Towards a Social Media-based Disease Surveillance System for Early Detection of Influenza-like Illnesses: A T witter Case Study in Wales (2025)

Abstract: Social media offers the potential to provide detection of outbreaks or public health incidents faster than traditional reporting mechanisms. In this paper, we developed and tested a pipeline to produce alerts of influenza-like illness (ILI) using Twitter data. Data was collected from the Twitter API, querying keywords referring to ILI symptoms and geolocated to Wales. Tweets that described first-hand descriptions of symptoms (as opposed to non-personal descriptions) were classified using transformer-based language models specialised on social media (BERTweet and TimeLMs), which were trained on a manually labelled dataset matching the above criteria. After gathering this data, weekly tweet counts were applied to the regression-based Noufaily algorithm to identify exceedances throughout 2022. The algorithm was also applied to counts of ILI-related GP consultations for comparison. Exceedance detection applied to the classified tweet counts produced alerts starting four weeks earlier than by using GP consultation data. These results demonstrate the potential to facilitate advanced preparedness for unexpected increases in healthcare burdens.

## introduction surveillance of symptoms of infectious diseases in the population, also known as syndromic surveillance, is an important public health function to provide warning of an incoming epidemic and prepare healthcare systems for increased demand. such surveillance systems traditionally rely on clinical data, for example, general practitioner (gp) consultations, ambulance call-outs, sickness-related absences, and access to telephone advice services. such data sources can be slow due to the reporting mechanisms they rely on. in recent years, there has been increasing interest in leveraging social media to develop an early * joint first author warning detection (ewd) system for infectious diseases @xcite @xcite . social media provides a rapid, and high-volume data source, offering the potential to provide reliable detection of a disease outbreak or public health incident faster than traditional reporting mechanisms. such data sources will enable more rapid and timely response to anticipate increased demand on health services. ## methods in order to detect potential outbreaks in social media, we primarily rely on twitter data (section 2.1) and an automatic nlp-based classification methodology (section 2.2). then, we present the gp consultation data we use as a comparison (section 2.3) and our methodology to identify outbreaks based on data (section 2.4). ## results in this section, we present both the classification results that are used as basis for our analysis (section 3.1), and the results of our exceedance detection algorithm based on the classification output (section 3.2). ## discussion this study has demonstrated the utility of using social-media data to provide early exceedance alerts of influenza-like illnesses (ilis) earlier than routine clinical data. the classified tweet counts produced an exceedance alert 4 weeks lead time on routine clinical data. it can supplement existing evidence for practitioners to assess if a season has started earlier than anticipated or is more extreme than usual, and provide public health authorities a valuable tool to prepare for an incoming surge in demands on the healthcare system. furthermore, this study shows the importance of correctly classifying first-hand accounts of symptoms, as matching on ili-related keywords alone produced counts that are heavily biased by mass media content, not reflective of prevalence of symptoms in the community. we show that tweet counts using keyword matching alone failed to produce any alarms. despite the fact that the classifier strug-gles with some particular linguistic patterns and contexts, the classified tweet counts does show a significant increase in tweet counts that coincide with an increase in gp consultations. ## conclusion in this paper, we demonstrate that social-media based syndromic surveillance is capable of providing an advance warning of healthcare burdens earlier than traditional syndromic indicators. the results are encouraging and suggest that an adoption of social media indicators to supplement traditional disease surveillance is feasible with current technology. we also demonstrate the importance of nlp-based classification in identifying references to first-hand accounts of symptoms experienced. nonetheless, validation of these conclusions with larger datasets is warranted. furthermore, issues around access to social media data, digital representation and changing patterns of social media engagement should be considered when using social media data for syndromic surveillance.