Data analytics to evaluate the impact of infectious disease on economy: Case study of COVID-19 pandemic

Investigation of the effects of temperature and relative humidity on the propagation of COVID-19 in different climatic zones

This study aims to evaluate the effects of temperature and relative humidity on the propagation of COVID-19 for indoor heating, ventilation, and air conditioning design and policy development in different climate zones. We proposed a cumulative lag model with two specific parameters of specific average temperature and specific relative humidity to evaluate the impact of temperature and relative humidity on COVID-19 transmission by calculating the relative risk of cumulative effect and the relative risk of lag effect. We considered the temperature and relative humidity corresponding to the relative risk of cumulative effect or the relative risk of lag effect equal to 1 as the thresholds of outbreak. In this paper, we took the overall relative risk of cumulative effect equal to 1 as the thresholds. Data on daily new confirmed cases of COVID-19 since January 1, 2021, to December 31, 2021, for three sites in each of four climate zones similar to cold, mild, hot summer and cold winter, and hot summer and warm winter were selected for this study. Temperature and relative humidity had a lagged effect on COVID-19 transmission, with peaking the relative risk of lag effect at a lag of 3-7 days for most regions. All regions had different parameters areas with the relative risk of cumulative effect greater than 1. The overall relative risk of cumulative effect was greater than 1 in all regions when specific relative humidity was higher than 0.4, and when specific average temperature was higher than 0.42. In areas similar to hot summer and cold winter, temperature and the overall relative risk of cumulative effect were highly monotonically positively correlated. In areas similar to hot summer and warm winter, there was a monotonically positive correlation between relative humidity and the overall relative risk of cumulative effect. This study provides targeted recommendations for indoor air and heating, ventilation, and air conditioning system control strategies and outbreak prevention strategies to reduce the risk of COVID-19 transmission. In addition, countries should combine vaccination and non-pharmaceutical control measures, and strict containment policies are beneficial to control another pandemic of COVID-19 and similar viruses.

Can Stringent Government Initiatives Lead to Global Economic Recovery Rapidly during the COVID-19 Epidemic?

This paper investigates the effectiveness of government measures implemented against COVID-19 and the factors influencing a country's economic growth from a global perspective. With the help of the data of the Government Response Stringency Index (GRSI), Google mobility, and confirmed COVID-19 daily cases, we conducted a panel model for 105 countries and regions from 11 March 2020 to 31 June 2021 to explore the effects of response policies in different countries against the pandemic. First, the results showed that staying in residential places had the strongest correlation with confirmed cases. Second, in countries with higher government stringency, stay-at-home policies carried out in the early spread of the pandemic had the most effective the impact. In addition, the results have also been strictly robustly analyzed by applying the propensity score matching (PSM) method. Third, after reconstructing a panel data of 47 OECD countries, we further concluded that governments should take stricter restrictive measures in response to COVID-19. Even though it may also cause a shock to the market in the short term, this may not be sustainable. As long as the policy response is justified, it will moderate the negative effect on the economy over time, and finally have a positive effect.

Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review

Aim

The use of information-based solutions such as dashboards is on the rise for taking fact-based actions against the COVID-19 crisis. This scoping review aimed to comprehensively investigate COVID-19 dashboards from different technical perspectives.

Subject and methods

Three main bibliographic databases, PubMed, Web of Science, and Scopus, were searched on 28 August 2021 to retrieve relevant studies. Arksey and O'Malley's (Int J Soc Res Methodol 8(1):19-32, 2005) methodological framework and the enhanced version of this methodology developed by Levac et al. (Implement Sci 5(1):1-9, 2010) were adopted for conducting this review.

Results

In total, 26 articles were included. The COVID-19 dashboards mainly focused on the infected (n = 25), deceased (n = 17), and recovered cases (n = 13), as well as the performed test (n = 10). Most of the dashboards were interactive, with public accessibility targeting various user groups. While some dashboards were both informative and supportive (38%), most were mainly informative (92%). The dashboard data were generally analyzed using simple techniques (58%) and delivered through web-based applications (88%).

Conclusion

Dashboards can help immediately manage, analyze, and summarize a huge amount of information about a COVID-19 outbreak. The findings revealed that the developed COVID-19 dashboards share more or less analogous characteristics that could lay the groundwork for designing and developing dashboards for any other pandemic.

The sources of the Kuznets relationship between the COVID-19 mortality rate and economic performance

This paper discusses the findings of an empirical analysis of the Kuznets, or reverse U-shaped relationship, between the COVID-19 mortality rate and economic performance. In the early stages of economic development, the COVID-19 mortality rate is anticipated to rise with rising economic activity and urbanization. Eventually, the mortality rate decreases at higher economic development levels as people and the government are more capable of investing in disease abatement measures. The quality of political institutions, wealth distribution, urbanization, vaccination rate, and improvements in healthcare systems are hypothesized to affect the COVID-19 mortality rate. Examining this relationship can be effective in understanding the change in the COVID-19 mortality rate at different economic performance stages and in identifying appropriate preventive measures. This study employed the negative binomial regression to model a cross-sectional dataset of 137 countries. Results indicated that the relationship between the per-head gross domestic product (GDP) level and the COVID-19 mortality rate appeared to follow a pattern like the Kuznets curve, implying that changes in institutional quality, healthcare advancements, wealth distribution, urbanization, vaccination rate, and the percentage of the elderly population were significant in explaining the relationship. Improvement of the healthcare system has a notable effect on lowering the COVID-19 mortality rate under more effective government conditions. Additionally, the results suggested that a higher per-head GDP is required to reverse the rising trend of the mortality rate under higher income inequality. Based on these results, preventive measures, and policies to reduce COVID-19 mortalities were recommended in the conclusion section.

Public Attitude towards Nuclear and Renewable Energy as a Factor of Their Development in a Circular Economy Frame: Two Case Studies

Nowadays, most countries of the world are implementing the transition to the low-carbon economy which implies the need to carry out a full-scale eco-modernization of the energy sector. Green energy may be identified as one of the core concerns of energy sector modernization as it allows a considerable decrease in emissions of harmful substances into the atmosphere. Therefore, nuclear and renewable energy may become key areas of global energy development in the near future, which is also in agreement with circular economy concepts. However, public opinion (and other controversial visions/aspects) is one of the barriers to their development. The purpose of this study is to analyze the relationship between attitudes towards nuclear and renewable energy in two countries: a EU country (Italy) and a non-EU country (Russia), considering the level of their development. The authors conducted a survey among residents regarding their attitude towards nuclear and renewable energy, as well as their attitude to the present energy policy. The cluster analysis technique was used to analyze the results. The obtained results confirmed the dependence between the level of development of nuclear and renewable energy and the public attitude towards it. The national energy policy also might influence public opinion on the development of nuclear or renewable energy. The authors identified public attitude as one of the key factors in the development of energy and the achievement of environmental and social sustainability.

Slovak parents' mental health and socioeconomic changes during the COVID-19 pandemic

The changes in people's mental health have become one of the hot topics during the COVID-19 pandemic. Parents have been said to be among the most vulnerable groups in terms of the imposed anti-pandemic measures. The present paper analyzes the trends in mental health indicators in a sample of Slovak parents (N = 363) who participated in four waves of data collection over a year and a half of the COVID-19 pandemic. The mental health indicators were represented by general levels of depression and anxiety as well as COVID-related stress and anxiety. While there were only minor changes in depression and anxiety, the dynamic in COVID-related stress and especially anxiety was more noteworthy. Besides some exceptions, the results hold even after controlling for the socioeconomic situation. The gender differences in the mental health trends were found to be negligible. Overall, we observed no substantial deterioration in the mental health indicators across the four waves of the study.

DeepCOVIDNet: Deep Convolutional Neural Network for COVID-19 Detection from Chest Radiographic Images

The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic that has infected millions of people causing millions of deaths around the world. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the standard screening method for COVID-19 detection but it requires specific molecular-biology training. Moreover, the general workflow is difficult e.g. sample collection, processing time, and analysis expertise, etc. Chest radiographic image analysis can be a good alternative screening method that is faster, more efficient, and requires minimal clinical or molecular biology trained laboratory personnel. Early studies have shown that abnormalities on the chest radiographic images are likely to be the consequence of COVID-19 infection. In this study, we propose DeepCOVIDNet, a deep learning based COVID-19 detection model. Our proposed deep-learning model is a multiclass classifier that can distinguish COVID-19, viral pneumonia, bacterial pneumonia, and healthy chest X-ray images. Our proposed model classifies radiographic images into four distinct classes and achieves the accuracy of 89.47% along with a high degree of precision, recall and F1 score. On a different dataset setting (COVID-19, bacterial pneumonia, viral pneumonia) our model achieves the maximum accuracy of 98.25%. We demonstrate generalizability of our proposed method using 5-fold cross validation for COVID-19 vs pneumonia and COVID-19 vs healthy classification that also manifests promising results.