusion criteria.Results: A total of 263 registered interventional trials were included in the study. We defined the sample size index (SI) as the total number of patients needed by the trials divided by the total number of patients diagnosed with COVID-19. A total of 84,341 patients had been diagnosed with COVID-19 in China as of 26 April 2020, and the included trials had a combined sample size of 31,156 patients (SI: 0.37). After control of the COVID-19 epidemic was achieved in China (February 18, 2020), the SI was 1.54, suggesting that the number of patients needed by the trials was greater than the number of newly diagnosed patients. The SIs in 8 out of 26 provinces in mainland China were >1.Conclusions: Our results suggested a clear over registration of COVID-19 trials in China, especially after control of the pandemic was achieved, preventing the generation of high-quality evidence.

scholarly journals Differential equation analysis on COVID-19

2020 ◽  
Vol 2 (1) ◽  
pp. 01-11
Author(s):  
Bin Zhao
Keyword(s):  
Infectious Diseases ◽  
Mathematical Models ◽  
Hubei Province ◽  
Chinese Government ◽  
Second Phase ◽  
The Novel ◽  
The World ◽  
Response Measures ◽  
The Government ◽  
Novel Coronavirus

Background: An infectious disease caused by a novel coronavirus called COVID-19 has raged across the world since December 2019. The novel coronavirus first appeared in Wuhan, China, and quickly spread to Asia and now many countries around the world are affected by the epidemic. The deaths of many patients, including medical staff, caused social panic, media attention, and high attention from governments and world organizations. Today, with the joint efforts of the government, the doctors and all walks of life, the epidemic in Hubei Province has been brought under control, preventing its spread from affecting the lives of the people. Because of its rapid spread and serious consequences, this sudden novel coronary pneumonia epidemic has become an important social hot spot event. Through the analysis of the novel coronary pneumonia epidemic situation, we can also have a better understanding of sudden infectious diseases in the future, so that we can take more effective response measures, establish a truly predictable and provide reliable and sufficient information for prevention and control model. Methods: We establish different models according to the different developments of the epidemic situation, different time points, and different response measures taken by the government. To be specific, during the period of 2020.1.23-2020.2.7, the traditional SIR model is adopted; during the period of 2020.2.8-2020.3.30, according to the scientific research results, it was considered that the novel coronary pneumonia has a latent period, so in the later phase of epidemic development, the government has effectively isolated patients, thus we adopt the SEIQR model accordingly. During the period of 2020.3.31-2020.5.16, because more asymptomatic infected people were found, we use the SEIQLR model to fit. Finally, through a SEIR simulator, considering the susceptible number, the latent number, the infected number, the cured number, death number and other factors, we simulate the change of various numbers of people from the beginning to the next 180 days of novel coronary pneumonia. Findings: The results based on the analysis of differential equations and kinetic models show that through the prediction of the model established in the first phase, the epidemic situation of novel coronary pneumonia in Hubei Province was controlled at the end of March, which is in line with the actual situation. The rest of Hubei province, except for Wuhan, lifted control of the departure channel from 0:00 am on March 25, and Wuhan was also unblocked on April 8. Through the establishment of the second-phase model, it is found that the epidemic situation will reach its peak in mid-February. For example, the quarantine admission of the hospital declined after mid-February, which is inseparable from the measures to build square cabin hospitals in early February so that more and more patients can be admitted. The model established in the third phase shows that the epidemic had been completely controlled by the end of May, which is also in line with the reality. Because in mid-May, the Wuhan government conducted a nucleic acid test on all the citizens to screen for asymptomatic infected persons to fundamentally control the spread of novel coronary pneumonia. Interpretation: Hubei Province, as the center of the initial outbreak of novel coronary pneumonia, people were forced to be isolated at home during the Spring Festival, the most important Chinese holiday, and the whole society was in a state of suspension of work and study. The Chinese government had taken many measures in response to the epidemic, such as shutting down the city, vigorously building square cabin hospitals, and prohibiting people from gathering. At the beginning of May this year, the epidemic in Hubei Province was finally effectively controlled. For ordinary citizens, we should not cause unnecessary panic about the unknown novel coronavirus. Instead, we should fully understand and be familiar with this virus. In addition to the relevant medical knowledge, we should also understand the spread of infectious diseases through appropriate mathematical models. By mathematical models, we can understand the degree of harm of infectious diseases, when to control it, how to stop it, and use scientific views to reveal the original face of the novel coronavirus to the public without causing social panic.

scholarly journals Incorporating Human Movement Data to Improve Epidemiological Estimates for 2019-nCoV

2020 ◽  
Author(s):  
Zhidong Cao ◽  
Qingpeng Zhang ◽  
Xin Lu ◽  
Dirk Pfeiffer ◽  
Lei Wang ◽  
...  
Keyword(s):  
Latent Infection ◽  
Human Mobility ◽  
Mainland China ◽  
Human Movement ◽  
Hubei Province ◽  
The Novel ◽  
Movement Data ◽  
Control And Prevention ◽  
Novel Coronavirus ◽  
Reporting Data

AbstractEstimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the city’s lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts.One Sentence SummaryA geo-stratified debiasing approach incorporating human movement data was developed to improve modeling of the 2019-nCoV epidemic.

scholarly journals Lessons from South Korea Regarding the Early Stage of the COVID-19 Outbreak

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 229 ◽  
Author(s):  
Min Cheol Chang ◽  
Jong Hyun Baek ◽  
Donghwi Park
Keyword(s):  
South Korea ◽  
Early Stage ◽  
Diagnostic Testing ◽  
Medical Community ◽  
Online Newspapers ◽  
Early Stages ◽  
Rapid Transmission ◽  
The Government ◽  
Testing Patient ◽  
Novel Coronavirus

South Korea has experienced difficulty in controlling the spread of the novel coronavirus disease (COVID-19) during the early stages of the outbreak. South Korea remains passionately determined to protect Koreans against COVID-19 and through trial and error hopes to improve the strategies used to limit the outbreak. Here, we review how COVID-19 spread and what prevention strategies were implemented during the early stages of the outbreak in South Korea. We investigated online newspapers published in South Korea from 21 January 2020 to 20 March 2020, and reviewed academic medical articles related to COVID-19. Additionally, we acquired data on COVID-19 cases through the official website for COVID-19 in South Korea. To date, numerous measures have been applied by the government and the medical community during the early stages of the COVID-19 outbreak including the reporting of methods for diagnostic testing, patient classification, the introduction of drive-through screening centers, COVID-19 preventive measures, implementation of government policies for the shortage of face masks, and entry restrictions. Here, we present data from the early stages of the COVID-19 outbreak and measures to prevent its spread in South Korea. We believe that sharing the experience of South Korea during the COVID-19 outbreak can help other countries to implement strategies to prevent its rapid transmission.

scholarly journals Analysis and prediction of the 2019 novel coronavirus pneumonia epidemic in China based on an individual-based model

2020 ◽  
Author(s):  
Zuiyuan Guo ◽  
Dan Xiao
Keyword(s):  
Stochastic Model ◽  
Control Measures ◽  
Cumulative Number ◽  
Individual Based Model ◽  
Whole Process ◽  
Coordinate Descent Algorithm ◽  
The Government ◽  
Prevention And Control Measures ◽  
Novel Coronavirus ◽  
And Control

Abstract We developed a stochastic model to simulate the process of the epidemic of novel coronavirus pneumonia in China. The study provides valuable reference to understand the transmission mechanism of the novel coronavirus and evaluate the influence of intervention meaures. We established a stochastic individual-based model, and simulated the whole process of occurrence, development, and control of the epidemic, and the infectors and patients leaving Hubei Province before the traffic was closed. Additionally, the R0 and the number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R0 at the initial stage of the epidemic predicted by the model was 4.97 (95% confidence interval [CI], 4.82- 5.17). Before the traffic lockdown in Hubei , an estimated 2000 (95% CI, 1982–2030) infectors and patients left Hubei and traveled throughout the country. The model estimated that as of March 15, the cumulative number of laboratory-confirmed patients in Hubei and other provinces would reach 42,739 (95% CI, 32734-55472) and 12,870 (95% CI, 11520-14572), respectively. If the government had taken prevention and control measures 1 day later, the cumulative number of laboratory- confirmed patients in the whole country would increase by 32.1%. If the lockdown of Hubei was taken 1 day in advance, it is estimated that the cumulative number of laboratory-confirmed patients in other provinces would decrease by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. Intervention measurements nationwide have effectively curbed human-to-human transmission of the virus.

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