Epidemiological characteristics and transmission dynamics of the COVID-19 outbreak in Hohhot, China: a time-varying SQEIAHR model analysis

Predicting the transmission dynamics of novel coronavirus infection in Shanxi province after the implementation of the "Class B infectious disease Class B management" policy

Background

China managed coronavirus disease 2019 (COVID-19) with measures against Class B infectious diseases, instead of Class A infectious diseases, in a major shift of its epidemic response policies. We aimed to generate robust information on the transmission dynamics of novel coronavirus infection in Shanxi, a province located in northern China, after the implementation of the "Class B infectious disease Class B management" policy.

Methods

We consolidated infection data in Shanxi province from December 6, 2022 to January 14, 2023 through a network questionnaire survey and sentinel surveillance. A dynamics model of the SEIQHCVR was developed to track the infection curves and effective reproduction number (R t ).

Results

Our model was effective in estimating the trends of novel coronavirus infection, with the coefficient of determination (R 2 ) above 90% in infections, inpatients, and critically ill patients. The number of infections in Shanxi province as well as in urban and rural areas peaked on December 20, 2022, with the peak of inpatients and critically ill patients occurring 2 to 3 weeks after the peak of infections. By the end of January 2023, 87.72% of the Shanxi residents were predicted to be infected, and the outbreak subsequently subsided. A small wave of COVID-19 infections may re-emerge at the end of April. In less than a month, the R t values of positive infections, inpatients and critically ill patients were all below 1.0.

Conclusion

The outbreak in Shanxi province is currently at a low prevalence level. In the face of possible future waves of infection, there is a strong need to strengthen surveillance and early warning.

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.

Epidemiological and clinical characteristics of COVID-19 reinfection during the epidemic period in Yangzhou city, Jiangsu province

Background

With the continuous progress of the epidemic of coronavirus disease 2019 (COVID-19) infection and the constant mutation of the virus strain, reinfection occurred in previously infected individuals and caused waves of the epidemic in many countries. Therefore, we aimed to explore the characteristics of COVID-19 reinfection during the epidemic period in Yangzhou and provide a scientific basis for assessing the COVID-19 situation and optimizing the allocation of medical resources.

Methods

We chose previously infected individuals of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reported locally in Yangzhou city from January 2020 to November 30, 2022. A telephone follow-up of cases was conducted from February to March 2023 to collect the COVID-19 reinfection information. We conducted a face-to-face survey on that who met the definition of reinfection to collect information on clinical symptoms, vaccination status of COVID-19, and so on. Data were analyzed using SPSS 19.0.

Results

Among the 999 eligible respondents (92.24% of all the participants), consisting of 42.28% males and 57.72% females, the reinfection incidence of females was significantly higher than that of male cases (χ2 = 5.197, P < 0.05); the ages of the respondents ranged from 1 to 91 years, with the mean age of 42.28 (standard deviation 22.73) years; the most of the sufferers were infected initially with Delta variant (56.88%), followed by the Omicron subvariants BA.1/BA.2 (39.52%). Among all the eligible respondents, 126 (12.61%) reported COVID-19 reinfection appearing during the epidemic period, and the intervals between infections were from 73 to 1,082 days. The earlier the initial infection occurred, the higher the reinfection incidence and the reinfection incidence was significantly increased when the interval was beyond 1 year (P < 0.01) .119 reinfection cases (94.4%) were symptomatic when the most common symptoms included fever (65.54%) and cough (61.34%); compared with the initial infection cases, the proportion of clinical symptoms in the reinfected cases was significantly higher (P < 0.01). The reinfection incidence of COVID-19 vaccination groups with different doses was statistically significant (P < 0.01). Fewer reinfections were observed among the respondents with three doses of COVID-19 vaccination compared to the respondents with two doses (χ2 = 14.595, P < 0.001) or without COVID-19 vaccination (χ2 =4.263, P = 0.039).

Conclusion

After the epidemic period of COVID-19, the reinfection incidence varied with different types of SARS-CoV-2 strains. The reinfection incidence was influenced by various factors such as virus characteristics, vaccination, epidemic prevention policies, and individual variations. As the SARS-CoV-2 continues to mutate, vaccination and appropriate personal protection have practical significance in reducing the risk of reinfection.