Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling.

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Inexpensive High-Throughput Multiplexed Biomarker Detection Using Enzymatic Metallization with Cellphone-Based Computer Vision.

Multiplexed biomarker detection can play a critical role in reliable and comprehensive disease diagnosis and prediction of outcome. Enzyme-linked immunosorbent assay (ELISA) is the gold standard method for immunobinding-based biomarker detection. However, this is currently expensive, limited to centralized laboratories, and usually limited to the detection of a single biomarker at a time. We present a low-cost, smartphone-based portable biosensing platform for high-throughput, multiplexed, sensitive, and quantitative detection of biomarkers from single, low-volume drops (<1 µL) of clinical samples. Biomarker binding to spotted capture antigens is converted, via enzymatic metallization, to the localized surface deposition of amplified, dry-stable, silver metal spots whose darkness is proportional to biomarker concentration. A custom smartphone application is developed, which uses real-time computer vision to enable easy optical detection of the deposited metal spots and sensitive and reproducible quantification of the biomarkers. We demonstrate the use of this platform for high-throughput, multiplexed detection of multiple viral antigen-specific antibodies from convalescent COVID-19 patient serum as well as vaccine-elicited antibody responses from uninfected vaccine-recipient serum and show that distinct multiplexed antibody fingerprints are observed among them.

A Multisensor System Embedded in a Computer Mouse for Occupational Stress Detection.

Occupational stress is a major challenge in modern societies, related with many health and economic implications. Its automatic detection in an office environment can be a key factor toward effective management, especially in the post-COVID era of changing working norms. The aim of this study is the design, development and validation of a multisensor system embedded in a computer mouse for the detection of office work stress. An experiment is described where photoplethysmography (PPG) and galvanic skin response (GSR) signals of 32 subjects were obtained during the execution of stress-inducing tasks that sought to simulate the stressors present in a computer-based office environment. Kalman and moving average filters were used to process the signals and appropriately formulated algorithms were applied to extract the features of pulse rate and skin conductance. The results found that the stressful periods of the experiment significantly increased the participants' reported stress levels while negatively affecting their cognitive performance. Statistical analysis showed that, in most cases, there was a highly significant statistical difference in the physiological parameters measured during the different periods of the experiment, without and with the presence of stressors. These results indicate that the proposed device can be part of an unobtrusive system for monitoring and detecting the stress levels of office workers.

Screen-viewing behaviours of children before and after the 2020-21 COVID-19 lockdowns in the UK: a mixed methods study.

BACKGROUND: Restrictions during the COVID-19 pandemic have led to increased screen-viewing among children, especially during strict periods of lockdown. However, the extent to which screen-viewing patterns in UK school children have changed post lockdowns is unclear. The aim of this paper is to examine how screen-viewing changed in 10-11-year-old children over the 2020-21 COVID-19 pandemic, how this compares to before the pandemic, and the influences on screen-viewing behaviour. METHODS: This is a mixed methods study with 10-11-year-olds from 50 schools in the Greater Bristol area, UK. Cross-sectional questionnaire data on minutes of weekday and weekend television (TV) viewing and total leisure screen-viewing were collected pre-COVID-19 in 2017-18 (N = 1,296) and again post-lockdowns in 2021 (N = 393). Data were modelled using Poisson mixed models, adjusted for age, gender, household education and seasonality, with interactions by gender and household education. Qualitative data were drawn from six focus groups (47 children) and 21 one-to-one parent interviews that explored screen-viewing behaviour during the pandemic and analysed using the framework method. RESULTS: Total leisure screen-viewing was 11% (95% CI: 12%-18%) higher post-lockdown compared to pre-COVID-19 on weekdays, and 8% (95% CI: 6%-10%) on weekends, equating to around 12-15 min. TV-viewing (including streaming) was higher by 68% (95% CI: 63%-74%) on weekdays and 80% (95% CI: 75%-85%) on weekend days. Differences in both were higher for girls and children from households with lower educational attainment. Qualitative themes reflected an unavoidable increase in screen-based activities during lockdowns, the resulting habitualisation of screen-viewing post-lockdown, and the role of the parent in reducing post-2020/21 lockdown screen-viewing. CONCLUSIONS: Although screen-viewing was higher post-lockdown compared to pre-COVID-19, the high increases reported during lockdowns were not, on average, sustained post-lockdown. This may be attributed to a combination of short-term fluctuations during periods of strict restrictions, parental support in regulating post-lockdown behaviour and age-related, rather than COVID-19-specific, increases in screen-viewing. However, socio-economic differences in our sample suggest that not all families were able to break the COVID-19-related adoption of screen-viewing, and that some groups may need additional support in managing a healthy balance of screen-viewing and other activities following the lockdowns.

Using an objective computer task (QbTest) to aid the identification of attention deficit hyperactivity disorder (ADHD) in the Children and Young People Secure Estate (CYPSE): a feasibility randomised controlled trial.

OBJECTIVES: QbTest has been shown to improve time to decision/diagnosis for young people with attention deficit hyperactivity disorder (ADHD). The aim was to assess the feasibility of QbTest for young people in prison. DESIGN: Single-centre feasibility randomised controlled trial (RCT), with 1:1 allocation. Concealed random allocation using an online pseudorandom list with random permuted blocks of varying sizes. SETTING: One Young Offenders Institution in England. PARTICIPANTS: 355 young people aged 15-18 years displaying possible symptoms of ADHD were assessed for eligibility, 69 were eligible to take part and 60 were randomised. INTERVENTION: QbTest-a computer task measuring attention, activity and impulsivity. MAIN OUTCOME MEASURES: Eligibility, recruitment and retention rates and acceptability of randomisation and trial participation. RESULTS: Of the 355 young people assessed for eligibility, 69 were eligible and 60 were randomised (n=30 QbTest plus usual care; n=30 usual care alone). The study achieved the specified recruitment target. Trial participation and randomisation were deemed acceptable by the majority of participants. 78% of young people were followed up at 3 months, but only 32% at 6 months, although this was also affected by COVID-19 restrictions. Secondary outcomes were mixed. Participants including clinical staff were mostly supportive of the study and QbTest; however, some young people found QbTest hard and there were issues with implementation of the ADHD care pathway. There were no serious adverse events secondary to the study or intervention and no one was withdrawn from the study due to an adverse event. CONCLUSIONS: With adaptations, a fully powered RCT may be achievable to evaluate the effectiveness of QbTest in the assessment of ADHD in the Children and Young People Secure Estate, with time to decision (days) as the primary outcome measure. However, further programme developmental work is required to address some of the challenges highlighted prior to a larger trial. TRIAL REGISTRATION NUMBER: ISRCTN17402196.

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

OBJECTIVES: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. DESIGN: Single-site, observational cohort study. SETTING: An urban, academic hospital in Boston, Massachusetts, USA. PARTICIPANTS: We enrolled adult hospital staff entering the hospital at a key ingress point. INTERVENTIONS: Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. OUTCOME MEASURES: Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. RESULTS: One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy.Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important.

Computer Visual Syndrome in Medical Students From a Private University in Paraguay: A Survey Study.

Purpose: To determine the prevalence and factors associated with computer vision syndrome in medical students at a private university in Paraguay. Methods: A survey study was conducted in 2021 in a sample of 228 medical students from the Universidad del Pacífico, Paraguay. The dependent variable was CVS, measured with the Computer Visual Syndrome Questionnaire (CVS-Q). Its association with covariates (hours of daily use of notebook, smartphone, tablet and PC, taking breaks when using equipment, use of preventive visual measures, use of glasses, etc.) was examined. Results: The mean age was 22.3 years and 71.5% were women. CVS was present in 82.5% of participants. Higher prevalence of CVS was associated with wearing a framed lens (PR = 1.11, 95% CI: 1.03-1.20). In contrast, taking a break when using electronic equipment at least every 20 min and every 1 h reduced 7% (PR = 0.93, 95% CI: 0.87-0.99) and 6% (PR = 0.94, 95% CI: 0.89-0.99) the prevalence of CVS, respectively. Conclusion: Eight out of 10 students experienced CVS during the COVID-19 pandemic. The use of framed lenses increased the presence of CVS, while taking breaks when using electronic equipment at least every 20 min and every 1 h reduced CVS.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.

Visual ergonomics for changing work environments in the COVID-19 pandemic.

BACKGROUND: The coronavirus 2019 (COVID-19) pandemic has brought about change in the work environment, increasing remote and hybrid mode of work, presenting a compelling need to study visual ergonomics in this new work environment. OBJECTIVE: To assess computer vision symptoms and visual ergonomics in remote and hybrid work settings during the COVID-19 pandemic with a focus on eye to screen relationship. METHODS: The computer-vision symptom scale (CVSS17) questionnaire and questions about human factors and ergonomics were included in the survey conducted in September 2021. Sixty-six working professionals (mean age 37 years±5), working from home (n = 44) or in hybrid mode (n = 22) were included in the study. Cramer's V was used for the correlation coefficient between two categorical variables for assessing eye health in changing work environments. RESULTS: Compared to our previous study, the correlation between computer vision syndrome (CVS) symptoms is markedly higher. The population working in hybrid mode experienced eye heaviness with strain to see well (V = 0.6872, p = 0.002) and dryness in the eyes (V = 0.5912, p = 0.0179). The population working from home who are bothered by surrounding lights also report dryness in the eyes (V = 0.3846, p = 0.0005). Screen use hours are higher in work from home situations (43% work more than 9 hrs) than those in hybrid mode of work (4% work more than 9 hrs). CONCLUSION: A definite increase in CVS in most of the population working remotely or in hybrid environments is established through this study. User-friendly strategies for raising awareness of applied visual ergonomics can prevent rampant onset of CVS in the working population.

Computer and Furniture Affecting Musculoskeletal Problems and Work Performance in Work From Home During COVID-19 Pandemic.

OBJECTIVES: We surveyed how home-working conditions, specifically furniture and computer use, affected self-reported musculoskeletal problems and work performance. METHODS: Questionnaires from 4112 homeworkers were analyzed. The relationship between subjective musculoskeletal problems or work performance and working conditions were determined by logistic regression analyses. RESULTS: More than half the homeworkers used a work desk, work chair, and laptop computer. However, approximately 20% of homeworkers used a low table, floor chair/floor cushion, or other furniture that was different from the office setup. Using a table of disproportionate size and height, sofa, floor cushion, and floor chair were associated with neck/shoulder pain or low back pain. Disproportionate table and chair, floor cushion, and tablet computer were associated with poor work performance. CONCLUSIONS: Disproportionate desk and chair, floor cushion/chair, and computer with small screen may affect musculoskeletal problems and home-working performance.

Computer-Aided Tools and Resources for Fungal Pathogens: An Application of Reverse Vaccinology for Mucormycosis.

Increasing fungal infections in immunocompromised hosts are a growing concern for global public health. Along with treatments, preventive measures are required. The emergence of reverse vaccinology has opened avenues for using genomic and proteomic data from pathogens in the design of vaccines. In this work, we present a comprehensive collection of various computational tools and databases with potential to aid in vaccine development. The ongoing pandemic has directed attention toward the increasing number of mucormycosis infections in COVID-19 patients. As a case study, we developed a computational pipeline for assisting vaccine development for mucormycosis. We obtained 6 proteins from 29,447 sequences from UniProtKB as potential vaccine candidates against mucormycosis, fulfilling multiple criteria. These criteria included potential characteristics, namely adhesin properties, surface or extracellular localization, antigenicity, no similarity to any human proteins, nonallergenicity, stability in vitro, and expression in fungal cells. These six proteins were predicted to have B cell and T cell epitopes, proinflammatory inducing peptides, and orthologs in several mucormycosis-causing species. These data could aid in vaccine development against mucormycosis for at-risk individuals.

[A new approach in the diagnosis of lung lesions in patients with COVID-19: lung ultrasound protocol versus CT scan].

AIM: To develop a protocol for ultrasound diagnostics of COVID-19 pneumonia and to assess the diagnostic capabilities of the method in comparison with computer tomography (CT). MATERIALS AND METHODS: The study included 59 patients with a new coronavirus infection. In order to identify changes in the lung tissue characteristic of a new coronavirus infection, we used a special protocol for ultrasound of the lungs, which was developed by us in such a way that the data obtained were compared by segment with the results of CT of the lungs. RESULTS: When comparing the results of lung ultrasound with the data of CT diagnostics, according to the new protocol, the percentage of lung tissue damage during ultrasound of the lungs averaged 70.8% in the group [62.5; 87.5], and according to the results of CT 70.0% [60.0; 72.5] (p=0.427). Thus, the ultrasound of the lung lesions was almost completely consistent with the changes revealed by CT. In order to assess the diagnostic value of lung ultrasound in identifying severe lung tissue lesions corresponding to CT 34, ROC analysis was performed, which showed the high diagnostic value of lung ultrasound in identifying severe lung tissue lesions. CONCLUSION: A new protocol was developed for assessing the severity of lung tissue damage according to ultrasound data, which showed a high diagnostic value in detecting COVID-19 pneumonia in comparison with CT. The results obtained give reason to recommend this protocol of ultrasound of the lungs as a highly sensitive method in diagnosing the severity of COVID-19 pneumonia. Its application is very important for dynamic examination of patients, especially in conditions of low availability of CT.

Diagnostic performance of corona virus disease 2019 chest computer tomography image recognition based on deep learning: Systematic review and meta-analysis.

BACKGROUND: To analyze the diagnosis performance of deep learning model used in corona virus disease 2019 (COVID-19) computer tomography(CT) chest scans. The included sample contains healthy people, confirmed COVID-19 patients and unconfirmed suspected patients with corresponding symptoms. METHODS: PubMed, Web of Science, Wiley, China National Knowledge Infrastructure, WAN FANG DATA, and Cochrane Library were searched for articles. Three researchers independently screened the literature, extracted the data. Any differences will be resolved by consulting the third author to ensure that a highly reliable and useful research paper is produced. Data were extracted from the final articles, including: authors, country of study, study type, sample size, participant demographics, type and name of AI software, results (accuracy, sensitivity, specificity, ROC, and predictive values), other outcome(s) if applicable. RESULTS: Among the 3891 searched results, 32 articles describing 51,392 confirmed patients and 7686 non-infected individuals met the inclusion criteria. The pooled sensitivity, the pooled specificity, positive likelihood ratio, negative likelihood ratio and the pooled diagnostic odds ratio (OR) is 0.87(95%CI [confidence interval]: 0.85, 0.89), 0.85(95%CI: 0.82, 0.87), 6.7(95%CI: 5.7, 7.8), 0.14(95%CI: 0.12, 0.16), and 49(95%CI: 38, 65). Further, the AUROC (area under the receiver operating characteristic curve) is 0.94(95%CI: 0.91, 0.96). Secondary outcomes are specific sensitivity and specificity within subgroups defined by different models. Resnet has the best diagnostic performance, which has the highest sensitivity (0.91[95%CI: 0.87, 0.94]), specificity (0.90[95%CI: 0.86, 0.93]) and AUROC (0.96[95%CI: 0.94, 0.97]), according to the AUROC, we can get the rank Resnet > Densenet > VGG > Mobilenet > Inception > Effficient > Alexnet. CONCLUSIONS: Our study findings show that deep learning models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic.

Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.

BACKGROUND: Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it's very important to be accurate in the early stages of diagnosis and treatment. RESULTS: We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology's. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. CONCLUSIONS: To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.

Computer-Aided Discovery and Redesign for Respiratory Sensitization: A Tiered Mechanistic Model to Deliver Robust Performance Across a Diverse Chemical Space.

Asthma is among the most common occupational diseases with considerable public health and economic costs. Chemicals that induce hypersensitivity in the airways can cause respiratory distress and comorbidities with respiratory infections such as COVID. Robust predictive models for this end point are still elusive due to the lack of an experimental benchmark and the over-reliance of existing in silico tools on structural alerts and structural (vs chemical) similarities. The Computer-Aided Discovery and REdesign (CADRE) platform is a proven strategy for providing robust computational predictions for hazard end points using a tiered hybrid system of expert rules, molecular simulations, and quantum mechanics calculations. The recently developed CADRE model for respiratory sensitization is based on a highly curated data set of structurally diverse chemicals with high-fidelity biological data. The model evaluates absorption kinetics in lung mucosa using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines subsequent reactivity with cell proteins via quantum-mechanics calculations using a multi-tiered regression. The model affords an accuracy above 0.90, with a series of external validations based on literature data in the range of 0.88-0.95. The model is applicable to all low-molecular-weight organics and can inform not only chemical substitution but also chemical redesign to advance development of safer alternatives.

Development and validation of automated computer-aided risk scores to predict in-hospital mortality for emergency medical admissions with COVID-19: a retrospective cohort development and validation study.

OBJECTIVES: There are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2). DESIGN: Logistic regression model development and validation study. SETTING: Two acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data). PARTICIPANTS: Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19_N: age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+. RESULTS: The risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19_N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and CARMc19_N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19_NB had better sensitivity and similar specificity. CONCLUSIONS: We have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Relationships Between the Physical Work Environment, Postures and Musculoskeletal Pain During COVID-19: A Survey of Frequent Computer Users.

OBJECTIVE: To understand the flexible work practices during the COVID-19 pandemic and their impact on work-related musculoskeletal disorders (MSDs) and depression in frequent computer users. METHODS: An e-survey determined the extent of workplace changes and MSD, and the relationships between them using descriptive-statistics and chi-squared tests. RESULTS: Of 700 who commenced the survey, 511 were analyzed. Since the pandemic commenced, 80% of respondents reported they were working more from home; and 89% reported some musculoskeletal pain. Compared with prepandemic, more people worked in nonergonomic environments, computer configurations and body postures. Work location was associated with upper back pain ( P = 0.011); body posture with headache ( P = 0.027) and low back pain ( P = 0.003). CONCLUSION: Nonergonomic work environments of frequent computer users during COVID-19 are related to having upper back pain, whereas nonergonomic postures are related to having headache and low back pain.

A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2.

PURPOSE: No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers. METHOD: To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load). RESULTS: Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities. CONCLUSIONS: This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.