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1.
Vaccine ; 40(49): 7141-7150, 2022 Nov 22.
Article in English | MEDLINE | ID: covidwho-2086812

ABSTRACT

The mass vaccination program has been actively promoted since the end of 2020. However, waning immunity, antibody-dependent enhancement (ADE), and increased transmissibility of variants make the herd immunity untenable and the implementation of dynamic zero-COVID policy challenging in China. To explore how long the vaccination program can prevent China at low resurgence risk, and how these factors affect the long-term trajectory of the COVID-19 epidemics, we developed a dynamic transmission model of COVID-19 incorporating vaccination and waning immunity, calibrated using the data of accumulative vaccine doses administered and the COVID-19 epidemic in 2020 in mainland China. The prediction suggests that the vaccination coverage with at least one dose reach 95.87%, and two doses reach 77.92% on 31 August 2021. However, despite the mass vaccination, randomly introducing infected cases in the post-vaccination period causes large outbreaks quickly with waning immunity, particularly for SARS-CoV-2 variants with higher transmissibility. The results showed that with the current vaccination program and 50% of the population wearing masks, mainland China can be protected at low resurgence risk until 8 January 2023. However, ADE and higher transmissibility for variants would significantly shorten the low-risk period by over 1 year. Furthermore, intermittent outbreaks can occur while the peak values of the subsequent outbreaks decrease, indicating that subsequent outbreaks boosted immunity in the population level, further indicating that follow-up vaccination programs can help mitigate or avoid the possible outbreaks. The findings revealed that the integrated effects of multiple factors: waning immunity, ADE, relaxed interventions, and higher variant transmissibility, make controlling COVID-19 challenging. We should prepare for a long struggle with COVID-19, and not entirely rely on the COVID-19 vaccine.

2.
Journal of Shandong University ; 58(10):95-99, 2020.
Article in Chinese | GIM | ID: covidwho-1975282

ABSTRACT

Objective: To investigate the transmission characteristics of a family cluster outbreak of coronavirus disease 2019 (COVID-19) in Xi-an, in order to provide reference for prevention and control efforts.

3.
J Theor Biol ; 549: 111205, 2022 09 21.
Article in English | MEDLINE | ID: covidwho-1907378

ABSTRACT

Several local outbreaks have occurred and been suppressed with the dynamic zero-COVID policy and widely promoted vaccination program implemented in China. The epidemic duration and final size vary significantly in different cities, which may be attributed to different implementation patterns and intensities of non-pharmaceutical interventions (NPIs). It's important to capture the underlying mechanism to explore more efficient implementation patterns of NPIs in order to prevent the future resurgence. In this study, outbreaks caused by Delta variant in Xi'an, Yangzhou and Guangzhou in 2021 are chosen as the examples. A novel model dividing the population into three groups is proposed to describe the heterogeneity of control interventions. The model is calibrated and key parameters related to NPIs are estimated by using multi-source epidemic data. The estimation results show a lower transmission probability but a higher initial reproduction number in Xi'an. Sensitivity analysis are conducted to investigate the impact of various control measures in different epidemic phases. The results identify the vital role of enhancing closed-off management, strengthening tracing and testing intensities, on shortening the epidemic durations and reducing the final size. Further, we find that sufficiently implemented closed-off management would prevent the city from lockdown. Strengthening the tracing other than the testing strategy in the initial stage is more effective on containing the epidemic in a shorter duration with less infections.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control , Disease Outbreaks/prevention & control , Humans , Quarantine
4.
Frontiers in public health ; 10, 2022.
Article in English | EuropePMC | ID: covidwho-1749552

ABSTRACT

Introduction Modeling on infectious diseases is significant to facilitate public health policymaking. There are two main mathematical methods that can be used for the simulation of the epidemic and prediction of optimal early warning timing: the logistic differential equation (LDE) model and the more complex generalized logistic differential equation (GLDE) model. This study aimed to compare and analyze these two models. Methods We collected data on (coronavirus disease 2019) COVID-19 and four other infectious diseases and classified the data into four categories: different transmission routes, different epidemic intensities, different time scales, and different regions, using R2 to compare and analyze the goodness-of-fit of LDE and GLDE models. Results Both models fitted the epidemic curves well, and all results were statistically significant. The R2 test value of COVID-19 was 0.924 (p < 0.001) fitted by the GLDE model and 0.916 (p < 0.001) fitted by the LDE model. The R2 test value varied between 0.793 and 0.966 fitted by the GLDE model and varied between 0.594 and 0.922 fitted by the LDE model for diseases with different transmission routes. The R2 test values varied between 0.853 and 0.939 fitted by the GLDE model and varied from 0.687 to 0.769 fitted by the LDE model for diseases with different prevalence intensities. The R2 test value varied between 0.706 and 0.917 fitted by the GLDE model and varied between 0.410 and 0.898 fitted by the LDE model for diseases with different time scales. The GLDE model also performed better with nation-level data with the R2 test values between 0.897 and 0.970 vs. 0.731 and 0.953 that fitted by the LDE model. Both models could characterize the patterns of the epidemics well and calculate the acceleration weeks. Conclusion The GLDE model provides more accurate goodness-of-fit to the data than the LDE model. The GLDE model is able to handle asymmetric data by introducing shape parameters that allow it to fit data with various distributions. The LDE model provides an earlier epidemic acceleration week than the GLDE model. We conclude that the GLDE model is more advantageous in asymmetric infectious disease data simulation.

5.
J Med Virol ; 94(7): 3121-3132, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1750404

ABSTRACT

Growing evidence has shown that anti-COVID-19 nonpharmaceutical interventions (NPIs) can support prevention and control of various infectious diseases, including intestinal diseases. However, most studies focused on the short-term mitigating impact and neglected the dynamic impact over time. This study is aimed to investigate the dynamic impact of anti-COVID-19 NPIs on hand, foot, and mouth disease (HFMD) over time in Xi'an City, northwestern China. Based on the surveillance data of HFMD, meteorological and web search data, Bayesian Structural Time Series model and interrupted time series analysis were performed to quantitatively measure the impact of NPIs in sequent phases with different intensities and to predict the counterfactual number of HFMD cases. From 2013 to 2021, a total number of 172,898 HFMD cases were reported in Xi'an. In 2020, there appeared a significant decrease in HFMD incidence (-94.52%, 95% CI: -97.54% to -81.95%) in the first half of the year and the peak period shifted from June to October by a small margin of 6.74% compared to the previous years of 2013 to 2019. In 2021, the seasonality of HFMD incidence gradually returned to the bimodal temporal variation pattern with a significant average decline of 61.09%. In particular, the impact of NPIs on HFMD was more evident among young children (0-3 years), and the HFMD incidence reported in industrial areas had an unexpected increase of 51.71% in 2020 autumn and winter. Results suggested that both direct and indirect NPIs should be implemented as effective public health measures to reduce infectious disease and improve surveillance strategies, and HFMD incidence in Xi'an experienced a significant rebound to the previous seasonality after a prominent decline influenced by the anti-COVID-19 NPIs.


Subject(s)
COVID-19 , Communicable Diseases , Hand, Foot and Mouth Disease , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , Child , Child, Preschool , China/epidemiology , Hand, Foot and Mouth Disease/epidemiology , Hand, Foot and Mouth Disease/prevention & control , Humans , Incidence , Seasons
6.
China Tropical Medicine ; 20(9):853-856, 2020.
Article in Chinese | GIM | ID: covidwho-890728

ABSTRACT

Objective: To explore the transmission characteristics of the typical clusters of coronavirus diseases 2019 (COVID-19) in Xi'an, so as to provide scientific basis for optimizing the control strategy of COVID-19.

7.
Chinese Journal of Nosocomiology ; 30(6):834-838, 2020.
Article in Chinese | GIM | ID: covidwho-822403

ABSTRACT

OBJECTIVE: To explore the early features of COVID-19 epidemic in Shaanxi Province so as to provide scientific basis for optimizing the prevention strategies and evaluating the effects of interventions. METHODS: The epidemic data that were reported through official networks of Shaanxi Province from Dec. 31, 2019 to Feb. 13, 2020 and the case data from Chinese Information System for Disease Control and Prevention were collected, the population data during the same period were obtained from Shaanxi Statistical Yearbook. The descriptive epidemiological analysis was performed by using Excel and ArcGIS software, the transmission dynamics model of COVID-19 was built based on Berkeley Madonna software experiment platform, and the rules of occurrence and progression of the disease were observed. RESULTS: By Feb. 13, 2020, the accumulative confirmed cases of COVID-19 reached 230 in Shaanxi Province, and the incidence rate was about 0.59 per 100 000. The male cases were more than the female cases, and the patients aged between 40 and 50 years old were dominant. The COVID-19 was highly prevalent in Xi'an, Ankang and Hanzhong. The SEIAR model showed that the basic regeneration index(R0) of the epidemic in Shaanxi Province was about 2.95, concluding that the beginning of Feb. 2020 was the peak period of outbreak of COVID-19 in Shaanxi Province. CONCLUSION: The COVID-19 epidemic in Shaanxi province shows a fast spreading trend. The theoretical number of confirmed cases that is predicted based on the SEIAR model can provide basis for prevention and control of the COVID-19 epidemic and curb the spread of the epidemic.

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