Discrepancies Between Classic and Digital Epidemiology in Searching for the Mayaro Virus: Preliminary Qualitative and Quantitative Analysis of Google Trends

Trends in Online Search Activity and the Correlation with Daily New Cases of Monkeypox among 102 Countries or Territories

Research assessing the trend in online search activity on monkeypox (mpox) and the correlation with the mpox epidemic at the global and national level is scarce. The trend of online search activity and the time-lag correlations between it and daily new mpox cases were estimated by using segmented interrupted time-series analysis and Spearman correlation coefficient (rs), respectively. We found that after the declaration of a Public Health Emergency of International Concern (PHEIC), the proportion of countries or territories with increasing changes in online search activity was lowest in Africa (8.16%, 4/49), and a downward trend in online search activity was highest in North America (8/31, 25.81%). The time-lag effect of global online search activity on daily new cases was significant (rs = 0.24). There were eight countries or territories with significant time-lag effect; the top three countries or territories were Brazil (rs = 0.46), United States (rs = 0.24), and Canada (rs = 0.24). Interest behavior in mpox was insufficient, even after the declaration of PHEIC, especially in Africa and North America. Online search activity could be used as an early indicator of the outbreak of mpox at the global level and in epidemic countries.

Association between public attention and monkeypox epidemic: A global lag-correlation analysis

The human monkeypox has become a public health problem globally. Google Trends Index (GTI) is an indicator of public attention, being potential for infectious disease outbreak surveillance. In this study, we used lag-correlation analysis to evaluate the spearman correlation coefficients between public attention and monkeypox epidemic by -36 to +36 days-lag in top 20 countries with most cumulated cases until September 30, 2022, the meta-analyses were performed to pool the coefficients of countries among all lags. We also constructed vector autoregression model and Granger-causality test to probe the significance of GTI in monkeypox forecasting. The strongest spearman correlation was found at lag +13 day (r = 0.53, 95% confidence interval: 0.371-0.703, p < 0.05). Meta-analysis showed significantly positive correlation when the lag was from -12 to +36 day, which was most notable on the third posterior day (lag +3 day). The pooled spearman correlation coefficients were all above 0.200 when the lag ranged from +1 to +20 day, and the causality of GTI for daily case was significant in worldwide and multiple countries. The findings suggested a robust association between 13-days-priority GTI and daily cases worldwide. This work introduced a potential monitor indicator on the early warning and surveillance of monkeypox outbreak.

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study

Background

Previous studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions.

Objective

The aim of this study is to investigate the relationship between Google Trends searches of symptoms associated with COVID-19 and confirmed COVID-19 cases and deaths. We aim to develop predictive models to forecast the COVID-19 epidemic based on a combination of Google Trends searches of symptoms and conventional COVID-19 metrics.

Methods

An open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal component analysis (PCA) and time series modeling. The application facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected the data of nine countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (error, trend, seasonality; autoregressive integrated moving average; and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root mean square error (RMSE) of the first principal component (PC1). The predictive abilities of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only.

Results

The degree of correlation and the best time lag varied as a function of the selected country and topic searched; in general, the optimal time lag was within 15 days. Overall, predictions of PC1 based on both search terms and COVID-19 traditional metrics performed better than those not including Google searches (median 1.56, IQR 0.90-2.49 versus median 1.87, IQR 1.09-2.95, respectively), but the improvement in prediction varied as a function of the selected country and time frame. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median 0.90, IQR 0.50-1.53 versus median 2.27, IQR 1.62-3.74, respectively).

Conclusions

The inclusion of digital online searches in statistical models may improve the nowcasting and forecasting of the COVID-19 epidemic and could be used as one of the surveillance systems of COVID-19 disease. We provide a free web application operating with nearly real-time data that anyone can use to make predictions of outbreaks, improve estimates of the dynamics of ongoing epidemics, and predict future or rebound waves.

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics.

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews

Background: Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the methodological complexity of demonstrating their measurable impact on human health. Even though new big data sources have shown unprecedented potential for disease diagnosis and outbreak detection, we need to investigate results in the existing literature to gain a comprehensive understanding of their impact on and benefits to human health. Objective: A systematic review of systematic reviews on identifying digital data sources and their impact area on people's health, including challenges, opportunities, and good practices. Methods: A multidatabase search was performed. Peer-reviewed papers published between January 2010 and November 2020 relevant to digital data sources on health were extracted, assessed, and reviewed. Results: The 64 reviews are covered by three domains, that is, universal health coverage (UHC), public health emergencies, and healthier populations, defined in WHO's General Programme of Work, 2019-2023, and the European Programme of Work, 2020-2025. In all three categories, social media platforms are the most popular digital data source, accounting for 47% (N = 8), 84% (N = 11), and 76% (N = 26) of studies, respectively. The second most utilized data source are electronic health records (EHRs) (N = 13), followed by websites (N = 7) and mass media (N = 5). In all three categories, the most studied impact of digital data sources is on prevention, management, and intervention of diseases (N = 40), and as a tool, there are also many studies (N = 10) on early warning systems for infectious diseases. However, they could also pose health hazards (N = 13), for instance, by exacerbating mental health issues and promoting smoking and drinking behavior among young people. Conclusions: The digital data sources presented are essential for collecting and mining information about human health. The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems. This study also identified the apparent gap in systematic reviews investigating the novel big data streams, Internet of Things (IoT) data streams, and sensor, mobile, and GPS data researched using artificial intelligence, complex network, and other computer science methods, as in this domain systematic reviews are not common.

A New Era of Epidemiology: Digital Epidemiology for Investigating the COVID-19 Outbreak in China

A novel pneumonia-like coronavirus disease (COVID-19) caused by a novel coronavirus named SARS-CoV-2 has swept across China and the world. Public health measures that were effective in previous infection outbreaks (eg, wearing a face mask, quarantining) were implemented in this outbreak. Available multidimensional social network data that take advantage of the recent rapid development of information and communication technologies allow for an exploration of disease spread and control via a modernized epidemiological approach. By using spatiotemporal data and real-time information, we can provide more accurate estimates of disease spread patterns related to human activities and enable more efficient responses to the outbreak. Two real cases during the COVID-19 outbreak demonstrated the application of emerging technologies and digital data in monitoring human movements related to disease spread. Although the ethical issues related to using digital epidemiology are still under debate, the cases reported in this article may enable the identification of more effective public health measures, as well as future applications of such digitally directed epidemiological approaches in controlling infectious disease outbreaks, which offer an alternative and modern outlook on addressing the long-standing challenges in population health.

Insights into the autoimmune aspect of premature ovarian insufficiency

Premature ovarian insufficiency (POI) refers to a continuum of decreasing ovarian function in women before the age of 40. To date, the cause of POI in the majority of cases remain unresolved. Many cases has been linked to genetic, toxic, infections, enzymatic and iatrogenic causes. A key function of the immune system is to identify and differentiate "self" and "non self" i.e. tolerance. Loss of self-tolerance results in an immune response against self-tissues and thus autoimmunity. Various investigations have highlighted the role of autoimmunity and its pertinence to POI. Several potential immune antigenic targets in the ovary have been reported to be involved in autoantibody induced autoimmune attack. The presence of lymphocytic oöphorits in ovarian samples of patients with POI provides histopathological evidence of autoimmune ovarian involvement. Finally, POI is strongly associated with other autoimmune conditions including for instance Addison disease, autoimmune polyglandular syndrome (APS) -1, APS-4, hypothyroidism, and diabetes mellitus among other autoimmune diseases. Taken together, these lines of evidence provide strong basis that support the role of autoimmunity as a potential cause of disease etiopathogenesis. Continuing research is increasingly providing more insight into the complex disease process. The aim of this review is to summarize the current literature related to the autoimmune nature of POI.

Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study

BACKGROUND

Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases.

OBJECTIVE

In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public's interest.

METHODS

We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern.

RESULTS

Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model.

CONCLUSIONS

An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams-Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

BACKGROUND

West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus.

OBJECTIVE

This study aimed at exploring the potential predictive power of West Nile virus-related Web searches.

METHODS

Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis.

RESULTS

In the regression analysis, an association between Web searches and "real-world" epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages.

CONCLUSIONS

Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

Correlation between Google Trends on dengue fever and national surveillance report in Indonesia

Background: Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. No study was performed to measure the correlation between country wide official dengue reports and Google Trends data in Indonesia. Objective: This study aims to measure the correlation between Google Trends data on dengue fever and the Indonesian national surveillance report. Methods: This research was a quantitative study using time series data (2012-2016). Two sets of data were analyzed using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the correlation between those data. Results: Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for three defined search terms with R-value range from 0.921 to 0.937 (p ≤ 0.05, overall period) which showed increasing trend in epidemic periods (2015-2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Conclusions: Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information-seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

Can Google Trends data improve forecasting of Lyme disease incidence?

BACKGROUND

Online activity-based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick-borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends.

METHODS

Data on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching "Borreliose" in Germany for the "last 5 years" as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set.

RESULTS

Forecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values-expanded model. The difference between the predictive performances was insignificant (Diebold-Mariano Test, p-value = 0.4152).

CONCLUSION

Google Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.

Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature

Aim

According to the World Health Organization (WHO), communicable tropical and sub-tropical diseases occur solely, or mainly in the tropics, thriving in hot, and humid conditions. Some of these disorders termed as neglected tropical diseases are particularly overlooked. Communicable tropical/sub-tropical diseases represent a diverse group of communicable disorders occurring in 149 countries, favored by tropical and sub-tropical conditions, affecting more than one billion people and imposing a dramatic societal and economic burden.

Methods

A systematic review of the extant scholarly literature was carried out, searching in PubMed/MEDLINE and Scopus. The search string used included proper keywords, like big data, nontraditional data sources, social media, social networks, infodemiology, infoveillance, novel data streams (NDS), digital epidemiology, digital behavior, Google Trends, Twitter, Facebook, YouTube, Instagram, Pinterest, Ebola, Zika, dengue, Chikungunya, Chagas, and the other neglected tropical diseases.

Results

47 original, observational studies were included in the current systematic review: 1 focused on Chikungunya, 6 on dengue, 19 on Ebola, 2 on Malaria, 1 on Mayaro virus, 2 on West Nile virus, and 16 on Zika. Fifteen were dedicated on developing and validating forecasting techniques for real-time monitoring of neglected tropical diseases, while the remaining studies investigated public reaction to infectious outbreaks. Most studies explored a single nontraditional data source, with Twitter being the most exploited tool (25 studies).

Conclusion

Even though some studies have shown the feasibility of utilizing NDS as an effective tool for predicting epidemic outbreaks and disseminating accurate, high-quality information concerning neglected tropical diseases, some gaps should be properly underlined. Out of the 47 articles included, only 7 were focusing on neglected tropical diseases, while all the other covered communicable tropical/sub-tropical diseases, and the main determinant of this unbalanced coverage seems to be the media impact and resonance. Furthermore, efforts in integrating diverse NDS should be made. As such, taking into account these limitations, further research in the field is needed.