The spread of COVID-19 led countries around the world to adopt lockdown measures of varying stringency, with the purpose of restricting the movement of people. However, the effectiveness of these measures on mobility has been markedly different. Employing a difference-in-differences design, we analyse the effectiveness of movement restrictions across different countries. We disentangle the role of regulation (stringency measures) from the role of people's knowledge about the spread of COVID-19. We proxy COVID-19 knowledge by using Google Trends data on the term "Covid". We find that lockdown measures have a higher impact on mobility the more people learn about COVID-19. This finding is driven by countries with low levels of trust in institutions and low levels of education.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863948 | PMC |
http://dx.doi.org/10.1016/j.jce.2022.02.004 | DOI Listing |
3 Biotech
February 2025
Shree. S. K. Patel College of Pharmaceutical Education and Research, Ganpat University, Kherva, Gujarat 384012 India.
This review assesses the antiviral capabilities of antimicrobial peptides (AMPs) against SARS-CoV-2 and other respiratory viruses, focussing on their therapeutic potential. AMPs, derived from natural sources, exhibit promising antiviral properties by disrupting viral membranes, inhibiting viral entry, and modulating host immune responses. Preclinical studies demonstrate that peptides such as defensins, cathelicidins, and lactoferrin can effectively reduce SARS-CoV-2 replication and inhibit viral spread.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states.
View Article and Find Full Text PDFJ Infect Public Health
January 2025
Public health Laboratory, The regional laboratory, Jazan Health Cluster, Jazan, Saudi Arabia.
Background: Patients with severe COVID-19 may require intensive care unit (ICU) admission to manage life-threatening complications. However, ICU admission is associated with an increased risk of acquiring nosocomial infections caused by multidrug-resistant (MDR) bacteria, particularly carbapenem-resistant Enterobacterale (CRE). Enterobacter cloacae complex (ECC), a group of closely related species including Enterobacter cloacae, is a common cause of healthcare-associated infections (HAIs).
View Article and Find Full Text PDFMol Pharm
January 2025
Ningbo No.2 Hospital, Ningbo, Zhejiang 315010, P. R. China.
At the end of 2019, SARS-CoV-2 emerged and rapidly spread, having a profound negative impact on human health and socioeconomic conditions. In response to this unprecedented global health crisis, significant advancements were made in the mRNA vaccine technology. In this study, we have compared the difference between two SARS-CoV-2 receptor-binding domain (RBD) mRNA-Lipid nanoparticle (LNP) vaccines prepared from two different ionizable cationic lipids: ALC-0315 and MC3.
View Article and Find Full Text PDFPLoS One
January 2025
Centro Ricerche Enrico Fermi, Rome, Italy.
The Covid-19 pandemic has sparked renewed attention to the risks of online misinformation, emphasizing its impact on individuals' quality of life through the spread of health-related myths and misconceptions. In this study, we analyze 6 years (2016-2021) of Italian vaccine debate across diverse social media platforms (Facebook, Instagram, Twitter, YouTube), encompassing all major news sources-both questionable and reliable. We first use the symbolic transfer entropy analysis of news production time-series to dynamically determine which category of sources, questionable or reliable, causally drives the agenda on vaccines.
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