Forecasting AIDS prevalence in the United States using online search traffic data

Over the past decade and with the increasing use of the Internet, the assessment of health issues using online search traffic data has become an integral part of Health Informatics. Internet data in general and from Google Trends in particular have been shown to be valid and valuable in predictions, forecastings, and nowcastings; and in detecting, tracking, and monitoring diseases’ outbreaks and epidemics. Empirical relationships have been shown to exist between Google Trends’ data and official data in several health topics, with the science of infodemiology using the vast amount of information available online for the assessment of public health and policy matters. The aim of this study is to provide a method of forecasting AIDS prevalence in the US using online search traffic data from Google Trends on AIDS related terms. The results at first show that significant correlations between Google Trends’ data and official health data on AIDS prevalence (2004–2015) exist in several States, while the estimated forecasting models for AIDS prevalence show that official health data and Google Trends data on AIDS follow a logarithmic relationship. Overall, the results of this study support previous work on the subject suggesting that Google data are valid and valuable for the analysis and forecasting of human behavior towards health topics, and could further assist with Health Assessment in the US and in other countries and regions with valid available official health data

Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis

Big Data Analytics have become an integral part of Health Informatics over the past years, with the analysis of Internet data being all the more popular in health assessment in various topics. In this study, we first examine the geographical distribution of the online behavioral variations towards Chlamydia, Gonorrhea, Syphilis, Tuberculosis, and Hepatitis in the United States by year from 2004 to 2017. Next, we examine the correlations between Google Trends data and official health data from the ‘Centers for Disease Control and Prevention’ (CDC) on said diseases, followed by estimating linear regressions for the respective relationships. The results show that Infoveillance can assist with exploring public awareness and accurately measure the behavioral changes towards said diseases. The correlations between Google Trends data and CDC data on Chlamydia cases are statistically significant at a national level and in most of the states, while the forecasting exhibits good performing results in many states. For Hepatitis, significant correlations are observed for several US States, while forecasting also exhibits promising results. On the contrary, several factors can affect the applicability of this forecasting method, as in the cases of Gonorrhea, Syphilis, and Tuberculosis, where the correlations are statistically significant in fewer states. Thus this study highlights that the analysis of Google Trends data should be done with caution in order for the results to be robust. In addition, we suggest that the applicability of this method is not that trivial or universal, and that several factors need to be taken into account when using online data in this line of research. However, this study also supports previous findings suggesting that the analysis of real-time online data is important in health assessment, as it tackles the long procedure of data collection and analysis in traditional survey methods, and provides us with information that could not be accessible otherwise

Infodemiology and Infoveillance: Scoping Review

Background: Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective: The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results: Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). Conclusions: The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research

Google Trends in Infodemiology and Infoveillance: Methodology Framework

Background: The use of Internet data is increasingly integrated in Health Informatics research and is becoming a useful tool in exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on what is trending and on the variations of the online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior towards health topics and in predicting diseases’ occurrence and outbreaks. Objective: Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data, and at presenting the first methodology framework in using Google Trends in Infodemiology and Infoveillance, consisting of the main factors that need to be taken into account for a solid methodology base. Methods: We provide a step-by-step guide for the methodology that needs to be followed when researching with Google Trends; essential for robust results in this line of research. Results: At first, an overview of the tool and the data are presented, followed by the analysis of the key methodological points for ensuring the robustness of the results, i.e., selecting the appropriate keyword(s), region(s), period, and category. Conclusions: In the era of Big Data, the analysis of online queries is all the more integrated in health research. This article presents and analyzes the key points that need to be considered for a solid methodology basis when using Google Trends data, which is crucial for ensuring the value and validity of the results

Accessibility of HIV Testing in Baton Rouge Metropolitan Statistical Area

This study examines HIV testing accessibility in the Baton Rouge Metropolitan Statistical Area (BR MSA) using the two-step floating catchment area (2SFCA) method to calculate accessibility scores for free, low-cost and all other HIV testing facilities. The two goals of this research are to apply accessibility estimation methods to HIV testing facilities, and to examine the accessibility of HIV testing facilities in the BR MSA. To achieve these goals, this study uses several research methods. The data about HIV testing providers and their locations were collected through Internet searches. By means of a fieldwork, the data were checked, revealing that only 20% of the free HIV testing providers found online are active and free. Almost all free testing providers are clustered in the largest cities, many facilities claimed as “free” turned out to be “low-cost” instead. A disaggregation technique with a linear regression was used to acquire the HIV prevalence rate at the census tract level, because it is only available at the parish/county level. To address accessibility questions, geographical methods, including mapping, the 2SFCA method, and the hot spot analysis were used. The low-cost testing providers are allocated equally throughout the study area and partly compensate the lack of free HIV testing providers for people outside of the largest cities. Almost all population of the BR MSA has access to HIV testing facilities, low-cost and fully charged, within a 30-minute driving time threshold. However, people living in the outskirts of the BR MSA have no access to free HIV testing providers even within a 40-minute driving time threshold

Time Trend of the Suicide Incidence in India: a Statistical Modelling

Background: It is estimated that over 100,000 people die by suicide in India every year. India alone contributes to more than 10% of suicides in the world. The suicide rate in India has been increasing steadily and has reached 11.2 (per 100,000 of population) in 2011 registering 78% increase over the value of 1980 (6.3). Objective: Objective of the study was to forecasts the suicide incidence of India up to 2020. Material and Methods: Theoretical statistics was used for the statistical modelling of the retrospective data of suicide incidence data of 1989-2011 years collected from National Crime Records Bureau (NCRB). Results: Using curve fitting method, Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power and Exponential growth models were validated. Cubic Model was the best fitted model with R2>0.90, p<0.01. Suicide incidence of India has an increasing trend. In 2020, it is estimated that the suicide incidence of India will be 109814 with CI [ 86,593, 133,034] for male, 76224 with CI [55,151, 97,297] for female and 186038 with CI [145,605, 226,471] for total [both male and female]. Conclusion: Suicide incidence of India has an increasing trend. India requires the involvement of all governments and other organizations to contribute to the cause of suicide awareness and prevention through activities, events, conferences and campaigns to solve this public health problem

Big data, factor clave para la sociedad del conocimiento

We are currently in an era of information explosion that affects our life in one way or another. Because of this, the transformation of huge databases into knowledge has become one of the tasks of greatest interest to society in general. Big Data was born as an instrument for knowledge due to the inability of current computer systems to store and process large volumes of data. The knowledge society arises from the use of technologies such as Big Data. The purpose of this article is to analyze the influence of Big Data on the knowledge society through a review of the state of the art supported by research articles and books published in the last 15 years, which allow us to put these two terms into context, understand their relationship and highlight the influence of Big Data as a generator of knowledge for today's society. The concept of Big Data, and its main applications to society will be defined. The concept of the Information Society is addressed and the main challenges it has are established. The relationship between both concepts is determined. And finally the conclusions are established. In order to reduce the digital divide, it is imperative to make profound long-term changes in educational models and public policies on investment, technology and employment that allow the inclusion of all social classes. In this sense, knowledge societies with the help of Big Data are called to be integrative elements and transform the way they are taught and learned, the way they are investigated, new social and economic scenarios are simulated, the brand decisions in Companies and share knowledge.Actualmente estamos en una época de explosión de información que afecta de una u otra manera nuestra vida. Debido a esto, la transformación de enormes bases de datos en conocimiento se ha convertido en una de las tareas de mayor interés para la sociedad en general. Big Data nace como instrumento para el conocimiento ante la incapacidad de los sistemas informáticos actuales para&nbsp; almacenar&nbsp; y&nbsp; procesar&nbsp; grandes&nbsp; volúmenes&nbsp; de&nbsp; datos.&nbsp; La&nbsp; sociedad&nbsp; de&nbsp; conocimiento&nbsp; surge del uso de tecnologías como del Big Data. El presente artículo tiene por objetivo analizar la influencia del Big Data sobre la sociedad del conocimiento por medio de una revisión del estado del arte soportada en artículos de investigación y libros publicados en los últimos 15 años, que permitan colocar en contexto estos dos términos, entender su relación y poner de manifiesto la influencia del Big Data como generador de conocimiento para la sociedad actual. Se definirá el concepto de Big Data, y sus principales aplicaciones a la sociedad. Se aborda el concepto de&nbsp; Sociedad&nbsp; de&nbsp; la&nbsp; Información&nbsp; y&nbsp; se&nbsp; establecen&nbsp; los&nbsp; principales&nbsp; desafíos&nbsp; que&nbsp; esta&nbsp; posee.&nbsp; Se determina la relación entre ambos conceptos. Y Finalmente se establecen las conclusiones. A fin de disminuir la brecha digital, es imperativo realizar cambios profundos a largo plazo en los modelos&nbsp; educativos&nbsp; y&nbsp; las&nbsp; políticas&nbsp; públicas&nbsp; sobre&nbsp; inversión,&nbsp; tecnología&nbsp; y&nbsp; empleo&nbsp; que&nbsp; permitan&nbsp; la&nbsp; inclusión&nbsp; de&nbsp; todas&nbsp; las&nbsp; clases&nbsp; sociales.&nbsp; En&nbsp; este&nbsp; sentido,&nbsp; las&nbsp; sociedades&nbsp; del&nbsp; conocimiento con&nbsp; la&nbsp; ayuda&nbsp; de&nbsp; Big&nbsp; Data&nbsp; están&nbsp; llamadas&nbsp; a&nbsp; ser&nbsp; elementos&nbsp; integradores&nbsp; y&nbsp; a&nbsp; transformar&nbsp; la&nbsp; forma&nbsp; en&nbsp; que&nbsp; se&nbsp; enseñan&nbsp; y&nbsp; aprenden,&nbsp; la&nbsp; forma&nbsp; en&nbsp; que&nbsp; se&nbsp; investigan,&nbsp; se&nbsp; simulan&nbsp; nuevos&nbsp; escenarios sociales y económicos, la marca decisiones en empresas y compartir conocimiento

Tendencias y estacionalidad de las búsquedas de información, realizadas a través de Google, sobre síndrome metabólico y salud laboral: estudio infodemiológico

[EN] Objective: This study aimed to analyse and relate the population interest through information search trends, on Metabolic Syndrome (MS) with the Occupational Health (OH). Method: Ecological and correlational study of the Relative Search Volume (RSV) obtained from Google Trends query, segmented into 3 searched periods concerning antiquity; date of query: September 30, 2023. Results: The lowest mean of the RSV was for the MS Topic (2.23 ± 0.87), albeit there was a positive correlation in the RSV amid MS and OH (R = 0.56; p 0.05), and significant differences were demonstrated in the information search between developed and undeveloped countries (p > 0.05). Conclusions: Through their information searches, the whole population showed to have a dearth of knowledge of MS than of its component diseases. A relationship was found between the information searches carried out on MS and OH. The study of information search trends may provide useful information on the population’s interest in the disease data, as well as would gradually allow the analysis of differences in popularity, or interest even between different countries. [ES] Objetivo: Este estudio tuvo como objetivo analizar y relacionar el interés de la población, a través de tendencias de búsqueda de información, sobre el Síndrome Metabólico (MS) con la Salud Laboral (OH). Método: Estudio ecológico y correlacional del Volumen Relativo de Búsqueda (RSV) obtenido de la consulta de Google Trends, segmentado en 3 períodos buscados relacionados con la antigüedad; fecha de consulta: 30 de septiembre de 2023. Resultados: La media más baja del RSV fue para el tema MS (2,23 ± 0,87), aunque hubo una correlación positiva en el RSV entre MS y OH (R = 0,56; p 0,05), y se demostraron diferencias significativas en la búsqueda de información entre países desarrollados y no desarrollados (p > 0,05). Conclusiones: A través de sus búsquedas de información, toda la población demostró tener un menor conocimiento sobre la MS que sobre las enfermedades que la componen. Se encontró relación entre las búsquedas de información realizadas sobre MS y OH. El estudio de las tendencias de búsqueda de información puede proporcionar información útil sobre el interés de la población por los datos de enfermedades, así como permitiría gradualmente analizar diferencias en popularidad, o interés incluso entre distintos países.S