# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 3 AI and Public Health Disease Surveillance  
Rapid epidemic detection and real-time monitoring are critical objectives of event-based surveillance to minimize the morbidity and mortality caused by infectious diseases and to enable the preparation of rapid response public health services and personnel. Automated event-based surveillance systems have the potential capacity to aggregate and sort vast amounts of heterogeneous data for patterns and abnormalities using forms of AI [39]. AI machine learning classifiers play an important role in real-time analysis and trend predictions [40]. AI applications used with social media sources can be effective in providing trends for climatic and socio-economic contexts [38; 41]. Social networks can further provide an efficient method for risk communication to prevent disease outbreaks and serve as real-time dissemination channels for declaration of pandemic events [42]. Machine learning models analysing internet and open health data sources have precedence across a number of disease surveillance projects to improve infectious disease surveillance and prediction, e.g., malaria [43], dengue fever [44], and cholera [45]. In the example of influenza, algorithmic and machine learning models have been applied to influenza tracking for nearly two decades. [PERSON] et al. [32] indicate that the use of internet-based sources for disease surveillance is traceable to 2006, with early work focusing on influenza. Influenza can be highly contagious and easily spreads as people move about and travel, for instance, making tracking and forecasting flu activity a challenge. To address these challenges, researchers have proposed methods utilizing internet-based sources, mobile phones, and other event-based surveillance methods. Research in the use of internet data has demonstrated a positive predictive value of the incidence of infectious diseases, e.g., seasonal influenza [46], but it has shown in some cases to have a low predictive value [47]. To monitor the volume of influenza cases, there is an increasing use of \"flu tracker\" apps, whereby flu symptoms are reported using crowdsourced platforms to improve the global surveillance of influenza [35; 48]. The U.S. Centres for Disease Control and Prevention (CDC) developed the FluView app, which draws on the U.S. influenza-like illness surveillance network (ILINet) that records the percentage of patients reporting to outpatient clinics with symptoms of influenza-like illness (ILI), such as fever, a sore throat, or cough, over the total number of patient visits, for example. The data are integrated in the weekly joint WHO and European Centre for Disease Prevention and Control (ECDC) COVID-19 and influenza (www.flunewseurope.org (accessed on 15 January 2022)) bulletins [49]. These symptom measurements are an established indicator of historical ILI activity, but an important drawback is that they require several days to collect from individual healthcare providers, causing a delay and reducing the opportunity for real-time situational awareness and data analysis. In an evaluation of surveillance systems, it was found that the systems issued alerts for human cases between 1.9 and 6.1 days, on average, before WHO reports [30]. As monitoring is improved over time, the heuristics improve as their outputs are confirmed against a set threshold for the epidemiological attributes of extracted health events [22]. Prior to returning the extracted information, the surveillance system aggregates the extracted events into outbreaks, across several documents and sources based on this 'learning'. The Computational Health Informatics Program (CHIP) at Boston Children's Hospital is among numerous research groups developing forecasting techniques combined with machine learning to provide more predictive indicators and estimates of local flu activity. An application called ARGONet (autoregression with general online information) leverages information from electronic health records, flu-related Google searches, and historical flu activity in a given location.