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1.
Lancet ; 398(10303): 843-855, 2021 09 04.
Article in English | MEDLINE | ID: covidwho-2106189

ABSTRACT

BACKGROUND: A previous efficacy trial found benefit from inhaled budesonide for COVID-19 in patients not admitted to hospital, but effectiveness in high-risk individuals is unknown. We aimed to establish whether inhaled budesonide reduces time to recovery and COVID-19-related hospital admissions or deaths among people at high risk of complications in the community. METHODS: PRINCIPLE is a multicentre, open-label, multi-arm, randomised, controlled, adaptive platform trial done remotely from a central trial site and at primary care centres in the UK. Eligible participants were aged 65 years or older or 50 years or older with comorbidities, and unwell for up to 14 days with suspected COVID-19 but not admitted to hospital. Participants were randomly assigned to usual care, usual care plus inhaled budesonide (800 µg twice daily for 14 days), or usual care plus other interventions, and followed up for 28 days. Participants were aware of group assignment. The coprimary endpoints are time to first self-reported recovery and hospital admission or death related to COVID-19, within 28 days, analysed using Bayesian models. The primary analysis population included all eligible SARS-CoV-2-positive participants randomly assigned to budesonide, usual care, and other interventions, from the start of the platform trial until the budesonide group was closed. This trial is registered at the ISRCTN registry (ISRCTN86534580) and is ongoing. FINDINGS: The trial began enrolment on April 2, 2020, with randomisation to budesonide from Nov 27, 2020, until March 31, 2021, when the prespecified time to recovery superiority criterion was met. 4700 participants were randomly assigned to budesonide (n=1073), usual care alone (n=1988), or other treatments (n=1639). The primary analysis model includes 2530 SARS-CoV-2-positive participants, with 787 in the budesonide group, 1069 in the usual care group, and 974 receiving other treatments. There was a benefit in time to first self-reported recovery of an estimated 2·94 days (95% Bayesian credible interval [BCI] 1·19 to 5·12) in the budesonide group versus the usual care group (11·8 days [95% BCI 10·0 to 14·1] vs 14·7 days [12·3 to 18·0]; hazard ratio 1·21 [95% BCI 1·08 to 1·36]), with a probability of superiority greater than 0·999, meeting the prespecified superiority threshold of 0·99. For the hospital admission or death outcome, the estimated rate was 6·8% (95% BCI 4·1 to 10·2) in the budesonide group versus 8·8% (5·5 to 12·7) in the usual care group (estimated absolute difference 2·0% [95% BCI -0·2 to 4·5]; odds ratio 0·75 [95% BCI 0·55 to 1·03]), with a probability of superiority 0·963, below the prespecified superiority threshold of 0·975. Two participants in the budesonide group and four in the usual care group had serious adverse events (hospital admissions unrelated to COVID-19). INTERPRETATION: Inhaled budesonide improves time to recovery, with a chance of also reducing hospital admissions or deaths (although our results did not meet the superiority threshold), in people with COVID-19 in the community who are at higher risk of complications. FUNDING: National Institute of Health Research and United Kingdom Research Innovation.


Subject(s)
Budesonide/administration & dosage , COVID-19/drug therapy , Glucocorticoids/administration & dosage , Administration, Inhalation , Aged , Bayes Theorem , COVID-19/mortality , Female , Hospitalization , Humans , Male , Middle Aged , Prospective Studies , Risk Factors , SARS-CoV-2 , Treatment Outcome
2.
PLoS One ; 17(10): e0268160, 2022.
Article in English | MEDLINE | ID: covidwho-2079683

ABSTRACT

BACKGROUND: Rapid diagnostics are vital for curving the transmission and control of the COVID-19 pandemic. Although many commercially available antigen-based rapid diagnostic tests (Ag-RDTs) for the detection of SARS-CoV-2 are recommended by the WHO, their diagnostic performance has not yet been assessed in Ethiopia. So far, the vast majority of studies assessing diagnostic accuracies of rapid antigen tests considered RT-PCR as a reference standard, which inevitably leads to bias when RT-PCR is not 100% sensitive and specific. Thus, this study aimed to evaluate the diagnostic performance of Panbio™ jointly with the RT-PCR for the detection of SARS-CoV-2. METHODS: A prospective cross-sectional study was done from July to September 2021 in Addis Ababa, Ethiopia, during the third wave of the pandemic involving two health centers and two hospitals. Diagnostic sensitivity and specificity of Panbio™ and RT-PCR were obtained using Bayesian Latent-Class Models (BLCM). RESULTS: 438 COVID-19 presumptive clients were enrolled, 239 (54.6%) were females, of whom 196 (44.7%) had a positive RT-PCR and 158 (36.1%) were Panbio™ positive. The Panbio™ and RT-PCR had a sensitivity (95% CrI) of 99.6 (98.4-100) %, 89.3 (83.2-97.6) % and specificity (95% CrI) of 93.4 (82.3-100) %, and 99.1 (97.5-100) %, respectively. Most of the study participants, 318 (72.6%) exhibited COVID-19 symptoms; the most reported was cough 191 (43.6%). CONCLUSION: As expected the RT-PCR performed very well with a near-perfect specificity and a high, but not perfect sensitivity. The diagnostic performance of Panbio™ is coherent with the WHO established criteria of having a sensitivity ≥80% for Ag-RDTs. Both tests displayed high diagnostic accuracies in patients with and without symptoms. Hence, we recommend the use of the Panbio™ for both symptomatic and asymptomatic individuals in clinical settings for screening purposes.


Subject(s)
COVID-19 , SARS-CoV-2 , Female , Humans , Male , SARS-CoV-2/genetics , COVID-19/diagnosis , COVID-19/epidemiology , Reverse Transcriptase Polymerase Chain Reaction , Pandemics , Cross-Sectional Studies , Ethiopia/epidemiology , Bayes Theorem , Prospective Studies , Sensitivity and Specificity , Antigens, Viral/analysis
3.
Sci Rep ; 12(1): 17561, 2022 Oct 20.
Article in English | MEDLINE | ID: covidwho-2077116

ABSTRACT

The purpose of this work was to review and synthesise the evidence on the comparative effectiveness of neutralising monoclonal antibody (nMAB) therapies in individuals exposed to or infected with SARS-CoV-2 and at high risk of developing severe COVID-19. Outcomes of interest were mortality, healthcare utilisation, and safety. A rapid systematic review was undertaken to identify and synthesise relevant RCT evidence using a Bayesian Network Meta-Analysis. Relative treatment effects for individual nMABs (compared with placebo and one another) were estimated. Pooled effects for the nMAB class compared with placebo were estimated. Relative effects were combined with baseline natural history models to predict the expected risk reductions per 1000 patients treated. Eight articles investigating four nMABs (bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab) were identified. All four therapies were associated with a statistically significant reduction in hospitalisation (70-80% reduction in relative risk; absolute reduction of 35-40 hospitalisations per 1000 patients). For mortality, ICU admission, and invasive ventilation, the risk was lower for all nMABs compared with placebo with moderate to high uncertainty due to small event numbers. Rates of serious AEs and infusion reactions were comparable between nMABs and placebo. Pairwise comparisons between nMABs were typically uncertain, with broadly comparable efficacy. In conclusion, nMABs are effective at reducing hospitalisation among infected individuals at high-risk of severe COVID-19, and are likely to reduce mortality, ICU admission, and invasive ventilation rates; the effect on these latter outcomes is more uncertain. Widespread vaccination and the emergence of nMAB-resistant variants make the generalisability of these results to current patient populations difficult.


Subject(s)
Antineoplastic Agents, Immunological , COVID-19 , Humans , SARS-CoV-2 , Network Meta-Analysis , Bayes Theorem , Antibodies, Monoclonal/therapeutic use , Antibodies, Neutralizing
4.
Sci Rep ; 12(1): 17417, 2022 Oct 18.
Article in English | MEDLINE | ID: covidwho-2077093

ABSTRACT

The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their performance with machine learning classifiers. In machine learning, the Naive Bayes classifier achieved the highest accuracy of 97%, whereas the ResNet50 CNN model attained the highest accuracy of 99%. Hence the deep learning networks outperformed well compared to the machine learning techniques in the classification of Covid-19 images.


Subject(s)
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , Bayes Theorem , Machine Learning , Tomography, X-Ray Computed , Lung/diagnostic imaging
5.
Sci Rep ; 12(1): 13490, 2022 08 05.
Article in English | MEDLINE | ID: covidwho-2077088

ABSTRACT

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19/epidemiology , Canada , Humans , Pandemics , RNA, Viral , Waste Water , Wastewater-Based Epidemiological Monitoring
6.
Vaccine ; 40(47): 6864-6872, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-2069777

ABSTRACT

BACKGROUND: In the face of rapid emerging variants of concern (VOCs) with potential of evading immunity from Beta to Omicron and uneven distribution of different vaccine brands, a mix-match strategy has been considered to enhance immunity. However, whether increasing immunogenicity using such a mix-match can lead to high clinical efficacy, particularly when facing Omicron pandemic, still remains elusive without using the traditional phase 3 trial. The aim of this study is to demonstrate how to evaluate correlates of protection (CoP) of the mix-match vaccination. METHODS: Data on neutralizing antibody (NtAb) titers and clinical efficacy against Wuhan or D614G strains of homologous ChAdOx1 nCov-19 or mRNA-1273 and heterologous vaccination were extracted from previous studies for demonstration. The reductions in NtAb titers of homologous vaccination against Beta, Delta, and Omicron variants were obtained from literatures. A Bayesian inversion method was used to derive CoP from homologous to mix-match vaccine. Findings The predicted efficacy of ChAdOx1 nCov-19 and mRNA-1273 for Wuhan or D614G strains was 93 % (89 %-97 %). Given 8 âˆ¼ 11-fold, 2 âˆ¼ 5.5-fold, and 32.5 âˆ¼ 36-fold reduction of NtAb for Beta, Delta, and Omicron variants compared with D614G, the corresponding predictive efficacy of the mix-match ranged from 75.63 % to 73.87 %, 84.87 % to 81.25 %, and 0.067 % to 0.059 %, respectively. Interpretations While ChAdOx1 nCov-19 and mRNA-1273 used for demonstrating how to timely evaluate CoP for the mix-match vaccine still provides clinical efficacy against Beta and Delta VOCs but it appears ineffective for Omicron variants, which highlights the urgent need for next generation vaccine against Omicron variant.


Subject(s)
COVID-19 , Influenza Vaccines , Humans , COVID-19 Vaccines , COVID-19/prevention & control , Antibodies, Viral , Bayes Theorem , ChAdOx1 nCoV-19 , SARS-CoV-2 , Antibodies, Neutralizing , Vaccination
7.
PLoS Comput Biol ; 18(10): e1010618, 2022 10.
Article in English | MEDLINE | ID: covidwho-2065098

ABSTRACT

In infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Humans , Bayes Theorem , SARS-CoV-2 , Reproduction
8.
Med Eng Phys ; 109: 103904, 2022 11.
Article in English | MEDLINE | ID: covidwho-2061652

ABSTRACT

OBJECTIVE: Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity. APPROACH: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method. MAIN RESULTS: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility. SIGNIFICANCE: The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.


Subject(s)
COVID-19 , Photoplethysmography , Humans , Photoplethysmography/methods , COVID-19/diagnosis , Signal Processing, Computer-Assisted , Bayes Theorem , Heart Rate , Algorithms
9.
Med Biol Eng Comput ; 60(12): 3475-3496, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2060011

ABSTRACT

The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networks method-based data-analysis module, which is used to specify the severity of coronavirus symptoms and classify cases as mild, moderate, and severe, and (2) autonomous decision-making module-based association rules mining method. This method allows the autonomous generation of the adequate decision based on the FP-growth algorithm and the distance between objects. To build the Bayesian Network model, we propose a novel data-based method that enables to effectively learn the network's structure, namely, MIGT-SL algorithm. The experimentations are performed over pre-processed discrete dataset. The proposed algorithm allows to correctly generate 74%, 87.5%, and 100% of the original structure of ALARM, ASIA, and CANCER networks. The proposed Bayesian model performs well in terms of accuracy with 96.15% and 94.77%, respectively, for binary and multi-class classification. The developed decision-making model is evaluated according to its utility in solving the decisional problem, and its accuracy of proposing the adequate decision is about 97.80%.


Subject(s)
COVID-19 , Humans , Bayes Theorem , Algorithms
10.
JAMA ; 328(16): 1604-1615, 2022 10 25.
Article in English | MEDLINE | ID: covidwho-2058991

ABSTRACT

Importance: Some individuals experience persistent symptoms after initial symptomatic SARS-CoV-2 infection (often referred to as Long COVID). Objective: To estimate the proportion of males and females with COVID-19, younger or older than 20 years of age, who had Long COVID symptoms in 2020 and 2021 and their Long COVID symptom duration. Design, Setting, and Participants: Bayesian meta-regression and pooling of 54 studies and 2 medical record databases with data for 1.2 million individuals (from 22 countries) who had symptomatic SARS-CoV-2 infection. Of the 54 studies, 44 were published and 10 were collaborating cohorts (conducted in Austria, the Faroe Islands, Germany, Iran, Italy, the Netherlands, Russia, Sweden, Switzerland, and the US). The participant data were derived from the 44 published studies (10 501 hospitalized individuals and 42 891 nonhospitalized individuals), the 10 collaborating cohort studies (10 526 and 1906), and the 2 US electronic medical record databases (250 928 and 846 046). Data collection spanned March 2020 to January 2022. Exposures: Symptomatic SARS-CoV-2 infection. Main Outcomes and Measures: Proportion of individuals with at least 1 of the 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after SARS-CoV-2 infection in 2020 and 2021, estimated separately for hospitalized and nonhospitalized individuals aged 20 years or older by sex and for both sexes of nonhospitalized individuals younger than 20 years of age. Results: A total of 1.2 million individuals who had symptomatic SARS-CoV-2 infection were included (mean age, 4-66 years; males, 26%-88%). In the modeled estimates, 6.2% (95% uncertainty interval [UI], 2.4%-13.3%) of individuals who had symptomatic SARS-CoV-2 infection experienced at least 1 of the 3 Long COVID symptom clusters in 2020 and 2021, including 3.2% (95% UI, 0.6%-10.0%) for persistent fatigue with bodily pain or mood swings, 3.7% (95% UI, 0.9%-9.6%) for ongoing respiratory problems, and 2.2% (95% UI, 0.3%-7.6%) for cognitive problems after adjusting for health status before COVID-19, comprising an estimated 51.0% (95% UI, 16.9%-92.4%), 60.4% (95% UI, 18.9%-89.1%), and 35.4% (95% UI, 9.4%-75.1%), respectively, of Long COVID cases. The Long COVID symptom clusters were more common in women aged 20 years or older (10.6% [95% UI, 4.3%-22.2%]) 3 months after symptomatic SARS-CoV-2 infection than in men aged 20 years or older (5.4% [95% UI, 2.2%-11.7%]). Both sexes younger than 20 years of age were estimated to be affected in 2.8% (95% UI, 0.9%-7.0%) of symptomatic SARS-CoV-2 infections. The estimated mean Long COVID symptom cluster duration was 9.0 months (95% UI, 7.0-12.0 months) among hospitalized individuals and 4.0 months (95% UI, 3.6-4.6 months) among nonhospitalized individuals. Among individuals with Long COVID symptoms 3 months after symptomatic SARS-CoV-2 infection, an estimated 15.1% (95% UI, 10.3%-21.1%) continued to experience symptoms at 12 months. Conclusions and Relevance: This study presents modeled estimates of the proportion of individuals with at least 1 of 3 self-reported Long COVID symptom clusters (persistent fatigue with bodily pain or mood swings; cognitive problems; or ongoing respiratory problems) 3 months after symptomatic SARS-CoV-2 infection.


Subject(s)
COVID-19 , Cognition Disorders , Fatigue , Respiratory Insufficiency , Adolescent , Adult , Aged , Child , Child, Preschool , Female , Humans , Male , Middle Aged , Young Adult , Bayes Theorem , COVID-19/complications , COVID-19/epidemiology , Fatigue/epidemiology , Fatigue/etiology , Pain/epidemiology , Pain/etiology , SARS-CoV-2 , Syndrome , Cognition Disorders/epidemiology , Cognition Disorders/etiology , Respiratory Insufficiency/epidemiology , Respiratory Insufficiency/etiology , Internationality , Global Health/statistics & numerical data , Mood Disorders/epidemiology , Mood Disorders/etiology
11.
BMC Med ; 20(1): 342, 2022 09 27.
Article in English | MEDLINE | ID: covidwho-2053903

ABSTRACT

BACKGROUND: In vitro drug screening studies have indicated that camostat mesilate (FOY-305) may prevent SARS-CoV-2 infection into human airway epithelial cells. This study was conducted to investigate whether camostat mesilate is an effective treatment for SARS-CoV-2 infection (COVID-19). METHODS: This was a multicenter, double-blind, randomized, parallel-group, placebo-controlled study. Patients were enrolled if they were admitted to a hospital within 5 days of onset of COVID-19 symptoms or within 5 days of a positive test for asymptomatic patients. Severe cases (e.g., those requiring oxygenation/ventilation) were excluded. Patients were enrolled, randomized, and allocated to each group using an interactive web response system. Randomization was performed using a minimization method with the factors medical institution, age, and underlying diseases (chronic respiratory disease, chronic kidney disease, diabetes mellitus, hypertension, cardiovascular diseases, and obesity). The patients, investigators/subinvestigators, study coordinators, and other study personnel were blinded throughout the study. Patients were administered camostat mesilate (600 mg qid; four to eight times higher than the clinical doses in Japan) or placebo for up to 14 days. The primary efficacy endpoint was the time to the first two consecutive negative tests for SARS-CoV-2. RESULTS: One-hundred fifty-five patients were randomized to receive camostat mesilate (n = 78) or placebo (n = 77). The median time to the first test was 11.0 days (95% confidence interval [CI]: 9.0-12.0) in the camostat mesilate group and 11.0 days (95% CI: 10.0-13.0) in the placebo group. Conversion to negative viral status by day 14 was observed in 45 of 74 patients (60.8%) in the camostat mesilate group and 47 of 74 patients (63.5%) in the placebo group. The primary (Bayesian) and secondary (frequentist) analyses found no significant differences in the primary endpoint between the two groups. No additional safety concerns beyond those already known for camostat mesilate were identified. CONCLUSIONS: Camostat mesilate did not substantially reduce the time to viral clearance, based on upper airway viral loads, compared with placebo for treating patients with mild to moderate SARS-CoV-2 infection with or without symptoms. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04657497. Japan Registry for Clinical Trials, jRCT2031200198.


Subject(s)
COVID-19 , Bayes Theorem , COVID-19/drug therapy , Double-Blind Method , Esters/adverse effects , Esters/therapeutic use , Guanidines/adverse effects , Guanidines/therapeutic use , Humans , SARS-CoV-2 , Treatment Outcome
12.
Am J Respir Crit Care Med ; 205(11): 1300-1310, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-2053493

ABSTRACT

Rationale: The most beneficial positive end-expiratory pressure (PEEP) selection strategy in patients with acute respiratory distress syndrome (ARDS) is unknown, and current practice is variable. Objectives: To compare the relative effects of different PEEP selection strategies on mortality in adults with moderate to severe ARDS. Methods: We conducted a network meta-analysis using a Bayesian framework. Certainty of evidence was evaluated using grading of recommendations assessment, development and evaluation methodology. Measurements and Main Results: We included 18 randomized trials (4,646 participants). Compared with a lower PEEP strategy, the posterior probability of mortality benefit from a higher PEEP without lung recruitment maneuver (LRM) strategy was 99% (risk ratio [RR], 0.77; 95% credible interval [CrI], 0.60-0.96, high certainty), the posterior probability of benefit of the esophageal pressure-guided strategy was 87% (RR, 0.77; 95% CrI, 0.48-1.22, moderate certainty), the posterior probability of benefit of a higher PEEP with brief LRM strategy was 96% (RR, 0.83; 95% CrI, 0.67-1.02, moderate certainty), and the posterior probability of increased mortality from a higher PEEP with prolonged LRM strategy was 77% (RR, 1.06; 95% CrI, 0.89-1.22, low certainty). Compared with a higher PEEP without LRM strategy, the posterior probability of increased mortality from a higher PEEP with prolonged LRM strategy was 99% (RR, 1.37; 95% CrI, 1.04-1.81, moderate certainty). Conclusions: In patients with moderate to severe ARDS, higher PEEP without LRM is associated with a lower risk of death than lower PEEP. A higher PEEP with prolonged LRM strategy is associated with increased risk of death when compared with higher PEEP without LRM.


Subject(s)
Positive-Pressure Respiration , Respiratory Distress Syndrome , Adult , Bayes Theorem , Humans , Lung , Network Meta-Analysis , Positive-Pressure Respiration/methods , Respiratory Distress Syndrome/therapy
13.
PLoS One ; 17(10): e0275011, 2022.
Article in English | MEDLINE | ID: covidwho-2054353

ABSTRACT

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients' age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load's impact on patients' infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19 Testing , Humans , Viral Load
14.
Transbound Emerg Dis ; 69(5): e2230-e2239, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2053012

ABSTRACT

Foot-and-mouth disease (FMD) affects the livestock industry and socioeconomic sustainability of many African countries. The success of FMD control programs in Africa depends largely on understanding the dynamics of FMD virus (FMDV) spread. In light of the recent outbreaks of FMD that affected the North-Western African countries in 2018 and 2019, we investigated the evolutionary phylodynamics of the causative serotype O viral strains all belonging to the East-Africa 3 topotype (O/EA-3). We analyzed a total of 489 sequences encoding the FMDV VP1 genome region generated from samples collected from 25 African and Western Asian countries between 1974 and 2019. Using Bayesian evolutionary models on genomic and epidemiological data, we inferred the routes of introduction and migration of the FMDV O/EA-3 topotype at the inter-regional scale. We inferred a mean substitution rate of 6.64 × 10-3  nt/site/year and we predicted that the most recent common ancestor for our panel of samples circulated between February 1967 and November 1973 in Yemen, likely reflecting the epidemiological situation in under sampled cattle-exporting East African countries. Our study also reinforces the role previously described of Sudan and South Sudan as a frequent source of FMDVs spread. In particular, we identified two transboundary routes of O/EA-3 diffusion: the first from Sudan to North-East Africa, and from the latter into Israel and Palestine AT; a second from Sudan to Nigeria, Cameroon, and from there to further into West and North-West Africa. This study highlights the necessity to reinforce surveillance at an inter-regional scale in Africa and Western Asia, in particular along the identified migration routes for the implementation of efficient control measures in the fight against FMD.


Subject(s)
Foot-and-Mouth Disease Virus , Foot-and-Mouth Disease , Animals , Bayes Theorem , Cattle , Disease Outbreaks/veterinary , Foot-and-Mouth Disease/epidemiology , Foot-and-Mouth Disease Virus/genetics , Nigeria/epidemiology , Phylogeny , Serogroup
15.
Transbound Emerg Dis ; 69(5): e1734-e1748, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2052999

ABSTRACT

Equine influenza virus (EIV) is a highly contagious pathogen of equids, and a well-known burden in global equine health. EIV H3N8 variants seasonally emerged and resulted in EIV outbreaks in the United States and worldwide. The present study evaluated the pattern of cross-regional EIV H3N8 spread and evolutionary characteristics at US and global scales using Bayesian phylogeography with balanced subsampling based on regional horse population size. A total of 297 haemagglutinin (HA) sequences of global EIV H3N8 were collected from 1963 to 2019 and subsampled to global subset (n = 67), raw US sequences (n = 100) and US subset (n = 44) datasets. Discrete trait phylogeography analysis was used to estimate the transmission history of EIV using four global and US genome datasets. The North American lineage was the major source of globally dominant EIV variants and spread to other global regions. The US EIV strains generally spread from the southern and midwestern regions to other regions. The EIV H3N8 accumulated approximately three nucleotide substitutions per year in the HA gene under heterogeneous local positive selection. Our findings will guide better decision making of target intervention strategies of EIV H3N8 infection and provide the better scheme of genomic surveillance in the United States and global equine health.


Subject(s)
Horse Diseases , Influenza A Virus, H3N8 Subtype , Influenza, Human , Orthomyxoviridae Infections , Animals , Bayes Theorem , Hemagglutinins , Horse Diseases/epidemiology , Horses , Humans , Influenza A Virus, H3N8 Subtype/genetics , Nucleotides , Orthomyxoviridae Infections/epidemiology , Orthomyxoviridae Infections/veterinary , Phylogeography
16.
Transbound Emerg Dis ; 69(5): e1445-e1459, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2052989

ABSTRACT

The Mexican lineage H5N2 low pathogenic avian influenza viruses (LPAIVs) were first detected in 1994 and mutated to highly pathogenic avian influenza viruses (HPAIVs) in 1994-1995 causing widespread outbreaks in poultry. By using vaccination and other control measures, the HPAIVs were eradicated but the LPAIVs continued circulating in Mexico and spread to several other countries. To get better resolution of the phylogenetics of this virus, the full genome sequences of 44 H5N2 LPAIVs isolated from 1994 to 2011, and 6 detected in 2017 and 2019, were analysed. Phylogenetic incongruence demonstrated genetic reassortment between two separate groups of the Mexican lineage H5N2 viruses between 2005 and 2010. Moreover, the recent H5N2 viruses reassorted with previously unidentified avian influenza viruses. Bayesian phylogeographic results suggested that mechanical transmission involving human activity is the most probable cause of the virus spillover to Central American, Caribbean, and East Asian countries. Increased infectivity and transmission of a 2011 H5N2 LPAIV in chickens compared to a 1994 virus demonstrates improved adaptation to chickens, while low virus shedding, and limited contact transmission was observed in mallards with the same 2011 virus. The sporadic increase in basic amino acids in the HA cleavage site, changes in potential N-glycosylation sites in the HA, and truncations of PB1-F2 should be further examined in relation to the increased infectivity and transmission in poultry. The genetic changes that occur as this lineage of H5N2 LPAIVs continues circulating in poultry is concerning not only because of the effect of these changes on vaccination efficacy, but also because of the potential of the viruses to mutate to the highly pathogenic form. Continued vigilance and surveillance efforts, and the pathogenic and genetic characterization of circulating viruses, are required for the effective control of this virus.


Subject(s)
Influenza A Virus, H5N2 Subtype , Influenza A virus , Influenza in Birds , Amino Acids, Basic/genetics , Animals , Bayes Theorem , Chickens , Humans , Influenza A Virus, H5N2 Subtype/genetics , Influenza A virus/genetics , Mexico/epidemiology , Phylogeny , Poultry
17.
Ann Epidemiol ; 75: 67-72, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2041516

ABSTRACT

PURPOSE: Early warning in the travel origins is crucial to prevent disease spreading. When travel origins have delays in reporting disease outbreaks, the exported cases could be used to estimate the epidemic. METHODS: We developed a Bayesian model to jointly estimate the epidemic prevalence and detection delay using the exported cases and their arrival and detection dates. We used simulation studies to discuss potential biases generated by the exported cases. We proposed a hypothesis testing framework to determine the epidemic severity. RESULTS: We applied the method to the early phase of the COVID-19 epidemic of Wuhan, United States, Italy, and Iran and found that the indicators estimated from the exported cases were consistent with the domestic data under certain scenarios. The exported cases could generate various biases if not modeled properly. We presented the required number of exported cases for determining different severity levels of the outbreak. CONCLUSIONS: The exported case data is a good addition to the domestic data but also has its drawbacks. Utilizing the diagnosis resources from all countries, we advocate that countries work collaboratively to strengthen the global infectious disease surveillance system.


Subject(s)
COVID-19 , Communicable Diseases , Epidemics , Humans , COVID-19/epidemiology , Bayes Theorem , Disease Outbreaks , Communicable Diseases/epidemiology , China/epidemiology
18.
PLoS One ; 17(9): e0274590, 2022.
Article in English | MEDLINE | ID: covidwho-2039421

ABSTRACT

BACKGROUND: A re-emergence of COVID-19 occurred in the northeast of China in early 2021. Different levels of non-pharmaceutical interventions, from mass testing to city-level lockdown, were implemented to contain the transmission of SARS-CoV-2. Our study is aimed to evaluate the impact of multi-level control measures on the second-wave SARS-CoV-2 transmission in the most affected cities in China. METHODS: Five cities with over 100 reported COVID-19 cases within one month from Dec 2020 to Feb 2021 were included in our analysis. We fitted the exponential growth model to estimate basic reproduction number (R0), and used a Bayesian approach to assess the dynamics of the time-varying reproduction number (Rt). We fitted linear regression lines on Rt estimates for comparing the decline rates of Rt across cities, and the slopes were tested by analysis of covariance. The effect of non-pharmaceutical interventions (NPIs) was quantified by relative Rt reduction and statistically compared by analysis of variance. RESULTS: A total of 2,609 COVID-19 cases were analyzed in this study. We estimated that R0 all exceeded 1, with the highest value of 3.63 (1.36, 8.53) in Haerbin and the lowest value of 2.45 (1.44, 3.98) in Shijiazhuang. Downward trends of Rt were found in all cities, and the starting time of Rt < 1 was around the 12th day of the first local COVID-19 cases. Statistical tests on regression slopes of Rt and effect of NPIs both showed no significant difference across five cities (P = 0.126 and 0.157). CONCLUSION: Timely implemented NPIs could control the transmission of SARS-CoV-2 with low-intensity measures for places where population immunity has not been established.


Subject(s)
COVID-19 , SARS-CoV-2 , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Disease Control , Humans
19.
PLoS One ; 17(9): e0274421, 2022.
Article in English | MEDLINE | ID: covidwho-2039414

ABSTRACT

BACKGROUND: Zhejiang, ranked in the top three in HFMD (hand, foot, and mouth disease) incidence, is located in the Yangtze River Delta region of southeast China. Since 2016, the EV71 vaccine has been promoted in Zhejiang Province. This study aimed to investigate the trend and seasonal variation characteristics of HFMD from 2010 to 2021 and estimate the reduction in enterovirus 71 infection after vaccine use. METHODS: The data on HFMD cases in Zhejiang Province from January 2010 to December 2021 were obtained from this network system. Individual information on cases and deaths was imported, and surveillance information, including demographic characteristics and temporal distributions, was computed by the system. The Joinpoint regression model was used to describe continuous changes in the incidence trend. The BSTS (Bayesian structural time-series models) model was used to estimate the monthly number of cases from 2017 to 2021 based on the observed monthly incidence during 2010-2016 by accounting for seasonality and long-term trends. The seasonal variation characteristics of HFMD pathogens were detected by wavelet analysis. RESULTS: From 2010 to 2021, the annual incidence rate fluctuated between 98.81 cases per 100,000 in 2020 and 435.63 cases per 100,000 in 2018, and 1711 severe HFMD cases and 106 fatal cases were reported in Zhejiang Province, China. The annual percent change (APC) in EV71 cases was -30.72% (95% CI: -45.10 to -12.50) from 2016 to 2021. The wavelet transform of total incidence and number of cases of the three pathogens all showed significant periodicity on the 1-year scale. The average 2-year scale periodicity was significant for the total incidence, EV71 cases and Cox A16 cases, but the other enterovirus cases showed significant periodicity on the 30-month scale. The 6-month scale periodicity was significant for the total incidence, EV71 case and Cox A16 case but not for the other enteroviruses case. The relative error percentage of the performance of the BSTS model was 0.3%. The estimated number of cases from 2017 to 2021 after the EV-A71 vaccines were used was 9422, and the reduction in the number of cases infected with the EV71 virus was 73.43% compared to 70.80% when the impact of the COVID-19 epidemic in 2020 was excluded. CONCLUSIONS: Since 2010, the incidence of EV71 infections has shown an obvious downward trend. All types of viruses showed significant periodicity on the 1-year scale. The periodicity of the biennial peak is mainly related to EV71 and Cox A16 before 2017 and other enteroviruses since 2018. The half-year peak cycle of HFMD was mainly caused by EV71 and Cox A6 infection. The expected incidence will be 2.76 times(include the cases of 2020) and 2.43 times(exclude the cases of 2020) higher than the actual value assuming that the measures of vaccination are not taken. EV71 vaccines are very effective and should be administered in the age window between 5 months and 5 years.


Subject(s)
COVID-19 , Enterovirus A, Human , Enterovirus Infections , Enterovirus , Hand, Foot and Mouth Disease , Vaccines , Antigens, Viral , Bayes Theorem , China/epidemiology , Hand, Foot and Mouth Disease/epidemiology , Hand, Foot and Mouth Disease/prevention & control , Humans , Infant
20.
PLoS One ; 17(7): e0270789, 2022.
Article in English | MEDLINE | ID: covidwho-2039353

ABSTRACT

BACKGROUND: India has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI's). METHODS: We analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models. FINDINGS: Up to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4-3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29-20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4-6.4] days, and case fatality rate was 2.5% [2.4-2.7] which increased with age. CONCLUSION: We found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding.


Subject(s)
COVID-19 , Contact Tracing , Bayes Theorem , COVID-19/epidemiology , Child , Humans , India/epidemiology , Retrospective Studies , SARS-CoV-2
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