'Dynamic zero-COVID' policy and viral clearance during an omicron wave in Tianjin, China: a city-wide retrospective observational study

Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis

BACKGROUND

The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the "dynamic zero-COVID" policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions.

METHODS

Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period.

RESULTS

(1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature. (2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days. (3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90-1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4-6 days in more than 50% of mass outbreaks. (4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping.

CONCLUSION

Overall, our findings suggest the variation in the infection rates during the "dynamic zero-COVID" policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic transmissions, judging peak points, and predicting peak infection cases using different patterns.

Epidemiological and laboratory characteristics of Omicron infection in a general hospital in Guangzhou: a retrospective study

The COVID-19 pandemic caused by SARS-CoV-2 has emerged as a major global public health concern. In November 2022, Guangzhou experienced a significant outbreak of Omicron. This study presents detailed epidemiological and laboratory data on Omicron infection in a general hospital in Guangzhou between December 1, 2022, and January 31, 2023. Out of the 55,296 individuals tested, 12,346 were found to be positive for Omicron. The highest prevalence of positive cases was observed in the 20 to 39 age group (24.6%), while the lowest was in children aged 0 to 9 years (1.42%). Females had a higher incidence of infection than males, accounting for 56.6% of cases. The peak time of Omicron infection varied across different populations. The viral load was higher in older adults and children infected with Omicron, indicating age-related differences. Spearman's rank correlation analysis revealed positive correlations between Ct values and laboratory parameters in hospitalized patients with Omicron infection. These parameters included CRP (rs = 0.059, p = 0.009), PT (rs = 0.057, p = 0.009), INR (rs = 0.055, p = 0.013), AST (rs = 0.067, p = 0.002), LDH (rs = 0.078, p = 0.001), and BNP (rs = 0.063, p = 0.014). However, EO (Eosinophil, rs = -0.118, p < 0.001), BASO (basophil, rs = -0.093, p < 0.001), and LY (lymphocyte, rs = -0.069, p = 0.001) counts showed negative correlations with Ct values. Although statistically significant, the correlation coefficients between Ct values and these laboratory indices were very low. These findings provide valuable insights into the epidemiology of Omicron infection, including variations in Ct values across gender and age groups. However, caution should be exercised when utilizing Ct values in clinical settings for evaluating Omicron infection.

Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach

BACKGROUND

COVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to COVID-19. Previous studies have reported changes in smoking behavior and influencing factors during the COVID-19 period, but none of them explored the main influencing factor and high-risk populations for smoking behavior during this period.

METHODS

We conducted a nationwide survey and obtained 21,916 valid data. Logistic regression was used to examine the relationships between each potential influencing factor (sociodemographic characteristics, perceived social support, depression, anxiety, and self-efficacy) and smoking outcomes. Then, variables related to smoking behavior were included based on the results of the multiple logistic regression, and the classification and regression tree (CART) method was used to determine the high-risk population for increased smoking behavior during COVID-19 and the most profound influencing factors on smoking increase. Finally, we used accuracy to evaluated the performance of the tree.

RESULTS

The strongest predictor of smoking behavior during the COVID-19 period is acceptance degree of passive smoking. The subgroup with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years is identified as the highest smoking risk (34%). The accuracy of classification and regression tree is 87%.

CONCLUSION

The main influencing factor is acceptance degree of passive smoking. More knowledge about the harm of secondhand smoke should be promoted. For high-risk population who smoke, the "mask protection" effect during the COVID-19 pandemic should be fully utilized to encourage smoking cessation.

Individual preferences, government policy, and COVID-19: A game-theoretic epidemiological analysis

Purpose

The ongoing COVID-19 pandemic imposes serious short-term and long-term health costs on populations. Restrictive government policy measures decrease the risks of infection, but produce similarly serious social, mental health, and economic problems. Citizens have varying preferences about the desirability of restrictive policies, and governments are thus forced to navigate this tension in making pandemic policy. This paper analyses the situation facing government using a game-theoretic epidemiological model.

Methodology

We classify individuals into health-centered individuals and freedom-centered individuals to capture the heterogeneous preferences of citizens. We first use the extended Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) model (adding individual preferences) and the signaling game model (adding government) to analyze the strategic situation against the backdrop of a realistic model of COVID-19 infection.

Findings

We find the following: 1. There exists two pooling equilibria. When health-centered and freedom-centered individuals send anti-epidemic signals, the government will adopt strict restrictive policies under budget surplus or balance. When health-centered and freedom-centered individuals send freedom signals, the government chooses not to implement restrictive policies. 2. When governments choose not to impose restrictions, the extinction of an epidemic depends on whether it has a high infection transmission rate; when the government chooses to implement non-pharmacological interventions (NPIs), whether an epidemic will disappear depends on how strict the government's restrictions are.

Originality/value

Based on the existing literature, we add individual preferences and put the government into the game as a player. Our research extends the current form of combining epidemiology and game theory. By using both we get a more realistic understanding of the spread of the virus and combine that with a richer understanding of the strategic social dynamics enabled by game theoretic analysis. Our findings have important implications for public management and government decision-making in the context of COVID-19 and for potential future public health emergencies.

Effects of dynamic zero COVID-19 policy on anxiety status and lifestyle changes of pregnant women in rural South China: a survey-based analysis by propensity score matching method

Introduction

The coronavirus disease 2019 (COVID-19) pandemic triggered a global public health crisis and has brought an unprecedented impact on pregnant women. The problems faced by pregnant women in the rural areas of China during the epidemic are different from those in urban areas. Although the epidemic situation in China has gradually improved, studying the impact of the previous dynamic zero COVID-19 policy on the anxiety status and lifestyle of pregnant women in rural areas of China, is still necessary.

Methods

A cross-sectional survey of pregnant women in rural South China was conducted from September 2021 to June 2022.Using questionnaires, sociodemographic characteristics, anxiety status, physical activity, sleep quality, and dietary status of the population were collected. Using the propensity score matching method, the effect of the dynamic zero COVID-19 strategy on the anxiety status and lifestyle of pregnant women was analyzed.

Results

Among the pregnant women in the policy group (n = 136) and the control group (n = 680), 25.7 and 22.4% had anxiety disorders, 83.1 and 84.7% had low or medium levels of physical activity, and 28.7 and 29.1% had sleep disorders, respectively. However, no significant difference (p > 0.05) was observed between the two groups. Compared with control group, the intake of fruit in the policy group increased significantly (p = 0.019), whereas that of aquatic products and eggs decreased significantly (p = 0.027). Both groups exhibited an unreasonable dietary structure and poor compliance with the Chinese dietary guidelines for pregnant women (p > 0.05). The proportion of pregnant women in the policy group, whose intake of stable food (p = 0.002), soybean, and nuts (p = 0.004) was less than the recommended amount, was significantly higher than that in the control group.

Discussion

The dynamic zero COVID-19 strategy had little impact on the anxiety status, physical activity, and sleep disorders of pregnant women in the rural areas of South China. However, it affected their intake of certain food groups. Improving corresponding food supply and organized nutritional support should be addressed as a strategic approach to improve the health of pregnant women in rural South China during the pandemic.

Molecular epidemiological characteristics of SARS-CoV-2 in imported cases from 2021 to 2022 in Zhejiang Province, China

Backgrounds

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a global threat since 2020. The emergence of the Omicron variant in 2021, which replaced Delta as the dominant variant of concern, has had a significant adverse impact on the global economy and public health. During this period, Zhejiang Province implemented dynamic zeroing and focused on preventing imported cases. This study aimed to gain clear insight into the characteristics of imported COVID-19 cases in Zhejiang Province.

Methods

We conducted a systematic molecular epidemiological analysis of 146 imported cases between July 2021 and November 2022 in Zhejiang Province. Virus samples with cycle threshold (Ct) value less than 32 were performed next generation sequencing. Basing the whole genome sequence obtained after quality control and assembly of reads, the whole genome variation map and phylogenetic tree were constructed and further analyzed.

Results

Our study identified critical months and populations for surveillance, profiled the variation of various lineages, determined the evolutionary relationships among various lineages of SARS-CoV-2, and compared the results in Zhejiang with those obtained worldwide during this period.

Conclusion

The continuous molecular epidemiological surveillance of imported cases of COVID-19 in Zhejiang Province during 2021 to 2022 is consistent with the global epidemic trend.

Transmission of SARS-CoV-2 Omicron Variant under a Dynamic Clearance Strategy in Shandong, China

SARS-CoV-2 Omicron caused a large wave of COVID-19 cases in China in spring 2022. Shandong was one of the most affected regions during this epidemic yet was also among those areas that were able to quickly contain the transmission. We aimed to investigate the origin, genetic diversity, and transmission patterns of the Omicron epidemic in Shandong under a dynamic clearance strategy. We generated 1,149 Omicron sequences, performed phylogenetic analysis, and interpreted results in the context of available epidemiological information. We observed that there were multiple introductions of distinct Omicron sublineages into Shandong from foreign countries and other regions in China, while a small number of introductions led to majority of local cases. We found evidence suggesting that some local clusters were potentially associated with foreign imported cases. Superspreading events and cryptic transmissions contributed to the rapid spread of this epidemic. We identified a BA.1.1 genome with the R493Q reversion mutation in the spike receptor binding domain, potentially associated with an escape from vaccine and Omicron infection elicited neutralizing immunity. Our findings illustrated how the dynamic clearance strategy constrained this epidemic's size, duration, and geographical distribution. IMPORTANCE Starting in March 2022, the Omicron epidemic caused a large wave of COVID-19 cases in China. Shandong was one of the most affected regions during this epidemic but was also among those areas that were able to quickly contain the transmission. We investigated the origin, genetic diversity, and transmission patterns of Omicron epidemic in Shandong under a dynamic clearance strategy. We found that there were multiple introductions of distinct Omicron sublineages into Shandong from foreign countries and other regions in China, while a small number of introductions led to most local cases. We found evidence suggesting that some local clusters were associated with foreign imported cases. Superspreading events and cryptic transmissions contributed to the rapid spread of this epidemic. Our study illustrated the transmission patterns of Omicron epidemic in Shandong and provided a looking glass onto this epidemic in China.