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An unproven "nicotine hypothesis" that indicates nicotine's therapeutic potential for COVID-19 has been proposed in recent literature. This study is about Twitter posts that misinterpret this hypothesis to make baseless claims about benefits of smoking and vaping in the context of COVID-19. We quantify the presence of such misinformation and characterize the tweeters who post such messages.

Twitter premium API was used to download tweets (n = 17,533) that match terms indicating (a) nicotine or vaping themes, (b) a prophylactic or therapeutic effect,

(c) COVID-19 (January-July 2020) as a conjunctive query. A constraint on the length of the span of text containing the terms in the tweets allowed us to focus on those that convey the therapeutic intent. We hand-annotated these filtered tweets and built a classifier that identifies tweets that extrapolate the nicotine hypothesis to smoking/vaping with a positive predictive value of 85%. We analyzed the frequently used terms in author bios, top Web links, anconfirmation bias when exposed to inadvertent or deliberate framing of scientific discourse that may border on misinformation. find more This calls for circumspection and additional planning in countering such narratives as the COVID-19 pandemic continues to ravage our world. Our results also serve as a cautionary tale in how social media can be leveraged to spread misleading information about tobacco products in the wake of pandemics.There has been a markedly renewed interest in factors associated with pneumonia, a leading cause of death worldwide, due to its frequent concurrence with pandemics of influenza and Covid-19 disease. Reported predisposing factors to both bacterial pneumonia and pandemic viral lower respiratory infections are wintertime occurrence, older age, obesity, pre-existing cardiopulmonary conditions and diabetes. Also implicated are age-related neurodegenerative diseases that cause parkinsonism and dementia. find more We investigated the prevalence of autopsy-proven pneumonia in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND), a longitudinal clinicopathological study, between the years 2006 and 2019 and before the beginning of the Covid-19 pandemic. Of 691 subjects dying at advanced ages (mean 83.4), pneumonia was diagnosed postmortem in 343 (49.6%). There were 185 subjects without dementia or parkinsonism while clinicopathological diagnoses for the other subjects included 319 with Alzheimer's disease dementia, 127 with idiopathic Parkinson's disease, 72 with dementia with Lewy bodies, 49 with progressive supranuclear palsy and 78 with vascular dementia. Subjects with one or more of these neurodegenerative diseases all had higher pneumonia rates, ranging between 50 and 61%, as compared to those without dementia or parkinsonism (40%). In multivariable logistic regression models, male sex and a non-summer death both had independent contributions (ORs of 1.67 and 1.53) towards the presence of pneumonia at autopsy while the absence of parkinsonism or dementia was a significant negative predictor of pneumonia (OR 0.54). Male sex, dementia and parkinsonism may also be risk factors for Covid-19 pneumonia. The apolipoprotein E4 allele, as well as obesity, chronic obstructive pulmonary disease, diabetes, hypertension, congestive heart failure, cardiomegaly and cigarette smoking history, were not significantly associated with pneumonia, in contradistinction to what has been reported for Covid-19 disease.The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020-2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021-2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. link= find more We found that behavioral interventions offer a cost-effective means of epidemic control grouthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.Lasting immunity will be critical for overcoming the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, factors that drive the development of high titers of anti-SARS-CoV-2 antibodies and how long those antibodies persist remain unclear. Our objective was to comprehensively evaluate anti-SARS-CoV-2 antibodies in a clinically diverse COVID-19 convalescent cohort at defined time points to determine if anti-SARS-CoV-2 antibodies persist and to identify clinical and demographic factors that correlate with high titers. Using a novel multiplex assay to quantify IgG against four SARS-CoV-2 antigens, a receptor binding domain-angiotensin converting enzyme 2 inhibition assay, and a SARS-CoV-2 neutralization assay, we found that 98% of COVID-19 convalescent subjects had anti-SARS-CoV-2 antibodies five weeks after symptom resolution (n=113). Further, antibody levels did not decline three months after symptom resolution (n=79). link2 As expected, greater disease severity, older age, male sex, obesity, and higher Charlson Comorbidity Index score correlated with increased anti-SARS-CoV-2 antibody levels. link3 We demonstrated for the first time that COVID-19 symptoms, namely fever, abdominal pain, diarrhea and low appetite, correlated consistently with higher anti-SARS-CoV-2 antibody levels. link2 Our results provide new insights into the development and persistence of anti-SARS-CoV-2 antibodies.While several clinical and immunological parameters correlate with disease severity and mortality in SARS-CoV-2 infection, work remains in identifying unifying correlates of coronavirus disease 2019 (COVID-19) that can be used to guide clinical practice. Here, we examine saliva and nasopharyngeal (NP) viral load over time and correlate them with patient demographics, and cellular and immune profiling. We found that saliva viral load was significantly higher in those with COVID-19 risk factors; that it correlated with increasing levels of disease severity and showed a superior ability over nasopharyngeal viral load as a predictor of mortality over time (AUC=0.90). A comprehensive analysis of immune factors and cell subsets revealed strong predictors of high and low saliva viral load, which were associated with increased disease severity or better overall outcomes, respectively. Saliva viral load was positively associated with many known COVID-19 inflammatory markers such as IL-6, IL-18, IL-10, and CXCL10, as well as type 1 immune response cytokines. Higher saliva viral loads strongly correlated with the progressive depletion of platelets, lymphocytes, and effector T cell subsets including circulating follicular CD4 T cells (cTfh). Anti-spike (S) and anti-receptor binding domain (RBD) IgG levels were negatively correlated with saliva viral load showing a strong temporal association that could help distinguish severity and mortality in COVID-19. Finally, patients with fatal COVID-19 exhibited higher viral loads, which correlated with the depletion of cTfh cells, and lower production of anti-RBD and anti-S IgG levels. Together these results demonstrated that viral load, as measured by saliva but not nasopharyngeal, is a dynamic unifying correlate of disease presentation, severity, and mortality over time.Perinatal transmission of COVID-19 is poorly understood and many neonatal intensive care units' (NICU) policies minimize mother-infant contact to prevent transmission. We present our unit's approach and ways it may impact neonatal microbiome acquisition. We attended COVID-19 positive mothers' deliveries from March-August 2020. Delayed cord clamping and skin-to-skin were avoided and infants were admitted to the NICU. No parents' visits were allowed and discharge was arranged with COVID-19 negative family members. link3 Maternal breast milk was restricted in the NICU. All twenty-one infants tested negative at 24 and 48 hours and had average hospital stays of nine days. 40% of mothers expressed breastmilk and 60% of infants were discharged with COVID-19 negative caregivers. Extended hospital stays, no skin-to-skin contact, limited maternal milk use, and discharge to caregivers outside primary residences, potentially affect the neonatal microbiome. Future studies are warranted to explore how ours and other centers' similar policies influence this outcome.

Population mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19.

To examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC.

This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on tmedia platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Population mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

COVID-19 has rapidly emerged as a global public health threat with infections recorded in nearly every country. Responses to COVID-19 have varied in intensity and breadth, but generally have included domestic and international travel limitations, closure of non-essential businesses, and repurposing of health services. While these interventions have focused on testing, treatment, and mitigation of COVID-19, there have been reports of interruptions to diagnostic, prevention, and treatment services for other public health threats.

We conducted a scoping review to characterize the early impact of COVID-19 on HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition.

A scoping literature review was completed using searches of PubMed and preprint servers (medRxiv/bioRxiv) from January 1st to October 31st, 2020, using Medical Subject Headings (MeSH) terms related to SARS-CoV-2 or COVID-19 and HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. Empiric studies reporting original data collection or mathematical models were included, and available data synthesized by region.