Multiple Confidence Intervals and Surprisal Intervals to Avoid Significance Fallacy.

Overconfidence in statistical results in medicine is fueled by improper practices and historical biases afflicting the concept of statistical significance. In particular, the dichotomization of significance (i.e., significant vs. not significant), blending of Fisherian and Neyman-Pearson approaches, magnitude and nullification fallacies, and other fundamental misunderstandings distort the purpose of statistical investigations entirely, impacting their ability to inform public health decisions or other fields of science in general. For these reasons, the international statistical community has attempted to propose various alternatives or different interpretative modes. However, as of today, such misuses still prevail. In this regard, the present paper discusses the use of multiple confidence (or, more aptly, compatibility) intervals to address these issues at their core. Additionally, an extension of the concept of confidence interval, called surprisal interval (S-interval), is proposed in the realm of statistical surprisal. The aforementioned is based on comparing the statistical surprise to an easily interpretable phenomenon, such as obtaining S consecutive heads when flipping a fair coin. This allows for a complete departure from the notions of statistical significance and confidence, which carry with them longstanding misconceptions.

Correction: Multiple Confidence Intervals and Surprisal Intervals to Avoid Significance Fallacy.

[This corrects the article DOI: 10.7759/cureus.51964.].

There is a need for more precise models to assess the determinants of health crises like COVID-19.

The COVID-19 pandemic has had a significant impact on global mortality. While the causal relationship between SARS-CoV-2 and the anomalous increase in deaths is established, more precise and complex models are needed to determine the exact weight of epidemiological factors involved. Indeed, COVID-19 behavior is influenced by a wide range of variables, including demographic characteristics, population habits and behavior, healthcare performance, and environmental and seasonal risk factors. The bidirectional causality between impacted and impacting aspects, as well as confounding variables, complicates efforts to draw clear, generalizable conclusions regarding the effectiveness and cost-benefit ratio of non-pharmaceutical health countermeasures. Thus, it is imperative that the scientific community and health authorities worldwide develop comprehensive models not only for the current pandemic but also for future health crises. These models should be implemented locally to account for micro-differences in epidemiological characteristics that may have relevant effects. It is important to note that the lack of a universal model does not imply that local decisions have been unjustified, and the request to decrease scientific uncertainty does not mean denying the evidence of the effectiveness of the countermeasures adopted. Therefore, this paper must not be exploited to denigrate either the scientific community or the health authorities.

A Framework to Avoid Significance Fallacy.

This manuscript presents a concise approach to tackle the widespread misuse of statistical significance in scientific research, focusing on public health. It offers practical guidance for conducting accurate statistical evaluations and promoting easily understandable results based on actual evidence. When conducting a statistical study to inform decision-making, it is recommended to follow a step-by-step sequence while considering various factors. Firstly, multiple target hypotheses should be adopted to assess the compatibility of experimental data with different models. Reporting all P-values in full, rounded in order to have a single non-zero significant digit, enhances transparency and reduces the likelihood of exaggerating the state of the evidence. Detailed documentation of the procedures used to evaluate the compatibility between test assumptions and data should be provided for rigorous assessment. A descriptive evaluation of results can be aided by using statistical compatibility ranges, which help avoid misrepresenting the evidence. Separately evaluating and reporting statistical compatibility and effect size prevents the magnitude fallacy. Additionally, reporting measures of statistical effect size enables evaluation of sectoral relevance, such as clinical significance. Multiple compatibility intervals, such as 99%, 95%, and 90% confidence intervals, should be reported to allow readers to assess the variation of P-values based on the width of the interval. These recommendations aim to enhance the robustness and interpretability of statistical analyses and promote transparent reporting of findings. The author encourages journal adoption of similar frameworks to enhance scientific rigor, particularly in the field of medical science.

Common Statistical Errors in Scientific Investigations: A Simple Guide to Avoid Unfounded Decisions.

During my experience as an author, peer reviewer, and editor during COVID-19, I have encountered - and committed - various errors related to the interpretation and use of statistical measures and tests. Primarily concerning health sciences such as epidemiology, infodemiology, and public health, the evidence used to inform a conclusion carries an extremely high weight as it translates into decisions made to preserve the population's well-being. Therefore, the aforementioned evidence must be reliable. This short guide discusses the most common and dangerous mistakes I have experienced during my scientific journey. Real and invented examples have been proposed and analyzed in detail, showing possible interpretations, both correct and incorrect, and their consequences. Such a framework makes it clear that a statistical test alone cannot answer any scientific questions. Indeed, the interpretation of results and the verification of assumptions and test eligibility - subject to the author's evaluation - are crucial components of the integrity of the scientific investigation. Before using a test or adopting a measure, we must ask ourselves the following fundamental questions: Are there valid reasons to explore my research question? Am I sure my approach can fully and adequately answer my research question? Am I sure that my model's assumptions - basic and hidden - are sufficiently satisfied? How could violating those assumptions affect the validity of the results and stakeholders? Is the effect size relevant regardless of statistical significance?

Are We Sure We Fully Understand What an Infodemic Is? A Global Perspective on Infodemiological Problems.

Infodemic is defined as an information epidemic that can lead to engaging in dangerous behavior. Although the most striking manifestations of the latter occurred on social media, some studies show that dismisinformation is significantly influenced by numerous additional factors, both web-based and offline. These include social context, age, education, personal knowledge and beliefs, mood, psychological defense mechanisms, media resonance, and how news and information are presented to the public. Moreover, various incorrect scientific practices related to disclosure, publication, and training can also fuel such a phenomenon. Therefore, in this opinion article, we seek to provide a comprehensive overview of the issues that need to be addressed to bridge the gap between science and the public and build resilience to the infodemic. In particular, we stress that the infodemic cannot be curbed by simply disproving every single false or misleading information since the belief system and the cultural or educational background are chief factors regarding the success of fake news. For this reason, we believe that the process of forming a critical sense should begin with children in schools (ie, when the mind is more receptive to new ways of learning). Furthermore, we also believe that themes such as scientific method and evidence should be at the heart of the university education of a future scientist. Indeed, both the public and scientists must be educated on the concepts of evidence and validity of sources, as well as learning how to dialogue appropriately with each other. Finally, we believe that the scientific publishing process could be greatly improved by paying reviewers for their work and by ceasing to pursue academic success at all costs.

Google Trends as a Predictive Tool for COVID-19 Vaccinations in Italy: Retrospective Infodemiological Analysis.

Background: Google Trends is an infoveillance tool widely used by the scientific community to investigate different user behaviors related to COVID-19. However, several limitations regarding its adoption are reported in the literature. Objective: This paper aims to provide an effective and efficient approach to investigating vaccine adherence against COVID-19 via Google Trends. Methods: Through the cross-correlational analysis of well-targeted hypotheses, we investigate the predictive capacity of web searches related to COVID-19 toward vaccinations in Italy from November 2020 to November 2021. The keyword "vaccine reservation" query (VRQ) was chosen as it reflects a real intention of being vaccinated (V). Furthermore, the impact of the second most read Italian newspaper (vaccine-related headlines [VRH]) on vaccine-related web searches was investigated to evaluate the role of the mass media as a confounding factor. Fisher r-to-z transformation (z) and percentage difference (δ) were used to compare Spearman coefficients. A regression model V=f(VRH, VRQ) was built to validate the results found. The Holm-Bonferroni correction was adopted (P*). SEs are reported. Results: Simple and generic keywords are more likely to identify the actual web interest in COVID-19 vaccines than specific and elaborated keywords. Cross-correlations between VRQ and V were very strong and significant (min r²=0.460, P*<.001, lag 0 weeks; max r²=0.903, P*<.001, lag 6 weeks). The remaining cross-correlations have been markedly lower (δ>55.8%; z>5.8; P*<.001). The regression model confirmed the greater significance of VRQ versus VRH (P*<.001 vs P=.03, P*=.29). Conclusions: This research provides preliminary evidence in favor of using Google Trends as a surveillance and prediction tool for vaccine adherence against COVID-19 in Italy. Further research is needed to establish the appropriate use and limits of Google Trends for vaccination tracking. However, these findings prove that the search for suitable keywords is a fundamental step to reduce confounding factors. Additionally, targeting hypotheses helps diminish the likelihood of spurious correlations. It is recommended that Google Trends be leveraged as a complementary infoveillance tool by government agencies to monitor and predict vaccine adherence in this and future crises by following the methods proposed in this paper.

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends.

BACKGROUND: Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy. OBJECTIVE: This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series. METHODS: We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis. RESULTS: Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006). CONCLUSIONS: Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19-related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy.

The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants.

World Health Organization is losing online credibility towards health-sensitive topics: Infodemiological analysis of Facebook users' reactions.

Background: The scientific infodemic constitutes one of the greatest threats to public health and safety today. The credibility of the main dissemination agencies is an essential tool for adhering to measures to preserve public health. Methods: The study is a longitudinal retrospective conducted on a web platform to investigate netizens' infodemic attitude towards World Health Organization. Reactions such as "like," "love," "affection," "surprise," "sadness," "anger," and "derision" were collected under World Health Organization (WHO) Facebook posts on climate change (from 2019 to 2022) and vaccines (from 2021 to 2022). Descriptive statistics, linear regression, and correlation methods were implemented to identify possible trends and relationships with the COVID-19 vaccination campaign. Results: These findings showed a worrying increase in derision reactions about climate change-related posts (up to 22% in November 2022, with a quadratically growing trend over time since December 2020). Furthermore, infodemic reactions such as anger and especially derision made up the majority of emotional reactions to vaccine-related posts since 2021 and up to 44% of total reactions in November 2022 (median since July 2021=9%, IQR: 4%-14%). Finally, there is evidence of a correlation between the start of the COVID-19 vaccination campaign and public distrust towards the WHO, even for issues unrelated to vaccines such as climate change. Conclusion: Based on what is known in the literature, these preliminary findings signal that the WHO is losing online public credibility towards extremely relevant issues for global health. Infodemiological interventions in accordance with the recent literature are urgently required.

Influence of Mass Media on Italian Web Users During the COVID-19 Pandemic: Infodemiological Analysis.

BACKGROUND: Concurrently with the COVID-19 pandemic, the world has been facing a growing infodemic, which has caused severe damage to economic and health systems and has often compromised the effectiveness of infection containment regulations. Although this infodemic has spread mainly through social media, there are numerous occasions on which mass media outlets have shared dangerous information, giving resonance to statements without a scientific basis. For these reasons, infoveillance and infodemiology methods are increasingly exploited to monitor information traffic on the web and make epidemiological predictions. OBJECTIVE: The purpose of this paper is to estimate the impact of Italian mass media on users' web searches to understand the role of press and television channels in both the infodemic and the interest of Italian netizens in COVID-19. METHODS: We collected the headlines published from January 2020 to March 2021 containing specific COVID-19-related keywords published on PubMed, Google, the Italian Ministry of Health website, and the most-read newspapers in Italy. We evaluated the percentages of infodemic terms on these platforms. Through Google Trends, we searched for cross-correlations between newspaper headlines and COVID-19-related web searches. Finally, we analyzed the web interest in infodemic content posted on YouTube. RESULTS: During the first wave of COVID-19, the Italian press preferred to draw on infodemic terms (rate of adoption: 1.6%-6.3%) and moderately infodemic terms (rate of adoption: 88%-94%), while scientific sources favored the correct names (rate of adoption: 65%-88%). The correlational analysis showed that the press heavily influenced users in adopting terms to identify the novel coronavirus (cross-correlations of ≥0.74 to ≤0.89, P value <.001; maximum lag=1 day). The use of scientific denominations by the press reached acceptable values only during the third wave (approximately 80%, except for the television services Rai and Mediaset). Web queries about COVID-19 symptoms also appeared to be influenced by the press (best average correlation=0.92, P<.007). Furthermore, web users showed pronounced interest in YouTube videos of an infodemic nature. Finally, the press gave resonance to serious "fake news" on COVID-19, which caused pronounced spikes of interest from web users. CONCLUSIONS: Our results suggest that the Italian mass media have played a decisive role in spreading the COVID-19 infodemic and addressing netizens' web interest, thus favoring the adoption of terms that are unsuitable for identifying COVID-19. Therefore, the directors of news channels and newspapers should be more cautious, and government dissemination agencies should exert more control over such news stories.

The Impact of Cheering on Sports Performance: Comparison of Serie A Statistics Before and During COVID-19.

BACKGROUND:  The role of cheering in home advantage in sports performance is unclear. As anti-coronavirus disease 2019 (COVID-19) restrictive measures have prevented crowds from entering stadiums, analysis of the past two football seasons can reveal important details. OBJECTIVE:  This paper aims to compare the last two football seasons in Italy with the previous six, highlighting changes due to the absence of cheering. METHODS:  We compared the average percentages of points obtained in home matches from 2013 to 2019 with those in the timelapse 2019-2021. The same operation was performed with referee statistics, such as fouls, penalties, and cards awarded against home teams. To do this, we used Welch's t-test and percentage increases. Pearson and Spearman's correlations were searched between the percentages of points collected in home matches and total points earned from 2013 to 2021. RESULTS: The average percentage of points collected by teams in home matches dropped by 8% (Welch's t = -4.3). The negative correlations between home collected points and total points in 2013-2019 timelapse have significantly diminished during the last two seasons (Welch's t = 6.2), approaching zero. Penalties against home teams have increased by 30% (Welch's t = 2.6), reaching 51.4%. CONCLUSIONS:  This research provides statistical evidence supporting the crowd's impact on sports and refereeing performance in Serie A. However, our results also suggest that part of the home advantage is linked to factors independent of the audience. Future research can deepen the above phenomena from a theoretical-psychological point of view.

The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends.

BACKGROUND: COVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti-COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information. OBJECTIVE: The aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users. METHODS: Google Trends was used to monitor users' web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net-an Italian website specializing in detecting online hoaxes-and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre-COVID-19) and 2020-2021 (post-COVID-19) were compared through percentage difference (∆%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9. RESULTS: The COVID-19 pandemic drastically increased Italian netizens' interest in conspiracies (∆% ∈ [60, 288], t ∈ [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre-COVID-19 period (average RSV=80±2.8, t min=1.8, ∆min%=+12.4, min∆SD%=-25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t 1=11.3 vs t 2=4.5; Δ1%=+157.6 vs Δ2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ∆%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04% of the entire web interest in the pandemic. CONCLUSIONS: COVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users' interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect-related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users' response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception.

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research.

Background: Alongside the COVID-19 pandemic, government authorities around the world have had to face a growing infodemic capable of causing serious damages to public health and economy. In this context, the use of infoveillance tools has become a primary necessity. Objective: The aim of this study is to test the reliability of a widely used infoveillance tool which is Google Trends. In particular, the paper focuses on the analysis of relative search volumes (RSVs) quantifying their dependence on the day they are collected. Methods: RSVs of the query coronavirus + covid during February 1-December 4, 2020 (period 1), and February 20-May 18, 2020 (period 2), were collected daily by Google Trends from December 8 to 27, 2020. The survey covered Italian regions and cities, and countries and cities worldwide. The search category was set to all categories. Each dataset was analyzed to observe any dependencies of RSVs from the day they were gathered. To do this, by calling i the country, region, or city under investigation and j the day its RSV was collected, a Gaussian distribution X i = X ( σ i , x ¯ i ) was used to represent the trend of daily variations of x i j = R S V s i j . When a missing value was revealed (anomaly), the affected country, region or city was excluded from the analysis. When the anomalies exceeded 20% of the sample size, the whole sample was excluded from the statistical analysis. Pearson and Spearman correlations between RSVs and the number of COVID-19 cases were calculated day by day thus to highlight any variations related to the day RSVs were collected. Welch's t-test was used to assess the statistical significance of the differences between the average RSVs of the various countries, regions, or cities of a given dataset. Two RSVs were considered statistical confident when t < 1.5 . A dataset was deemed unreliable if the confident data exceeded 20% (confidence threshold). The percentage increase Δ was used to quantify the difference between two values. Results: Google Trends has been subject to an acceptable quantity of anomalies only as regards the RSVs of Italian regions (0% in both periods 1 and 2) and countries worldwide (9.7% during period 1 and 10.9% during period 2). However, the correlations between RSVs and COVID-19 cases underwent significant variations even in these two datasets ( M a x   | Δ |   =   +   625 % for Italian regions, and M a x   | Δ | =   + 175 %   for countries worldwide). Furthermore, only RSVs of countries worldwide did not exceed confidence threshold. Finally, the large amount of anomalies registered in Italian and international cities' RSVs made these datasets unusable for any kind of statistical inference. Conclusion: In the considered timespans, Google Trends has proved to be reliable only for surveys concerning RSVs of countries worldwide. Since RSVs values showed a high dependence on the day they were gathered, it is essential for future research that the authors collect queries' data for several consecutive days and work with their RSVs averages instead of daily RSVs, trying to minimize the standard errors until an established confidence threshold is respected. Further research is needed to evaluate the effectiveness of this method.

Relationships between Demographic, Geographic, and Environmental Statistics and the Spread of Novel Coronavirus Disease (COVID-19) in Italy.

BACKGROUND: Since January 2020, the coronavirus disease 2019 (COVID-19) pandemic has raged around the world, causing nearly a million deaths and hundreds of severe economic crises. In this scenario, Italy has been one of the most affected countries. OBJECTIVE: This study investigated significant correlations between COVID-19 cases and demographic, geographical, and environmental statistics of each Italian region from February 26 to August 12, 2020. We further investigated the link between the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and particulate matter (PM) 2.5 and 10 concentrations before the lockdown in Lombardy. METHODS: All demographic data were obtained from the AdminStat Italia website, and geographic data were from the Il Meteo website. The collection frequency was one week. Data on PM2.5 and PM10 average daily concentrations were collected from previously published articles. We used Pearson's coefficients to correlate the quantities that followed a normal distribution, and Spearman's coefficient to correlate quantities that did not follow a normal distribution. RESULTS: We found significant strong correlations between COVID-19 cases and population number in 60.0% of the regions. We also found a significant strong correlation between the spread of SARS-CoV-2 in the various regions and their latitude, and with the historical averages (last 30 years) of their minimum temperatures. We identified a significant strong correlation between the number of COVID-19 cases until August 12 and the average daily concentrations of PM2.5 in Lombardy until February 29, 2020. No significant correlation with PM10 was found in the same long periods. However, we found that 40 µg/m^3 for PM2.5 and 50 µg/m^3 for PM10 are plausible thresholds beyond which particulate pollution clearly favors the spread of SARS-CoV-2. CONCLUSION: Since SARS-CoV-2 is correlated with historical minimum temperatures and PM10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in enclosed places and to avoid exposure to the cold.

The Impact of COVID-19 on Italian Web Users: A Quantitative Analysis of Regional Hygiene Interest and Emotional Response.

BACKGROUND: Between the end of February and the beginning of June 2020, Italy was certainly one of the worst affected countries in the world by the coronavirus disease 2019 (COVID-19) pandemic. During this period, Web interest in the novel coronavirus underwent a drastic surge. OBJECTIVE: The aim of this study was to quantitatively analyze the impact of COVID-19 on Web searches related to hygiene-preventive measures and emotional-psychological aspects as well as to estimate the effectiveness and limits of online information during an epidemic. We looked for significant correlations between COVID-19 relative search volumes and cases per region to understand the interest of the average Italian Web user during international, national, and regional COVID-19 situations. By doing so, it will be possible to deduce the mental and physical health of the population. METHODS: We used the Google Trends tool, which returns normalized values called relative search volumes (RSV), ​​ranging from 0 to 100 according to the Web popularity of a group of queries. By comparing the RSVs in periods before and after the outbreak of the novel coronavirus in Italy, we derived the impact of COVID-19 on the activity of Italian netizens towards novel coronavirus itself, specifically regarding hygiene, prevention, and psychological well-being. Furthermore, we calculated Pearson's correlations ρ between all these queries and COVID-19 cases for each region. We chose a p-value ([Formula: see text]) threshold α=.1. RESULTS: The general Web interest in COVID-19 in Italy waned, as did the correlation with the official number of cases per region (p<.1 only until March 14). Web interest was similarly distributed across the regions (average search volume [ASV]=92, standard deviation [SD]=6). We found that all trends depend significantly on the number of COVID-19 cases at the national but not international or regional levels. Between February 20 and June 10, Web interest related to hygiene and prevention increased by 116% and 901%, respectively, compared to those from January 1 to February 19, 2020 (95%CIs: [115.3, 116.3], [850.3, 952.2]). Significant correlations between regional cumulative Web searches and COVID-19 cases were found between February 26 and March 7 ([Formula: see text]=.43, 95%CI: [.42, .44], p=.07). During the COVID-19 pandemic until June 10, 2020, national Web searches of the generic terms "fear" and "anxiety" grew by 8% and 21%, respectively (95%CIs: [8.0, 8.2], [20.4, 20.6]), compared to those of the period of January 1, 2018 - December 29, 2019. We found cyclically significant correlations between negative emotions related to the novel coronavirus and COVID-19 official data. CONCLUSIONS: Italian netizens showed a marked interest in the COVID-19 pandemic only when this became a direct national problem. Web searches have rarely been correlated with the number of cases per region; we conclude that the danger was perceived similarly in all regions. The period of maximum effectiveness of online information in relation to this type of situation is limited to three to four days from a specific key event. We suggest that all government agencies focus their Web disclosure efforts over that time. We found cyclical correlations with Web searches related to negative feelings such as anxiety, depression, fear, and stress. Therefore, to identify mental and physical health problems among the population, it suffices to observe slight variations in the trend of related Web queries.

Modeling the Epidemiological Trend and Behavior of COVID-19 in Italy.

As of May 14, 2020, Italy has been one of the red hotspots for the COVID-19 pandemic. In particular, the regions of Emilia Romagna, Piedmont, and especially Lombardy were the most affected and had to face very serious health emergencies, which brought them to the brink of collapse. Since the virus has demonstrated local properties, i.e., greater severity and contagiousness in specific regions, the aim of this study is to model the complex behavior of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy. In particular, we further investigated the results of other articles on the correlation with particulate matter pollution 10 (PM 10) and 2.5 (PM 2.5) by extending the research at the intra-regional level, as well as calculated a more plausible number of those infected compared to those officially declared by Civil Protection. Through a computational simulation of the Susceptible-Exposed-Infectious-Recovered (S.E.I.R.) model, we also estimated the most representative basic reproduction number [Formula: see text] for these three regions from February 22 to March 14, 2020. In doing so, we have been able to evaluate the consistency of the first containment measures until the end of April, as well as identify possible SARS-CoV-2 local behavior mutations and specificities.

Correction: Modeling the Epidemiological Trend and Behavior of COVID-19 in Italy.

[This corrects the article DOI: 10.7759/cureus.9884.].