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Background: A dengue vaccine, dengvaxia, was licensed for the first time in 2015. It was approved for use in 11 countries where dengue infection is endemic, including the Philippines. In November 2017, controversy arose in the Philippines regarding the dengvaxia vaccine. We hypothesized that the dengvaxia controversy might be correlated with immunization coverage in the Philippines. Methods: We performed an analytical and infodemiological study on web‐based interest in dengvaxia, both globally and in 18 dengue endemic countries, from 2015 to 2020 using Google Trends™. Comparisons were made with search trends for the components of the National Immunization Program (NIP) and vaccine coverage by computing the Pearson product–moment correlation coefficient (r) between each variable. Results: Among the 18 countries included, the Philippines had the highest search volume index for dengvaxia, with peaks in searches coinciding with that of worldwide search trends. There was no correlation between the relative search volume for dengvaxia with that of vaccines included in the NIP in the Philippines from 2015 to 2020. There was no significant correlation between web‐based interest in dengvaxia and the estimated immunization coverage from 2015 to 2019. Conclusion: There was no significant correlation between web‐based interest in dengvaxia, the vaccines in the NIP, and national immunization coverage. [ABSTRACT FROM AUTHOR]

Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data as well as interoperate fragmented systems, leading to longer transfer and processing of data. As such, this study presents the use of real-time latent data from social media, particularly from Twitter, to complement existing disease surveillance efforts. By being able to classify infodemiological (health-related) tweets, this study is able to produce a range of possible disease incidences of Dengue and Typhoid Fever within the Western Visayas region in the Philippines. Both diseases showed a strong positive correlation (R >. 70) between the number of tweets and surveillance data based on official records of the Philippine Health Agency. Regression equations were derived to determine a numerical range of possible disease incidences given certain number of tweets. As an example, the study shows that 10 infodemiological tweets represent the presence of 19-25 Dengue Fever incidences at the provincial level. [ABSTRACT FROM AUTHOR]

Finding innovative ICT solutions to enhance the Philippines’ health sector is part and parcel of the Philippine eHealth Strategic Framework and Plan 2020 program . This study sees the opportunity of using collected Twitter data to create a model that processes tweets to produce a dataset that may be relevant in the field of epidemiology and infodemiology . Through the collection of relevant tweets, future studies may make use of the output of this research for various purposes, such as the improvement of epidemiological systems of the Department of Health in support of the eHealth strategy. In this study, we used the Naïve-Bayes classification model, an efficient text classifier, to create a model that determines whether a tweet is “infodemiological” or not. From the collected 18,044 tweets, we have narrowed it down to 1,090 tweets (6.04%) that can be used in epidemiology. Using this as a dataset for training and testing, the model was able to classify 79.91% of tweets correctly. This research shows that it is indeed feasible to collect and classify enough infodemiological tweets in the Filipino language, which in turn can be used for future infodemiological studies. [ABSTRACT FROM AUTHOR]

Due to the real-time acquisition of big data from the Internet, analysis of Google queries is now recognized as a valuable tool to explore and predict human behavior and interests. It was suggested that online data can be correlated with actual health data. Although the data are not structured nor systematic, the huge data from search engines can easily identify trends concerning diseases and other health concepts from a population perspective. Moreover, Internet data with the use of web search advertising nowadays may not only reveal the interest of the general population but also of the healthcare industry as reflected by the bid prices in search terms for medications. We aimed to compare the interests of the general population using monthly search volumes from Google and the healthcare industry using bid prices in web searches. Data used in this study were obtained from the Google Ads Application Programming Interface (API). This study evaluated the population's interest in neurological disorders by using search volumes related to neurology, either disease diagnosis or medications. Bid values generated in API were used as a proxy for the interests of the healthcare industry. Spearman's rank-order correlation was performed between search volumes and bid prices to determine significance. Among the neurologic diseases listed, the most searched were attention deficit hyperactivity disorder, migraine, and Alzheimer's disease. The most commonly searched drugs were oral antihypertensives (amlodipine, losartan, carvedilol), lipid-lowering agents (atorvastatin, simvastatin, rosuvastatin), and antiplatelets (acetylsalicylic acid, clopidogrel). The other most searched drugs were analgesics such as acetaminophen, tramadol, diclofenac, and morphine. The correlational analysis did not reveal a statistically significant correlation between search volume and bid price for both neurologic diseases and medications. Web searches may reflect the interest of the general population and the healthcare industry. However, there was disagreement in the search interests of the general population and the scientific community, including the pharmaceutical industry. Further studies are necessary in order to align these interests for the common benefit of all stakeholders.

• There is an increase in online search behavior for seizure terms in the Philippines. • Such searches centered on seizure definition, symptoms, causes, types, and treatment. • Seasonal and psychosocial factors may influence Philippine online search behavior. • Rising Philippine online interest in seizures may hint at growing epilepsy awareness. The internet has been established as a rich source of health information that is well utilized by the public. Online health information-seeking behavior may reflect public awareness in particular health-related topics, including epilepsy and seizures. This study aimed to describe online search behavior for epilepsy and seizures in the Philippines through the examination of trends of Philippine-based Google search queries for epilepsy and seizure-related terms. The search volume indices (SVIs) for the keywords "epilepsy (disorder)" and "seizure (disease)" based on Philippine-based Google searches from January 2004 to July 2021 were generated using Google Trends. Descriptive analysis of search volume patterns, including related topics and queries, was performed. There was a recent relative increase in Philippine-based searches related to the term "seizure" with a concurrent relative decline in searches related to the term "epilepsy" over time. The Philippines had the fifth-highest SVI for "seizure" based on worldwide trends. Most searches centered on the definition, symptomatology, etiologies, classification, and treatment of seizures and epilepsy. Queries containing seizure-related terms rose in volume by more than 5000% during the seventeen-year period. Search volume tended to peak during the National Epilepsy Awareness Week in September but slumped during holiday months. Online search behavior in the Philippines for seizure-related terms appears to be on the rise and may reflect increasing public awareness in epilepsy. Factors including fears surrounding epileptic seizures, the stigma surrounding the term "epilepsy", and seasonal events including a regularly held epilepsy awareness week may influence online search behavior. [ABSTRACT FROM AUTHOR]