Coronavirus (Covid-19) pandemic: How may communication strategies influence our behaviours?

The COVID-19 pandemic and seeking information about condoms online: an infodemiology approach

Objectives: As condoms are effective tools for pathogen-avoidance in sexual intercourse, seeking information about condoms online may be a reactive response to the COVID-19 according to the behavioral immune system theory.Design: Taking an infodemiology perspective, this research employed multilevel analyses to examine how COVID-19 online query data (i.e., Google topic search terms Coronavirus and COVID-19) and coronavirus epidemiological data (i.e., COVID-19 cases per million and case fatality rate) would predict condom information seeking behavior online (i.e., Google topic search term Condom) throughout the pandemic across American states (Study 1) and 102 countries/territories (Study 2), after accounting for death-thought accessibility (i.e., illness-related searches), interest in birth control (i.e., birth-control-related searches), COVID-19 control policy, stay at home behavior, season, religious holidays, yearly trends, autocorrelation, and contextual variables such as HIV prevalence rate and socioeconomic development indicators (GINI index, urbanization, etc.).Results: When there were high levels of COVID-19 concerns in cyberspace in a given week, search volume for condoms increased from the previous week across American states and different countries/territories. By contrast, the effect of actual coronavirus threat was non-significant.Conclusion: Seeking information about condoms online could be a reactive response to high levels of COVID-19 concerns across different populations.

Risk-reducing strategies and labour vulnerability during the pandemic in India

The Covid-19 health disaster has created a labour crisis. We examine the impact of the Covid-19 induced state-level direct (such as providing free food, minimum income, and transportation services for the labourers) and indirect (such as skill mapping of the return migrants and allowing extended hour shifts in the factories) risk-reducing labour strategies on urban and rural employment rates in India. These risk-reducing labour strategies secure livelihood and discourage labourers from risking their lives by joining the workplace of high interpersonal human contact during the pandemic. This reduces employment rates. Specifically, direct risk-reducing labour strategies reduce employment in urban and rural areas, while indirect risk-reducing labour strategies lessen employment only in urban areas. The mitigating effect justifies the importance of the Keynesian interventionist resilience techniques that safeguard the labourers and reduce the risks during the disaster.

Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data

Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R² of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.

Community pharmacists' perceptions and experiences of stress during COVID-19

BACKGROUND

Pharmacists play a crucial role in the COVID-19 pandemic scenario, performing frontline roles for the community, and supporting the healthcare system. This study aimed at investigating stress and its correlates among this category of workers at a high risk of SARS-CoV-2 infection.

METHODS

The participants for this study were employees of the "Municipal Pharmaceutical Company" of L'Aquila (Italy). Data were collected using an anonymous, web-based, self-administered questionnaire. Two independent surveys were conducted, from June to July 2020, and in January 2021.

RESULTS

Two separate groups of respondents were involved: a total of 37 workers participated in the first survey (mean age 44.9 ± 10.7, 75.7% women) and 18 in the second survey (mean age 45.9 ± 9.2, 94.4% women). The average total scores of the perceived stress (GHQ-12 score) increased significantly from 15.5 to 18.2 (p = 0.0438), showing a moderate level of stress in the category investigated.

CONCLUSIONS

We observed a strong emotional exhaustion in the study sample of pharmacists, who reported high-risk perceptions and fear. A comprehensive assistance should be granted to support the well-being of healthcare workers who provide an essential service, despite the high risk of infection.

Does Quality of Care (QoC) Perception Influence the Quality of Life (QoL) in Women with Endometriosis? Results from an Italian Nationwide Survey during Covid Pandemic

(1) Background: Endometriosis is a chronic and progressive illness that generates a slew of issues, lowering the quality of life of women. The purpose of this study was to look at the quality of life in women with endometriosis and how it relates to the quality of care. (2) Methods: This study is an online survey performed in Italy during the COVID pandemic using the Italian version of the Health Questionnaire SF-36 and a questionnaire for assessing the quality of care received. (3) Results: 1052 women with a self-reported diagnosis of endometriosis participated in the survey. The mean levels of Physical Component Summary (PCS) and Mental Component Summary (MCS) were 38.89 ± 10.55 and 34.59 ± 11.17, respectively. A total of 77% of women judged the services they received positively, and 51% considered the coordination between healthcare professionals to be satisfactory. The satisfaction index mean was 23.11 ± 4.80. PCS was positively related to Occupation, high educational level, physical activity, and health care satisfaction. MCS was positively related to higher age, physical activity, and health care satisfaction. (4) Conclusions: The study indicated that satisfaction with health care was a significant predictor of QoL in women with endometriosis, for both physical and mental health.

Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review

The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was "coronavirus" (53.7%), followed by "COVID-19" (31.5%) and "COVID" (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.

E-Learning Satisfaction, Stress, Quality of Life, and Coping: A Cross-Sectional Study in Italian University Students a Year after the COVID-19 Pandemic Began

(1) Background: The objective was to investigate e-learning satisfaction in a sample of university students by evaluating quality of life, stress sociality, and coping. (2) Methods: This was an online survey involved 471 students attending the University of L′Aquila from June to July 2021. The primary goal was estimating the e-learning satisfaction as measured by the E-learning Satisfaction Scale, while the secondary outcomes were studying its relationship with demographic factors, the perception of quality of life, sociality, stress, and coping strategies using a hierarchical regression model. (3) Results: A total of 136 participants were males (29%); the mean age was 25 years. The results revealed that the satisfaction score was 30.6, and the mean stress level was 19.4. Students suffered psychologically and physically for 14 days a month. The sociality score was 36. With respect to coping strategies, students reported higher scores for “Acceptance” (6.0), “Active coping” (6.2), and “Planning” (6.4). E-learning satisfaction was significantly related to age and course attendance. It was positively associated with the social presence score and coping strategies such as self-blame and religion, while it was inversely related to stress and unhealthy days. (4) Conclusions: The students revealed a positive propensity to use e-learning despite the end of quarantine. Sociality, stress, quality of life, and coping seemed to play an important role in student′s e-learning satisfaction.

The heterogeneous regional effect of mobility on Coronavirus spread

The Coronavirus (COVID-19) pandemic struck global society in 2020. The pandemic required the adoption of public policies to control spread of the virus, underlining the mobility restrictions. Several studies show that these measures have been effective. Within the topic of Coronavirus spread, this original paper analyses the effect of mobility on Coronavirus spread in a heterogeneous regional context. A multiple dynamic regression model is used to control sub-national disparities in the effect of mobility on the spread of the Coronavirus, as well as to measure it at the context of Spanish regions. The model includes other relevant explanatory factors, such as wind speed, sunshine hours, vaccinated population and social awareness. It also develops a new methodology to optimise the use of Google trends data. The results reveal heterogeneity among regions, which has important implications for current and future pandemic containment strategies.

Accuracy and Timeliness of Knowledge Dissemination on COVID-19 Among People in Rural and Remote Regions of China at the Early Stage of Outbreak

Coronavirus disease 2019 (COVID-19) spread throughout China in January 2020. To contain the virus outbreak, the Chinese government took extraordinary measures in terms of public policy, wherein accurate and timely dissemination of information plays a crucial role. Despite all of the efforts toward studying this health emergency, little is known about the effectiveness of public policies that support health communication during such a crisis to disseminate knowledge for self-protection. Particularly, we focus on the accuracy and timeliness of knowledge dissemination on COVID-19 among people in remote regions—a topic largely omitted in existing research. In February 2020, at the early-stages of the COVID-19 outbreak, a questionnaire survey was carried out. In total, 8,520 participants from seven less economically developed provinces situated in the borderlands of China with large ethnic minority groups responded. We analyzed the data through poisson regression and logistic regression analyses. We found that (1) people in remote regions of China obtained accurate information on COVID-19. Further, they were able to take appropriate measures to protect themselves. (2) Result from both descriptive analysis and multivariable regression analysis revealed that there is no large difference in the accuracy of information among groups. (3) Older, less educated, and rural respondents received information with a significant delay, whereas highly educated, younger, urban residents and those who obtained information through online media were more likely to have received the news of the outbreak sooner and to be up to date on the information. This research provides evidence that disadvantage people in remote regions obtained accurate and essential information required to act in an appropriate manner in responses to the COVID-19 outbreak. However, they obtained knowledge on COVID-19 at a slower pace than other people; thus, further improvement in the timely dissemination of information among disadvantage people in remote regions is warranted.

Quality of Life among Pediatric Neurocognitive, Speech, and Psychomotor Rehabilitation Professionals during the COVID-19 Pandemic: A Longitudinal Study on an Italian Sample

Objective: The aim was to estimate the perceived quality of life and its relationship with sociodemographic and professional factors, perception of susceptibility to COVID-19, and stress. Design: It was a longitudinal study. Subjects: Professionals, working in Italian centers for pediatric neurocognitive, speech, and psychomotor rehabilitation. Methods: Participants were interviewed online twice during the COVID-19 outbreak in Italy. The questionnaire included: (i) The measures of health-related quality of life to perform the Summary Index of Unhealthy Days, (ii) modified items from the “Standard questionnaire on risk perception of an infectious disease outbreak” and (iii) the items of the General Health Questionnaire. Results: One hundred and thirty professionals out of 130 participated in the first interview, while only 50 therapists took part in the second interview (dropout rate: 61%). The Summary Index of Unhealthy Days was 8 days at the first interview, and it decreased to 6 days at the second interview; however, the reduction was not significant (F = 3.22; p = 0.079). The multivariable analysis showed that the rehabilitation providers with moderate or severe stress level were more likely to have a negative perception of the quality of life (ORadj = 7.155; 95% CI: 2.8–18.2), and this result was confirmed at the second interview. Conclusions: Our results showed that in a severe public health emergency, the mental health and quality of life of rehabilitation professionals must be a topic of focus to enhance psychological resilience, to prevent burnout and to reduce rehabilitation errors.

COVID-19 concerns in cyberspace predict human reduced dispersal in the real world: Meta-regression analysis of time series relationships across American states and 115 countries/territories

On the basis of parasite-stress theory of sociality and behavioral immune system theory, this research examined how concerns regarding the Coronavirus disease 2019 (COVID-19) in cyberspace (i.e., online search volume for coronavirus-related keywords) would predict human reduced dispersal in the real world (i.e., human mobility trends throughout the pandemic) between January 05, 2020 and May 22, 2021. Multiple regression analyses controlling for COVID-19 cases per million, case fatality rate, death-thought accessibility, government stringency index, yearly trends, season, religious holidays, and reduced dispersal in the preceding week were conducted. Meta-regression analysis of the multiple regression results showed that when there were high levels of COVID-19 concerns in cyberspace in a given week, the amount of time people spent at home increased from the previous week across American states (Study 1) and 115 countries/territories (Study 2). Across studies, the associations between COVID-19 concerns and reduced dispersal were stronger in areas of higher historical risks of infectious-disease contagion. Compared with actual coronavirus threat, COVID-19 concerns in cyberspace had significantly larger effects on predicting human reduced dispersal in the real world. Thus, online query data have invaluable implications for predicting large-scale behavioral changes in response to life-threatening events in the real world and are indispensable for COVID-19 surveillance.

The COVID-19 Pandemic in Brazil: Some Aspects and Tools

The article presents some aspects related to the COVID-19 pandemic in Brazil including public health, challenges facing healthcare workers and adverse impacts on the country's economy. Its main contribution is the availability of two web applications for online monitoring of the evolution of the pandemic in Brazil and South America. The applications provide the possibility to download data in different formats, view interactive maps and graphs of the cumulative confirmed cases, deaths and lethality rates, in addition to presenting plots of moving averages for states and municipalities. The predictions about new cases and new deaths caused by COVID-19, in states and regions of Brazil, are also reported using GAMLSS models. The forecasts can be easily used by public managers for effective decision-making.

Exploring the use of web searches for risk communication during COVID-19 in Germany

Risk communication during pandemics is an element of utmost importance. Understanding the level of public attention—a prerequisite for effective communication—implicates expensive and time-consuming surveys. We hypothesise that the relative search volume from Google Trends could be used as an indicator of public attention of a disease and its prevention measures. The search terms ‘RKI’ (Robert Koch Institute, national public health authority in Germany), ‘corona’ and ‘protective mask’ in German language were shortlisted. Cross-correlations between these terms and the reported cases from 15 February to 27 April were conducted for each German federal state. The findings were contrasted against a timeline of official communications concerning COVID-19. The highest correlations of the term ‘RKI’ with reported COVID-19 cases were found between lags of − 2 and − 12 days, meaning web searches were already performed from 2 to 12 days before case numbers increased. A similar pattern was seen for the term ‘corona’. Cross-correlations indicated that most searches on ‘protective mask’ were performed from 6 to 12 days after the peak of cases. The results for the term ‘protective mask’ indicate a degree of confusion in the population. This is supported by conflicting recommendations to wear face masks during the first wave. The relative search volumes could be a useful tool to provide timely and location-specific information on public attention for risk communication.