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1.
J Mol Diagn ; 23(9): 1085-1096, 2021 09.
Article in English | MEDLINE | ID: covidwho-1370607

ABSTRACT

Widespread high-throughput testing for identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR has been a foundation in the response to the coronavirus disease 2019 (COVID-19) pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing is widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Herein, we investigate a high-throughput, laboratory-developed SARS-CoV-2 RT-PCR assay to determine whether modeling can generate quality control metrics that identify false-positive (FP) results due to contamination. This study reviewed repeated clinical samples focusing on positive samples that test negative on re-extraction and PCR, likely representing false positives. To identify and predict false-positive samples, we constructed machine learning-derived models based on the extraction method used. These models identified variables associated with false-positive results across all methods, with sensitivities for predicting FP results ranging between 67% and 100%. Application of the models to all results predicted a total FP rate of 0.08% across all samples, or 2.3% of positive results, similar to reports for other RT-PCR tests for RNA viruses. These models can predict quality control parameters, enabling laboratories to generate decision trees that reduce interpretation errors, allow for automated reflex testing of samples with a high FP probability, improve workflow efficiency, and increase diagnostic accuracy for patient care.


Subject(s)
COVID-19 Nucleic Acid Testing/methods , RNA, Viral/isolation & purification , Reverse Transcriptase Polymerase Chain Reaction/methods , Automation, Laboratory , Carrier State/virology , Decision Support Systems, Clinical , False Positive Reactions , High-Throughput Nucleotide Sequencing/methods , Humans , Machine Learning , SARS-CoV-2/genetics , Viral Load , Workflow
3.
J Med Internet Res ; 23(7): e27448, 2021 07 21.
Article in English | MEDLINE | ID: covidwho-1319560

ABSTRACT

BACKGROUND: The beginning of the COVID-19 pandemic presented many sudden challenges regarding food, including grocery shopping changes (eg, reduced store hours, capacity restrictions, and empty store shelves due to food hoarding), restaurant closures, the need to cook more at home, and closures of food access programs. Eat Well Saskatchewan (EWS) implemented a 16-week social media campaign, #eatwellcovid19, led by a dietitian and nutrition student that focused on sharing stories submitted by the Saskatchewan public about how they were eating healthy during the COVID-19 pandemic. OBJECTIVE: The goal of this study was to describe the implementation of the #eatwellcovid19 social media campaign and the results from the evaluation of the campaign, which included campaign performance using social media metrics and experiences and perspectives of campaign followers. METHODS: Residents of Saskatchewan, Canada, were invited to submit personal stories and experiences to EWS about how they were eating healthy during the COVID-19 pandemic from April to August 2020. Each week, one to three stories were featured on EWS social media platforms-Facebook, Instagram, and Twitter-along with evidence-based nutrition information to help residents become more resilient to challenges related to food and nutrition experienced during the COVID-19 pandemic. Individuals who submitted stories were entered into a weekly draw for a Can $100 grocery gift card. Social media metrics and semistructured qualitative interviews of campaign followers were used to evaluate the #eatwellcovid19 campaign. RESULTS: In total, 75 stories were submitted by 74 individuals on a variety of topics (eg, grocery shopping, traditional skills, and gardening), and 42 stories were featured on social media. EWS shared 194 #eatwellcovid19 posts across social media platforms (Facebook: n=100; Instagram: n=55; and Twitter: n=39). On Facebook, #eatawellcovid19 reached 100,571 followers and left 128,818 impressions, resulting in 9575 engagements. On Instagram, the campaign reached 11,310 followers, made 14,145 impressions, and received 823 likes and 15 comments. On Twitter, #eatwellcovid19 made 15,199 impressions and received 424 engagements. Featured story submission posts had the best engagement on Facebook and the most likes and comments on Instagram. The EWS social media pages reported increases in their following during the campaign (Instagram: +30%; Facebook: +14%; and Twitter: +12%). Results from the interviews revealed that there were two types of campaign followers: those who appreciated hearing the stories submitted by followers, as it helped them to feel connected to the community during social isolation, and those who appreciated the evidence-based information. CONCLUSIONS: Numerous stories were submitted to the #eatwellcovid19 social media campaign on various topics. On Instagram and Facebook, posts that featured these stories had the highest engagement. During this campaign, EWS's social media following increased by more than 10% on each platform. The approach used for the #eatwellcovid19 campaign could be considered by others looking to develop health promotion campaigns.


Subject(s)
COVID-19 , Diet, Healthy , Health Promotion/organization & administration , Health Promotion/statistics & numerical data , Pandemics , Qualitative Research , Social Media/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/epidemiology , Emotions , Female , Humans , Male , Middle Aged , SARS-CoV-2 , Saskatchewan/epidemiology , Young Adult
4.
JMIR Form Res ; 5(7): e28656, 2021 Jul 06.
Article in English | MEDLINE | ID: covidwho-1270975

ABSTRACT

BACKGROUND: With improved accessibility to social media globally, health researchers are capitalizing on social media platforms to recruit participants for research studies. This has particularly been the case during the COVID-19 pandemic, when researchers were not able to use traditional methods of recruitment. Nevertheless, there is limited evidence on the feasibility of social media for recruiting a national sample. OBJECTIVE: This paper describes the use of social media as a tool for recruiting a national sample of adults to a web-based survey during the COVID-19 pandemic. METHODS: Between August and October 2020, participants were recruited through Facebook via two advertisement campaigns (paid option and no-cost option) into a web-based survey exploring the relationship between social determinants of health and well-being of adults during the COVID-19 pandemic. Data were analyzed using SPSS software and Facebook metrics that were autogenerated by Facebook Ads Manager. Poststratification weights were calculated to match the Australian population on the basis of gender, age, and state or territory based on the 2016 Australian census data. RESULTS: In total, 9594 people were reached nationally with the paid option and potentially 902,000 people were reached through the no-cost option, resulting in a total of 1211 survey responses. The total cost of the advertisement campaign was Aus $649.66 (US $489.23), resulting in an overall cost per click of Aus $0.25 (US $0.19). CONCLUSIONS: Facebook is a feasible and cost-effective method of recruiting participants for a web-based survey, enabling recruitment of population groups that are considered hard to reach or marginalized. Recruitment through Facebook facilitated diversity, with participants varying in socioeconomic status, geographical location, educational attainment, and age.

5.
Biomed Signal Process Control ; 69: 102814, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1252535

ABSTRACT

BACKGROUND AND OBJECTIVE: SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19. METHOD: In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed. RESULT: This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis. CONCLUSION: The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery.

6.
Bioinformatics ; 2021 May 20.
Article in English | MEDLINE | ID: covidwho-1236218

ABSTRACT

SUMMARY: The web platform 3DBionotes-WS integrates multiple Web Services and an interactive Web Viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic Electron Microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context we have developed 3DBionotes-COVID-19 as an answer to: (1) The need to explore multi-omics data in a unified context with a special focus on structural information and (2) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryogenic Electron Microscopy. AVAILABILITY: https://3dbionotes.cnb.csic.es/ws/covid19. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

7.
Radiology ; 300(2): E328-E336, 2021 08.
Article in English | MEDLINE | ID: covidwho-1136121

ABSTRACT

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.


Subject(s)
COVID-19/complications , Radiography, Thoracic/methods , Sarcopenia/complications , Sarcopenia/diagnostic imaging , Tomography, X-Ray Computed/methods , Aged , Female , Humans , Italy , Male , Middle Aged , Muscle, Skeletal/diagnostic imaging , Predictive Value of Tests , Retrospective Studies , SARS-CoV-2
8.
J Med Internet Res ; 22(12): e24286, 2020 12 03.
Article in English | MEDLINE | ID: covidwho-978988

ABSTRACT

BACKGROUND: The emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths worldwide. Without a coordinated national public health plan informed by surveillance with actionable metrics, the United States has been ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance has incomplete ascertainment and is limited by the use of standard surveillance metrics. Although many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic. OBJECTIVE: The aim of this study is to develop dynamic metrics for public health surveillance that can inform worldwide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision making and to identify where and when corrective action is required to prevent outbreaks. METHODS: Using a longitudinal trend analysis study design, we extracted COVID-19 data from global public health registries. We used an empirical difference equation to measure daily case numbers for our use case in 50 US states and the District of Colombia as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Examination of the United States and state data demonstrated that most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk, and persistence. Larger US states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic, and other states have alarming rates of speed, acceleration, jerk, and 7-day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the United States enters its third wave of COVID-19, all 50 states and the District of Colombia have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota), and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) new infections per 100,000 people. CONCLUSIONS: Standard surveillance techniques such as daily and cumulative infections and deaths are helpful but only provide a static view of what has already occurred in the pandemic and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as signals when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allow us to zero in on where transmissions are increasing, meaning corrective action can be applied with precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary while preserving the economy in other US areas.


Subject(s)
COVID-19/prevention & control , COVID-19/transmission , Public Health Surveillance , COVID-19/epidemiology , COVID-19/mortality , Humans , Longitudinal Studies , Pandemics/prevention & control , Pandemics/statistics & numerical data , Public Health , Registries , SARS-CoV-2 , United States/epidemiology
9.
Laryngoscope Investig Otolaryngol ; 5(5): 796-806, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-734145

ABSTRACT

Objectives: There is a need to develop a medical device which can accurately measure normal and abnormal nasal breathing which the patient can better understand in addition to being able to diagnose the cause for their nasal obstruction.The aim is to evaluate the accuracy of the nasal acoustic device (NAD) in diagnosing the common causes for nasal obstruction and diagnosing normal and abnormal (nasal obstruction) nasal breathing. Methods: This pilot study recruited 27 patients with allergic rhinitis (AR), chronic rhinosinusitis (CRS), and a deviated nasal septum (DNS) which represents the common causes for NO and 26 controls (with normal nasal breathing). Nasal breathing sounds were recorded by the NAD akin to two small stethoscopes placed over the left and right nasal ala. The novel outcome metrics for the NAD include inspiratory nasal acoustic score (INA) score, expiratory nasal acoustic (ENA) score and the inspiratory nasal obstruction balance index (NOBI). The change in acoustic score following decongestant is key in this diagnostic process. Results: Pre-decongestant ENA score was used to detect the presence of nasal obstruction in patients compared to controls, with a sensitivity of 0.81 (95% CI: 0.66-0.96) and a specificity of 0.77 (0.54-1.00). Post-decongestant percentage change in INA score was used to identify the presence of AR or CRS, with a sensitivity of 0.87 (0.69-1.00) and specificity of 0.72 (0.55-0.89) for AR; and a sensitivity of 0.92 (0.75-1.00) and specificity of 0.69 (0.52-0.86) for CRS. Post-decongestant inspiratory NOBI was used to identify DNS, with a sensitivity of 0.77 (0.59-0.95) and specificity of 0.94 (0.82-1.00). Conclusion: We have demonstrated that the NAD can help distinguish between normal and abnormal nasal breathing and help diagnose AR, CRS, and DNS. Such a device has not been invented and could revolutionize COVID-19 recovery telemedicine. Level of Evidence: Diagnostic accuracy study-Level III.

10.
Chaos Solitons Fractals ; 138: 109945, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-436317

ABSTRACT

COVID-19 declared as a global pandemic by WHO, has emerged as the most aggressive disease, impacting more than 90% countries of the world. The virus started from a single human being in China, is now increasing globally at a rate of 3% to 5% daily and has become a never ending process. Some studies even predict that the virus will stay with us forever. India being the second most populous country of the world, is also not saved, and the virus is spreading as a community level transmitter. Therefore, it become really important to analyse the possible impact of COVID-19 in India and forecast how it will behave in the days to come. In present work, prediction models based on genetic programming (GP) have been developed for confirmed cases (CC) and death cases (DC) across three most affected states namely Maharashtra, Gujarat and Delhi as well as whole India. The proposed prediction models are presented using explicit formula, and impotence of prediction variables are studied. Here, statistical parameters and metrics have been used for evaluated and validate the evolved models. From the results, it has been found that the proposed GEP-based models use simple linkage functions and are highly reliable for time series prediction of COVID-19 cases in India.

11.
Head Neck ; 42(7): 1674-1680, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-291714

ABSTRACT

BACKGROUND: Outpatient telemedicine consultations are being adopted to triage patients for head and neck cancer. However, there is currently no established structure to frame this consultation. METHODS: For suspected referrals with cancer, we adapted the Head and Neck Cancer Risk Calculator (HaNC-RC)-V.2, generated from 10 244 referrals with the following diagnostic efficacy metrics: 85% sensitivity, 98.6% negative predictive value, and area under the curve of 0.89. For follow-up patients, a symptom inventory generated from 5123 follow-up consultations was used. A customized Excel Data Tool was created, trialed across professional groups and made freely available for download at www.entintegrate.co.uk/entuk2wwtt, alongside a user guide, protocol, and registration link for the project. Stakeholder support was obtained from national bodies. RESULTS: No remote consultations were refused by patients. Preliminary data from 511 triaging episodes at 13 centers show that 77.1% of patients were discharged directly or have had their appointments deferred. DISCUSSION: Significant reduction in footfall can be achieved using a structured triaging system. Further refinement of HaNC-RC-V.2 is feasible and the authors welcome international collaboration.


Subject(s)
Continuity of Patient Care , Coronavirus Infections/epidemiology , Head and Neck Neoplasms/diagnosis , Head and Neck Neoplasms/epidemiology , Pneumonia, Viral/epidemiology , Referral and Consultation , Risk Assessment/methods , Triage/organization & administration , Betacoronavirus , COVID-19 , Clinical Decision-Making , Evidence-Based Practice , Humans , Medical Oncology/methods , Pandemics , Predictive Value of Tests , Remote Consultation , SARS-CoV-2 , Symptom Assessment , United Kingdom/epidemiology
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