Automatic text filtering using limited supervision learning for epidemic intelligence

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Increased risk of HIV and other drug-related harms associated with injecting in public places: national bio-behavioural survey of people who inject drugs

Background: Whilst injecting drugs in public places is considered a proxy for high risk behaviour among people who inject drugs (PWID), studies quantifying its relationship with multiple drug-related harms are lacking and none have examined this in the context of an ongoing HIV outbreak (located in Glasgow, Scotland). We aimed to: 1) estimate the prevalence of public injecting in Scotland and associated risk factors; and 2) estimate the association between public injecting and HIV, current HCV, overdose, and skin and soft tissue infections (SSTI). Methods: Cross-sectional, bio-behavioural survey (including dried blood spot testing to determine HIV and HCV infection) of 1469 current PWID (injected in last 6 months) recruited by independent interviewers from 139 harm reduction services across Scotland during 2017–18. Primary outcomes were: injecting in a public place (yes/no); HIV infection; current HCV infection; self-reported overdose in the last year (yes/no) and SSTI the last year (yes/no). Multi-variable logistic regression was used to determine factors associated with public injecting and to estimate the association between public injecting and drug-related harms (HIV, current HCV, overdose and SSTI). Results: Prevalence of public injecting was 16% overall in Scotland and 47% in Glasgow city centre. Factors associated with increased odds of public injecting were: recruitment in Glasgow city centre (aOR=5.45, 95% CI 3.48–8.54, p<0.001), homelessness (aOR=3.68, 95% CI 2.61–5.19, p<0.001), high alcohol consumption (aOR=2.42, 95% CI 1.69–3.44, p<0.001), high injection frequency (≥4 per day) (aOR=3.16, 95% CI 1.93–5.18, p<0.001) and cocaine injecting (aOR=1.46, 95% CI 1.00 to 2.13, p = 0.046). Odds were lower for those receiving opiate substitution therapy (OST) (aOR=0.37, 95% CI 0.24 to 0.56, p<0.001) and older age (per year increase) (aOR=0.97, 95% CI 0.95 to 0.99, p = 0.013). Public injecting was associated with an increased risk of HIV infection (aOR=2.11, 95% CI 1.13–3.92, p = 0.019), current HCV infection (aOR=1.49, 95% CI 1.01–2.19, p = 0.043), overdose (aOR=1.59, 95% CI 1.27–2.01, p<0.001) and SSTI (aOR=1.42, 95% CI 1.17–1.73, p<0.001). Conclusions: These findings highlight the need to address the additional harms observed among people who inject in public places and provide evidence to inform proposals in the UK and elsewhere to introduce facilities that offer safer drug consumption environments

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness

Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. In this paper we adapt existing bio-surveillance algorithms to detect localised spikes in Twitter activity corresponding to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which fully describe the event and by linking to highly relevant news articles. We apply our methods to outbreaks of illness and events strongly affecting sentiment. In both case studies we are able to detect events verifiable by third party sources and produce high quality summaries

Mobile Sensing, Simulation and Machine-learning Techniques: Improving Observations in Public Health

Entering an era where mobile phones equipped with numerous sensors have become an integral part of our lives and wearable devices such as activity trackers are very popular, studying and analyzing the data collected by these devices can give insights to the researchers and policy makers about the ongoing illnesses, outbreaks and public health in general. In this regard, new machine learning techniques can be utilized for population screening, informing centers of disease control and prevention of potential threats and outbreaks. Big data streams if not present, will limit investigating the feasibility of such new techniques in this domain. To overcome this shortcoming, simulation models even if grounded by small-size data can represent a simple platform of the more complicated systems and then be utilized as safe and still precise environments for generating synthetic ground truth big data. The objective of this thesis is to use an agent-based model (ABM) which depicts a city consisting of restaurants, consumers, and an inspector, to investigate the practicability of using smartphones data in the machine-learning component of Hidden Markov Model trained by synthetic ground-truth data generated by the ABM model to detect food-borne related outbreaks and inform the inspector about them. To this end, we also compared the results of such arrangement with traditional outbreak detection methods. We examine this method in different formations and scenarios. As another contribution, we analyzed smart phone data collected through a real world experiment where the participants were using an application Ethica Data on their phones named. This application as the first platform turning smartphones into micro research labs allows passive sensor monitoring and sending over context-dependent surveys. The collected data was later analyzed to get insights into the participants' food consumption patterns. Our results indicate that Hidden Markov Models supplied with smart phone data provide accurate systems for foodborne outbreak detection. The results also support the applicability of smart phone data to obtain information about foodborne diseases. The results also suggest that there are some limitations in using Hidden Markov Models to detect the exact source of outbreaks

Integration and Visualization Public Health Dashboard: The medi plus board Pilot Project

Traditional public health surveillance systems would benefit from integration with knowledge created by new situation-aware realtime signals from social media, online searches, mobile/sensor networks and citizens' participatory surveillance systems. However, the challenge of threat validation, cross-verification and information integration for risk assessment has so far been largely untackled. In this paper, we propose a new system, medi+board, monitoring epidemic intelligence sources and traditional case-based surveillance to better automate early warning, cross-validation of signals for outbreak detection and visualization of results on an interactive dashboard. This enables public health professionals to see all essential information at a glance. Modular and configurable to any 'event' defined by public health experts, medi+board scans multiple data sources, detects changing patterns and uses a configurable analysis module for signal detection to identify a threat. These can be validated by an analysis module and correlated with other sources to assess the reliability of the event classified as the reliability coefficient which is a real number between zero and one. Events are reported and visualized on the medi+board dashboard which integrates all information sources and can be navigated by a timescale widget. Simulation with three datasets from the swine flu 2009 pandemic (HPA surveillance, Google news, Twitter) demonstrates the potential of medi+board to automate data processing and visualization to assist public health experts in decision making on control and response measures

Should outbreak response immunization be recommended for measles outbreaks in middle- and low-income countries? An update.

Measles caused mortality in >164,000 children in 2008, with most deaths occurring during outbreaks. Nonetheless, the impact and desirability of conducting measles outbreak response immunization (ORI) in middle- and low-income countries has been controversial. World Health Organization guidelines published in 1999 recommended against ORI in such settings, although recently these guidelines have been reversed for countries with measles mortality reduction goals

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

Machine learning in drug supply chain management during disease outbreaks: a systematic review

The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks

PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes