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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-307881

ABSTRACT

No, they can't. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained;neither can the end of the epidemic after the turning point. An SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the mid-term is not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given.

2.
Kans J Med ; 14: 108-110, 2021.
Article in English | MEDLINE | ID: covidwho-1204406

ABSTRACT

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, aka COVID-19) virus has evolved into a World Health Organization-declared pandemic which has strained our regional critical care and hospital resources. METHODS: A Critical Care Task Force was established between Kansas City area intensive care units to allow for preparedness for potential surges by sharing of bed capacity both in the ICU and hospital, and ventilator capacity as well as weekly web-based meetings to share resource concerns and best practice. This Task Force also collected patient information to understand the dynamics of community impact and resource needs better. This effort allowed for compilation and dissemination of information regarding data that describe characteristics of patients with COVID-19 compared to a random sample of medical ICU patients with conditions other than COVID-19.Demographic and therapeutic factors affecting patients admitted to medical intensive care units in the Kansas City metro area are reported from May 5, 2020 until June 2, 2020 using a retrospective case-control study examining gender, race, and therapeutic options including modes of ventilation, vasopressor requirements, renal-replacement therapy, and disposition. RESULTS: During data collection, patients being treated for COVID-19 in intensive care units in the Kansas City metropolitan area were more likely to be older, less likely to be white, and less likely to be immunosuppressed as compared to those being treated for non-COVID illnesses. They were more likely to require non-invasive ventilation and undergo prone positioning but were equally likely to require invasive ventilation and other organ supportive therapy. CONCLUSIONS: Hospitalized patients being treated for COVID-19 in the Kansas City metropolitan area have similar demographics to those being reported in the U.S. including age and race. Additionally, establishing a Critical Care Task Force in response to the pandemic allowed for preparation for a potential surge, establishing capacity, and disseminating timely information to policy makers and critical care workers on the front line.

3.
Genome Med ; 13(1): 66, 2021 04 21.
Article in English | MEDLINE | ID: covidwho-1197350

ABSTRACT

BACKGROUND: The large airway epithelial barrier provides one of the first lines of defense against respiratory viruses, including SARS-CoV-2 that causes COVID-19. Substantial inter-individual variability in individual disease courses is hypothesized to be partially mediated by the differential regulation of the genes that interact with the SARS-CoV-2 virus or are involved in the subsequent host response. Here, we comprehensively investigated non-genetic and genetic factors influencing COVID-19-relevant bronchial epithelial gene expression. METHODS: We analyzed RNA-sequencing data from bronchial epithelial brushings obtained from uninfected individuals. We related ACE2 gene expression to host and environmental factors in the SPIROMICS cohort of smokers with and without chronic obstructive pulmonary disease (COPD) and replicated these associations in two asthma cohorts, SARP and MAST. To identify airway biology beyond ACE2 binding that may contribute to increased susceptibility, we used gene set enrichment analyses to determine if gene expression changes indicative of a suppressed airway immune response observed early in SARS-CoV-2 infection are also observed in association with host factors. To identify host genetic variants affecting COVID-19 susceptibility in SPIROMICS, we performed expression quantitative trait (eQTL) mapping and investigated the phenotypic associations of the eQTL variants. RESULTS: We found that ACE2 expression was higher in relation to active smoking, obesity, and hypertension that are known risk factors of COVID-19 severity, while an association with interferon-related inflammation was driven by the truncated, non-binding ACE2 isoform. We discovered that expression patterns of a suppressed airway immune response to early SARS-CoV-2 infection, compared to other viruses, are similar to patterns associated with obesity, hypertension, and cardiovascular disease, which may thus contribute to a COVID-19-susceptible airway environment. eQTL mapping identified regulatory variants for genes implicated in COVID-19, some of which had pheWAS evidence for their potential role in respiratory infections. CONCLUSIONS: These data provide evidence that clinically relevant variation in the expression of COVID-19-related genes is associated with host factors, environmental exposures, and likely host genetic variation.


Subject(s)
Bronchi , COVID-19/genetics , Respiratory Mucosa , SARS-CoV-2 , Adult , Aged , Aged, 80 and over , Angiotensin-Converting Enzyme 2/genetics , Asthma/genetics , COVID-19/immunology , Cardiovascular Diseases/genetics , Cardiovascular Diseases/immunology , Gene Expression , Genetic Variation , Humans , Middle Aged , Obesity/genetics , Obesity/immunology , Pulmonary Disease, Chronic Obstructive/genetics , Quantitative Trait Loci , Risk Factors , Smoking/genetics
4.
Front Med (Lausanne) ; 8: 630209, 2021.
Article in English | MEDLINE | ID: covidwho-1121692

ABSTRACT

Rationale: Coronavirus disease 2019 (COVID-19) can cause disruption of the renin-angiotensin system in the lungs, possibly contributing to pulmonary capillary leakage. Thus, angiotensin receptor blockers (ARBs) may improve respiratory failure. Objective: Assess safety of losartan for use in respiratory failure related to COVID-19 (NCT04335123). Methods: Single arm, open label trial of losartan in those hospitalized with respiratory failure related to COVID-19. Oral losartan (25 mg daily for 3 days, then 50 mg) was administered from enrollment until day 14 or hospital discharge. A post-hoc external control group with patients who met all inclusion criteria was matched 1:1 to the treatment group using propensity scores for comparison. Measures: Primary outcome was cumulative incidence of any adverse events. Secondary, explorative endpoints included measures of respiratory failure, length of stay and vital status. Results: Of the 34 participants enrolled in the trial, 30 completed the study with a mean age SD of 53.8 ± 17.7 years and 17 males (57%). On losartan, 24/30 (80%) experienced an adverse event as opposed to 29/30 (97%) of controls, with a lower average number of adverse events on losartan relative to control (2.2 vs. 3.3). Using Poisson regression and controlling for age, sex, race, date of enrollment, disease severity at enrollment, and history of high-risk comorbidities, the incidence rate ratio of adverse events on losartan relative to control was 0.69 (95% CI: 0.49-0.97) Conclusions: Losartan appeared safe for COVID-19-related acute respiratory compromise. To assess true efficacy, randomized trials are needed.

5.
Proc Natl Acad Sci U S A ; 117(42): 26190-26196, 2020 10 20.
Article in English | MEDLINE | ID: covidwho-811483

ABSTRACT

Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained; neither can the end of the epidemic after the turning point. A susceptible-infected-removed (SIR) model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slowdown in the number of newly infected individuals during the expansion phase allows one to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast. The impossibility to predict in the midterm is not due to wrong or incomplete data, since it persists in error-free, synthetically produced datasets and does not necessarily improve by using larger datasets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective, or phenomenological models and supports that only probabilities of different outcomes can be confidently given.


Subject(s)
Coronavirus Infections/epidemiology , Forecasting , Pneumonia, Viral/epidemiology , Betacoronavirus , COVID-19 , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Forecasting/methods , Humans , Models, Biological , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , SARS-CoV-2 , Spain/epidemiology , Uncertainty
6.
Am J Respir Crit Care Med ; 202(1): 83-90, 2020 07 01.
Article in English | MEDLINE | ID: covidwho-155109

ABSTRACT

Rationale: Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). ACE2 (angiotensin-converting enzyme 2), and TMPRSS2 (transmembrane protease serine 2) mediate viral infection of host cells. We reasoned that differences in ACE2 or TMPRSS2 gene expression in sputum cells among patients with asthma may identify subgroups at risk for COVID-19 morbidity.Objectives: To determine the relationship between demographic features and sputum ACE2 and TMPRSS2 gene expression in asthma.Methods: We analyzed gene expression for ACE2 and TMPRSS2, and for ICAM-1 (intercellular adhesion molecule 1) (rhinovirus receptor as a comparator) in sputum cells from 330 participants in SARP-3 (Severe Asthma Research Program-3) and 79 healthy control subjects.Measurements and Main Results: Gene expression of ACE2 was lower than TMPRSS2, and expression levels of both genes were similar in asthma and health. Among patients with asthma, male sex, African American race, and history of diabetes mellitus were associated with higher expression of ACE2 and TMPRSS2. Use of inhaled corticosteroids (ICS) was associated with lower expression of ACE2 and TMPRSS2, but treatment with triamcinolone acetonide did not decrease expression of either gene. These findings differed from those for ICAM-1, where gene expression was increased in asthma and less consistent differences were observed related to sex, race, and use of ICS.Conclusions: Higher expression of ACE2 and TMPRSS2 in males, African Americans, and patients with diabetes mellitus provides rationale for monitoring these asthma subgroups for poor COVID-19 outcomes. The lower expression of ACE2 and TMPRSS2 with ICS use warrants prospective study of ICS use as a predictor of decreased susceptibility to SARS-CoV-2 infection and decreased COVID-19 morbidity.


Subject(s)
Asthma , Coronavirus Infections , Coronavirus , Pandemics , Pneumonia, Viral , Adrenal Cortex Hormones , Betacoronavirus , COVID-19 , Demography , Humans , Male , Prospective Studies , SARS-CoV-2 , Sputum
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