Inactivated COVID-19 vaccine booster dose shortened the viral shedding time of patients infected with the Omicron variant BA.2.

OBJECTIVES: We assessed the effect of inactivated COVID-19 vaccine boosting immunization on the viral shedding time for patients infected with the Omicron variant BA.2. METHODS: We performed a real-world study by analyzing the outbreak data of patients infected with the COVID-19 Omicron variant BA.2 from March to May 2022 in Shanghai, China. Patients were categorized into three groups, including not fully vaccinated (zero and one dose), fully vaccinated (two doses), and booster-vaccinated (three doses). RESULTS: A total of 4443 patients infected with COVID-19 were included in the analysis. The proportion of viral shedding within 14 days in the three groups was 94.7%, 95.5%, and 96.7%, respectively (P <0.001). After adjusting for sex, age, underlying conditions, and clinical symptoms, the booster vaccination had a 29% increased possibility (hazard ratio: 1.29, 95% confidence interval: 1.18-1.41) of no detectable viral shedding within 14 days, whereas the fully vaccinated group had an 11% increased possibility of no detectable viral shedding (hazard ratio: 1.11, 95% confidence interval: 1.01-1.23). The effect of booster vaccination was more significant in males, the elderly, and people with underlying conditions or symptomatic infections. CONCLUSION: Our study confirmed that the booster vaccination could significantly shorten the viral shedding time of patients infected with the Omicron variant BA.2.

Effects of short-term ambient particulate matter exposure on the risk of severe COVID-19.

OBJECTIVES: Previous studies have suggested a relationship between outdoor air pollution and the risk of coronavirus disease 2019 (COVID-19). However, there is a lack of data related to the severity of disease, especially in China. This study aimed to explore the association between short-term exposure to outdoor particulate matter (PM) and the risk of severe COVID-19. METHODS: We recruited patients diagnosed with COVID-19 during a recent large-scale outbreak in eastern China caused by the Delta variant. We collected data on meteorological factors and ambient air pollution during the same time period and in the same region where the cases occurred and applied a generalized additive model (GAM) to analyze the effects of short-term ambient PM exposure on the risk of severe COVID-19. RESULTS: A total of 476 adult patients with confirmed COVID-19 were recruited, of which 42 (8.82%) had severe disease. With a unit increase in PM10, the risk of severe COVID-19 increased by 81.70% (95% confidence interval [CI]: 35.45, 143.76) at a lag of 0-7 days, 86.04% (95% CI: 38.71, 149.53) at a lag of 0-14 days, 76.26% (95% CI: 33.68, 132.42) at a lag of 0-21 days, and 72.15% (95% CI: 21.02, 144.88) at a lag of 0-28 days. The associations remained significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. With a unit increase in PM2.5, the risk of severe COVID-19 increased by 299.08% (95% CI: 92.94, 725.46) at a lag of 0-7 days, 289.23% (95% CI: 85.62, 716.20) at a lag of 0-14 days, 234.34% (95% CI: 63.81, 582.40) at a lag of 0-21 days, and 204.04% (95% CI: 39.28, 563.71) at a lag of 0-28 days. The associations were still significant at lags of 0-7 days, 0-14 days, and 0-28 days in the multipollutant models. CONCLUSIONS: Our results indicated that short-term exposure to outdoor PM was positively related to the risk of severe COVID-19, and that reducing air pollution may contribute to the control of COVID-19.

[A comparative study of time series models in predicting COVID-19 cases].

Objective: To compare the performances of different time series models in predicting COVID-19 in different countries. Methods: We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020. Results: For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively. Conclusions: The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.

Are we ready to deal with a global COVID-19 pandemic? Rethinking countries' capacity based on the Global Health Security Index.

BACKGROUND: The Global Health Security (GHS) Index has been developed to measure a country's capacity to cope with a public health emergency; however, evidence for whether it corresponds to the response to a global pandemic is lacking. This study performed a multidimensional association analysis to explore the correlation between the GHS Index and COVID-19-associated morbidity, mortality, and disease increase rate (DIR) in 178 countries (regions). METHODS: The GHS Index and COVID-19 pandemic data - including total cases per million (TCPM), total deaths per million (TDPM), and daily growth rate - were extracted from online databases. The Spearman correlation coefficient was applied to describe the strength of the association between the GHS Index, sociological characteristics, and the epidemic situation of COVID-19. DIRs were compared, and the impact of the GHS Index on the DIR by the time of "lockdown" was visualized. RESULTS: The overall GHS Index was positively correlated with TCPM and TDPM, with coefficients of 0.34 and 0.41, respectively. Countries categorized into different GHS Indextiers had different DIRs before implementing lockdown measures. However, no significant difference was observed between countries in the middle and upper tiers after implementing lockdown measures. The correlation between GHS Index and DIR was positive five days before lockdown measures were taken, but it became negative 13 days later. CONCLUSIONS: The GHS Index has limited value in assessing a country's capacity to respond to a global pandemic. Nevertheless, it has potential value in determining the country's ability to cope with a local epidemic situation.