Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics.

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Returning persons with SARS-CoV-2 to the field of play in professional golf: a risk assessment and risk reduction approach.

Objectives: This pilot study aimed to see whether a risk assessment and risk reduction approach was a practical and feasible approach, as compared with standard isolation for fully vaccinated, asymptomatic persons positive for SARS-CoV-2. Methods: This prospective cohort study included all players and caddies participating in two large professional golf events from 7 to 20 February 2022 in South Africa. Fully vaccinated persons testing positive who were asymptomatic were subject to risk assessment and risk reduction measures to protect the integrity of the event. Asymptomatic individuals who could socially distance in outdoor areas were allowed to participate. Close contacts were subject to daily rapid antigen tests and asked to prioritise outdoor space. Results: The protocols put in place for the events were practical, feasible, and well accepted by event participants and staff during the study period. There was a total of 378 player-week episodes and 378 caddie-week episodes during the study period. Three persons tested positive while registered at events during the study period (0.4% of person episodes). The positive tests were returned from two players and one caddie, all of which were asymptomatic at the time of testing. There was one high-risk contact who consistently returned negative antigen tests. There was no evidence of transmission. Conclusions: The approach was practical and feasible. A risk assessment and risk reduction approach allowed fully vaccinated asymptomatic persons with SARS-CoV-2 to participate in golf, an outdoor sport where social distancing is possible, compared with standard isolation.

Risk assessment and implementation of risk reduction measures is not associated with increased transmission of SARS-CoV-2 compared with standard isolation at professional golf events.

Objectives: The purpose of this prospective study was to report incidence and transmission of SARS-CoV-2, among professional golfers and essential support staff undergoing risk assessment and enhanced risk reduction measures when considered a close contact as opposed to standard isolation while competing on the DP World Tour during the 2021 season. Methods: This prospective cohort study included all players and essential support staff participating in 26 DP World Tour events from 18 April 2021 to 21 November 2021. High-risk contacts were isolated for 10 days. Moderate-risk contacts received education regarding enhanced medical surveillance, had daily rapid antigen testing for 5 days, with reverse transcriptase-polymerase chain reaction (RT-PCR) tesing on day 5, mandated mask use and access to outside space for work purposes only. Low-risk contacts typically received rapid antigen testing every 48 hours and RT-PCR testing on day 5. Results: The total study cohort compromised 13 394 person-weeks of exposure. There were a total of 30 positive cases over the study period. Eleven contacts were stratified as 'high risk'. Two of these subsequently tested positive for SARS-CoV-2. There were 79 moderate-risk contact and 73 low-risk contacts. One moderate-risk contact subsequently tested positive for SARS-CoV-2 but did not transmit the virus. All other contacts, remained negative and asymptomatic to the end of the tournament week. Conclusions: A risk assessment and risk reduction-based approach to contact tracing was safe in this professional golf event setting when Alpha and Delta were the predominant variants. It enabled professional golfers and essential support staff to work.

Assessing the risk of SARS-CoV-2 transmission in international professional golf.

OBJECTIVES: There is no published data on the incidence or risk of SARS-CoV-2 transmission when playing golf, a sport played outdoors where social distancing is possible. The purpose of this prospective study was to report incidence and transmission regarding SARS-CoV-2, of professional golfers competing on the PGA European Tour across 23 events in 11 countries. METHODS: Daily symptom and temperature checks and weekly reverse transcriptase PCR (RT-PCR) screening were performed to determine potential carriage of SARS-CoV-2. Onset and type of symptomology were analysed. Gene expression and cycle thresholds (Cts) were reviewed for all positive cases. Repeat PCR testing was performed on all positive players. RT-PCR analysis included human housekeeping genes and various RNA genes specific for SARS-CoV-2. RESULTS: During the study period, there were 2900 RT-PCR tests performed on 195 professional golfers competing on the European Tour. Four players tested positive on-site during the study period (0.14% of tests; positive results were declared with Ct <40). Two positive tests were returned as part of routine protocols, while two reported a history of close contact with an individual who had tested positive for SARS-CoV-2 and were isolated and target tested. All were asymptomatic at time of testing, with three developing symptoms subsequently. None required hospital admission. There was no transmission from player to player. CONCLUSION: Golf is an outdoor sport where social distancing is possible, meaning risks can be low if guidance is followed by participants. Risk of transmission of SARS-CoV-2 can be mitigated by highly accurate RT-PCR testing of participants and by setting up a safe bubble that includes testing players and support staff, as well as all persons coming into contact with them during the course of the tournament, for example, drivers and hotel staff. This report can also provide reassurance for participants and policy makers regarding community golf, which can be encouraged for the health benefits it provides, in a relatively low-risk environment, with minimal risk of transmission by observing sensible viral hygiene protocols.

Pilot evaluation of risk assessment and enhanced protocols regarding contacts at an international professional golf event.

OBJECTIVES: The aim of this study was to assess whether a risk assessment and managed risk approach to contact tracing was practical and feasible at the Gran Canaria Lopesan Open 2021 and could inform further pilot work regarding disease transmission during elite sporting events. METHODS: This prospective cohort study included all international attendees. All participants required a minimum of one negative reverse transcriptase PCR (RT-PCR) test prior to travelling to each tournament. High-risk contacts were isolated for 10 days. Moderate-risk contacts received education regarding enhanced medical surveillance, had daily rapid antigen testing for 5 days, with RT-PCR day 5, mandated mask use and access to outside space for work purposes only. Low-risk contacts received rapid antigen testing every 48 hours and PCR testing on day 5. RESULTS: A total of 550 persons were accredited and were required to undergo RT-PCR testing before the event. Two of these tests were positive (0.36%). Of these, case 1 had 1 high, 23 moderate and 48 low-risk contacts. Case 2 did not have any significant travel history within 2 days of positive test and had one high-risk contact. There were no further positive tests on site in the wider cohort of attendees, from a total of 872 RT-PCR and 198 rapid antigen tests. CONCLUSIONS: This pilot study showed it is practical, feasible and well accepted to provide enhanced (daily) virus testing and risk-mitigating measures at a professional golf event. Further study is required to assess the efficacy of these interventions; however, no transmission was found in this pilot study.

Public health considerations regarding golf during the COVID-19 pandemic: a narrative review.

BACKGROUND: Golf is a sport played worldwide by >60 million people from a variety of backgrounds and abilities. Golf's contribution to physical and mental health benefits are becoming increasingly recognised. Countries have adopted a range of restrictions to playing golf during the COVID-19 pandemic. AIMS: The purpose of this narrative review was to (1) explore the literature related to the possible health benefits and risks of playing golf during the COVID-19 pandemic and (2) provide recommendations on golf-related activity from the relevant available literature. RESULTS: Golf can provide health-enhancing physical activity. Regular physical activity is associated with physical/mental health, immune system and longevity benefits. Sense of belonging and life satisfaction significantly improved when golfing restrictions were relaxed after the first lockdown in the UK. Golf is an outdoor sport, where social distancing is possible, and if rules are followed, risk of COVID-19 transmission is likely to be low. CONCLUSIONS: Policy-makers and governing bodies should support the promotion of golf because participation brings wide ranging benefits for physical health and mental well-being. When effective risk reduction measures are used, the benefits of playing golf in most circumstances outweigh the risk of transmission.

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

IMPACT-Restart: the influence of COVID-19 on postoperative mortality and risk factors associated with SARS-CoV-2 infection after orthopaedic and trauma surgery.

AIMS: The primary aim of this study was to assess the independent association of the coronavirus disease 2019 (COVID-19) on postoperative mortality for patients undergoing orthopaedic and trauma surgery. The secondary aim was to identify factors that were associated with developing COVID-19 during the postoperative period. METHODS: A multicentre retrospective study was conducted of all patients presenting to nine centres over a 50-day period during the COVID-19 pandemic (1 March 2020 to 19 April 2020) with a minimum of 50 days follow-up. Patient demographics, American Society of Anesthesiologists (ASA) grade, priority (urgent or elective), procedure type, COVID-19 status, and postoperative mortality were recorded. RESULTS: During the study period, 1,659 procedures were performed in 1,569 patients. There were 68 (4.3%) patients who were diagnosed with COVID-19. There were 85 (5.4%) deaths postoperatively. Patients who had COVID-19 had a significantly lower survival rate when compared with those without a proven SARS-CoV-2 infection (67.6% vs 95.8%, p < 0.001). When adjusting for confounding variables (older age (p < 0.001), female sex (p = 0.004), hip fracture (p = 0.003), and increasing ASA grade (p < 0.001)) a diagnosis of COVID-19 was associated with an increased mortality risk (hazard ratio 1.89, 95% confidence interval (CI) 1.14 to 3.12; p = 0.014). A total of 62 patients developed COVID-19 postoperatively, of which two were in the elective and 60 were in the urgent group. Patients aged > 77 years (odds ratio (OR) 3.16; p = 0.001), with increasing ASA grade (OR 2.74; p < 0.001), sustaining a hip (OR 4.56; p = 0.008) or periprosthetic fracture (OR 14.70; p < 0.001) were more likely to develop COVID-19 postoperatively. CONCLUSION: Perioperative COVID-19 nearly doubled the background postoperative mortality risk following surgery. Patients at risk of developing COVID-19 postoperatively (patients > 77 years, increasing morbidity, sustaining a hip or periprosthetic fracture) may benefit from perioperative shielding. Cite this article: Bone Joint J 2020;102-B(12):1774-1781.

Patch dynamics modeling framework from pathogens' perspective: Unified and standardized approach for complicated epidemic systems.

Mathematical models are powerful tools to investigate, simulate, and evaluate potential interventions for infectious diseases dynamics. Much effort has focused on the Susceptible-Infected-Recovered (SIR)-type compartment models. These models consider host populations and measure change of each compartment. In this study, we propose an alternative patch dynamic modeling framework from pathogens' perspective. Each patch, the basic module of this modeling framework, has four standard mechanisms of pathogen population size change: birth (replication), death, inflow, and outflow. This framework naturally distinguishes between-host transmission process (inflow and outflow) and within-host infection process (replication) during the entire transmission-infection cycle. We demonstrate that the SIR-type model is actually a special cross-sectional and discretized case of our patch dynamics model in pathogens' viewpoint. In addition, this patch dynamics modeling framework is also an agent-based model from hosts' perspective by incorporating individual host's specific traits. We provide an operational standard to formulate this modular-designed patch dynamics model. Model parameterization is feasible with a wide range of sources, including genomics data, surveillance data, electronic health record, and from other emerging technologies such as multiomics. We then provide two proof-of-concept case studies to tackle some of the existing challenges of SIR-type models: sexually transmitted disease and healthcare acquired infections. This patch dynamics modeling framework not only provides theoretical explanations to known phenomena, but also generates novel insights of disease dynamics from a more holistic viewpoint. It is also able to simulate and handle more complicated scenarios across biological scales such as the current COVID-19 pandemic.

Four Challenges Associated With Current Mathematical Modeling Paradigm of Infectious Diseases and Call for a Shift.

Mathematical models are critical tools to characterize COVID-19 dynamics and take action accordingly. We identified 4 major challenges associated with the current modeling paradigm (SEIR) that hinder the efforts to accurately characterize the emerging COVID-19 and future epidemics. These challenges included (1) lack of consistent definition of "case"; (2) discrepancy between patient-level clinical insights and population-level modeling efforts; (3) lack of adequate inclusion of individual behavioral and social influence; and (4) allowing little flexibility of including new evidence and insights when our knowledge evolved rapidly during the pandemic. Therefore, these challenges made the current SEIR modeling paradigm less practical to handle the complex COVID-19 and future pandemics. Novel and more reliable data sources and alternative modeling paradigms are needed to address these issues.