Preventing the next pandemic: Use of artificial intelligence for epidemic monitoring and alerts

Emerging infections are a continual threat to public health security, which can be improved by use of rapid epidemic intelligence and open-source data. Artificial intelligence systems to enable earlier detection and rapid response by governments and health can feasibly mitigate health and economic impacts of serious epidemics and pandemics. EPIWATCH is an artificial intelligence-driven outbreak early-detection and monitoring system, proven to provide early signals of epidemics before official detection by health authorities

Socioeconomic differentials in the immediate mortality effects of the national Irish smoking ban

This article has been made available through the Brunel Open Access Publishing Fund.Background: Consistent evidence has demonstrated that smoking ban policies save lives, but impacts on health inequalities are uncertain as few studies have assessed post-ban effects by socioeconomic status (SES) and findings have been inconsistent. The aim of this study was to assess the effects of the national Irish smoking ban on ischemic heart disease (IHD), stroke, and chronic obstructive pulmonary disease (COPD) mortality by discrete and composite SES indicators to determine impacts on inequalities. Methods: Census data were used to assign frequencies of structural and material SES indicators to 34 local authorities across Ireland with a 2000–2010 study period. Discrete indicators were jointly analysed through principal component analysis to generate a composite index, with sensitivity analyses conducted by varying the included indicators. Poisson regression with interrupted time-series analysis was conducted to examine monthly age and gender-standardised mortality rates in the Irish population, ages ≥35 years, stratified by tertiles of SES indicators. All models were adjusted for time trend, season, influenza, and smoking prevalence. Results: Post-ban mortality reductions by structural SES indicators were concentrated in the most deprived tertile for all causes of death, while reductions by material SES indicators were more equitable across SES tertiles. The composite indices mirrored the results of the discrete indicators, demonstrating that post-ban mortality decreases were either greater or similar in the most deprived when compared to the least deprived for all causes of death. Conclusions: Overall findings indicated that the national Irish smoking ban reduced inequalities in smoking-related mortality. Due to the higher rates of smoking-related mortality in the most deprived group, even equitable reductions across SES tertiles resulted in decreases in inequalities. The choice of SES indicator was influential in the measurement of effects, underscoring that a differentiated analytical approach aided in understanding the complexities in which structural and material factors influence mortality

Influenza vaccine as a coronary intervention for prevention of myocardial infarction.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. Influenza is one of the leading infectious causes of morbidity and mortality globally, and evidence is accumulating that it can precipitate acute myocardial infarction (AMI). This is thought to be due to a range of factors including inflammatory release of cytokines, disruption of atherosclerotic plaques and thrombogenesis, which may acutely occlude a coronary artery. There is a large body of observational and clinical trial evidence that shows that influenza vaccine protects against AMI. Estimates of the efficacy of influenza vaccine in preventing AMI range from 15% to 45%. This is a similar range of efficacy compared with the accepted routine coronary prevention measures such as smoking cessation (32-43%), statins (19-30%) and antihypertensive therapy (17-25%). Influenza vaccine should be considered as an integral part of CVD management and prevention. While it is recommended in many guidelines for patients with CVD, rates of vaccination in risk groups aged <65 years are very low, in the range of 30%. The incorporation of vaccination into routine CVD prevention in patient care requires a clinical practice paradigm change

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Cross-sectional survey of changes in knowledge, attitudes and practice of mask use in Sydney and Melbourne during the 2020 COVID-19 pandemic

Objectives Since mask uptake and the timing of mask use has the potential to influence the control of the COVID-19 pandemic, this study aimed to assess the changes in knowledge toward mask use in Sydney and Melbourne, Australia, during the 2020 COVID-19 pandemic. Design An observational study, using a cross-sectional survey, was distributed to adults in Sydney and Melbourne, Australia, during July-August 2020 (survey 1) and September 2020 (survey 2), during the COVID-19 pandemic in Australia. Setting and participants Participants aged 18 years or older and living in either Sydney or Melbourne. Primary and secondary outcome measures Demographics, risk measures, COVID-19 severity and perception, mask attitude and uptake were determined in this study. Results A total of 700 participants completed the survey. In both Sydney and Melbourne, a consistent decrease was reported in almost all risk-mitigation behaviours between March 2020 and July 2020 and again between March 2020 and September 2020. However, mask use and personal protective equipment use increased in both Sydney and Melbourne from March 2020 to September 2020. There was no significant difference in mask use during the pandemic between the two cities across both timepoints (1.24 (95% CI 0.99 to 1.22; p=0.072)). Perceived severity and perceived susceptibility of COVID-19 infection were significantly associated with mask uptake. Trust in information on COVID-19 from both national (1.77 (95% CI 1.29 to 2.44); p<0.000)) and state (1.62 (95% CI 1.19 to 2.22); p=0.003)) government was a predictor of mask use across both surveys. Conclusion Sydney and Melbourne both had high levels of reported mask wearing during July 2020 and September 2020, consistent with the second wave and mask mandates in Victoria, and cluster outbreaks in Sydney at the time. High rates of mask compliance may be explained by high trust levels in information from national and state government, mask mandates, risk perceptions, current outbreaks and the perceived level of risk of COVID-19 infection at the time

COVID-19 outbreaks in aged-care facilities in Australia

Background: Aged-care facilities (ACF’s) provide unique challenges when implementing infection control methods for respiratory outbreaks such as COVID-19. Research on this highly vulnerable setting is lacking and there was no national reporting data of COVID-19 cases in ACFs in Australia early in the pandemic. We aimed to estimate the burden of aged-care worker (ACW) infections and outbreaks of COVID-19 in Australian aged-care. Methods: A line list of publicly available aged-care related COVID-19 reported cases from January 25 to June 10, 2020 was created and was enhanced by matching data extracted from media reports of aged-care related COVID-19 relevant outbreaks and reports. Rate ratios (RR) were used to predict risk of infection in ACW and aged-care residents, and were calculated independently, by comparing overall cases to ACW and aged-care residents' cases. Results: A total of 14 ACFs with COVID-19 cases were recorded by June 2020 nationwide, with a high case fatality rate (CFR) of 50% (n = 34) and 100% (n = 3) seen in two ACFs. Analysis on the resident risk found that the COVID-19 risk is 1.27 times higher (unadjusted RR 1.27 95% confidence interval [CI] 1.00 to1.61; P = 0.047) as compared with the risk of infection in the general population. In over 60% of cases identified in ACFs, the source of infection in the index case was unknown. A total of 28 deaths associated within ACFs were reported, accounting for 54.9% of total deaths in New South Wales and 26.9% of total deaths in Australia. Conclusions: This high-risk population requires additional prevention and control measures, such as routine testing of all staff and patients regardless of symptoms. Prompt isolation and quarantine as soon as a case is confirmed within a facility is essential

Treatment outcomes of various types of tuberculosis in Pakistan, 2006 and 2007

Measuring treatment outcome is important for successful tuberculosis (TB) control programmes. The purpose of this study was to examine the outcomes of various types of TB cases registered in Pakistan over a 2-year period and compare those outcomes among the different provinces and regions of the country. A retrospective, cohort study was conducted in which TB treatment outcome reports were reviewed. Of the 349 694 pulmonary TB cases registered in Pakistan during 2006 and 2007, 309 154 (88.4%) were treated successfully. Treatment success was significantly higher in new smear-positive cases and lower in retreatment cases. Among the provinces and regions, treatment success was significantly higher in 4 out of 8 provinces. Treatment success needs to be improved, particularly in retreatment cases. The national TB control programme should review the provincial and regional programmes and learn lessons from well-performing programmes. Patient factors that may affect the treatment outcome should be also studied

The association between acute flaccid myelitis (AFM) and enterovirus D68 (EV-D68) – what is the evidence for causation?

© 2018, European Centre for Disease Prevention and Control (ECDC). All rights reserved. Background: Enterovirus D68 (EV-D68) has historically been a sporadic disease, causing occasional small outbreaks of generally mild infection. In recent years, there has been evidence of an increase in EV-D68 infections globally. Large outbreaks of EV-D68, with thousands of cases, occurred in the United States, Canada and Europe in 2014. The outbreaks were associated temporally and geographically with an increase in clusters of acute flaccid myelitis (AFM). Aims: We aimed to evaluate a causal association between EV-D68 and AFM. ?Methods: Using data from the published and grey literature, we applied the Bradford Hill criteria, a set of nine principles applied to examine causality, to evaluate the relationship between EV-D68 and AFM. Based on available evidence, we defined the Bradford Hill Criteria as being not met, or met minimally, partially or fully. Results: Available evidence applied to EV-D68 and AFM showed that six of the Bradford Hill criteria were fully met and two were partially met. The criterion of biological gradient was minimally met. The incidence of EV-D68 infections is increasing worldwide. Phylogenetic epidemiology showed diversification from the original Fermon and Rhyne strains since the year 2000, with evolution of a genetically distinct outbreak strain, clade B1. Clade B1, but not older strains, is associated with AFM and is neuropathic in animal models. Conclusion: While more research is needed on dose–response relationship, application of the Bradford Hill criteria supported a causal relationship between EV-D68 and AFM

Harnessing Tweets for Early Detection of an Acute Disease Event

Background: Melbourne, Australia, witnessed a thunderstorm asthma outbreak on 21 November 2016, resulting in over 8,000 hospital admissions by 6 p.m. This is a typical acute disease event. Because the time to respond is short for acute disease events, an algorithm based on time between events has shown promise. Shorter the time between consecutive incidents of the disease, more likely the outbreak. Social media posts such as tweets can be used as input to the monitoring algorithm. However, due to the large volume of tweets, a large number of alerts may be produced. We refer to this problem as alert swamping. Methods: We present a four-step architecture for the early detection of the acute disease event, using social media posts (tweets) on Twitter. To curb alert swamping, the first three steps of the algorithm ensure the relevance of the tweets. The fourth step is a monitoring algorithm based on time between events. We experiment with a dataset of tweets posted in Melbourne from 2014 to 2016, focusing on the thunderstorm asthma outbreak in Melbourne in November 2016. Results: Out of our 18 experiment combinations, three detected the thunderstorm asthma outbreak up to 9 hours before the time mentioned in the official report, and five were able to detect it before the first news report. Conclusions: With appropriate checks against alert swamping in place and the use of a monitoring algorithm based on time between events, tweets can provide early alerts for an acute disease event such as thunderstorm asthma

Estimated Mask Use and Temporal Relationship to COVID-19 Epidemiology of Black Lives Matter Protests in 12 Cities

Background: There is an increased risk of SARS-CoV-2 transmission during mass gatherings and a risk of asymptomatic infection. We aimed to estimate the use of masks during Black Lives Matter (BLM) protests and whether these protests increased the risk of COVID-19. Two reviewers screened 496 protest images for mask use, with high inter-rater reliability. Protest intensity, use of tear gas, government control measures, and testing rates were estimated in 12 cities. A correlation analysis was conducted to assess the potential effect of mask use and other measures, adjusting for testing rates, on COVID-19 epidemiology 4 weeks (two incubation periods) post-protests. Mask use ranged from 69 to 96% across protests. There was no increase in the incidence of COVID-19 post-protest in 11 cities. After adjusting for testing rates, only Miami, which involved use of tear gas and had high protest intensity, showed a clear increase in COVID-19 after one incubation period post-protest. No significant correlation was found between incidence and protest factors. Our study showed that protests in most cities studied did not increase COVID-19 incidence in 2020, and a high level of mask use was seen. The absence of an epidemic surge within two incubation periods of a protest is indicative that the protests did not have a major influence on epidemic activity, except in Miami. With the globally circulating highly transmissible Alpha, Delta, and Omicron variants, layered interventions such as mandated mask use, physical distancing, testing, and vaccination should be applied for mass gatherings in the future