Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

• MeSH
• behaviorální vědy * metody   trendy   MeSH
• COVID-19 * epidemiologie   etnologie   prevence a kontrola   MeSH
• komunikace MeSH
• kultura MeSH
• lékařská praxe založená na důkazech * metody   MeSH
• lidé MeSH
• pandemie * prevence a kontrola   MeSH
• sociální normy MeSH
• veřejné zdravotnictví metody   trendy   MeSH
• vůdcovství MeSH
• vytváření politiky * MeSH
• zdravotní politika * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The COVID-19 pandemic in the first months of 2020 posed an unprecedented threat to the health of the world's population. In this longitudinal design study, we elaborated the typology of 27 European countries based on the complete beginnings of the ongoing COVID-19 pandemic based on health indicators and contextual variables. Two-step analysis using factor scores to run a cluster analysis identifying 5 consistent groups of countries. We then analyze the relationship between the GHS predictive index, the restrictions and health care expenditures within countries categorized into 5 clusters. An analysis of the early stages of a pandemic confirmed that in countries where anti-pandemic measures were rapidly and consistently in place, the spread of the virus was suppressed more rapidly and the first wave of pandemics in these countries was incomparably more benign than in countries with later responses and milder restrictive measures.

• Klíčová slova
• COVID-19 pandemic, European countries, Factory and cluster analyses, GHS predictive Index, Suppression and mitigation, Typology,
• Publikační typ
• časopisecké články MeSH

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

• Klíčová slova
• ANFIS, Adaptive Network-based Fuzzy Inference System, ANN, Artificial Neural Network, AU, Australia, Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory, Bi-GRU, Bidirectional Gated Recurrent Unit, Bi-LSTM, Bidirectional Long Short-Term Memory, Bidirectional, COVID-19 Prediction, COVID-19, Coronavirus Disease 2019, Conv-LSTM, Convolutional Long Short Term Memory, Convolutional Long Short Term Memory (Conv-LSTM), DL, Deep Learning, DLSTM, Delayed Long Short-Term Memory, Deep learning, EMRO, Eastern Mediterranean Regional Office, ES, Exponential Smoothing, EV, Explained Variance, GRU, Gated Recurrent Unit, Gated Recurrent Unit (GRU), IR, Iran, LR, Linear Regression, LSTM, Long Short-Term Memory, Lasso, Least Absolute Shrinkage and Selection Operator, Long Short Term Memory (LSTM), MAE, Mean Absolute Error, MAPE, Mean Absolute Percentage Error, MERS, Middle East Respiratory Syndrome, ML, Machine Learning, MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation, MSE, Mean Square Error, MSLE, Mean Squared Log Error, Machine learning, New Cases of COVID-19, New Deaths of COVID-19, PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses, RMSE, Root Mean Square Error, RMSLE, Root Mean Squared Log Error, RNN, Repetitive Neural Network, ReLU, Rectified Linear Unit, SARS, Serious Intense Respiratory Disorder, SARS-COV, SARS coronavirus, SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2, SVM, Support Vector Machine, VAE, Variational Auto Encoder, WHO, World Health Organization, WPRO, Western Pacific Regional Office,
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The numbers of coronavirus disease 2019 (COVID-19) deaths per million people differ widely across countries. Often, the causal effects of interventions taken by authorities are unjustifiably concluded based on the comparison of pure mortalities in countries where interventions consisting different strategies have been taken. Moreover, the possible effects of other factors are only rarely considered. METHODS: We used data from open databases (European Centre for Disease Prevention and Control, World Bank Open Data, The BCG World Atlas) and publications to develop a model that could largely explain the differences in cumulative mortality between countries using non-interventional (mostly socio-demographic) factors. RESULTS: Statistically significant associations with the logarithmic COVID-19 mortality were found with the following: proportion of people aged 80 years and above, population density, proportion of urban population, gross domestic product, number of hospital beds per population, average temperature in March and incidence of tuberculosis. The final model could explain 67% of the variability. This finding could also be interpreted as follows: less than a third of the variability in logarithmic mortality differences could be modified by diverse non-pharmaceutical interventions ranging from case isolation to comprehensive measures, constituting case isolation, social distancing of the entire population and closure of schools and borders. CONCLUSIONS: In particular countries, the number of people who will die from COVID-19 is largely given by factors that cannot be drastically changed as an immediate reaction to the pandemic and authorities should focus on modifiable variables, e.g. the number of hospital beds.

• MeSH
• COVID-19 mortalita   MeSH
• dospělí MeSH
• HIV infekce epidemiologie   MeSH
• hrubý domácí produkt MeSH
• hustota populace MeSH
• incidence MeSH
• komorbidita MeSH
• kouření epidemiologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• městské obyvatelstvo statistika a číselné údaje   MeSH
• mladiství MeSH
• nadváha epidemiologie   MeSH
• obložnost MeSH
• pandemie prevence a kontrola   MeSH
• poskytování zdravotní péče organizace a řízení   MeSH
• prevalence MeSH
• SARS-CoV-2 * MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• socioekonomické faktory MeSH
• teplota MeSH
• tuberkulóza epidemiologie   MeSH
• věkové rozložení MeSH
• veřejné zdravotnictví MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH

Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are specific to the coronavirus crisis and correspond to phrases related to nonpharmaceutical intervention policies to fight physical contagion. We show that during this period, fear of the coronavirus - manifested as excess search volume - represents a timely and valuable data source for forecasting stock price variation around the world.

• Klíčová slova
• Coronavirus, Google Trends, Panic, Stock market, Uncertainty,
• Publikační typ
• časopisecké články MeSH

Patients with multiple myeloma (MM) seem to be at increased risk for more severe COVID-19 infection and associated complications due to their immunocompromised state, the older age and comorbidities. The European Myeloma Network has provided an expert consensus statement in order to guide therapeutic decisions in the era of the COVID-19 pandemic. Patient education for personal hygiene and social distancing measures, along with treatment individualization, telemedicine and continuous surveillance for early diagnosis of COVID-19 are essential. In countries or local communities where COVID-19 infection is widely spread, MM patients should have a PCR test of nasopharyngeal swab for SARS-CoV-2 before hospital admission, starting a new treatment line, cell apheresis or ASCT in order to avoid ward or community spread and infections. Oral agent-based regimens should be considered, especially for the elderly and frail patients with standard risk disease, whereas de-intensified regimens for dexamethasone, bortezomib, carfilzomib and daratumumab should be used based on patient risk and response. Treatment initiation should not be postponed for patients with end organ damage, myeloma emergencies and aggressive relapses. Autologous (and especially allogeneic) transplantation should be delayed and extended induction should be administered, especially in standard risk patients and those with adequate MM response to induction. Watchful waiting should be considered for standard risk relapsed patients with low tumor burden, and slow biochemical relapses. The conduction of clinical trials should continue with appropriate adaptations to the current circumstances. Patients with MM and symptomatic COVID-19 disease should interrupt anti-myeloma treatment until recovery. For patients with positive PCR test for SARS-CoV-2, but with no symptoms for COVID-19, a 14-day quarantine should be considered if myeloma-related events allow the delay of treatment. The need for surveillance for drug interactions due to polypharmacy is highlighted. The participation in international COVID-19 cancer registries is greatly encouraged.

• MeSH
• Betacoronavirus izolace a purifikace   MeSH
• čas zasáhnout při rozvinutí nemoci statistika a číselné údaje   MeSH
• COVID-19 MeSH
• kontrola infekce metody   MeSH
• koronavirové infekce epidemiologie   prevence a kontrola   přenos   virologie   MeSH
• lidé MeSH
• management nemoci MeSH
• mnohočetný myelom terapie   virologie   MeSH
• pandemie prevence a kontrola   MeSH
• SARS-CoV-2 MeSH
• směrnice pro lékařskou praxi jako téma normy   MeSH
• telemedicína * MeSH
• virová pneumonie epidemiologie   prevence a kontrola   přenos   virologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH