# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 3 AI and Public Health Disease Surveillance
In a research study, ARGO was shown to outperform Google Flu Trends (GFT) [50], a flu forecasting system developed by Google that operated from 2008 to 2015, but notably failed to accurately predict peaks in the flu season [25; 48], as described by [PERSON] et al. 2020. To improve its accuracy, ARGONet also draws on the spatial-temporal patterns of flu spread in neighbouring areas. \"It exploits the fact that the presence of flu in nearby locations may increase the risk of experiencing a disease outbreak at a given location\" [50]. In disease outbreaks before COVID-19, the application of AI was limited because of a shortage of the data needed to provide rapid updates. The millions of feeds about coronavirus on social media and news sites are allowing algorithms to generate near-real-time information for the public health services tracking its spread. For instance, the disease surveillance system HealthMap based at Boston Children's Hospital uses natural language processing and machine learning to analyse data from government reports, social media, news sites in 15 languages, and other informal sources to constantly track the spread of outbreaks [51]. The future developments at the time of this study include building a symptom-checker to assess the symptoms of coronavirus that distinguish it from seasonal flu. In the emergence of COVID-19, risk analysis companies using AI-enabled tools were able to provide early detection of the coronavirus, before the WHO was informed. An AI epidemiologist in the Canadian-based company BlueDot ([[https://bluedot.global/](https://bluedot.global/)]([https://bluedot.global/](https://bluedot.global/)) (accessed on 1 November 2021)) is widely reported as the first source to reveal news of the COVID-19 outbreak in late December 2019 [2]. BlueDot analysed data from news reports, airline ticketing, and animal disease outbreaks to predict areas that would be prone to the outbreak, expanding from regions in China [51]. Data sources and AI played an important role in various countries' responses to the pandemic [18]. Another company using AI capabilities is Metabiota ([[https://www.metabiota.com/](https://www.metabiota.com/)]([https://www.metabiota.com/](https://www.metabiota.com/)) (accessed on 1 November 2021)) based in San Francisco, which offers an epidemic tracker and a near-term forecasting model of disease spread. Metabiota, which also used analytics to track flight data accurately, anticipated that countries such as Japan, Thailand, Taiwan, and South Korea would be at risk of a coronavirus outbreak days before any case was reported in any of those countries [51; 52]. Interest in syndromic surveillance based on social media data has greatly increased in recent years, leading to what might be termed \"nowcasting\" [50; 53; 54], that is providing predictions of disease levels which incorporate data from current social media activity but can include rolling data sources such as ever-hospitalized cases and persons tested [53]. The early detection and analysis of epidemics based on mining Twitter messages is a common approach in nowcasting [53; 54]. The VAC Medi + Board dashboard provides visualizations of vaccination threads extracted from Twitter, for example, illustrating the diffusion of information, the most influential users, and impact groups [55; 56]. Over the course of the swine flu (H1N1) pandemic in 2009, novel big data streams from social media channels, in correlation with traditional surveillance reporting, demonstrated a potential for early-warning systems for infection disease outbreaks detecting a peak in an outbreak of swine flu up to two weeks before the official public health authorities [57]. [PERSON] et al. [34] reports that including Twitter data on influenza or influenza symptoms can improve nowcasting performance to a greater degree than was possible with search surveillance using Google Trends. During the 2012-2013 influenza season, a Twitter-based AI surveillance application also consistently predicted and mirrored CDC data with 85% accuracy [34].