The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe.

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C–20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to −0.0142;p < 0.05) and negative (coefficient: −0.0496 to −0.0248;p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was −10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public. Graphical Image 1

Corrigendum: Knowledge, Attitude, and Practice Survey of COVID-19 Among Healthcare Students During the COVID-19 Outbreak in China: An Online Cross-Sectional Survey.

[This corrects the article DOI: 10.3389/fpubh.2021.742314.].

Determinants of COVID-19 Vaccine Acceptance and Hesitancy: A Health Care Student-Based Online Survey in Northwest China.

Background: With the spread of COVID-19 around the world, herd immunity through vaccination became a key measure to control the pandemic, but high uptake of vaccine is not guaranteed. Moreover, the actual acceptance of COVID-19 vaccination and associated factors remain uncertain among health care students in Northwest China. Methods: A cross-sectional survey of a sample of 631 health care students was performed using a questionnaire developed through Wen Juan Xing survey platform to collect information regarding their attitudes, beliefs, and acceptance of COVID-19 vaccination. Binary logistic regression analyses were performed to identify the association between vaccination willingness and demographics, attitudes, and beliefs to determine the factors that actually effect acceptance and hesitancy of COVID-19 vaccine among health care students. Results: Overall, 491 (77.81%) students actually received the COVID-19 vaccine, and of the 140 unvaccinated, 69 were hesitant and 71 rejected. Binary logistic regression analysis showed that the actually vaccinated individuals were those who mostly believed in the effectiveness of the COVID-19 vaccine (OR = 2.94, 95%CI: 1.37, 6.29), those who mostly felt it is their responsibility to receive the vaccine to protect others from infection (OR = 2.75, 95%CI: 1.45, 5.23), with less previous experience about other vaccines (OR = 1.70, 95%CI: 1.06, 2.72), students who mostly thought COVID-19 to be very severe (OR = 1.77, 95%CI: 1.07, 2.93), and students who mostly thought the COVID-19 vaccine was one of the best protection measures (OR = 1.68, 95%CI: 1.03, 2.76). Concerns about side effects of vaccines (OR = 0.30, 95%CI: 0.18, 0.51) and the use of personal protective behavior as an alternative to the COVID-19 vaccination (OR = 0.16, 95%CI: 0.06, 0.39) hindered the vaccine acceptance. Conclusions: Our study showed higher COVID-19 vaccine acceptance among healthcare students. However, the individuals with vaccine hesitancy and rejection were still worrying. Vaccine safety and effectiveness issues continue to be a major factor affecting students' acceptance. To expand vaccine coverage in response to the COVID-19 pandemic, appropriate vaccination strategies and immunization programs are essential, especially for those with negative attitudes and beliefs.

Non-viral vectors for RNA delivery.

RNA-based therapy is a promising and potential strategy for disease treatment by introducing exogenous nucleic acids such as messenger RNA (mRNA), small interfering RNA (siRNA), microRNA (miRNA) or antisense oligonucleotides (ASO) to modulate gene expression in specific cells. It is exciting that mRNA encoding the spike protein of COVID-19 (coronavirus disease 2019) delivered by lipid nanoparticles (LNPs) exhibits the efficient protection of lungs infection against the virus. In this review, we introduce the biological barriers to RNA delivery in vivo and discuss recent advances in non-viral delivery systems, such as lipid-based nanoparticles, polymeric nanoparticles, N-acetylgalactosamine (GalNAc)-siRNA conjugate, and biomimetic nanovectors, which can protect RNAs against degradation by ribonucleases, accumulate in specific tissue, facilitate cell internalization, and allow for the controlled release of the encapsulated therapeutics.

Clinical and virological impact of single and dual infections with influenza A (H1N1) and SARS-CoV-2 in adult inpatients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mimics the influenza A (H1N1) virus in terms of clinical presentation, transmission mechanism, and seasonal coincidence. Comprehensive data for the clinical severity of adult patients co-infected by both H1N1 and SARS-CoV-2, and, particularly, the relationship with PCR cycle threshold (Ct) values are not yet available. All participants in this study were tested for H1N1 and SARS-CoV-2 simultaneously at admission. Demographic, clinical, treatment, and laboratory data were extracted from electronic medical records and compared among adults hospitalized for H1N1 infection, SARS-CoV-2 infection and co-infection with both viruses. Ct values for viral RNA detection were further compared within SARS-CoV-2 and co-infection groups. Score on seven-category ordinal scale of clinical status at day 7 and day 14 were assessed. Among patients with monoinfection, H1N1 infection had higher frequency of onset symptoms but lower incidence of adverse events during hospitalization than SAR-CoV-2 infection (P < 0.05). Co-infection had an increased odds of acute kidney injury, acute heart failure, secondary bacterial infections, multilobar infiltrates and admittance to ICU than monoinfection. Score on seven-category scale at day 7 and day 14 was higher in patients with coinfection than patients with SAR-CoV-2 monoinfection (P<0.05). Co-infected patients had lower initial Ct values (referring to higher viral load) (median 32) than patients with SAR-CoV-2 monoinfection (median 36). Among co-infected patients, low Ct values were significantly and positively correlated with acute kidney injury and ARDS (P = 0.03 and 0.02, respectively). Co-infection by SARS-CoV-2 and H1N1 caused more severe disease than monoinfection by either virus in adult inpatients. Early Ct value could provide clues for the later trajectory of the co-infection. Multiplex molecular diagnostics for both viruses and early assessment of SAR-CoV-2 Ct values are recommended to achieve optimal treatment for improved clinical outcome.

Knowledge, Attitude, and Practice Survey of COVID-19 Among Healthcare Students During the COVID-19 Outbreak in China: An Online Cross-Sectional Survey.

Background: The ongoing coronavirus disease (COVID-19) outbreak has placed the healthcare system and student training under considerable pressure. However, the plights of healthcare students in the COVID-19 period have drawn limited attention in China. Methods: A cross-sectional on-line survey was undertaken between January and March 2020 to explore the COVID-19 knowledge, attitude, and practice (KAP) survey among Chinese healthcare students. Demographic information and data on KAP were obtained using a self-reported questionnaire. The percentage KAP scores were categorized as good or poor. Independent predictors of good knowledge of COVID-19 were ascertained to use a logistic regression model. Results: Of the 1,595 participants, 85.9% (1,370) were women, 53.4% were junior college students, 65.8% majoring in nursing, and 29.8% had received training on COVID-19. The overall median percentage for good KAP was 51.6% with knowledge of 28.3%, attitude 67.8%, and practice 58.6%, respectively. Independent predictors of good knowledge of COVID-19 were being students ≥25 (95% CI = 0.27-0.93, P = 0.02), those taking bachelor degrees (95% CI = 1.17-2.07, P = 0.00), and those having participated in COVID-19 treatment training. Conclusions: The result of this study revealed suboptimal COVID-19-related KAP among healthcare students in China. To effectively control future outbreaks of COVID-19, there is a need to implement public sensitization programs to improve the understanding of COVID-19 and address COVID-19-related myths and misconceptions, especially among healthcare students.