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.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Learning to rank query expansion terms for COVID-19 scholarly search.

OBJECTIVE: With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need. MATERIALS AND METHODS: For the sake of training and evaluation, we use the widely adopted TREC-COVID benchmark. Given a query, the proposed framework utilizes a contextual and a domain-specific neural language model to generate a set of candidate query expansion terms that enrich the original query. Moreover, the framework includes a multi-head attention mechanism that is trained alongside a learning-to-rank model for re-ranking the list of generated expansion candidate terms. The original query and the top-ranked expansion terms are posed to the PubMed search engine for retrieving relevant scholarly articles to an information need. The framework, CQED, can have four different variations, depending upon the learning path adopted for training and re-ranking the candidate expansion terms. RESULTS: The model drastically improves the search performance, when compared to the original query. The performance improvement in comparison to the original query, in terms of RECALL@1000 is 190.85% and in terms of NDCG@1000 is 343.55%. Additionally, the model outperforms all existing state-of-the-art baselines. In terms of P@10, the model that has been optimized based on Precision outperforms all baselines (0.7987). On the other hand, in terms of NDCG@10 (0.7986), MAP (0.3450) and bpref (0.4900), the CQED model that has been optimized based on an average of all retrieval measures outperforms all the baselines. CONCLUSION: The proposed model successfully expands queries posed to PubMed, and improves search performance, as compared to all existing baselines. A success/failure analysis shows that the model improved the search performance of each of the evaluated queries. Moreover, an ablation study depicted that if ranking of generated candidate terms is not conducted, the overall performance decreases. For future work, we would like to explore the application of the presented query expansion framework in conducting technology-assisted Systematic Literature Reviews (SLR).

Shoulder Surgery Google Search Trends during the COVID-19 Pandemic.

The Coronavirus disease 2019 (COVID-19) pandemic has strained many healthcare systems. Google Trends is a tool that provides information on online interest in selected keywords and topics over time. The purpose of this study is to describe the effect of the COVID-19 pandemic on online interest in elective shoulder pathology. Online search pattern data were obtained via Google Trends from November 2019 to November 2020 using the search terms 'orthopedic surgery' and 'shoulder pathology' search terms. Relative search volume index (SVI) graphs were generated from this data and the 7-day average of new COVID-19 cases in the United States. Orthopaedic surgery and shoulder pathology search trends decreased during March 2020 with a sudden rise in the 7-day average of new COVID-19 cases. After March 2020, orthopaedic surgery and shoulder pathology search terms approached pre-COVID-19 pandemic values despite continued increases in the 7-day average of new COVID-19 cases. (Journal of Surgical Orthopaedic Advances 32(1):014-016, 2023).

Impact assessment of immunization and the COVID-19 pandemic on varicella across Europe using digital epidemiology methods: A descriptive study.

BACKGROUND: Varicella is usually a mild disease in children but may be life-threatening, especially in adolescents and adults. Infection control measures implemented during the Coronavirus Disease 2019 (COVID-19) pandemic may have suppressed varicella transmission, potentially creating an 'immunity debt', particularly in countries without universal varicella vaccination. OBJECTIVES: To assess trends in Google search engine queries for varicella keywords as a proxy for varicella infection rates and to evaluate the effect of universal varicella vaccination on these trends. A further objective was to assess the impact of the COVID-19 pandemic on varicella keyword search query trends in countries with and without universal varicella vaccination. METHODS: This study used the keyword research tool, Google Trends, to evaluate trends in time series of the relative search query popularity of language-specific varicella keywords in 28 European countries from January 2015 through December 2021. The Google Ads Keyword Planner tool was used to evaluate absolute search volumes from March 2018 through December 2021. RESULTS: The relative search query popularity of varicella keywords displayed marked seasonal variation. In all 28 countries, the relative search query popularity of varicella keywords declined after the start of the COVID-19 pandemic (March 2020), compared with pre-pandemic levels (range, -18% to -70%). From April 2020 to July 2021, a period of intense COVID-19 transmission and infection control, absolute search volumes for varicella keywords were lower than pre-pandemic levels but rebounded after July 2021, when infection control measures were relaxed. CONCLUSION: This evaluation of search query trends demonstrated that search query data could be used as a proxy for trends in varicella infection rates and revealed that transmission of varicella may have been suppressed during the COVID-19 pandemic. Consideration should be given to using search query data to better understand the burden of varicella, particularly in countries where surveillance systems are inadequate.

Increasing digitalization is associated with anxiety and depression: A Google Ngram analysis.

The prevalence of anxiety disorders and depression are rising worldwide. Studies investigating risk factors on a societal level leading to these rises are so far limited to social-economic status, social capital, and unemployment, while most such studies rely on self-reports to investigate these factors. Therefore, our study aims to evaluate the impact of an additional factor on a societal level, namely digitalization, by using a linguistic big data approach. We extend related work by using the Google Books Ngram Viewer (Google Ngram) to retrieve and adjust word frequencies from a large corpus of books (8 million books or 6 percent of all books ever published) and to subsequently investigate word changes in terms of anxiety disorders, depression, and digitalization. Our analyses comprise and compare data from six languages, British English, German, Spanish, Russian, French, and Italian. We also retrieved word frequencies for the control construct "religion". Our results show an increase in word frequency for anxiety, depression, and digitalization over the last 50 years (r = .79 to .89, p < .001), a significant correlation between the frequency of anxiety and depression words (r = .98, p < .001), a significant correlation between the frequency of anxiety and digitalization words (r = .81, p < .001), and a significant correlation between the frequency of depression and anxiety words (r = .81, p < .001). For the control construct religion, we found no significant correlations for word frequency over the last 50 years and no significant correlation between the frequency of anxiety and depression words. Our results showed a negative correlation between the frequency of depression and religion words (r = -.25, p < .05). We also improved the method by excluding terms with double meanings detected by 73 independent native speakers. Implications for future research and professional and clinical implications of these findings are discussed.

Population well-being and the COVID-19 vaccination program in Chile: evidence from Google Trends.

OBJECTIVE: We analyze the dynamics of the mental well-being of the Chilean population in response to the progress of the vaccination strategy implemented by the government. STUDY DESIGN: This study aims at investigating the possibility of using Google Trends as an instrument for tracking mental well-being of the Chilean population. METHODS: We use the volume of searches for keywords in Google Trends (GT) related to Anguish, Anxiety, Depression, and Stress as a proxy for population well-being. Using event study methods, we analyze social attention reactions to news about the vaccination program. We implement a Difference-in-Difference-in-Differences estimation to estimate changes in population welfare by socio-economic status induced by the progress of inoculation. RESULTS: We show that social attention to mental health problems is sensitive to news about the vaccination program. Moreover, and most importantly, we find that mental well-being responds positively to the percentage of inoculated people. This phenomenon appear to be permanent and affected by socio-economic status, with the wealthier population experiencing greater improvements than the less wealthy. CONCLUSIONS: During the COVID-19 vaccination program in Chile, social attention to mental health problems appears to be sensitive to news about the vaccination program. There is also strong evidence of socio-economic status-induced heterogeneity in population responses to program implementation. The above phenomena appears to be permanent and cannot be attributed to either socio-economic segregation in access to vaccines or to the highly stratified schedule of the vaccination program.

Research and Public Interest in Mindfulness in the COVID-19 and Post-COVID-19 Era: A Bibliometric and Google Trends Analysis.

Public and research interest in mindfulness has been growing, and the Coronavirus disease 2019 (COVID-19) pandemic seems to have accelerated this growth. This study was conducted to investigate the public and research interest in mindfulness in the context of COVID-19. The term 'Mindfulness' was searched in Google Trends, and data were collected from December 2004 to November 2022. The relationship between the relative search volume (RSV) of 'Mindfulness' and that of related topics was analyzed, and 'Top related topics and queries' for the search term 'Mindfulness' were investigated. For bibliometric analysis, a search was conducted in the Web of Science database. Keyword co-occurrence analysis was conducted, and a two-dimensional keyword map was constructed using VOSviewer software. Overall, the RSV of 'Mindfulness' increased slightly. The RSVs of 'Mindfulness' and 'Antidepressants' showed an overall significant positive correlation (r = 0.485) but a statistically significant negative correlation during the COVID-19 era (-0.470). Articles on mindfulness in the context of COVID-19 were closely related to depression, anxiety, stress, and mental health. Four clusters of articles were identified, including 'mindfulness', 'COVID-19', 'anxiety and depression', and 'mental health'. These findings may provide insights into potential areas of interest and identify ongoing trends in this field.

The Effect of the COVID-19 Pandemic on Digital Health-Seeking Behavior: Big Data Interrupted Time-Series Analysis of Google Trends.

BACKGROUND: Due to the emergency responses early in the COVID-19 pandemic, the use of digital health in health care increased abruptly. However, it remains unclear whether this introduction was sustained in the long term, especially with patients being able to decide between digital and traditional health services once the latter regained their functionality throughout the COVID-19 pandemic. OBJECTIVE: We aim to understand how the public interest in digital health changed as proxy for digital health-seeking behavior and to what extent this change was sustainable over time. METHODS: We used an interrupted time-series analysis of Google Trends data with break points on March 11, 2020 (declaration of COVID-19 as a pandemic by the World Health Organization), and December 20, 2020 (the announcement of the first COVID-19 vaccines). Nationally representative time-series data from February 2019 to August 2021 were extracted from Google Trends for 6 countries with English as their dominant language: Canada, the United States, the United Kingdom, New Zealand, Australia, and Ireland. We measured the changes in relative search volumes of the keywords online doctor, telehealth, online health, telemedicine, and health app. In doing so, we capture the prepandemic trend, the immediate change due to the announcement of COVID-19 being a pandemic, and the gradual change after the announcement. RESULTS: Digital health search volumes immediately increased in all countries under study after the announcement of COVID-19 being a pandemic. There was some variation in what keywords were used per country. However, searches declined after this immediate spike, sometimes reverting to prepandemic levels. The announcement of COVID-19 vaccines did not consistently impact digital health search volumes in the countries under study. The exception is the search volume of health app, which was observed as either being stable or gradually increasing during the pandemic. CONCLUSIONS: Our findings suggest that the increased public interest in digital health associated with the pandemic did not sustain, alluding to remaining structural barriers. Further building of digital health capacity and developing robust digital health governance frameworks remain crucial to facilitating sustainable digital health transformation.

Suspended Particulate Matter Analysis of Pre and During Covid Lockdown Using Google Earth Engine Cloud Computing: A Case Study of Ukai Reservoir.

Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.

How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data.

Controversy exists about the impact of the COVID-19 pandemic on dietary habits, with studies demonstrating both benefits and drawbacks of this period. We analyzed Google Trends data on specific terms and arguments related to different foods (i.e., fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, and sugar-sweetened beverages) in order to evaluate the interest of Italian people before and during the COVID-19 pandemic. Joinpoint regression models were applied to identify the possible time points at which public interest in foods changed (i.e., joinpoints). Interestingly, public interest in specific food categories underwent substantial changes during the period under examination. While some changes did not seem to be related to the COVID-19 pandemic (i.e., legumes and red meat), public interest in fruit, vegetables, milk, and whole grains increased significantly, especially during the first lockdown. It should be noted, however, that the interest in food-related issues returned to prepandemic levels after the first lockdown period. Thus, more efforts and ad hoc designed studies should be encouraged to evaluate the duration and direction of the COVID-19 pandemic's influence.

Estimation of Lockdowns' Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data.

Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.

COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures.

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.

COVID-19 and Dr. Google: Parents' changing experience using online health information about their children's health during the pandemic.

AIMS: To identify how the COVID-19 pandemic influences parents' use of the internet, including social media, when seeking health-related information about the pandemic relevant to their children. METHODS: This study employed semi-structured interviews to explore the factors affecting parents of young children when information-seeking online about their children's health related to the COVID-19 pandemic. Parents of children with and without chronic health conditions were interviewed in July and August 2020. Interviews were audio-recorded and transcribed verbatim, then analysed using theoretical thematic analysis, based on Social Cognitive Theory. RESULTS: Through interviews with 13 parents, we identified a myriad of factors that affected parents' internet searching. The decision to access online health information and the regulation of its usage was multifaceted and relied upon the interactions between environmental triggers and parents' information needs, personal attitudes, and circumstances. Overall, parents felt supported by online health information during the COVID-19 pandemic, and the majority were confident in their ability to navigate the plethora of online health information. However, parents of children with chronic conditions had unmet information needs in relation to COVID-19 and their children's condition. CONCLUSIONS: Understanding parents' attitudes and behaviours when seeking online health information that is relevant to their children during a global pandemic can inform the optimisation of online health content delivery to parents.

The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data.

BACKGROUND: Respiratory syncytial virus (RSV) is a major cause of respiratory infection in children. Despite usually following a consistent seasonal pattern, the 2020-2021 RSV season in many countries was delayed and changed in magnitude. OBJECTIVE: This study aimed to test if these changes can be attributed to nonpharmaceutical interventions (NPIs) instituted around the world to combat SARS-CoV-2. METHODS: We used the internet search volume for RSV, as obtained from Google Trends, as a proxy to investigate these abnormalities. RESULTS: Our analysis shows a breakdown of the usual correlation between peak latency and magnitude during the year of the pandemic. Analyzing latency and magnitude separately, we found that the changes therein are associated with implemented NPIs. Among several important interventions, NPIs affecting population mobility are shown to be particularly relevant to RSV incidence. CONCLUSIONS: The 2020-2021 RSV season served as a natural experiment to test NPIs that are likely to restrict RSV spread, and our findings can be used to guide health authorities to possible interventions.

Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.

BACKGROUND: Due to the COVID-19 pandemic, health information related to COVID-19 has spread across news media worldwide. Google is among the most used internet search engines, and the Google Trends tool can reflect how the public seeks COVID-19-related health information during the pandemic. OBJECTIVE: The aim of this study was to understand health communication through Google Trends and news coverage and to explore their relationship with prevention and control of COVID-19 at the early epidemic stage. METHODS: To achieve the study objectives, we analyzed the public's information-seeking behaviors on Google and news media coverage on COVID-19. We collected data on COVID-19 news coverage and Google search queries from eight countries (ie, the United States, the United Kingdom, Canada, Singapore, Ireland, Australia, South Africa, and New Zealand) between January 1 and April 29, 2020. We depicted the characteristics of the COVID-19 news coverage trends over time, as well as the search query trends for the topics of COVID-19-related "diseases," "treatments and medical resources," "symptoms and signs," and "public measures." The search query trends provided the relative search volume (RSV) as an indicator to represent the popularity of a specific search term in a specific geographic area over time. Also, time-lag correlation analysis was used to further explore the relationship between search terms trends and the number of new daily cases, as well as the relationship between search terms trends and news coverage. RESULTS: Across all search trends in eight countries, almost all search peaks appeared between March and April 2020, and declined in April 2020. Regarding COVID-19-related "diseases," in most countries, the RSV of the term "coronavirus" increased earlier than that of "covid-19"; however, around April 2020, the search volume of the term "covid-19" surpassed that of "coronavirus." Regarding the topic "treatments and medical resources," the most and least searched terms were "mask" and "ventilator," respectively. Regarding the topic "symptoms and signs," "fever" and "cough" were the most searched terms. The RSV for the term "lockdown" was significantly higher than that for "social distancing" under the topic "public health measures." In addition, when combining search trends with news coverage, there were three main patterns: (1) the pattern for Singapore, (2) the pattern for the United States, and (3) the pattern for the other countries. In the time-lag correlation analysis between the RSV for the topic "treatments and medical resources" and the number of new daily cases, the RSV for all countries except Singapore was positively correlated with new daily cases, with a maximum correlation of 0.8 for the United States. In addition, in the time-lag correlation analysis between the overall RSV for the topic "diseases" and the number of daily news items, the overall RSV was positively correlated with the number of daily news items, the maximum correlation coefficient was more than 0.8, and the search behavior occurred 0 to 17 days earlier than the news coverage. CONCLUSIONS: Our findings revealed public interest in masks, disease control, and public measures, and revealed the potential value of Google Trends in the face of the emergence of new infectious diseases. Also, Google Trends combined with news media can achieve more efficient health communication. Therefore, both news media and Google Trends can contribute to the early prevention and control of epidemics.

Searching for online information on the fit of children's footwear during the COVID-19 pandemic: an analysis of Google Trends data.

BACKGROUND: Selecting footwear with appropriate fit in children is challenging due the changes with foot size and dimensions which occur throughout childhood. Access to appropriate footwear is important but recent challenges with the COVID-19 pandemic resulted in closure of retail stores for prolonged periods where parents/carers could not physically purchase footwear for their children and the footwear industry suffered disruption to their supply chain, and falls in retail sales. Simultaneously increased use of social media platforms for health information seeking throughout the pandemic have been documented. This likely would have included parents/carers seeking information online to support footwear purchases for their children. The primary aim of this work was to explore how searches for online fitting information for children changed throughout the COVID-19 pandemic lockdown periods. A secondary aim was to identify how searches were influenced by footwear style. METHODS:  We employed Google Trends to obtain search engine traffic related to footwear fitting information for children. We collected data spanning the three years pre, during and post the main national lockdown for three eight-week windows: (1) first eight weeks of the U.K. national lockdown; (2) the first eight weeks of the calendaryear; (3) the eight weeks leading up to children going back-to-school for the new academic year in the U.K. The search terms reflected parents/carers searching for footwear fit information relating to children and were grouped by style of footwear: children, infants, babies and toddlers as well as school shoes. RESULTS: We identified increased searching for footwear fit information for children during the pandemic, which reduced following post pandemic in all except the searches which related to school shoes. We saw reductions in searching related to fit of school shoes as schools closed indefinitely and an increase in online searches with the pandemic. This was also maintained post-pandemic despite shops reopening, suggesting that some of these changes in information reflect new consumer behaviours which may continue. CONCLUSIONS:  Increased searches for online resources regarding footwear fit highlights the importance of ensuring high quality accessible online information on footwear fit is available to support those buying footwear for their children.

Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia.

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.