Effectiveness of control strategies for Coronavirus Disease 2019: a SEIR dynamic modeling study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".