Effectiveness of control strategies for Coronavirus Disease 2019: a SEIR dynamic modeling study
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Abstract
Background
Since its first case’s occurrence in Wuhan, China, the Coronavirus Disease 2019 (COVID-19) has been spreading rapidly to other provinces and neighboring countries. A series of intervention strategies have been implemented, but didn’t stop its spread.
Methods
Two mathematical models have been developed to simulate the current epidemic situation in the city of Wuhan and in other parts of China. Special considerations were given to the mobility of people for the estimation and forecast the number of asymptomatic infections, symptomatic infections, and the infections of super-spreading events (I sse ).
Findings
The basic reproductive number (R 0 ) was calculated for the period between 18 January 2020 and 16 February 2020: R 0 declined from 5.75 to 1.69 in Wuhan and from 6.22 to 1.67 in the entire country (not including the Wuhan area). At the same time, Wuhan is estimated to observe a peak in the number of confirmed cases around 6 February 2020. The number of infected individuals in the entire country (not including the Wuhan area) peaked around February 3. The results also show that the peak of new asymptomatic cases per day in Wuhan occurred on February 6, and the peak of new symptomatic infections have occurred on February 3. Concurrently, while the number of confirmed cases nationwide would continue to decline, the number of real-time COVID-19 inpatients in Wuhan has reached a peak of 13,030 on February 14 before it decreases. The model further shows that the COVID-19 cases will gradually wane by the end of April 2020, both in Wuhan and the other parts of China. The number of confirmed cases would reach the single digit on March 27 in Wuhan and March 19 in the entire country. The five cities with top risk index in China with the exclusion of Wuhan are: Huanggang, Xiaogan, Jingzhou, Chongqing, and Xiangyang city.
Interpretations
Although the national peak time has been reached, a significant proportion of asymptomatic patients and the infections of super-spreading events (I sse ) still exist in the population, indicating the potential difficulty for the prevention and control of the disease. As the Return-to-Work tide is approaching and upgrading, further measures (e.g., escalatory quarantine, mask wearing when going out, and sit apart when taking vehicles) will be particularly crucial to stop the COVID-19 in other cities outside of Wuhan.
What was already known about the topic concerned
Currently, a Coronavirus Disease 2019 (COVID-19) is thought to have emerged into the human population in Wuhan, and cases have been identified in neighboring provinces and other countries. In existing epidemiological studies, the basic reproduction number (R 0 ) of the virus were estimated between 1.4 and 5. Besides, it is of crucial importance to evaluate and improve different intervention strategies which have already implemented.
What new knowledge the manuscript contributes
In this study, two mathematical models were established to simulate the current epidemic situation and predict the future trend of the COVID-19. We found that with the implementation of different policies, R 0 continued to decline over time and the number of confirmed cases in Wuhan will peak on around February 6. Also, we estimated and forecast the number of asymptomatic infections, symptomatic infections, and infections of super-spreading events caused by the COVID-19 and the risk index of different cities.
Implications of all the available evidence
Our research has important practical implications for public health policy makers. Although the current prevention and control measures have made some significant inroads into controlling the epidemic, complete control has not yet been achieved. We recommend that self-isolation at home be strictly observed for a period of time in the future. Furthermore, our estimation of the number of asymptomatic people, super spreading and real-time inpatients would provide basis guidance for the hospital to arrange beds accordingly.
Article activity feed
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SciScore for 10.1101/2020.02.19.20025387: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, there is no enough data to provide accurate information about quarantine rates and detection rates at each stage of the epidemic, as the denominator could also be underestimated due to limited medical source for treatment, false positive testing kits. However, we have adjusted for the initial …
SciScore for 10.1101/2020.02.19.20025387: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, there is no enough data to provide accurate information about quarantine rates and detection rates at each stage of the epidemic, as the denominator could also be underestimated due to limited medical source for treatment, false positive testing kits. However, we have adjusted for the initial values of the parameters in the models based on the available survey data. Second, based on the changes in prevention and control modes and societal efforts at each stage, we have estimated the functions of the quarantine and detection rates in relation to time, in order to simulate the actual conditions as closely as possible. In addition, we were unable to obtain some relevant data that may be very important to the study, such as the exposure and possible infection rates of healthcare workers nationwide(19). Instead, we use the estimation for exposure and infection rate of that population from a survey (unpublished data) based on a big specialized hospital in Wuhan.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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