Effectiveness of control strategies for Coronavirus Disease 2019: a SEIR dynamic modeling study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».