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As the SARS-CoV-2 virus (COVID-19) continues to affect people across the globe, there is limited understanding of the long term implications for infected patients1–3. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually designed by clinicians, and not granular enough to understand the natural history or patient experiences of ‘long COVID’. In order to get a complete picture, there is a need to use patient generated data to track the long-term impact of COVID-19 on recovered patients in real time. There is a growing need to meticulously characterize these patients’ experiences, from infection to months post-infection, and with highly granular patient generated data rather than clinician narratives. In this work, we present a longitudinal characterization of post-COVID-19 symptoms using social media data from Twitter. Using a combination of machine learning, natural language processing techniques, and clinician reviews, we mined 296,154 tweets to characterize the post-acute infection course of the disease, creating detailed timelines of symptoms and conditions, and analyzing their symptomatology during a period of over 150 days.

We read the study by Goutines et al. on the presence of infections and nutritional status of internationally adopted children (IAC) in France (1). While the study provides valuable insight into the areas within the adoption process to enable safer child and public health policies, there are areas which require additional thought.

As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.

The wellbeing of doctors in training is currently under the spotlight. The British Medical Association (BMA) has committed to understanding issues of wellbeing amongst medical students. Medical students from England were asked to complete an online survey pertaining to wellbeing. 84 students responded. Results show 29% of respondents were given a mental health diagnosis whilst at medical school, and 82% could be classified as 'disengaged' and 85% 'exhausted' using the Oldenburg Burnout Scale. This demonstrates the need for further surveys with an increased number of respondents in order to gather more evidence surrounding these high rates of mental health issues. Support and preventative measures for medical students are required.

Doctors and medical students are working in a system which is affecting their mental wellbeing and their ability to provide the best possible care for patients. The British Medical Association conducted an online survey of doctors and medical students in October 2018. In total, 4347 responses were received and analysed. Doctors working the longest hours appear to be most vulnerable to psychological and emotional disturbance. Older and more senior doctors are most likely to report that their working environment has impacted on their condition. Medical students and junior doctors report the highest rate of having a formally diagnosed mental health condition in the last 12 months. This may be because they are in the vulnerable age group when psychiatric disorders start. Junior doctors were least likely to be aware of how to access help or support. Older doctors, those working as SAS (Staff, Associate Specialists and Specialty) doctors and overseas qualified doctors are most likely to say they have asked for support in managing a problem from their employer but that no support was provided. It is important to recognize that doctors, in spite of stress and poor wellbeing, continue to work hard, which has both advantages and disadvantages. These findings highlight that the environment in which doctors work, train, and study affects their mental health, and for this reason careful consideration needs to be given to the type and level of support provision available to them, as well as the ease of access and awareness of such support.