Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.

In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy severely resulting in high casualties and in the near breakdown of primary care. Due to these facts, the Covid CXR Hackathon-Artificial Intelligence for Covid-19 prognosis: aiming at accuracy and explainability challenge had been launched at the beginning of February 2022, releasing a new imaging dataset with additional clinical metadata for each accompanying chest X-ray (CXR). In this article we summarize our techniques at correctly diagnosing chest X-ray images collected upon admission for severity of COVID-19 outcome. In addition to X-ray imagery, clinical metadata was provided and the challenge also aimed at creating an explainable model. We created a best-performing, as well as, an explainable model that makes an effort to map clinical metadata to image features whilst predicting the prognosis. We also did many ablation studies in order to identify crucial parts of the models and the predictive power of each feature in the datasets. We conclude that CXRs at admission do not help the predicting power of the metadata significantly by itself and contain mostly information that is also mutually present in the blood samples and other clinical factors collected at admission.

Comparison of hesitancy between COVID-19 and seasonal influenza vaccinations within the general Hungarian population: a cross-sectional study.

BACKGROUND: The willingness to get COVID-19 or seasonal influenza vaccines has not yet been thoroughly investigated together, thus, this study aims to explore this notion within the general adult population. METHODS: The responses of 840 Hungarian participants were analysed who took part in a nationwide computer-assisted telephone interviewing. During the survey questions concerning various demographic characteristics, perceived financial status, and willingness to get the two types of vaccines were asked. Descriptive statistics, comparative statistics and word co-occurrence network analysis were conducted. RESULTS: 48.2% of participants were willing to get a COVID-19 vaccine, while this ratio for the seasonal influenza was only 25.7%. The difference was significant. Regardless of how the participants were grouped, based on demographic data or perceived financial status, the significant difference always persisted. Being older than 59 years significantly increased the willingness to get both vaccines when compared to the middle-aged groups, but not when compared to the younger ones. Having higher education significantly elevated the acceptance of COVID-19 vaccination in comparison to secondary education. The willingness of getting any type of COVID-19 vaccine correlated with the willingness to get both influenza and COVID-19. Finally, those who were willing to get either vaccine coupled similar words together to describe their thoughts about a COVID-19 vaccination. CONCLUSION: The overall results show a clear preference for a COVID-19 vaccine and there are several similarities between the nature of willingness to get either type of vaccine.

Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis.

Mobile phones have been used to monitor mobility changes during the COVID-19 pandemic but surprisingly few studies addressed in detail the implementation of practical applications involving whole populations. We report a method of generating a "mobility-index" and a "stay-at-home/resting-index" based on aggregated anonymous Call Detail Records of almost all subscribers in Hungary, which tracks all phones, examining their strengths and weaknesses, comparing it with Community Mobility Reports from Google, limited to smartphone data. The impact of policy changes, such as school closures, could be identified with sufficient granularity to capture a rush to shops prior to imposition of restrictions. Anecdotal reports of large scale movement of Hungarians to holiday homes were confirmed. At the national level, our results correlated well with Google mobility data, but there were some differences at weekends and national holidays, which can be explained by methodological differences. Mobile phones offer a means to analyse population movement but there are several technical and privacy issues. Overcoming these, our method is a practical and inexpensive way forward, achieving high levels of accuracy and resolution, especially where uptake of smartphones is modest, although it is not an alternative to smartphone-based solutions used for contact tracing and quarantine monitoring.