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

Infection Prevention and Control Response and Escalation Framework: Evaluation and application beyond a pandemic

BACKGROUND

The COVID-19 pandemic resulted in constant changes to Infection Prevention and Control (IPAC) recommendations, impacting clinician capacity to stay up to date. The COVID-19 IPAC Response and Escalation Framework (IPAC Framework), rarely reported or evaluated was developed to provide scalable IPAC guidance during the pandemic to health care in New South Wales (NSW), Australia.

METHODS

Using a thematic analysis approach, a qualitative study using an online, cross-sectional survey comprising 27 questions was sent to 248 key stakeholders. Participants were health workers with broad clinical and system representation with responsibilities for risk assessment, communicating, implementing, or monitoring the IPAC Framework.

RESULTS

The IPAC Framework provided a useful IPAC tool for the management of COVID-19 as perceived by 93% of respondents. The overwhelming majority (91%) reported the Framework provided enough information on IPAC strategies needed for COVID-19 that were aligned with transmission risk. Resources supporting the IPAC Framework were reported by most respondents (84%) as being widely accepted as the authoritative guidance.

CONCLUSIONS

An IPAC Framework is perceived as invaluable by clinicians and administrators to manage IPAC requirements in health care during a pandemic. The IPAC Framework can be applied more generally to support ongoing IPAC requirements.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

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

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

Impact of the cervical cancer awareness months on public interest in Japan: A Google Trends analysis, 2012–2021

The immunization and screening rates for human papillomavirus in Japan are lower than those in other countries. We aimed to evaluate the impact of cervical cancer awareness months on public attention using Google Trends analysis. Between 2012 and 2021, we analyzed the trends in relative search volumes (RSVs) for “Shikyuu-keigan” (cervical cancer in English) in Japan, during the cervical cancer awareness month (CCAM) in January and cervical cancer prevention awareness enhancement month (CCPAEM) in November. We performed a joinpoint regression analysis to identify a statistically significant trend change point. Additionally, we compared the mean RSVs of each awareness month with the rest of the year. Significant trend change points were observed, but none were found in CCAM and CCPAEM periods. Comparison of mean RSVs among CCAM, CCPAEM, and the rest of the months did not suggest any significant increases in RSVs during these awareness periods. In conclusion, CAM and CCPAEM did not raise public interest in cervical cancer in Japan. Although the results are based on internet users, the findings might suggest a need to develop a more effective and attractive approach to achieve the 90-70-90 targets of cervical cancer prevention by 2030.

Impact of Breast Cancer Awareness Month on Public Interest in the United States between 2012 and 2021: A Google Trends Analysis

Breast Cancer Awareness Month (BCAM) has a long history of over 30 years, established in 1985 to occur every October, and the National Breast Cancer Foundation now leads the operation. There have been no studies to evaluate the impact of the BCAM on public awareness of breast cancer. We analyzed the impact of BCAM on public awareness of breast cancer in the U.S. from 2012 to 2021 using the relative search volume (RSV) of Google Trends as a surrogate. We also analyzed the impact of Lung Cancer Awareness Month (LCAM) and Prostate Cancer Awareness Month (PCAM) on public awareness of lung and prostate cancer, respectively, to see differences in their effectiveness among the health observances for the top three most common cancers in the U.S. We performed a joinpoint regression analysis to identify statistically significant time points of a change in trend. There were joinpoints around BCAM for "Breast cancer" every year from 2012 to 2021, with a significant increase in the weekly RSVs from 21.9% to 46.7%. Except for 2013 and 2015 for "Lung cancer", when significant increases in the RSV at 1.8% and 1.2% per week were observed around LCAM, no joinpoints were noted around LCAM or PCAM. These results imply that BCAM has successfully improved the public awareness of breast cancer in the U.S. compared to other representative health observances, likely due to the effective involvement of non-medical industries, influencers affected by breast cancer, and an awareness symbol.

Impact of the world hand hygiene and global handwashing days on public awareness between 2016 and 2020: Google trends analysis

BACKGROUND

Scientific evidence suggest that hand hygiene as one of the most effective measures to control infection. To promote good hand hygiene practices, the World Health Organization introduced May 5 as World Hand Hygiene Day (WHHD), and international stakeholders established Global Handwashing Day (GHD) on October 15. However, its contributions to raising public awareness of hand hygiene is unclear.

METHODS

This study evaluates the impact of the WHHD and GHD on the public awareness of hand hygiene in Japan, the United Kingdom, the United States, and worldwide from 2016 to 2020, using the relative search volume of "Hand hygiene" in Google Trends as a surrogate. To identify a statistically significant timepoint of a trend change, we performed Joinpoint regression analysis.

RESULTS

Upticks of the relative search volumes as well as joinpoints were noted worldwide around the WHHD and GHD from 2016 to 2019, but no joinpoints were identified around the WHHD and GHD in 2020. No such changes were observed in Japan, the United Kingdom, and the United States during these periods.

CONCLUSIONS

While the WHHD was originally established to raise awareness of hand hygiene in healthcare facilities, our result suggests that the WHHD and GHD may not have effectively disseminated the importance of hand hygiene to the general public at a country level. Additional policy measures to advocate hand hygiene to the public are necessary to communicate its benefits.

'Let communities do their work': the role of mutual aid and self-help groups in the Covid-19 pandemic response

How to respond quickly, effectively, and sensitively to large-scale crises is debated at length in the aid sector. Institutional focuses on projects and outcomes have led to abundant literature on the efficacy of external interventions, while the actions of individuals and communities to meet their own needs remain under researched. This paper seeks to close the gap by joining global trends and specific case studies to explore the scale, breadth, and characteristics of citizen and community-led responses to the Covid-19 pandemic of 2020-21. Using mixed methods, it argues that mutual aid, self-help, and other spontaneous community measures were vital to the early response to Covid-19 globally. Such endeavours have limitations, however, which can be strengthened with the right national and international support. The paper concludes by calling on authorities and aid actors to widen their understanding of 'first response' and provide meaning ful support to mutual aid and local self-help initiatives now and in the future.

Impact of the World Antimicrobial Awareness Week on public interest between 2015 and 2020: A Google Trends analysis

OBJECTIVES

To evaluate the impact of the World Antimicrobial Awareness Week (WAAW) on public awareness of antimicrobial resistance using Google Trends analysis.

METHODS

The impact of WAAW on public awareness of 'antimicrobial resistance' (AMR), 'antibacterial', and 'antibiotics' in Japan, the UK, the United States, and worldwide from 2015 to 2020 was analyzed, using the relative search volume (RSV) of Google Trends as a surrogate. A joinpoint regression analysis was performed to identify a statistically significant time point of a change in trend.

RESULTS

No joinpoints around WAAW were identified in Japan, the United Kingdom, or the United States from 2015 to 2020 with RSVs of 'AMR', whereas increasing RSVs were noted worldwide in 2017 and 2020. Further, there were decreasing RSVs of 'antibiotics' in the first half of 2020, which could be due to the COVID-19 pandemic. The study results suggest that WAAW did little to improve public awareness of AMR in the selected countries despite its contribution worldwide.

CONCLUSIONS

This study implies that we need to develop a more effective method to improve public awareness to fight against AMR.

Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies and UML Modeling

OBJECTIVES

This paper describes a methodology for gathering requirements and early design of remote monitoring technology (RMT) for enhancing patient safety during pandemics using virtual care technologies. As pandemics such as COrona VIrus Disease (COVID-19) progress there is an increasing need for effective virtual care and RMT to support patient care while they are at home.

METHODS

The authors describe their work in conducting literature reviews by searching PubMed.gov and the grey literature for articles, and government websites with guidelines describing the signs and symptoms of COVID-19, as well as the progression of the disease. The reviews focused on identifying gaps where RMT could be applied in novel ways and formed the basis for the subsequent modelling of use cases for applying RMT described in this paper.

RESULTS

The work was conducted in the context of a new Home of the Future laboratory which has been set up at the University of Victoria. The literature review led to the development of a number of object-oriented models for deploying RMT. This modeling is being used for a number of purposes, including for education of students in health infomatics as well as testing of new use cases for RMT with industrial collaborators and projects within the smart home of the future laboratory.

CONCLUSIONS

Object-oriented modeling, based on analysis of gaps in the literature, was found to be a useful approach for describing, communicating and teaching about potential new uses of RMT.

Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study

COVID-19 mainly spreads through cough or sneeze droplets produced by an infected person. The viral particles are mostly present in the oral cavity. The risk of contracting COVID-19 is high in the dental profession due to the nature of procedures involved that produce aerosols. Along with other measures to limit the risk of infection, pre-procedural mouth rinses are beneficial in reducing the viral particles in the oral cavity. In this study, the antiviral efficacy of essential oil components has been determined specifically against SARS-CoV-2 by molecular docking and conceptual DFT approach. Based on the binding affinities of the components against the receptor binding domain of the S1 glycoprotein, cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active. The molecular descriptor values obtained through conceptual DFT also indicated the above-mentioned components to be active based on the correlation between the structure and the activity of the compounds. Therefore, pre-procedural mouth rinses with these components included may be specifically suitable for dental procedures during the COVID-19 period.

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Impact of the World Inflammatory Bowel Disease Day and Crohn's and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis

Background

More than 6 million people are affected by inflammatory bowel disease (IBD) globally. The World IBD Day (WID, May 19) and Crohn's and Colitis Awareness Week (CCAW, December 1-7) occur yearly as national health observances to raise public awareness of IBD, but their effects are unclear.

Objective

The aim of this study was to analyze the relationship between WID or CCAW and the public health awareness on IBD represented by the Google search engine query data.

Methods

This study evaluates the impact of WID and CCAW on the public awareness of IBD in the United States and worldwide from 2016 to 2020 by using the relative search volume of "IBD," "ulcerative colitis," and "Crohn's disease" in Google Trends. To identify significant time points of trend changes (joinpoints), we performed joinpoint regression analysis.

Results

No joinpoints were noted around the time of WID or CCAW during the study period in the search results of the United States. Worldwide, joinpoints were noted around WID in 2020 with the search for "IBD" and around CCAW in 2017 and 2019 with the search for "ulcerative colitis." However, the extents of trend changes were modest without statistically significant increases.

Conclusions

These results posed a question that WID and CCAW might not have worked as expected to raise public awareness of IBD. Additional studies are needed to precisely estimate the impact of health observances to raise the awareness of IBD.