Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model (preprint)

Background There may be evidence that COVID-19 affects illness patterns. This study aimed to estimate epidemiological trends in China and to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications.Methods The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges.Results The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% ~ -31%) from January to March 2020, by 24% (95% CI: -29% ~ -17%) from January to June 2020, by 15% (95% CI: -19% ~ -9.2%) from January to December 2020, by 11% (95% CI: -15% ~ -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% ~ -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% ~ -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 ~ 115,391).Conclusions After COVID-19 intervention, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

Modulation of the immune response to SARS-CoV-2 vaccination by NSAIDs.

Evidence is scarce to guide the use of nonsteroidal anti-inflammatory drugs (NSAIDs) to mitigate SARS-CoV-2 vaccine related adverse effects, given the possibility of blunting the desired immune response. In this pilot study, we deeply phenotyped a small number of volunteers who did or did not take NSAIDs concomitant with SARS-CoV-2 immunizations to seek initial information on the immune response. A SARS-CoV-2 vaccine specific RBD-IgG antibody response and efficacy in the evoked neutralization titers were evident irrespective of concomitant NSAID consumption. Given the sample size, only a large and consistent signal of immunomodulation would have been detectable, and this was not apparent. However, the information gathered may inform the design of a definitive clinical trial. Here, we report a series of divergent omics signals that invite additional hypotheses testing. Significance Statement A SARS-CoV-2 vaccine specific immune response was evident irrespective of concomitant NSAID consumption in a clinical pilot study of small sample size.

Effects of phenylpyridinone derivative on the replication of Porcine Deltacoronavirus

The purpose of this study was to investigate the antiviral effect of phenylpyridinone derivative JIB-04 on porcine deltacoronavirus (PDCoV), and explore the possible mechanism. Cell viability after treatment with different concentrations of JIB-04 was detected by CCK-8, and the 50% cytotoxic concentration (CC50) and 50% effective concentration (EC50) were calculated. TCID50 method was used to detect the effects of JIB-04 pretreatment and co-treatment on PDCoV replicationand the effect of JIB-04 treatment on virus attachment and penetration. Finally, qRT-PCR, TCID50 and Western blot methods were used to detect the effect of JIB-04 on virus replication at different times post infection. The results showed that JIB-04 did not affect the cell viability of LLC-PK cells at all tested concentrations, and CC50>640 micro mol.L-1, EC50=0.216 micro mol.L-1, and SI index is greater than 2 963. Compared with untreated virus infection group, JIB-04 treatment significantly reduced the virus titer (P < 0.001), but it had no effect on attachment or penetration of PDCoV. At 6 h post infection, compared with untreated virus infection group, virus titer in JIB-04 treatment group was significantly decreased (P < 0.01). At 12 and 24 h post infection, virus titer, genome copy number, and N protein expression level all significantly decreased (P < 0.01). JIB-04 has a low cytotoxicity and a high selective index, and can protect against PDCoV infection in vitro, making it a potential antiviral drug. JIB-04 can inhibit synthesis of viral RNA, protein and PDCoV replication.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

Considerations for the Safe Operation of Schools During the Coronavirus Pandemic.

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, providing safe in-person schooling has been a dynamic process balancing evolving community disease burden, scientific information, and local regulatory requirements with the mandate for education. Considerations include the health risks of SARS-CoV-2 infection and its post-acute sequelae, the impact of remote learning or periods of quarantine on education and well-being of children, and the contribution of schools to viral circulation in the community. The risk for infections that may occur within schools is related to the incidence of SARS-CoV-2 infections within the local community. Thus, persistent suppression of viral circulation in the community through effective public health measures including vaccination is critical to in-person schooling. Evidence suggests that the likelihood of transmission of SARS-CoV-2 within schools can be minimized if mitigation strategies are rationally combined. This article reviews evidence-based approaches and practices for the continual operation of in-person schooling.

Why DO citizens engage in government social media accounts during COVID-19 pandemic? A comparative study.

The worldwide COVID-19 outbreak that crushed the global economy and healthcare increased the public willingness to acquire more information and enthusiasm to engage online among billions of users through social networks. As more towns, cities, and regions turn to lockdown, government social media accounts (GSMAs) develop as a trustworthy source to obtain information about the COVID-19 pandemic. Thus, investigating the determinants and consequences of citizens' participation behavior on GSMAs is essential. Drawing on the self-determination theory (SDT) and civic volunteer model (CVM), we examine the influence of motivational factors (i.e., intrinsic extrinsic) on citizens' participation behavior on GSMAs, which leads to online civic behavior. Comparative research between China and Pakistan is carried out using data collected through an online survey. This study shows that information-seeking, political benefits, self-development, altruism, and perceived reciprocity are the critical antecedents of citizens' participatory behavior on GSMAs in both countries, resulting in online civic behavior. Furthermore, moderating results reveal that perceived connectivity moderates the relationship between certain motivational factors (intrinsic and extrinsic) and citizens' participatory behavior on GSMAs, whereas trust in government moderates the relationship between participatory behavior on GSMAs and online civic behavior during COVID-19. Theoretical and managerial implications are discussed in detail.