The second worldwide wave of interest in coronavirus since the COVID-19 outbreaks in South Korea, Italy and Iran: A Google Trends study

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

BACKGROUND

Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations.

METHOD

This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10-90 and 25-75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants.

RESULT

The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance.

CONCLUSION

Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Efficacy and safety of ultra-short wave diathermy on COVID-19 pneumonia: a pioneering study

Background

The ultra-short wave diathermy (USWD) is widely used to ameliorate inflammation of bacterial pneumonia, however, for COVID-19 pneumonia, USWD still needs to be verified. This study aimed to investigate the efficacy and safety of USWD in COVID-19 pneumonia patients.

Methods

This was a single-center, evaluator-blinded, randomized controlled trial. Moderate and severe COVID-19 patients were recruited between 18 February and 20 April 2020. Participants were randomly allocated to receive USWD + standard medical treatment (USWD group) or standard medical treatment alone (control group). The negative conversion rate of SARS-CoV-2 and Systemic Inflammatory Response Scale (SIRS) on days 7, 14, 21, and 28 were assessed as primary outcomes. Secondary outcomes included time to clinical recovery, the 7-point ordinal scale, and adverse events.

Results

Fifty patients were randomized (USWD, 25; control, 25), which included 22 males (44.0%) and 28 females (56.0%) with a mean (SD) age of 53 ± 10.69. The rates of SARS-CoV-2 negative conversion on day 7 (p = 0.066), day 14 (p = 0.239), day 21 (p = 0.269), and day 28 (p = 0.490) were insignificant. However, systemic inflammation by SIRS was ameliorated with significance on day 7 (p = 0.030), day 14 (p = 0.002), day 21 (p = 0.003), and day 28 (p = 0.011). Time to clinical recovery (USWD 36.84 ± 9.93 vs. control 43.56 ± 12.15, p = 0.037) was significantly shortened with a between-group difference of 6.72 ± 3.14 days. 7-point ordinal scale on days 21 and 28 showed significance (p = 0.002, 0.003), whereas the difference on days 7 and 14 was insignificant (p = 0.524, 0.108). In addition, artificial intelligence-assisted CT analysis showed a greater decrease in the infection volume in the USWD group, without significant between-group differences. No treatment-associated adverse events or worsening of pulmonary fibrosis were observed in either group.

Conclusion

Among patients with moderate and severe COVID-19 pneumonia, USWD added to standard medical treatment could ameliorate systemic inflammation and shorten the duration of hospitalization without causing any adverse effects.Clinical Trial Registration: chictr.org.cn, identifier ChiCTR2000029972.

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends

Introduction

Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system.

Methods

The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence.

Results

Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche.

Conclusion

Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Global monitoring of public interest in preventive measures against COVID-19 via analysis of Google Trends: an infodemiology and infoveillance study

OBJECTIVES

The COVID-19 pandemic has influenced people's concerns regarding infectious diseases and their preventive measures. However, the magnitude of the impact and the difference between countries are unclear. This study aimed to assess the magnitude of the impact of COVID-19 on public interest and people's behaviours globally in preventing infectious diseases while comparing international trends and sustainability.

DESIGN

An infodemiology and infoveillance study.

SETTING

The study employed a web-based data collection to delineate public interest regarding COVID-19 preventive measures using Google Trends.

PRIMARY AND SECONDARY OUTCOME MEASURES

A relative search volume was assigned to a keyword, standardising it from 0 to 100, with 100 representing the highest share of the term searches. The search terms "coronavirus", "wash hand", "social distancing", "hand sanitizer" and "mask" were investigated across 196 different countries and regions from July 2018 to October 2021 and weekly reports of the relative search volume were obtained. Persistence of interest was assessed by comparing the first 20 weeks with the last 20 weeks of the study period.

RESULTS

Although the relative search volume of "coronavirus" increased and was sustained at a significantly higher level (p<0.05) than before the pandemic declaration, globally, the trends and sustainability of the interest in preventable measures against COVID-19 varied between countries and regions.

CONCLUSIONS

Sustained interest in preventive measures differed globally, with regional differences noted among Asia, Europe, Africa and the Americas. The global differences should be considered for implementing effective interventions against COVID-19. The increased interest in preventive behaviours against COVID-19 may be related to overall infectious disease prevention.

Influence of the COVID-19 pandemic measures on incidence and representation of other infectious diseases in Germany: a lesson to be learnt

Aim

The COVID-19 pandemic resulted in a wide range of serious health, social and economic consequences. To counteract the pandemic, various measures and restrictions such as lockdowns, closures, social distancing, hygiene, and protective measures such as wearing face masks have been enforced. Apart from the COVID-19 pandemic, these measures also had effects on other transmittable diseases. This study therefore determined the impact on case numbers and interest for other infectious diseases as well.

Subject and methods

Anonymized data on reported case numbers from the German Robert Koch Institute and data from Google Trends about the search interest have been used in this study to track courses of infectious diseases before and during the coronavirus pandemic in Germany.

Results

The results of this analysis clearly demonstrated that the case numbers of influenza, whooping cough, measles, mumps, scarlet fever and chicken pox decreased in the pandemic years, most probably due to anti-pandemic measures in Germany. Additionally, the Google Trends analysis demonstrated public awareness, documented by a corresponding search interest, for the new topic COVID-19 and for other infectious diseases.

Conclusion

Online available data provided valuable sources for research purposes in infodemiology or infoveillance.

Representation of long COVID syndrome in the awareness of the population is revealed by Google Trends analysis

In some COVID-19 patients, symptoms persist for several weeks and sometimes, after the acute disease phase, these patients develop new symptoms, which then represents a transition into the so-called long COVID. The exact demarcation of the terms and generally applicable definitions are still discussed, but the phenomenon is most commonly referred to as long COVID.

In this study, Google Trends data have been used to track levels of public awareness for long COVID and some important symptoms during the course of the COVID-19 pandemic.

The results of this analysis clearly demonstrate the public interest in the new topic of long COVID, as documented by a corresponding search volume. This is related to the disease COVID-19, which is being spread by the corona pandemic. Relevant symptoms for COVID-19 or long COVID, for example ageusia and anosmia, only started to receive more public attention during the pandemic.

Therefore, Google Trends is a useful tool to demonstrate the population's awareness of certain infodemiological topics like long COVID.

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Long COVID plays a non-negligible role in the information needs, as evidenced by the growing search interest during the pandemic.

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Symptoms ageusia and anosmia became relatively well-known and relevant during the pandemic.

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Google Trends is a useful tool, well suited for demonstrating the population's awareness of certain infodemiological topics.

The supply and demand of news during COVID-19 and assessment of questionable sources production

Misinformation threatens our societies, but little is known about how the production of news by unreliable sources relates to supply and demand dynamics. We exploit the burst of news production triggered by the COVID-19 outbreak through an Italian database partially annotated for questionable sources. We compare news supply with news demand, as captured by Google Trends data. We identify the Granger causal relationships between supply and demand for the most searched keywords, quantifying the inertial behaviour of the news supply. Focusing on COVID-19 news, we find that questionable sources are more sensitive than general news production to people's interests, especially when news supply and demand mismatched. We introduce an index assessing the level of questionable news production solely based on the available volumes of news and searches. We contend that these results can be a powerful asset in informing campaigns against disinformation and providing news outlets and institutions with potentially relevant strategies.

An Effective Protocol for Management of International Arrivals at Risk in COVID-19 Pandemic: Experience From the Pre-Hospital Covid-19 Response Teams at Xi'an, China

Background

The coronavirus disease 2019 (COVID-19) outbreak within China has been well controlled and stabilized since early April 2020. Therefore, the current major focus in China is to prevent the introduction of COVID into China from international arrivals. To achieve this, pre-Hospital COVID-19 Response Teams (pHCRTs) have been established.

Context

The pHRCTs were established in Xi'an, China in early 2020. During the 12 months covered in this report, there were 356 international flight arrivals with over 5,000 COVID-19 Nucleic Acid Test (NAT) positive people, 500 of them with symptomatic COVID-19 and requiring admission to special hospitals. All other arrivals were managed in dedicated facilities by pHRCTs. The outcome measure of this report was the number of positive cases among the pHRCT members.

Details

Four hundred forty-two staff worked in the pHCRTs during the reporting period. Despite multiple throat swab PCR tests during their pHRCTs tour of duty, and the subsequent mandatory 14-day quarantine required before return to the general community, no staff became NAT positive.

Conclusion

The prevention of community transmission from imported cases is a vital part of the strategy to maintain the low numbers of cases in countries which have achieved control, or suppression of local internal cases. The program of pHCRTs described in this article gives successful protocols for transportation of patients who are infectious based on the minimal transmission of virus and staff safety. The strategies employed may prove useful in future pandemics.

Interest in dentistry in early months of the COVID-19 global pandemic: A Google Trends approach

Background

In early the COVID‐19 pandemic, routine dental treatments have been delayed due to the risk of disease transmission. This delay may lead public to search for information on the Internet for a solution.

Objectives

This study aims to evaluate the public interest in dentistry in the early months of the COVID‐19 global pandemic in the selected countries.

Methods

The daily numbers of new COVID‐19 cases were recorded for China, South Korea, Italy, Germany, Russia, Ukraine and Turkey. For these countries, Internet search interest of the keyword ‘dentistry’, ‘coronavirus’, ‘COVID‐19’, ‘SARS‐CoV‐2’ and ‘pandemic’ in the early months of the COVID‐19 pandemic was evaluated by using Google Trends data.

Results

In most countries included the public Internet search interest in ‘dentistry+coronavirus+COVID‐19+SARS‐CoV‐2+pandemic’ peaked prior to the peak of new COVID‐19 cases. While a statistically significant positive correlation was observed between the number of new cases and Google Trends data in China, South Korea, Italy and Germany, a statistically significant negative correlation was observed in Turkey.

Conclusion

The peak public interest in dentistry has been prior to the peak of COVID‐19 new cases in most countries. The use of Internet data can provide useful information about pandemics and many other diseases.

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.

Evaluation of COVID-19 trend in Iran; Population response to the recent pandemic overtime

Relative internet search volumes (RSV) is now being consider as a measurement of awareness for most of the trending topics. During the recent coronavirus disease (COVID-19) outbreak, many researchers used the RSVs to interpret the population responses to the pandemic in various ways. By using the RSVs searched by Persian language people, we demonstrated that the Iranian people increased their knowledge and awareness of COVID-19 during the early phases of the disease before the first peak. However, their relative searches about the COVID-19 and its clinical symptoms decreased gradually despite of the gradual rise of the confirmed cases. Their less tendency to seek information about the COVID-19 could be one of the possible explanation for the increasing number of confirmed cases even several days after easing the disease related lockdown.

Predicting New Daily COVID-19 Cases and Deaths Utilizing Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study

Background

Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction.

Objective

The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods.

Methods

We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics.

Results

GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models.

Conclusions

NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.

Interest in COVID-19 in Latin America and the Caribbean: an infodemiological study using Google Trends

Infodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean. Data from Google Trends from December 12, 2019, to April 25, 2020, regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators. The Latin American and Caribbean countries with the most interest for COVID-19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2 ± 1.2 days and 16.7 ± 15 days, respectively. The duration of public attention was 64.8 ± 12.5 days. The most popular topics related to COVID-19 were: the country's situation (100 ± 0) and coronavirus symptoms (36.82 ± 16.16). Most countries showed a strong or moderated (r = 0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35 ± 5.76 (r = 0.79). Interest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Less interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility.

The Impact of COVID-19 Infodemic on Depression and Sleep Disorders; Focusing On Uncertainty Reduction Strategies and Level of Interpretation Theory

Background

During the COVID-19 pandemic, information diffusion about the COVID-19 has attracted public attention through social media. The World Health Organization declared an infodemic of COVID-19 on February 15, 2020. Misinformation and disinformation, including overwhelming amounts of information about COVID-19 on social media, could promote adverse psychological effects.

Objective

This study used the Psychological Distance and Level of Construal theory (CLT) to predict peoples’ negative psychological symptoms from social media usage. In this study, the CLT intended to show peoples’ psychological proximity to objects and events with respect to the COVID-19 pandemic. Furthermore, this study links the uncertainty reduction strategy (URS) and CLT for COVID-19–related preventive behaviors and affective reactions to assess their effects on mental health problems.

Methods

A path model was tested (N=297) with data from a web-based survey to examine how social media usage behaviors are associated with URS and psychological distance with COVID-19 (based on the CLT), leading to preventive behaviors and affective reactions. Finally, the path model was used to examine how preventive behaviors and affective reactions are associated with mental health problems including anxiety and sleep disorder.

Results

After measuring participants’ social media usage behavior, we found that an increase in general social media usage led to higher use of the URS and lower construal level on COVID-19. The URS is associated with preventive behaviors, but the CLT did not show any association with preventive behaviors; however, it increases affective reactions. Moreover, increased preventive behavior showed negative associations with symptoms of mental health problems; that is, depression and sleep disorder. However, the affective reaction tends to be positively associated with depression and sleep disorder. Owing to the infodemic of COVID-19, the psychological perception of the pandemic negatively influenced users’ mental health problems.

Conclusions

Our results imply that the information from social media usage heightened concerns and had a lower construal level; this does not facilitate taking preventive actions but rather reinforces the negative emotional reaction and mental health problems. Thus, higher URS usage is desirable.

Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

Currently, many countries all over the world are facing the second wave of COVID-19. Therefore, this study aims to analyze the spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern during the first to the second waves in the South Sumatra Province of Indonesia. This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases and epidemic spread rate. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I, while the Pearson correlation was used to examining the relationship between meteorological factors and the epidemic spread rate. Most infected areas and the direction of virus spread were predicted using wind rose analysis. The results revealed that the epidemic rapidly spread from August 1 to December 1, 2020. The highest epidemic spread rate was observed in the Palembang district and in its peripheral areas (dense urban areas), while the lowest spread rate was found in the eastern and southern parts of South Sumatra Province (remote areas). The spatial correlation characteristic of the epidemic distribution exhibited a negative correlation and random distribution. Air temperature, wind speed, and precipitation have contributed to a significant impact on the high epidemic spread rate in the second wave. In summary, this study offers new insight for arranging control and prevention strategies against the potential of second wave strike.

Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India

OBJECTIVE

Digital surveillance has shown mixed results as a supplement to traditional surveillance. Google Trends™ (GT) (Google, Mountain View, CA, United States) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India.

METHODS

Data was obtained on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behavior in India between January 1 and May 31, 2020 in the form of relative search volume (RSV). We also used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests.

RESULTS

GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms "COVID 19," "COVID," "social distancing," "soap," and "lockdown" at the national level. In 5 high-burden states, high correlation was observed for these 5 terms along with "Corona." Peaks in RSV, both at the national level and in high-burden states corresponded with media coverage or government declarations on the ongoing pandemic.

CONCLUSION

The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage-induced curiosity, or health-seeking curiosity.

High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA

Objective: Incorporating spatial analyses and online health information queries may be beneficial in understanding the role of Google relative search volume (RSV) data as a secondary public health surveillance tool during pandemics. This study identified coronavirus disease 2019 (COVID-19) clustering and defined the predictability performance of Google RSV models in clustered and non-clustered areas of the USA.

Methods: Getis-Ord General and local G statistics were used to identify monthly clustering patterns. Monthly country- and state-level correlations between new daily COVID-19 cases and Google RSVs were assessed using Spearman's rank correlation coefficients and Poisson regression models for January–December 2020.

Results: Huge clusters involving multiple states were found, which resulted from various control measures in each state. This demonstrates the importance of state-to-state coordination in implementing control measures to tackle the spread of outbreaks. Variability in Google RSV model performance was found among states and time periods, possibly suggesting the need to use different frameworks for Google RSV data in each state. Moreover, the sign of correlation can be utilized to understand public responses to control and preventive measures, as well as in communicating risk.

Conclusion: COVID-19 Google RSV model accuracy in the USA may be influenced by COVID-19 transmission dynamics, policy-driven community awareness and past outbreak experiences.

COVID-19 Pandemic Impact on Substance Misuse: A Social Media Listening, Mixed Method Analysis

The restrictive measures adopted during the COVID-19 pandemic modified some previously consolidated drug use patterns. A focus on social networks allowed drug users to discuss, share opinions and provide advice during a worldwide emergency context. In order to explore COVID-19-related implications on drug trends/behaviour and on most popular psychotropic substances debated, the focus here was on the constantly updated, very popular, Reddit social platform’s posts and comments. A quantitative and qualitative analysis of r/Drugs and related subreddits, using a social media listening netnographic approach, was carried out. The post/comments analysed covered the time-frame December 2019–May 2020. Between December 2019 and May 2020, the number of whole r/Drugs subreddit members increased from 619,563 to 676,581 members, respectively, thus increasing by 9.2% by the end of the data collection. Both the top-level r/Drugs subreddit and 92 related subreddits were quantitatively analysed, with posts/comments related to 12 drug categories. The drugs most frequently commented on included cannabinoids, psychedelics, opiates/opioids, alcohol, stimulants and prescribed medications. The qualitative analysis was carried out focussing on four subreddits, relating to some 1685 posts and 3263 comments. Four main themes of discussion (e.g., lockdown-associated immunity and drug intake issues; drug-related behaviour/after-quarantine plans’ issues; lockdown-related psychopathological issues; and peer-to-peer advice at the time of COVID-19) and four categories of Redditors (e.g., those continuing the use of drugs despite the pandemic; the “couch epidemiologists”; the conspirationists/pseudo-science influencers; and the recovery-focused users) were tentatively identified here. A mixed-methods, social network-based analysis provided a range of valuable information on Redditors’ drug use/behaviour during the first phase of the COVID-19 pandemic. Further studies should be carried out focusing on other social networks as well as later phases of the pandemic.

The rise of infodemiology and infoveillance during COVID-19 crisis

We noticed an increase in the relative number of published papers on topics such as infoveillance, infodemiology and Google Trends. Collected PubMed data are from the period of January 2020 to March 2021 and were searched with the use of five keywords: infoveillance, infodemiology, Google Trends, diabetes and in silico. We compared an increase in the number of papers from PubMed with search interest expressed in Google Trends. Collected Google Trends data is from the same period, covering fifteen months starting January 2020 and were searched with the use of three search topics: coronavirus, lockdown and social distancing. The geographic setting for search engine users was worldwide. We propose a hypothesis that after increased interest in searches during the pandemic's initial months came an increased number of published papers on topics such as infoveillance, infodemiology and Google Trends.

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Google Trends data underline the importance and duration of the COVID-19 effects.

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PubMed data reveal an increase in the number of papers in infoveillance, infodemiology and Google Trends

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Current restrictions such as lockdown and social distancing are reflected in the number of internet searches

Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers

BACKGROUND

In the face of the COVID-19 pandemic, the German government and the 16 German federal states implemented a variety of nonpharmaceutical interventions (NPIs) to decelerate the spread of the SARS-CoV-2 virus and thus prevent a collapse of the health care system. These measures comprised, among others, social distancing, the temporary closure of shops and schools, and a ban of large public gatherings and meetings with people not living in the same household.

OBJECTIVE

It is fair to assume that the issued NPIs have heavily affected social life and psychological functioning. We therefore aimed to examine possible effects of this lockdown in conjunction with daily new infections and the state of the national economy on people's interests, motives, and other psychological states.

METHODS

We derived 249 keywords from the Google Trends database, tapping into 27 empirically and rationally selected psychological domains. To overcome issues with reliability and specificity of individual indicator variables, broad factors were derived by means of time series factor analysis. All domains were subjected to a change point analysis and time series regression analysis with infection rates, NPIs, and the state of the economy as predictors. All keywords and analyses were preregistered prior to analysis.

RESULTS

With the pandemic arriving in Germany, significant increases in people's search interests were observed in virtually all domains. Although most of the changes were short-lasting, each had a distinguishable onset during the lockdown period. Regression analysis of the Google Trends data confirmed pronounced autoregressive effects for the investigated variables, while forecasting by means of the tested predictors (ie, daily new infections, NPIs, and the state of economy) was moderate at best.

CONCLUSIONS

Our findings indicate that people's interests, motives, and psychological states are heavily affected in times of crisis and lockdown. Specifically, disease- and virus-related domains (eg, pandemic disease, symptoms) peaked early, whereas personal health strategies (eg, masks, homeschooling) peaked later during the lockdown. Domains addressing social life and psychosocial functioning showed long-term increases in public interest. Renovation was the only domain to show a decrease in search interest with the onset of the lockdown. As changes in search behavior are consistent over multiple domains, a Google Trends analysis may provide information for policy makers on how to adapt and develop intervention, information, and prevention strategies, especially when NPIs are in effect.

Association between traffic inflow and COVID-19 prevalence at the provincial level in South Korea

Objectives

To analyze the relationship between traffic inflow and COVID-19 prevalence in South Korea for formulating prevention policies for novel infections.

Methods

We evaluated traffic inflow and new COVID-19 cases in 8 regions of Korea from January 1, 2020, to January 31, 2021. The toll collection system (TCS) traffic volume for 2019–2020 and traffic inflow trends were analyzed using independent samples t-test and nonlinear regression, respectively. The association between TCS traffic volume and new COVID-19 cases by city was analyzed using correlation analysis.

Results

Traffic inflow volume in 2020 decreased 3.7% from 2019. The TCS traffic inflow trend in the 8 provinces decreased during the first COVID-19 wave, gradually increased until the second wave, decreased after the second wave, and showed a sharp decrease in the third wave. There was a positive correlation between the traffic inflow volume and new cases in Busan-Gyeongnam and Jeonbuk, but not in Daegu-Gyeongbuk or Gangwon.

Conclusions

A decrease in new COVID-19 cases in the regions was associated with increased traffic inflow volume. Therefore, the Korean government can establish preventive social distancing policies by identifying increases or decreases in traffic volume. These policies will also need to consider the distribution of vaccines in each area.

Epidemics, Public Sentiment, and Infectious Disease Equity Market Volatility

Background: This article studies the relationship between the COVID-19 epidemic, public sentiment, and the volatility of infectious disease equities from the perspective of the United States. We use weekly data from January 3, 2020 to March 7, 2021. This provides a sufficient dataset for empirical analysis. Granger causality test results prove the two-way relationship between the fluctuation of infectious disease equities and confirmed cases. In addition, confirmed cases will cause the public to search for COVID-19 tests, and COVID-19 tests will also cause fluctuations in infectious disease equities, but there is no reverse correlation. The results of this research are useful to investors and policy makers. Investors can use the number of confirmed cases to predict the volatility of infectious disease equities. Similarly, policy makers can use the intervention of retrieved information to stabilize public sentiment and equity market fluctuations, and integrate a variety of information to make more scientific judgments on the trends of the epidemic.

Google Trend Analysis and Paradigm Shift of Online Education Platforms during the COVID-19 Pandemic

Objective: The largest pandemic in history, the COVID-19 pandemic, has been declared a doomsday globally. The second wave spreading worldwide has devastating consequences in every sector of life. Several measures to contain and curb its infection have forged significant challenges for the education community. With an estimated 1.6 billion learners, the closure of schools and other educational institutions has impacted more than 90% of students worldwide from the elementary to tertiary level. Methods: In a view to studying impacts on student's fraternity, this article aims at addressing alternative ways of educating-more specifically, online education-through the analysis of Google trends for the past year. The study analyzed the platforms of online teaching and learning systems that have been enabling remote learning, thereby limiting the impact on the education system. Thorough text analysis is performed on an existing dataset from Kaggle to retrieve insight on the clustering of words that are more often looked at during this pandemic to find the general patterns of their occurrence. Findings: The results show that the coronavirus patients are the most trending patterns in word search clustering, with the education system being at the control and preventive measures to bring equilibrium in the system of education. There has been significant growth in online platforms in the last year. Existing assets of educational establishments have effectively converted conventional education into new-age online education with the help of virtual classes and other key online tools in this continually fluctuating scholastic setting. The effective usage of teaching tools such as Microsoft Teams, Zoom, Google Meet, and WebEx are the most used online platforms for the conduction of classes, and whiteboard software tools and learning apps such as Vedantu, Udemy, Byju's, and Whitehat Junior have been big market players in the education system over the pandemic year, especially in India. Conclusions: The article helps to draw a holistic approach of ongoing online teaching-learning methods during the lockdown and also highlights changes that took place in the conventional education system amid the COVID pandemic to overcome the persisting disruption in academic activities and to ensure correct perception towards the online procedure as a normal course of action in the new educational system. To fill in the void of classroom learning and to minimize the virus spread over the last year, digital learning in various schools and colleges has been emphasized, leading to a significant increase in the usage of whiteboard software platforms.

Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data

Background

The COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States.

Objective

We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events?

Methods

We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states.

Results

The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others.

Conclusions

COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Indian Paradigm of Orthopedic Care in Post-COVID-19 Era

INTRODUCTION

COVID-19 pandemic caused by the newly emerged strain of coronavirus (SARS-CoV-2) has had phenomenally casted its impact on the health-care systems globally. The rampant spread of contagiosity has challenged the solidarity of the medical fraternity of the developed and developing world. The rising turmoil enforces to trudge with stoicism and expresses the need for planning because of subjugating the prevailing conditions with judicial channelization of available resources. In many developed and developing countries, the resources such as appropriate equipment as well as personnel have been extended to combat the pandemic substantially. At the same time, the populous country such as India has taken a stand to cancel electively planned orthopedic surgeries. However, under the issued guidelines of apex authorities, trauma and emergency services had have been in continuity with a reorganized manner. Hereby, we discuss the present shift in paradigm in the field of orthopedics with an interplay of regenerative orthopedics and telemedicine and its pivotal role against the odds of the COVID-19 pandemic. Besides, we see over the future perspectives and challenges in the purview of resorting to an effective clinical practice in orthopedics specialty. Albeit these guidelines expound strategies to manage trauma and orthopedic cases amidst pandemics but the subsequent post-COVID-19 phase warrants explicable vision and planning. Indeed, resuming elective orthopedics surgical intervention in post-phase of pandemic shall definitively be a task invoking fundamental planning, especially in a resource-limited background. With the rollout of vaccines in the country, the scenario is in favor of returning to normalcy with evaluation for COVID-19 being added to the list of routine medical and surgical screening profiles.

Big data driven COVID-19 pandemic crisis management: potential approach for global health

INTRODUCTION

Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively.

METHODS

A systematic review guided by PRISMA guidelines has been performed to obtain the key elements.

RESULTS

This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data.

CONCLUSIONS

The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.

Genomic Variations in SARS-CoV-2 Genomes From Gujarat: Underlying Role of Variants in Disease Epidemiology

Humanity has seen numerous pandemics during its course of evolution. The list includes several incidents from the past, such as measles, Ebola, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), etc. The latest edition to this is coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of August 18, 2020, COVID-19 has affected over 21 million people from 180 + countries with 0.7 million deaths across the globe. Genomic technologies have enabled us to understand the genomic constitution of pathogens, their virulence, evolution, and rate of mutation, etc. To date, more than 83,000 viral genomes have been deposited in public repositories, such as GISAID and NCBI. While we are writing this, India is the third most affected country by COVID-19, with 2.7 million cases and > 53,000 deaths. Gujarat is the 11th highest affected state with a 3.48% death rate compared to the national average of 1.91%. In this study, a total of 502 SARS-CoV-2 genomes from Gujarat were sequenced and analyzed to understand its phylogenetic distribution and variants against global and national sequences. Further variants were analyzed from diseased and recovered patients from Gujarat and the world to understand its role in pathogenesis. Among the missense mutations present in the Gujarat SARS-CoV-2 genomes, C28854T (Ser194Leu) had an allele frequency of 47.62 and 7.25% in deceased patients from the Gujarat and global datasets, respectively. In contrast, the allele frequency of 35.16 and 3.20% was observed in recovered patients from the Gujarat and global datasets, respectively. It is a deleterious mutation present in the nucleocapsid (N) gene and is significantly associated with mortality in Gujarat patients with a p-value of 0.067 and in the global dataset with a p-value of 0.000924. The other deleterious variant identified in deceased patients from Gujarat (p-value of 0.355) and the world (p-value of 2.43E-06) is G25563T, which is located in Orf3a and plays a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct clusters under the GH clade of GISAID. This study will shed light on the viral haplotype in SARS-CoV-2 samples from Gujarat, India.

The Popularity of the Biologically-Based Therapies During Coronavirus Pandemic Among the Google Users in the USA, UK, Germany, Italy and France

Object

The aim of this retrospective infodemiological study was to evaluate people’s interests in biologically-based (B-B) complementary and alternative medicine (CAM) therapies such as herbs, foods, and supplements during the coronavirus pandemic via analysis of Google search engine statistics.

Design & settings

The category, period, and regions selected in the Google Trends were “health,” “15 January–15 May 2020,” in the United States of America (USA), the United Kingdom (UK), Germany, Italy, and France, respectively. The most commonly searched herbs, foods and supplements (n = 32) during the pandemic were determined from a pool of keywords (n = 1286) based on the terms’ relative search volumes (RSVs) within the last five years. Correlation analyses were conducted to investigate associations between coronavirus-related parameters with each keyword’s RSV for each country. Selected keywords (n = 25) were analyzed using the gtrendsR package in the R programming language; the ggplot2 package was used to visualize the data, the Prophet package was used to estimate the time series, and the dplyr package was used to create the data frame.

Results

Significantly strong positive correlations were identified between daily RSVs of the terms “black seed,” “vitamin C,” “zinc,” and “quercetin,” and search queries for “coronavirus” and “COVID-19” in the USA (Spearman’s correlation coefficient > 0.8, p < 0.05), and between the RSVs of the terms “vitamin C” and “zinc,” and daily search queries for “coronavirus” and/or “COVID-19” in the UK (Spearman’s correlation coefficient > 0.8, p < 0.05).

Conclusion

Google Trends can be a beneficial tool for following public interest in identifying outbreak-related misinformation, and scientific studies and statements from authorities and the media play a potential role in driving internet searches.

Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries

Effective public health response to novel pandemics relies on accurate and timely surveillance of pandemic spread, as well as characterization of the clinical course of the disease in affected individuals. We sought to determine whether Internet search patterns can be useful for tracking COVID-19 spread, and whether these data could also be useful in understanding the clinical progression of the disease in 32 countries across six continents. Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98-21.08) and 22.16 days (20.33-23.99), respectively. Cross-country ensemble averaging was used to derive average temporal profiles for each search term, which were combined to create a search-data-based view of the clinical course of disease progression. Internet search patterns revealed a clear temporal pattern of disease progression for COVID-19: Initial symptoms of fever, dry cough, sore throat and chills were followed by shortness of breath an average of 5.22 days (3.30-7.14) after initial symptom onset, matching the clinical course reported in the medical literature. This study shows that Internet search data can be useful for characterizing the detailed clinical course of a disease. These data are available in real-time at population scale, providing important benefits as a complementary resource for tracking pandemics, especially before widespread laboratory testing is available.

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

BACKGROUND

Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.

OBJECTIVE

Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department.

METHODS

Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics.

RESULTS

The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics.

CONCLUSIONS

The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Technology, Privacy, and User Opinions of COVID-19 Mobile Apps for Contact Tracing: Systematic Search and Content Analysis

BACKGROUND

Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting.

OBJECTIVE

The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review.

METHODS

This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed.

RESULTS

This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps.

CONCLUSIONS

This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users.

In the interest of public safety: rapid response to the COVID-19 epidemic in Vietnam

We describe the status of the COVID-19 epidemic in Vietnam, major response successes, factors that prompted implementation of certain public health actions, and the impact of these actions. In addition, information for three case studies is reported, with crucial learnings to inform future response. Findings from this study suggest that as early as 20 January 2020, Vietnam held a national risk assessment, established a national COVID-19 Response Plan and Technical Treatment and Care Guidelines, and prepared public health laboratories to accurately diagnose cases and hospitals to effectively treat patients. The first COVID-19 case was detected on 23 January. As of 30 September, there had been three waves of the COVID-19 epidemic totalling 1095 cases, and resulting in 35 deaths all among people with underlying health conditions. Evidence of potential transmission of SARS-CoV-2 from a commercial passenger flight inbound to Vietnam was reported. This study also highlights the importance of early technical preparedness, strong political commitment, multisectoral and multilevel efforts, increased resourcing and coordination towards an effective COVID-19 response. Controlling outbreaks in settings, such as crowded public places (bars and hospitals), within certain villages and over cities, required early detection, aggressive trace-test-quarantine efforts, a geographically extensive lockdown area and an adoption of several non-pharmaceutical interventions. Many low-income and middle-income countries have experienced their second or third wave of the COVID-19 epidemic, and they can learn from Vietnam's response across the three epidemic waves. Swift governmental action, strict border control measures, effective communication of health promotion measures, widespread community engagement, expanded testing capacity and effective social measures to slow the spread of SARS-CoV-2, are highly important in these locations.

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

Processes for Trauma Care at Six Level I Trauma Centers During the COVID-19 Pandemic

INTRODUCTION

As the COVID-19 pandemic spread, patient care guidelines were published and elective surgeries postponed. However, trauma admissions are not scheduled and cannot be postponed. There is a paucity of information available on continuing trauma care during the pandemic. The study purpose was to describe multicenter trauma care process changes made during the COVID-19 pandemic.

METHODS

This descriptive survey summarized the response to the COVID-19 pandemic at six Level I trauma centers. The survey was completed in 05/2020. Questions were asked about personal protective equipment, ventilators, intensive care unit (ICU) beds, and negative pressure rooms. Data were summarized as proportions.

RESULTS

The survey took an average of 5 days. Sixty-seven percent reused N-95 respirators; 50% sanitized them with 25% using ultraviolet light. One hospital (17%) had regional resources impacted. Thirty-three percent created ventilator allocation protocols. Most hospitals (83%) designated more beds to the ICU; 50% of hospitals designated an ICU for COVID-19 patients. COVID-19 patients were isolated in negative pressure rooms at all hospitals.

CONCLUSIONS

In response to the COVID-19 pandemic, Level I trauma centers created processes to provide optimal trauma patient care and still protect providers. Other centers can use the processes described to continue care of trauma patients during the COVID-19 pandemic.

Could amantadine interfere with COVID-19 vaccines based on the LNP-mRNA platform?

INTRODUCTION

Amantadine is a well-known medication with indications in neurology and infectious diseases. It is currently FDA approved for Parkinson's disease, drug-induced extrapyramidal symptoms, and influenza.

METHODS

The article is the author's original research hypothesis.

RESULTS

Because more people are going to be vaccinated and additional similar vaccines are going to be introduced, we should take into consideration the potential of amantadine to interfere with LNP-mRNA COVID-19 vaccine delivery into the target cells.

CONCLUSIONS

A more cautious approach to the patients taking amantadine as far as vaccination utilizing LNP-mRNA platform should be considered.

Prevalence of Depression, Anxiety, Distress and Insomnia and Related Factors in Healthcare Workers During COVID-19 Pandemic in Turkey

The purpose of this study was to evaluate the prevalence of depression, anxiety, distress, and insomnia and related factors in healthcare workers during the COVID-19 pandemic in Turkey. We applied the study survey online to HCWs during the pandemic in Turkey between 23 April and 23 May 2020. We used the sociodemographic data form, Patient Health Questionnaire-9, General Anxiety Disorder-7, Insomnia Severity Index, and Impact of Event Scale-Revised. Six hundred twenty (66.0%) of the 939 HCWs taking part in the study were female, 580 (61.8%) were physicians, 569 (60.6%) were working on the front line. Seven hundred twenty-nine (77.6%) participants exhibited depression, 565 (60.2%) anxiety, 473 (50.4%) insomnia, and 717 (76.4%) distress symptoms. Depression, anxiety, insomnia, and distress symptoms were significantly greater among females, individuals with a history of psychiatric illness, and individuals receiving psychiatric support during the COVID-19 pandemic. HCWs serving in Turkey during the COVID-19 pandemic experienced high levels of depression, anxiety, insomnia, and distress symptoms. Female gender, being a nurse, working on the front line, history of psychiatric illness, and being tested for COVID-19 were identified as risk factors for mental health problems.

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning

Background

Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide.

Methods

The “interest over time” and “interest by region” Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. The features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries.

Results

Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods and the models with different numbers of features.

Conclusions

Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.

Travel-related control measures to contain the COVID-19 pandemic: a rapid review

BACKGROUND

In late 2019, first cases of coronavirus disease 2019, or COVID-19, caused by the novel coronavirus SARS-CoV-2, were reported in Wuhan, China. Subsequently COVID-19 spread rapidly around the world. To contain the ensuing pandemic, numerous countries have implemented control measures related to international travel, including border closures, partial travel restrictions, entry or exit screening, and quarantine of travellers.

OBJECTIVES

To assess the effectiveness of travel-related control measures during the COVID-19 pandemic on infectious disease and screening-related outcomes.

SEARCH METHODS

We searched MEDLINE, Embase and COVID-19-specific databases, including the WHO Global Database on COVID-19 Research, the Cochrane COVID-19 Study Register, and the CDC COVID-19 Research Database on 26 June 2020. We also conducted backward-citation searches with existing reviews.

SELECTION CRITERIA

We considered experimental, quasi-experimental, observational and modelling studies assessing the effects of travel-related control measures affecting human travel across national borders during the COVID-19 pandemic. We also included studies concerned with severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) as indirect evidence. Primary outcomes were cases avoided, cases detected and a shift in epidemic development due to the measures. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome.

DATA COLLECTION AND ANALYSIS

One review author screened titles and abstracts; all excluded abstracts were screened in duplicate. Two review authors independently screened full texts. One review author extracted data, assessed risk of bias and appraised study quality. At least one additional review author checked for correctness of all data reported in the 'Risk of bias' assessment, quality appraisal and data synthesis. For assessing the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for observational studies concerned with screening, ROBINS-I for observational ecological studies and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed certainty of evidence with GRADE, and the review author team discussed ratings.

MAIN RESULTS

We included 40 records reporting on 36 unique studies. We found 17 modelling studies, 7 observational screening studies and one observational ecological study on COVID-19, four modelling and six observational studies on SARS, and one modelling study on SARS and MERS, covering a variety of settings and epidemic stages. Most studies compared travel-related control measures against a counterfactual scenario in which the intervention measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of travel restrictions, or a combination of measures. There were concerns with the quality of many modelling studies and the risk of bias of observational studies. Many modelling studies used potentially inappropriate assumptions about the structure and input parameters of models, and failed to adequately assess uncertainty. Concerns with observational screening studies commonly related to the reference test and the flow of the screening process. Studies on COVID-19 Travel restrictions reducing cross-border travel Eleven studies employed models to simulate a reduction in travel volume; one observational ecological study assessed travel restrictions in response to the COVID-19 pandemic. Very low-certainty evidence from modelling studies suggests that when implemented at the beginning of the outbreak, cross-border travel restrictions may lead to a reduction in the number of new cases of between 26% to 90% (4 studies), the number of deaths (1 study), the time to outbreak of between 2 and 26 days (2 studies), the risk of outbreak of between 1% to 37% (2 studies), and the effective reproduction number (1 modelling and 1 observational ecological study). Low-certainty evidence from modelling studies suggests a reduction in the number of imported or exported cases of between 70% to 81% (5 studies), and in the growth acceleration of epidemic progression (1 study). Screening at borders with or without quarantine Evidence from three modelling studies of entry and exit symptom screening without quarantine suggests delays in the time to outbreak of between 1 to 183 days (very low-certainty evidence) and a detection rate of infected travellers of between 10% to 53% (low-certainty evidence). Six observational studies of entry and exit screening were conducted in specific settings such as evacuation flights and cruise ship outbreaks. Screening approaches varied but followed a similar structure, involving symptom screening of all individuals at departure or upon arrival, followed by quarantine, and different procedures for observation and PCR testing over a period of at least 14 days. The proportion of cases detected ranged from 0% to 91% (depending on the screening approach), and the positive predictive value ranged from 0% to 100% (very low-certainty evidence). The outcomes, however, should be interpreted in relation to both the screening approach used and the prevalence of infection among the travellers screened; for example, symptom-based screening alone generally performed worse than a combination of symptom-based and PCR screening with subsequent observation during quarantine. Quarantine of travellers Evidence from one modelling study simulating a 14-day quarantine suggests a reduction in the number of cases seeded by imported cases; larger reductions were seen with increasing levels of quarantine compliance ranging from 277 to 19 cases with rates of compliance modelled between 70% to 100% (very low-certainty evidence).

AUTHORS' CONCLUSIONS

With much of the evidence deriving from modelling studies, notably for travel restrictions reducing cross-border travel and quarantine of travellers, there is a lack of 'real-life' evidence for many of these measures. The certainty of the evidence for most travel-related control measures is very low and the true effects may be substantially different from those reported here. Nevertheless, some travel-related control measures during the COVID-19 pandemic may have a positive impact on infectious disease outcomes. Broadly, travel restrictions may limit the spread of disease across national borders. Entry and exit symptom screening measures on their own are not likely to be effective in detecting a meaningful proportion of cases to prevent seeding new cases within the protected region; combined with subsequent quarantine, observation and PCR testing, the effectiveness is likely to improve. There was insufficient evidence to draw firm conclusions about the effectiveness of travel-related quarantine on its own. Some of the included studies suggest that effects are likely to depend on factors such as the stage of the epidemic, the interconnectedness of countries, local measures undertaken to contain community transmission, and the extent of implementation and adherence.

COVID-19 Blind Spots: A Consensus Statement on the Importance of Competent Political Leadership and the Need for Public Health Cognizance

As the COVID-19 pandemic continues, important discoveries and considerations emerge regarding the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pathogen; its biological and epidemiological characteristics; and the corresponding psychological, societal, and public health (PH) impacts. During the past year, the global community underwent a massive transformation, including the implementation of numerous nonpharmacological interventions; critical diversions or modifications across various spheres of our economic and public domains; and a transition from consumption-driven to conservation-based behaviors. Providing essential necessities such as food, water, health care, financial, and other services has become a formidable challenge, with significant threats to the existing supply chains and the shortage or reduction of workforce across many sectors of the global economy. Food and pharmaceutical supply chains constitute uniquely vulnerable and critically important areas that require high levels of safety and compliance. Many regional health-care systems faced at least one wave of overwhelming COVID-19 case surges, and still face the possibility of a new wave of infections on the horizon, potentially in combination with other endemic diseases such as influenza, dengue, tuberculosis, and malaria. In this context, the need for an effective and scientifically informed leadership to sustain and improve global capacity to ensure international health security is starkly apparent. Public health "blind spotting," promulgation of pseudoscience, and academic dishonesty emerged as significant threats to population health and stability during the pandemic. The goal of this consensus statement is to provide a focused summary of such "blind spots" identified during an expert group intense analysis of "missed opportunities" during the initial wave of the pandemic.

Rapid Decline in Online Search Queries for Hip and Knee Arthroplasties Concurrent With the COVID-19 Pandemic

BACKGROUND

In response to the coronavirus disease 2019 (COVID-19) pandemic, US hospitals have canceled elective surgeries. This decline in total joint arthroplasty (TJA) revenue may place financial strain on hospitals. Our goal was to quantify the impact of COVID-19 on the public interest in elective TJA.

METHODS

The Google Search Volume Index (GSVI) identified the terms "knee replacement," "hip replacement," and "orthopedic surgeon" as the most common to describe TJA. The term "elective surgery cancellation" was also analyzed. Weekly GSVI data were extracted between 04-01-2015 and 04-04-2020. Time series analysis was conducted and state GSVI values were compared with COVID-19 prevalence and unemployment claims.

RESULTS

The relative public interest in elective TJA has sharply declined since the WHO declaration of COVID-19 as a global pandemic. Between 03-01-2020 and 03-29-2020, the popularity of searches for "knee replacement", "hip replacement," and "orthopedic surgeon" dropped by 62.1%, 52.1%, and 44.3%, respectively. A concurrent spike was observed for the term "elective surgery cancellation." California, New Hampshire, Maine, and Nevada showed a low relative rate for TJA searches, and the highest increase in unemployment claims.

CONCLUSION

The onset of COVID-19 correlates with declining relative popularity of searches related to elective TJA. Higher volume of COVID-19 cases in certain states may correspond with lower relative search popularity, although this correlation remains unclear. These results portend the possibility of a decline in elective TJA case volume, further straining hospitals. Further research is required to inform stakeholders how best to proceed and determine any sustained effects from the current diminished relative interest in TJA.

LEVEL OF EVIDENCE

Level III.

Global Interest in Telehealth During COVID-19 Pandemic: An Analysis of Google Trends™

Background

Since the outbreak, healthcare systems across the globe are overcrowded with coronavirus disease (COVID-19) patients. To sustain the response towards the pandemic, many hospitals have adapted to virtual healthcare and telemedicine. Google™ has become the most widely used search engine over the years. Google Trends™ can be used to depict the public interest over a certain topic. The output of the Google Trends™ is displayed as relative search volume (RSV) which is the proportionate search volume regarding a specific topic comparative to the total search volume in a specific time and region. The primary aim of this study was to evaluate the relationship between the daily reported number of new COVID-19 cases and deaths and the corresponding changes in Google Trends™ RSV of telehealth over six months.

Methods

A retrospective study was conducted from January 21, 2020 to July 21, 2020. About 17 countries that reported the total number of cases greater than 200,000 in the situation report of July 21, 2020 were selected to be a part of this study. The daily reported new cases and deaths globally and of the selected countries were extracted from the World Health Organization (WHO) situation reports. The combination of keywords used for obtaining the RSV data through Google Trends™ was “telehealth”, “telemedicine”, “mHealth”, and “eHealth”. These words were used with the “+” feature of Google Trends™ with “1/21/2020 to 7/21/2020” as time range, “all categories” for the category, and “web search” for the type of search. The worldwide RSV as well as the RSVs of the selected countries were obtained from the Google Trends™ website. Spearman’s correlation coefficient (ρ) was used to determine the strength of the relationship between new cases or deaths and RSVs related to telehealth.

Results

A positive fair correlation was established between the global interest in telehealth and the new cases (ρ=0.307, p-value<0.001) and deaths (ρ=0.469, p-value<0.001) reported worldwide. The United States of America (USA), India, and Bangladesh were found to have a positive fair correlation between the public interest regarding telehealth and the emerging new COVID-19 cases and deaths. The United Kingdom (UK) and Italy demonstrated a positive poor correlation between the rising new cases or deaths and RSV. Similar statistics were noted for the daily new cases of Chile. For Turkey, a positive fair correlation between new deaths and RSV while a positive poor correlation between new cases and RSV was observed. No significant correlation was observed for the rest of the selected countries.

Conclusion

This study highlights the steadily rising public interest in telehealth during the COVID-19 pandemic. Telemedicine can provide the necessary remote consultation and healthcare for patients in the current situation. However, previous studies have shown that the majority of the countries are inadequately equipped for the digitization of the healthcare system. Therefore, it has become necessary to incorporate telemedicine into the healthcare system to combat any possible pandemic in the future.

Students' Acceptance of the COVID-19 Impact on Shifting Higher Education to Distance Learning in Poland

This paper is dedicated to the higher education institutions shifting towards distance learning processes due to the global pandemic situation caused by COVID-19 in 2020. The paper covers the pandemic situation in Poland generally, analyzing governmental ordinances and tracking the gradual extension of restrictions for educational institutions. The purpose of this study is to investigate the influence of Experience, Enjoyment, Computer Anxiety, and Self-Efficacy on students’ acceptance of shifting education to distance learning. The study tested and used the adapted General Extended Technology Acceptance Model for E-Learning (GETAMEL) in the context of coronavirus pandemic. The partial least squares method of structural equation modeling was employed to test the proposed research model. The study utilizes an online survey to obtain data from 1692 Polish undergraduate and graduate students in both full- and part-time study. The dataset was analyzed using SmartPLS 3 software. Results showed that the best predictor of student’s acceptance of shifting education to distance learning is Enjoyment, followed by Self-Efficacy. Both Perceived Ease of Use and Perceived Usefulness predict student’s Attitude Towards Using and Intention to Use the distance learning. The findings improve understanding regarding the acceptance of distance learning and this work is therefore of particular interest to teachers and practitioners of education.

Evaluation of the effect of the state of emergency for the first wave of COVID-19 in Japan

In this paper, we evaluate the effect of the state of emergency for the first wave of COVID-19 in Japan, 2020 from the viewpoint of mathematical modelling. In Japan, it was announced during the period of the state of emergency from April 7 to May 25, 2020 that the 80% reduction of the contact rate is needed to control the outbreak. By numerical simulation, we show that the reduction rate seems to have reached up to 86%. Moreover, we estimate the control reproduction numberR c during the period of the state of emergency asR c = 0.36 (95%CI, 0.34-0.39), and show that the effective reproduction numberR e after the lifting of the state of emergency could be greater than 1. This result suggests us that the second wave of COVID-19 in Japan could possibly occur if any effective intervention will not be taken again.

Fluctuation of Public Interest in COVID-19 in the United States: Retrospective Analysis of Google Trends Search Data

BACKGROUND

In the absence of vaccines and established treatments, nonpharmaceutical interventions (NPIs) are fundamental tools to control coronavirus disease (COVID-19) transmission. NPIs require public interest to be successful. In the United States, there is a lack of published research on the factors that influence public interest in COVID-19. Using Google Trends, we examined the US level of public interest in COVID-19 and how it correlated to testing and with other countries.

OBJECTIVE

The aim of this study was to determine how public interest in COVID-19 in the United States changed over time and the key factors that drove this change, such as testing. US public interest in COVID-19 was compared to that in countries that have been more successful in their containment and mitigation strategies.

METHODS

In this retrospective study, Google Trends was used to analyze the volume of internet searches within the United States relating to COVID-19, focusing on dates between December 31, 2019, and March 24, 2020. The volume of internet searches related to COVID-19 was compared to that in other countries.

RESULTS

Throughout January and February 2020, there was limited search interest in COVID-19 within the United States. Interest declined for the first 21 days of February. A similar decline was seen in geographical regions that were later found to be experiencing undetected community transmission in February. Between March 9 and March 12, 2020, there was a rapid rise in search interest. This rise in search interest was positively correlated with the rise of positive tests for SARS-CoV-2 (6.3, 95% CI -2.9 to 9.7; P<.001). Within the United States, it took 52 days for search interest to rise substantially after the first positive case; in countries with more successful outbreak control, search interest rose in less than 15 days.

CONCLUSIONS

Containment and mitigation strategies require public interest to be successful. The initial level of COVID-19 public interest in the United States was limited and even decreased during a time when containment and mitigation strategies were being established. A lack of public interest in COVID-19 existed in the United States when containment and mitigation policies were in place. Based on our analysis, it is clear that US policy makers need to develop novel methods of communicating COVID-19 public health initiatives.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Correlation between the Spread of COVID-19 and the Interest in Personal Protective Measures in Poland and Portugal

The pandemic of the coronavirus disease 2019 (COVID-19), has gained extensive coverage in public media and global news, generated international and national communication campaigns to educate the communities worldwide and raised the attention of everyone. The coronavirus has caused viral pneumonia in tens of thousands of people around the world, and the COVID-19 outbreak changed most countries’ routines and concerns and transformed social behaviour. This study explores the potential use of Google Trends (GT) in monitoring interest in the COVID-19 outbreak and, specifically, in personal protective equipment and hand hygiene, since these have been promoted by official health care bodies as two of the most protective measures. GT was chosen as a source of reverse engineering data, given the interest in the topic and the novelty of the research. Current data on COVID-19 are retrieved from GT using keywords in two languages—Portuguese and Polish. The geographical settings for GT are two countries: Poland and Portugal. The period under analysis is 20 January 2020, when the first cases outside China were known, to 15 June 2020. The results show that there is a correlation between the spread of COVID-19 and the search for personal protective equipment and hand hygiene and that GT can help, to a certain extent, understand people’s concerns, behaviour and reactions to sanitary problems and protection recommendations.

Ten considerations for effectively managing the COVID-19 transition

Governments around the world have implemented measures to manage the transmission of coronavirus disease 2019 (COVID-19). While the majority of these measures are proving effective, they have a high social and economic cost, and response strategies are being adjusted. The World Health Organization (WHO) recommends that communities should have a voice, be informed and engaged, and participate in this transition phase. We propose ten considerations to support this principle: (1) implement a phased approach to a 'new normal'; (2) balance individual rights with the social good; (3) prioritise people at highest risk of negative consequences; (4) provide special support for healthcare workers and care staff; (5) build, strengthen and maintain trust; (6) enlist existing social norms and foster healthy new norms; (7) increase resilience and self-efficacy; (8) use clear and positive language; (9) anticipate and manage misinformation; and (10) engage with media outlets. The transition phase should also be informed by real-time data according to which governmental responses should be updated.