Daily Numbers Of COVID-19 Cases Research Articles

Predicting COVID-19 pandemic waves including vaccination data with deep learning.

During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear. We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines. We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions. Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

Bayesian adaptive selection of basis functions for functional data representation

Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data.

Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series.

In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma2, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecastsindicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond.

Understanding the Impact of COVID-19 on the Volatility Dynamism of Brics Stock Market

The current study is aimed at examining the impact of COVID-19 on the volatility of stock market returns in the BRICS economies known as trading partners. Due to the pandemic situation all over the world, it had influence on the working of the stock markets as the investors were vigilant of their investment position. Since the World Bank declared the first confirmed case of covid-19 on 31st December 2019, hence the daily data of the stock indices of the each of the BRICS economies has been fetch ranging from 1st January 2020 to 31st march2021. The impact of Covid-19 has been studied by taking the number of daily Covid-19 cases in the respective economies. The study employed GARCH family of models such as GARCH(1,1), EGARCH(1,1) and MGARCH(1,1) in order to understand the nature and dynamics of volatility of the stock markets of BRICS economies so that investors can make decisions regarding portfolio management while investing in such economies acting as global trading partners. The study found that except China and South Africa, the rising number of cases has significant impact on BRICS economies volatility. The level of persistence is found to be high, revealing that it takes time to volatility to wipe out from the market, indicating that the BRICS stock market remained volatile for a longer period of time due to the arrival of Covid news.

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97–0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97–0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26–2.74) and 1.078 (95%CI 1.04–1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93–1.31) and 1.007 (95%CI 0.99–1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

Effects of corruption and income inequality on the reported number of COVID-19 cases and deaths: Evidence from a time series cross-sectional data analysis.

Corruption-income inequality nexus is likely to affect the healthcare services, which in turn affect a country's ability to suppress an epidemic. Widespread corruption in public sectors may influence the data inventory practices to control the recording and sharing of official statistics to avoid political disturbance or social problems caused by an epidemic. This empirical study examines the effects of income inequality, data inventory, and universal healthcare coverage on cross-country variation in reported numbers of COVID-19 cases and deaths in the presence of corruption in public sectors. Daily numbers of COVID-19 cases and deaths of selected 29 countries are integrated for the first 120 days of the epidemic in each country. COVID-19 dataset is then integrated with a dataset of different indices. Fixed effect panel model is applied to explore the effects of corruption perception, income inequality, open data inventory practice, and universal health coverage on the daily numbers of COVID-19 cases and deaths per million. Income inequality, corruption perception and open data inventory are found to significantly affect the number of confirmed cases and deaths. Countries with alarming income inequality are found to report 39.89 more COVID-19 cases per million, on average. Under a lower level of corruption, countries with lower level of open data inventory are expected to report 74.31 more COVID-19 cases but 1.43 less deaths per million. Given a higher level of corruption, countries with lower level of open data inventory are expected to report lower number of COVID-19 cases and deaths. Corruption demonstrates a significant influence on the size of the epidemic in terms of the number of COVID-19 cases and deaths. A country with higher level of corruption in public sector along with lower levels of open data inventory is expected to report lower number of COVID-19 cases and deaths.

Evaluation of the Effectiveness of Community Activities Restriction in Containing the Spread of COVID-19 in West Java, Indonesia Using Time-Series Clustering

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur.

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Recent literature has revealed a growing interest in methods for anticipating the demand for medical items and personnel at hospital, especially during turbulent scenarios such as the COVID-19 pandemic. In times like those, new variables appear and affect the once known demand behavior. This paper investigates the hypothesis that the combined Prophet-LSTM method results in more accurate forecastings for COVID-19 hospital Intensive Care Units (ICUs) demand than both standalone models, Prophet and LSTM (Long Short-Term Memory Neural Network). We also compare the model to well-established demand forecasting benchmarks. The model is tested to a representative Brazilian municipality that serves as a medical reference to other cities within its region. In addition to traditional time series components, such as trend and seasonality, other variables such as the current number of daily COVID-19 cases, vaccination rates, non-pharmaceutical interventions, social isolation index, and regional hospital beds occupation are also used to explain the variations in COVID-19 hospital ICU demand. Results indicate that the proposed method produced Mean Average Errors (MAE) from 13% to 45% lower than well established statistical and machine learning forecasting models, including the standalone models.

Air pollution and COVID-19 mortality and hospitalization: An ecological study in Iran

Exposure to air pollution can exacerbate the severe COVID-19 conditions, subsequently causing an increase in the death rate. In this study, we investigated the association between long-term exposure to air pollution and risks of COVID-19 hospitalization and mortality in Arak, Iran.Air pollution data was obtained from air quality monitoring stations located in Arak, including particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3) and carbon monoxide (CO). Daily numbers of Covid-19 cases including hospital admissions (hospitalization) and deaths (mortality) were obtained from a national data registry recorded by Arak University of Medical Sciences. A Poisson regression model with natural spline functions was applied to set the effects of air pollution on COVID-19 hospitalization and mortality.The percent change of COVID-19 hospitalization per 10 μg/m3 increase in PM2.5 and PM10 were 8.5% (95% CI 7.6 to 11.5) and 4.8% (95% CI 3 to 6.5), respectively. An increase of 10 μg/m3 in PM2.5 resulting in 5.6% (95% CI: 3.1–8.3%) increase in COVID-19 mortality. The percent change of hospitalization (7.7%, 95% CI 2.2 to 13.3) and mortality (4.5%, 95% CI 0.3 to 9.5) were positively significant per one ppb increment in SO2, while NO2, O3 and CO were inversely associated with hospitalization and mortality.Our findings strongly suggesting that a small increase in long-term exposure to PM2.5, PM10 and SO2 elevating risks of hospitalization and mortality related to COVID-19.

Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model

The rapid spread of the COVID-19 pandemic has triggered substantial economic and social disruptions worldwide. The number of infection-induced deaths in Senegal in particular and West Africa in general are minimal when compared with the rest of the world. We use count regression (statistical) models such as the generalized Waring regression model to forecast the daily confirmed COVID-19 cases in Senegal. The generalized Waring regression model has an advantage over other models such as the negative binomial regression model because it considers factors that cannot be observed or measured, but that are known to affect the number of daily COVID-19 cases. Results from this study reveal that the generalized Waring regression model fits the data better than most of the usual count regression models, and could better explain some of the intrinsic characteristics of the disease dynamics.

제주도 코로나-19 확산 패턴 분석

Researchers are able to adopt a text scrapping method to collect data from news articles when data are not available due to privacy protections. This study introduces the processes of text scrapping and analyzing texts of news articles from a local news server in Jeju-do. Since the Jeju government regularly discards the path information of COVID-19 patients, researchers who want to explore characteristics of places where a high number of confirmed cases occurred have predicaments in collecting relevant information. To overcome this challenge for social researchers, this study shows a text analysis process including pre-processing, calculating TF-IDF, creating word clouds, and conducting a word network analysis. The results from analyzing 4500 news articles confirm that there was a serial correlation between the number of daily COVID-19 cases and the number of articles and explore specific features of the places where COVID-19 patients went through. The article would help social researchers to use big data and text mining methods in order to overcome the difficulties of data collection in public administration.

Transmission of COVID-19 pandemic (Turkey) associated with short-term exposure of air quality and climatological parameters.

The study aims to investigate associations between air pollution, climate parameters, and the diffusion of COVID-19-confirmed cases in Turkey using Spearman's correlation test as an empirical methodology by Statgraphics Centurion XVI (version 16.1) and to determine the risk factors accelerating the spread of SARS-CoV-2 virus. The present study demonstrates the strong impacts of air pollutants and weather conditions on the transmission of COVID-19 morbidity. Particularly, O3 and PM10 from air quality parameters exhibited the strongest correlation with the number of daily cases in Kütahya (rs = -0.62; p < 0.05) and Sivas (rs = -0.62; p < 0.05) provinces, respectively. In meteorological parameters, rainfall showed the highest impact (rs = 0.76; p < 0.05) on the number of daily COVID-19 cases in Denizli distinct. Moreover, this study suggested that the diffusion of the novel coronavirus SARS-CoV-2 in regions with high levels of air pollution and low wind speed is dominant. To prevent the negative effects of the future pandemic crisis on public health and economic systems, manifold implications to encourage strategies to reduce air pollution in the polluted region such as being prevalent the usage of renewable energy technologies in particular electricity generation and sustainable policies such as improving the health system should be implemented by decision-makers.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-18403-4.

Correlation Analyses between Ultraviolet Radiation, Global Solar Radiation, and Metrological Variables and the COVID-19 Cases in Arid Climate

The transmission of infectious diseases is influenced by several meteorological factors. In this study, the influence of several such factors in the transmission of COVID-19 (from 26 March 2020 to 29 July 2021) in the arid weather of Riyadh, Saudi Arabia was investigated using the Spearman and Kendall rank tests. The factors considered were the average, maximum, and minimum values of air temperatures, air pressure, wind speed, relative humidity, absolute humidity, dew point temperatures, and the average values of the global solar radiation and ultraviolet radiation at bands A and B. The data on meteorological factors were obtained from the King Abdulaziz City for Science and Technology (KACST) weather station, whereas the data on the daily COVID-19 cases were obtained from the official webpage of the Saudi Arabian Ministry of Health (MOH). The results revealed that air temperature (average, minimum, and maximum) average and maximum wind speed, maximum dew point temperature, global solar radiation, and ultraviolet radiation at A and B bands are positively associated with the daily number of COVID-19 cases reported in Riyadh. However, relative humidity, atmospheric pressure (averages, minimum, and maximum) is anti-correlated with the number of daily COVID-19 cases, while absolute humidity exerts no influence. These results are in total agreement with some of the previously established studies and are either contradicted partly or totally with others conducted at several locations around the world. The results could help not only epidemiologists understand the behavior of COVID-19 against meteorological variables but also national and international organizations and healthcare policymakers devise control strategies to combat the virus.

Suppression May Improve Adaptation to Worry When Facing Uncertainty: Studying COVID-19 Pandemic.

The COVID-19 pandemic has been associated with increased uncertainty, fear and worry in everyone's life. The effect of changes in daily life has been studied widely, but we do not know how emotion-regulation strategies influence adaptation to a new situation to help them overcome worry in the face of uncertainty. Here, 1,064 self-selected Farsi speaking participants completed an online battery of questionnaires that measured fear of virus and illness, worry, intolerance of uncertainty, and emotion regulation (two subscales: reappraisal, suppression). We also documented the number of daily COVID-19 cases and deaths due to COVID-19 on the day in which participants completed the questionnaire. Our findings suggest a correlation between contamination fear and the number of daily-confirmed cases (r = 0.11), and the number of reported deaths due to COVID-19 (r = 0.09). Worry mediated the relationship between intolerance of uncertainty and fear of virus and illness (b = 0.16, 0.1141 < CI < 0.2113). In addition, suppression moderated the relationship between intolerance of uncertainty and worry (p < 0.01). Our results suggest that suppression (at least in the short term) can be an adaptive response to the worry associated with uncertainty. Suppression can reduce worry, which in turn can decrease fear of contamination and improve adaptation to social distancing requirements. Although, the observed correlations were significant, but considering the sample size, they are not strong, and they should be interpreted cautiously.

National and State Trends in Anxiety and Depression Severity Scores Among Adults During the COVID-19 Pandemic - United States, 2020-2021.

Recent studies indicate an increase in the percentage of adults who reported clinically relevant symptoms of anxiety and depression during the COVID-19 pandemic (1-3). For example, based on U.S. Census Bureau Household Pulse Survey (HPS) data, CDC reported significant increases in symptoms of anxiety and depressive disorders among adults aged ≥18 years during August 19, 2020-February 1, 2021, with the largest increases among adults aged 18-29 years and among those with less than a high school education (1). To assess more recent national trends, as well as state-specific trends, CDC used HPS data (4) to assess trends in reported anxiety and depression among U.S. adults in all 50 states and the District of Columbia (DC) during August 19, 2020-June 7, 2021 (1). Nationally, the average anxiety severity score increased 13% from August 19-31, 2020, to December 9-21, 2020 (average percent change [APC] per survey wave=1.5%) and then decreased 26.8% from December 9-21, 2020, to May 26-June 7, 2021 (APC=-3.1%). The average depression severity score increased 14.8% from August 19-31, 2020, to December 9-21, 2020 (APC=1.7%) and then decreased 24.8% from December 9-21, 2020, to May 26-June 7, 2021 (APC=-2.8%). State-specific trends were generally similar to national trends, with both anxiety and depression scores for most states peaking during the December 9-21, 2020, or January 6-18, 2021, survey waves. Across the entire study period, the frequency of anxiety and depression symptoms was positively correlated with the average number of daily COVID-19 cases. Mental health services and resources, including telehealth behavioral services, are critical during the COVID-19 pandemic.

The Need for a Paradigm Shift to Person-Centered Medicine during Pandemic Times

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has completely changed how the world looks at medicine. Unfortunately, the larger focus has been on the physical health only – epidemiology, clinical features, prevention of transmission, and management and there is very little focus on mental health and stigmatization. The 2019 coronavirus disease (COVID-19) has caused universal psychosocial impact by causing emotional disturbances, economic burden, and financial losses on a massive scale. Effects such as posttraumatic stress disorder, depression, anxiety, obsessive–compulsive symptoms, and insomnia in the post infection period have been reported among COVID-19 survivors. With disease progression, clinical symptoms become severe and infected patients may develop psychological problems. With exponential growth in the number of daily COVID-19 cases since March, 2021, India reported more than 400,000 new cases daily on May 1, 2021. India's COVID-19 surge could have become a regional disaster impacting all of south Asia. However, India has successfully avoided that disaster by strengthening the surveillance systems, imposing travel restrictions, lockdowns, and mandatory travel quarantine for individuals returning from infected areas. These were necessary to control the spread of SARS-CoV-2. The situation in India required urgent, bold measures and close cooperation between India and the global community. Currently, free vaccinations for the whole population are being given. With 1.4 billion people, this is going to be a massive effort. The pandemic and the aftermath need a paradigm shift from our traditional medical care models to one that is person centered. A person-centered model of care will be best solution here and all across the world. This is especially so, when we are fighting a disastrous pandemic.

Infodemics and its Effect on Mental Health of Medical Students of IMDC during COVID-19 Pandemic

IntroductionGlobally rising the number of daily COVID-19 cases has triggered the incredible surge in the information (and misinformation) regarding COVID-19. This misinformation was about numbers of cases, inaccurate guidelines, advertisement of unapproved treatments, and remedies as cures. The increasing prevalence of depression and anxiety was found to be linked directly to the misinformation catered from social media. The study aims to explore the mental health of medical students due to a surge in information and misinformation regarding Covid-19.Materials and MethodsThis cross-sectional study was conducted online from August 2020 to January, 2021. 227 MBBS students of IMDC (1st year to final year with age range 19-25 years) were invited to participate in an online survey through Google docs. Those who were on medication for anxiety and depression were excluded from the study. The use of the data for research purposes was explained and written consent in the first section of the online survey was given to all participants before filling the questionnaire which was divided into 3 sections. The first part was about Demographic information. The second part consisted of 5 questions about info-media use and the third part consisting of 12 questions about mental health including stress-related and behavioral changes, somatic symptoms, emotional disturbance, and cognitive functioning during the covid-19 pandemic. Ethical Approval was taken from the ethical committee of IMDC. A Chi-square test was applied to calculate the significance of qualitative variables and determine the prevalence of mental health.ResultsThe total number of students who participated in this survey were 227 (28.2% males and 71.8% females). Among types of info-media, Whats-app was used most frequently by MBBS students. Different questions were asked regarding Info-media usage and mental health. A significant association was found between gender (Both males and females) and feelings of fear and worry (p-value 0.004**), feeling socially isolated(p= 0.005**), difficulty in communicating with people (p = 0.021*), feeling confused in decision making as time progressed( 0.008**), lack of interest in studying or difficulty in concentration during this time (p 0.037*) and effect on memorizing ability (0.000)**.MBBS students who were regularly using Info-media showed a significant disturbance in mental health problems including stress-related and behavioral changes and somatic symptoms. They were found to be significantly fearful and worried (p-value 0.010*), had difficulty in communicating with people regarding covid-19(p-value 0.021*), and also felt a change in appetite-valuable 0.030*). No serious mental health problem was found related to cognitive functioning. ConclusionIn conclusion, our findings show that there is a high prevalence of mental health problems, which were positively associated with frequent use of info-media during the COVID-19 outbreak.

COVID-19 CASES, MEDIA ATTENTION AND SOCIAL MOOD

The study uses rolling regressions, both at global and country level, to analyze the impact of daily Covid-19 case numbers on four (Panic, Sentiment, Media coverage, and Fake news) indices. The indices are obtained from the Ravenpack Finance, while the daily Covid-19 cases and the policy response stringency index data is extracted from the Oxford COVID-19 Government Response Tracker. The results indicate that the impact of the number of daily Covid-19 cases on the indices is quite variable over time. Higher impact on the indices is reflected in periods where there is a significant surge in cases, in particular the initial surge in Spring, Summer and Fall. There is some evidence of diminished (increased) sensitivity of panic and media indices (fake news) to number of cases but this is not consistent across all countries. These results indicate that the public are concerned and respond to changes in the trends of the spread of the virus and highlight the importance of managing trends if halting its spread is not immediately feasible.Keywords: COVID-19, Ravenpack Indices, Media Attention, Stringency IndexJEL Classifications: G40, G41DOI: https://doi.org/10.32479/ijefi.11591

"TRUST IN PUBLIC INSTITUTIONS AND COMPLIANCE WITH MEASURES AGAINST COVID-19 PANDEMIC. CASE STUDY ON METROPOLITAN AREA OF CLUJ, ROMANIA"

The goal of this article is to analyze the level of citizens’ trust in different public institutions during the second wave of COVID-19 pandemic, and the influence of citizens’ trust on their compliance with the measures adopted to prevent the spread of the virus. The research was conducted between November and December 2020 on a sample of 700 residents of Metropolitan Area of Cluj, Romania. During the time of data collection, Romania registered the largest number of daily COVID-19 cases, therefore, citizens’ compliance with preventive measures was crucial to contain the spread of the virus. Citizens reported high levels of compliance with preventive measures. However, even though people were recommended to avoid meetings with relatives and friends, and participation to private events with large number of people, respondents reported that did not fully comply with social distancing requirements. Citizens have highest level of trust in the public institutions at local level, medical institutions and County Committees for Emergency Situations. The research found that trust in public institutions influences the compliance with preventive measures; however, the influence is weak and the trust in different institutions influences differently policy compliance.

Predicting the development trend of the second wave of COVID-19 in five European countries.

The second wave of COVID‐19 has caused a dramatic increase in COVID‐19 cases and deaths globally. An accurate prediction of its development trend is significant. We predicted the development trend of the second wave of COVID‐19 in five European countries, including France, Germany, Italy, Spain, and the UK. We first built models to predict daily numbers of COVID‐19 cases and deaths based on the data of the first wave of COVID‐19 in these countries. Based on these models, we built new models to predict the development trend of the second wave of COVID‐19. We predicted that the second wave of COVID‐19 would have peaked around on November 16, 2020, January 10, 2021, December 1, 2020, March 1, 2021, and January 10, 2021, in France, Germany, Italy, Spain, and the UK, respectively. It will be basically under control on April 26, 2021, September 20, 2021, August 1, 2021, September 15, 2021, and August 10, 2021, in these countries, respectively. Their total number of COVID‐19 cases will reach around 4,745,000, 7,890,000, 6,852,000, 8,071,000, and 10,198,000, respectively, and total number of COVID‐19 deaths will be around 262,000, 262,000, 231,000, 253,000, and 350,000 during the second wave of COVID‐19. The COVID‐19 mortality rate in the second wave of COVID‐19 is predicted to be about 3.4%, 3.5%, 3.4%, 3.4%, and 3.1% in France, Spain, Germany, France, and the UK. The second wave of COVID‐19 is expected to cause many more cases and deaths, last for a much longer time, and have a lower COVID‐19 mortality rate than the first wave.