Title: EPI-AI: Automated Understanding and Alerting of Disease Outbreaks from Global News Media
Funder: Economic and Social Research Council

Abstract: The EPI-AI project aims to revolutionise real-time digital disease surveillance (DDS) using Artificial Intelligence to extract and interpret data from online news streams in order to support expert decision making. Previous work in this area has tended to take a siloed approach, focusing either on Natural Language Processing (NLP) methods or spatial analysis with little consideration of ethics or social sciences. We will accomplish our goal by using an interdisciplinary approach that combines expertise from three disciplines - computer science, epidemiology, and bioethics - to develop novel machine learning and statistical models that are adapted to the structurally complex data and objectives of global epidemic surveillance. Our approach will integrate the results from deep learning methods, which have revolutionised performance in NLP in other areas, with data from disease risk surfaces and region-specific information on media biases using Bayesian hierarchal modelling. The aims of the EPI-AI project are: 1. To explore NLP techniques that can extend DDS coverage to resource-poor languages of public health significance including Indonesian, Farsi and Swahili; 2. To explore a range of 'few shot' relevancy classification techniques from minimum numbers of examples based on the noisy output of machine translation (MT); 3. To deal effectively with the issues of de-duplication and integration of events with existing spatio-temporal risk surfaces. This will involve the development and validation against human gold standards of novel NLP techniques to identify and link entities of epidemiological interest from news texts to unique identifiers in standard terminologies; 4. To incorporate evidence from online media into existing spatio-temporal risk surfaces: we will develop neural network and semantic methods so that media reports can be combined with existing evidence through Bayesian hierarchal models that update global risk assessments and generate alerts; 5. To generate equity-informed alerts to support public health decision-makers; 6. To deploy and maintain an online portal called EPI-AI to share real-time alerts with end users that summarise disease outbreak events and to validate these alerts against manual systems at end-user partners. The online portal will add evidence and insight to traditional sources of epidemiological data. As well as alerts the portal will include a database of summaries of news events (with entities linked to standard ontologies) including faceted searching, retrieval and data export. Our research on NLP will not be focussed on specific diseases. For spatial analysis and alerting, we will focus on priority infectious diseases as identified by our public health partners, including the WHO. These diseases include influenza (including avian influenza), Zika, and hemorrhagic fevers, such as Ebola and Marburg.

Lay abstract: Disease outbreaks, such as Zika, Ebola and SARS epidemics, are of the greatest importance to the international community and the UK/Canadian governments. Public health organisations need data as early as possible in an outbreak to respond rapidly and prevent human suffering. Traditional bio-surveillance relies on human laboratory networks, but these data are often unavailable in real-time, patchy in geographic coverage, and tuned to specific diseases. Digital disease surveillance (DDS) using Web-based news data overcomes some of these limitations, providing a critical supplement to traditional networks. However, current DDS systems rely to a large extent on manual screening of Web data for events of interest: a skilled and labour-intensive process given the volume, multilingualism, velocity and potential bias of news sources. Research has shown that there is significant potential to automate DDS. Natural Language Processing (NLP) has been in use since the early 2000s to efficiently detect and track health threats from outbreak news reports. For example, the Canadian GPHIN system, which detected the first evidence of SARS, uses a combination of NLP and human experts to sift through over 20K online news reports each day in nine languages. However, traditional automated approaches are insensitive to context that can help experts to interpret risk factors and fail to take account of possible data biases. Our goal in the EPI-AI project is to achieve a step-change in real-time automated DDS. Previous work has tended to take a siloed approach, focusing on Natural Language Processing methods or spatial analysis with little consideration of equality considerations that arise from biases in the data. We will use an interdisciplinary approach, combining expertise from three disciplines - computer science, epidemiology, and bioethics - to develop novel machine learning and statistical models adapted to the complex data and objectives of global epidemic surveillance. Benefits that we see include: (i) improved geographic precision and coverage; (ii) improved ability to understand the topical focus of a report; (iii) automated normalisation of risk factors to a standard terminology for integration of evidence across systems; (iv) automated spatio-temporal analysis of reports to update global risk maps and trigger alerts; and (v) provision of contextual information on potential media bias to support interpretation of alerts. This fundamentally interdisciplinary research will be closely aligned with key Canadian, UK and global public health stakeholders.

Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: INFECTION