New International Guidelines and Consensus on the Use of Lung Ultrasound.

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland.

In the article, the authors present a multi-agent model that simulates the development of the COVID-19 pandemic at the regional level. The developed what-if system is a multi-agent generalization of the SEIR epidemiological model, which enables predicting the pandemic's course in various regions of Poland, taking into account Poland's spatial and demographic diversity, the residents' level of mobility, and, primarily, the level of restrictions imposed and the associated compliance. The developed simulation system considers detailed topographic data and the residents' professional and private lifestyles specific to the community. A numerical agent represents each resident in the system, thus providing a highly detailed model of social interactions and the pandemic's development. The developed model, made publicly available as free software, was tested in three representative regions of Poland. As the obtained results indicate, implementing social distancing and limiting mobility is crucial for impeding a pandemic before the development of an effective vaccine. It is also essential to consider a given community's social, demographic, and topographic specificity and apply measures appropriate for a given region.

Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks.

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual's mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level.

Type and reliability of information about coronavirus most frequently shared by social media users.

Objective: To identify, investigate and categorize the most frequently shared content related to COVID-19 by social media users. Methods: The BuzzSumo analytic tool was used to identify the most frequently shared content about COVID-19 between July and August 2020. They were then analyzed and classified into eight main categories according to their topic. Results: Among 120 articles that were shared 6,189,187 times in total during the analyzed period, the most popular were those that referred to methods for decreasing COVID-19 spread and characteristics. No myths or misinformation were found in the most frequently shared articles. The most popular content included humorous yet educational videos. Conclusions: The most frequently shared content by social media users is reliable and refers to prevention in the first place. As humorous videos about prevention attracted the most attention, it seems an attractive and potentially effective strategy to foster online preventive behaviors during the pandemic. Lay Summary: The most popular articles that were shared more than 6 million times in total during the analyzed period of time referred methods for decreasing COVID-19 spread and COVID-19 characteristics. The Internet and social media provide countless opportunities and audiences to deliver accurate knowledge and recommendations on COVID-19 and may contribute to fostering preventive and responsible behaviors.

Modeling the Spatial and Temporal Spread of COVID-19 in Poland Based on a Spatial Interaction Model

This article describes an original methodology for integrating global SIR-like epidemic models with spatial interaction models, which enables the forecasting of COVID-19 dynamics in Poland through time and space. Mobility level, estimated by the regional population density and distances among inhabitants, was the determining variable in the spatial interaction model. The spatiotemporal diffusion model, which allows the temporal prediction of case counts and the possibility of determining their spatial distribution, made it possible to forecast the dynamics of the COVID-19 pandemic at a regional level in Poland. This model was used to predict incidence in 380 counties in Poland, which represents a much more detailed modeling than NUTS 3 according to the widely used geocoding standard Nomenclature of Territorial Units for Statistics. The research covered the entire territory of Poland in seven weeks of early 2021, just before the start of vaccination in Poland. The results were verified using official epidemiological data collected by sanitary and epidemiological stations. As the conducted analyses show, the application of the approach proposed in the article, integrating epidemiological models with spatial interaction models, especially unconstrained gravity models and destination (attraction) constrained models, leads to obtaining almost 90% of the coefficient of determination, which reflects the quality of the model's fit with the spatiotemporal distribution of the validation data.

Online Information of COVID-19: Visibility and Characterization of Highest Positioned Websites by Google between March and April 2020-A Cross-Country Analysis.

BACKGROUND: The WHO has used the term "infodemic" to describe the vast amount of false and true information that was making it difficult for people to find reliable information when they needed it. The infodemic spreads faster than COVID-19 itself. The main objective of the study was to characterize and analyze content about COVID-19 returned by Google during the pandemic and compare it between countries. METHODS: The study was conducted between 30 March and 27 April 2020. The information was searched through local Google websites using the "COVID-19", "Coronavirus", "SARS-CoV-2" and "fake news" keywords. The search was conducted in Australia, France, Germany, Italy, Poland, Singapore, Spain, UK and the USA. The total number of the analyzed webpages was 685. RESULTS: The most frequent types were News websites 47% (324/685) and Governmental 19% (131/685) while the least were Health portals 2% (17/685) and Scientific journals 5% (35/635), p < 0.001. United States and Australia had the highest share of Governmental websites. There was a positive correlation between the amount of preventive information and a number of SARS-CoV-2 infections in countries. The higher the number of tests performed, the higher was the amount of information about prevention available online. CONCLUSIONS: Online information is usually available on news and government websites and refers to prevention. There were differences between countries in types of information available online. The highest positioned (the first 20) websites for COVID-19, Coronavirus and SARS-CoV-2 keywords returned by Google include true information.

The Effects of Climate and Bioclimate on COVID-19 Cases in Poland

The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim of the study is to identify the effects of changes in air temperature on the number of COVID-19 infections in Poland. The hypothesis under consideration concerns an increase in the number of COVID-19 cases as temperature decreases. The spatial heterogeneity of the relationship under study during the first year and a half of the COVID-19 pandemic in Polish counties is thus revealed.

COVID-19 misinformation: Accuracy of articles about coronavirus prevention mostly shared on social media.

OBJECTIVE: To analyze accuracy of articles about COVID-19 prevention most frequently shared through social media platforms. METHODS: Identifying, using the Buzzsumo analytic tool, 30 most frequently shared articles in April 2020 about COVID-19 prevention and classifying them according to number of shares, accuracy, topic and sharing platform. Calculations were made using descriptive statistics tools and chi-square test. RESULTS: The top 30 articles about coronavirus prevention were shared 4904 160 times over a period of one month with 96.8% of all shares through Facebook. Most of the articles (80%) was found to be accurate, however they accounted for only 64% of shares. The inaccuracies referred mostly to handwashing. The most shared articles were about medications followed by masks and hand washing. CONCLUSIONS: Articles about coronavirus prevention are usually accurate, yet relatively less likely to be shared than inaccurate ones. Facebook remains a dominant social media platform for sharing content. Buzzsumo could be considered a tool in certain situations such as pandemic for health authorities to quickly investigate different health topics popular on social media. LAY SUMMARY: Most of the articles about COVID-19 prevention, identified as most frequently shared through social media platform during the pandemic, was found to be accurate. However, inaccurate content was more likely to be shared than by Facebook users compared with accurate content. This suggests the need for health authorities to monitor content shared on social media in extraordinary situations such as pandemics.