Long COVID: major findings, mechanisms and recommendations.

Long COVID is an often debilitating illness that occurs in at least 10% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. More than 200 symptoms have been identified with impacts on multiple organ systems. At least 65 million individuals worldwide are estimated to have long COVID, with cases increasing daily. Biomedical research has made substantial progress in identifying various pathophysiological changes and risk factors and in characterizing the illness; further, similarities with other viral-onset illnesses such as myalgic encephalomyelitis/chronic fatigue syndrome and postural orthostatic tachycardia syndrome have laid the groundwork for research in the field. In this Review, we explore the current literature and highlight key findings, the overlap with other conditions, the variable onset of symptoms, long COVID in children and the impact of vaccinations. Although these key findings are critical to understanding long COVID, current diagnostic and treatment options are insufficient, and clinical trials must be prioritized that address leading hypotheses. Additionally, to strengthen long COVID research, future studies must account for biases and SARS-CoV-2 testing issues, build on viral-onset research, be inclusive of marginalized populations and meaningfully engage patients throughout the research process.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H₀) or in combination with anomalous sensor data (H₁). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H₁ model significantly outperformed the H₀ model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H₀ model to the H₁ model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

Multimodal biomedical AI.

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

Operation Nasal Vaccine-Lightning speed to counter COVID-19.

Given the poor ability of intramuscular mRNA COVID-19 vaccines to induce robust immunity in the respiratory mucosa, a push for a nasal vaccine strategy is needed.

Smartphone apps in the COVID-19 pandemic.

At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.

Health-care league table shows we were warned

Four emerge as very strong contenders: those of Germany, the Netherlands, Norway and Taiwan, each with laudable features such "Which country has the worst health care?" as broad choice, excellent coordination of long-term care and affordability. (Regarding universal coverage, Taiwan's system is similar to the United Kingdom's, with care paid for by a single public authority, and mandatory public health insurance;Norway uses a single-payer model with limited private insurance;and Germany and the Netherlands have universal coverage with mandatory basic private insurance.) It is noteworthy that the same four have so far been among the most successful in managing COVID-19. When I led a review in 2018-19 to help the UK National Health Service to plan its future workforce and directions, I learnt that it has a body, Health Education England, sible for education and training, that helps the service to adapt to major changes such as incorporating genomics, digital medicine and artificial intelligence into daily medical practice.

Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands.

The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.

6 month serologic response to the Pfizer-BioNTech COVID-19 vaccine among healthcare workers.

AIM: Healthcare workers (HCWs) were among the first group of people vaccinated with the Pfizer-BioNTech Covid-19 vaccine (BNT162b2). Characterization of the kinetics of antibody response to vaccination is important to devise future vaccination strategies. To better characterize the antibody response to BNT162b2, we analyzed the kinetics of IgG and IgM antibody response to 5 different SARS-CoV-2 epitopes over a period of 6 months. METHODS AND RESULTS: An observational single-centered study was conducted to evaluate the temporal dynamics of anti-SARS-CoV-2 antibodies following immunization with two doses of BNT162b2. Anti-SARS-CoV-2 antibodies were assessed using the Maverick SARS-CoV-2 multi-antigen panel (Genalyte Inc.). Healthcare workers aged ≥18 receiving BNT162b2 vaccination who self-reported no prior symptoms of COVID-19 nor prior COVID-19 PCR test positivity, were included in this study. HCWs developed an IgG antibody response to SARS-CoV-2 Spike S1, Spike S1 receptor binding domain (RBD), Spike S1S2 and Spike S2 after vaccination. IgG response was observed at two weeks following immunization in most participant samples and continued to increase at week 4, but subsequently decreased significantly starting at 3 months and up to 6 months. In contrast, IgM response to respective epitopes was minimal. CONCLUSION: Multiplex results demonstrate that, contrary to natural infection, immunization with BNT162b2 produces minimal anti-Spike IgM response. Polyclonal IgG response to Spike declined at 3 months and continued to do so up to 6 months.

COVID-19 vaccine breakthrough infections.

Vaccine efficacy wanes over time but can be fully restored with a booster dose.

It's not too late.

As the Biden administration took office last January, with the pandemic peaking at more than 130,000 COVID-19 hospitalizations in the United States, there were high hopes for a new plan of "sticking to the science" and expectations that public health policies, communication, and trust would return to levels not seen for many years. That didn't happen. Why?

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms.

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

From mRNA sensing to vaccines.

The 2005 Immunity paper by Karikó et al. has been hailed as a cornerstone insight that directly led to the design and delivery of the mRNA vaccines against COVID-19. We asked experts in pathogen sensing, vaccine development, and public health to provide their perspective on the study and its implications.