Origin of the H1N1 (Russian influenza) pandemic of 1977-A risk assessment using the modified Grunow-Finke tool (mGFT).

In 1977, the Soviet Union (Union of Soviet Socialist Republics [USSR]) notified the World Health Organization (WHO) about an outbreak of H1N1 influenza, which later spread to many countries. The H1N1 strain of 1977 reappeared after being absent from the world for over 20 years. This pandemic simultaneously spread to several cities in the USSR and China. Many theories have been postulated to account for the emergence of this pandemic, including natural and unnatural origins. The purpose of this study was to use the modified Grunow-Finke risk assessment tool (modified Grunow-Finke tool [mGFT]) to investigate the origin of the 1977 H1N1 pandemic. Data was collected from WHO archives and published documents. The assessment of the pandemic's origin involved the utilization of a modified version of the original Grunow-Finke risk assessment tool (GFT). Using the mGFT, the final score was 37 out of 60 points (probability: 62%), indicating a high likelihood that the Russian influenza pandemic of 1977 was of unnatural origin. Several variables supported this finding, including the sudden re-emergence of a previously extinct strain, a genetic signature of laboratory modification for vaccine development, and unusual epidemiology. Inter-rater reliability was moderate to high. By applying the mGFT to the 1977 Russian influenza pandemic, we established a high probability that this pandemic was of unnatural origin. Although this is not definitive, it is consistent with the possibility that it originated from an incompletely attenuated live influenza vaccine. The mGFT is a useful risk analysis tool to evaluate the origin of epidemics.

Use of a risk assessment tool to determine the origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is contentious. Most studies have focused on a zoonotic origin, but definitive evidence such as an intermediary animal host is lacking. We used an established risk analysis tool for differentiating natural and unnatural epidemics, the modified Grunow-Finke assessment tool (mGFT) to study the origin of SARS-COV-2. The mGFT scores 11 criteria to provide a likelihood of natural or unnatural origin. Using published literature and publicly available sources of information, we applied the mGFT to the origin of SARS-CoV-2. The mGFT scored 41/60 points (68%), with high inter-rater reliability (100%), indicating a greater likelihood of an unnatural than natural origin of SARS-CoV-2. This risk assessment cannot prove the origin of SARS-CoV-2 but shows that the possibility of a laboratory origin cannot be easily dismissed.

Monitoring the burden of COVID-19 and impact of hospital transfer policies on Australian aged-care residents in residential aged-care facilities in 2020.

BACKGROUND: Residential aged-care facilities in Australia emerged as the high-risk setting the COVID-19 outbreaks due to community transmission. The vulnerable aged-care residents of these facilities suffered due to low hospital transfers and high mortality and morbidity rates. This study aimed to monitor and report the burden of COVID-19 in residential aged-care facilities across Australia and the impact of hospital transfer policies on resident hospitalisation during the first year of the pandemic. METHODS: We conducted a retrospective cohort study by collecting data from weekly aged-care outbreak reports published by open sources and official government sources between 1st March and 20th November 2020. A comprehensive line list of outbreaks was created using open-source data. The line list included the name of the facility, location, COVID-19 cases among residents, & staff, resident hospitalisations, mode of transmission, number of resident deaths, and state policies involving resident hospitalisation. We also searched the websites of these facilities to collect data on their COVID-19 policies for the residents, staff, and visitors. Statistical analyses were performed on the data obtained. RESULTS: 126 aged-care COVID-19 outbreaks were identified in Australia during the study period. The incidence rate of COVID-19 infections among aged-care residents in Australia was (1118.5 per 100,000 resident population) which is 10 times higher than the general population (107.6 per 100,000 population). The hospitalisation rate for aged-care residents in Australia was 0.93 per 100,000 population. The hospitalisation rate of aged-care residents in Victoria was 3.14 per 100,000 population despite having the highest COVID-19 cases. Excluding South Australia, all states followed ad-hoc case-by-case hospital transfer policies for aged-care residents. CONCLUSION: This study documented a higher risk of COVID-19 infection for aged-care residents and workers but found low hospitalisation rates among residents across Australia. The hospitalisation rates in Victoria were higher than the national average but low when considering the COVID-19 infection rates in the state. The hospitalisation rates could have been impacted due to the state hospital transfer policies at that time. Immediate transfer of infected residents to hospitals may improve their survival and reduce the risk of infection to the other residents, as healthcare settings have more advanced infection control measures and are well-equipped with trained staff and resources.

Modelling vaccination capacity at mass vaccination hubs and general practice clinics: a simulation study.

BACKGROUND: COVID-19 mass vaccination programs place an additional burden on healthcare services. We aim to model the queueing process at vaccination sites to inform service delivery. METHODS: We use stochastic queue network models to simulate queue dynamics in larger mass vaccination hubs and smaller general practice (GP) clinics. We estimate waiting times and daily capacity based on a range of assumptions about appointment schedules, service times and staffing and stress-test these models to assess the impact of increased demand and staff shortages. We also provide an interactive applet, allowing users to explore vaccine administration under their own assumptions. RESULTS: Based on our assumed service times, the daily throughput for an eight-hour clinic at a mass vaccination hub ranged from 500 doses for a small hub to 1400 doses for a large hub. For GP clinics, the estimated daily throughput ranged from about 100 doses for a small practice to almost 300 doses for a large practice. What-if scenario analysis showed that sites with higher staff numbers were more robust to system pressures and mass vaccination sites were more robust than GP clinics. CONCLUSIONS: With the requirement for ongoing COVID-19 booster shots, mass vaccination is likely to be a continuing feature of healthcare delivery. Different vaccine sites are useful for reaching different populations and maximising coverage. Stochastic queue networks offer a flexible and computationally efficient approach to simulate vaccination queues and estimate waiting times and daily throughput to inform service delivery.

Clinical and cardiac structural predictors of atrial fibrillation persistence.

AIMS: The persistence of atrial fibrillation (AF) has been associated with differential clinical outcomes, with studies showing that persistent and permanent AF results in increased morbidity and mortality when compared to the paroxysmal subtype. Given the established prognostic implications of AF subtype, we sought to discern the clinical and structural cardiac parameters associated with persistent/ permanent AF. MATERIALS AND METHODS: Consecutive patients admitted to our institution between January 2013 and January 2018 with a primary diagnosis of non-valvular AF who underwent comprehensive transthoracic echocardiography were retrospectively appraised. Assessment of clinical and echocardiographic parameters was undertaken and compared according to AF subtype. RESULTS: Of 1010 patients, 665 (mean age 66.8 ± 13.5 years, 53% men) had comprehensive transthoracic echocardiography on index admission and were included in the primary analysis. The majority of patients (n = 468; 70%) had paroxysmal AF while 197 (30%) had persistent/ permanent AF. Multivariable logistic regression analysis showed that heart failure (adjusted OR 3.135; 95% CI 2.099 to 4.682, P < .001), right atrial (RA) area ≥18 cm2 (adjusted OR 2.147; 95% CI 1.413 to 3.261, P < .001) and left atrial emptying fraction (LAEF) ≤34% (adjusted OR 2.959; 95% CI 1.991 to 4.398, P < .001) were independent predictors of persistent /permanent AF. CONCLUSIONS: The presence of heart failure, increased RA size and impaired LA function were associated with persistent/ permanent AF. These clinical and cardiac structural risk markers of AF persistence may identify a target population for early intervention to prevent adverse cardiovascular outcomes.

The potential impact of a recent measles epidemic on COVID-19 in Samoa.

BACKGROUND: The pandemic of COVID-19 has occurred close on the heels of a global resurgence of measles. In 2019, an unprecedented epidemic of measles affected Samoa, requiring a state of emergency to be declared. Measles causes an immune amnesia which can persist for over 2 years after acute infection and increases the risk of a range of other infections. METHODS: We modelled the potential impact of measles-induced immune amnesia on a COVID-19 epidemic in Samoa using data on measles incidence in 2018-2019, population data and a hypothetical COVID-19 epidemic. RESULTS: The young population structure and contact matrix in Samoa results in the most transmission occurring in young people < 20 years old. The highest rate of death is the 60+ years old, but a smaller peak in death may occur in younger people, with more than 15% of total deaths in the age group under 20 years old. Measles induced immune amnesia could increase the total number of cases by 8% and deaths by more than 2%. CONCLUSIONS: Samoa, which had large measles epidemics in 2019-2020 should focus on rapidly achieving high rates of measles vaccination and enhanced surveillance for COVID-19, as the impact may be more severe due to measles-induced immune paresis. This applies to other severely measles-affected countries in the Pacific, Europe and elsewhere.

Bayesian Phylogeography and Pathogenic Characterization of Smallpox Based on HA, ATI, and CrmB Genes.

Variola virus is at risk of re-emergence either through accidental release, bioterrorism, or synthetic biology. The use of phylogenetics and phylogeography to support epidemic field response is expected to grow as sequencing technology becomes miniaturized, cheap, and ubiquitous. In this study, we aimed to explore the use of common VARV diagnostic targets hemagglutinin (HA), cytokine response modifier B (CrmB), and A-type inclusion protein (ATI) for phylogenetic characterization as well as the representativeness of modelling strategies in phylogeography to support epidemic response should smallpox re-emerge. We used Bayesian discrete-trait phylogeography using the most complete data set currently available of whole genome (n = 51) and partially sequenced (n = 20) VARV isolates. We show that multilocus models combining HA, ATI, and CrmB genes may represent a useful heuristic to differentiate between VARV Major and subclades of VARV Minor which have been associated with variable case-fatality rates. Where whole genome sequencing is unavailable, phylogeography models of HA, ATI, and CrmB may provide preliminary but uncertain estimates of transmission, while supplementing whole genome models with additional isolates sequenced only for HA can improve sample representativeness, maintaining similar support for transmission relative to whole genome models. We have also provided empirical evidence delineating historic international VARV transmission using phylogeography. Due to the persistent threat of re-emergence, our results provide important research for smallpox epidemic preparedness in the posteradication era as recommended by the World Health Organisation.

Recalibration of the Grunow-Finke Assessment Tool to Improve Performance in Detecting Unnatural Epidemics.

Successful identification of unnatural epidemics relies on a sensitive risk assessment tool designed for the differentiation between unnatural and natural epidemics. The Grunow-Finke tool (GFT), which has been the most widely used, however, has low sensitivity in such differentiation. We aimed to recalibrate the GFT and improve the performance in detection of unnatural epidemics. The comparator was the original GFT and its application in 11 historical outbreaks, including eight confirmed unnatural outbreaks and three natural outbreaks. Three steps were involved: (i) removing criteria, (ii) changing weighting factors, and (iii) adding and refining criteria. We created a series of alternative models to examine the changes on the parameter likelihood of unnatural outbreaks until we found a model that correctly identified all the unnatural outbreaks and natural ones. Finally, the recalibrated GFT was tested and validated with data from an unnatural and natural outbreak, respectively. A total of 238 models were tested. Through the removal of criteria, increasing or decreasing weighting factors of other criteria, adding a new criterion titled "special insights," and setting a new threshold for likelihood, we increased the sensitivity of the GFT from 38% to 100%, and retained the specificity at 100% in detecting unnatural epidemics. Using test data from an unnatural and a natural outbreak, the recalibrated GFT correctly classified their etiology. The recalibrated GFT could be integrated into routine outbreak investigation by public health institutions and agencies responsible for biosecurity.

The role of pneumonia and secondary bacterial infection in fatal and serious outcomes of pandemic influenza a(H1N1)pdm09.

BACKGROUND: The aim of this study was to estimate the prevalence of pneumonia and secondary bacterial infections during the pandemic of influenza A(H1N1)pdm09. METHODS: A systematic review was conducted to identify relevant literature in which clinical outcomes of pandemic influenza A(H1N1)pdm09 infection were described. Published studies (between 01/01/2009 and 05/07/2012) describing cases of fatal or hospitalised A(H1N1)pdm09 and including data on bacterial testing or co-infection. RESULTS: Seventy five studies met the inclusion criteria. Fatal cases with autopsy specimen testing were reported in 11 studies, in which any co-infection was identified in 23% of cases (Streptococcus pneumoniae 29%). Eleven studies reported bacterial co-infection among hospitalised cases of A(H1N1)2009pdm with confirmed pneumonia, with a mean of 19% positive for bacteria (Streptococcus pneumoniae 54%). Of 16 studies of intensive care unit (ICU) patients, bacterial co-infection identified in a mean of 19% of cases (Streptococcus pneumoniae 26%). The mean prevalence of bacterial co-infection was 12% in studies of hospitalised patients not requiring ICU (Streptococcus pneumoniae 33%) and 16% in studies of paediatric patients hospitalised in general or pediatric intensive care unit (PICU) wards (Streptococcus pneumoniae 16%). CONCLUSION: We found that few studies of the 2009 influenza pandemic reported on bacterial complications and testing. Of studies which did report on this, secondary bacterial infection was identified in almost one in four patients, with Streptococcus pneumoniae the most common bacteria identified. Bacterial complications were associated with serious outcomes such as death and admission to intensive care. Prevention and treatment of bacterial secondary infection should be an integral part of pandemic planning, and improved uptake of routine pneumococcal vaccination in adults with an indication may reduce the impact of a pandemic.

Prevalence and genotypes of group A rotavirus among outpatient children under five years old with diarrhea in Beijing, China, 2011-2016.

BACKGROUND: Rotavirus is a leading cause of severe diarrheal disease, and one of the common causes of death in children aged under five years old. The dominant epidemic strains may change in different years in the same area. In order to provide evidence for rotavirus epidemic control and inform vaccine development, we analyzed epidemiological patterns and genetic characteristics of rotavirus in Beijing during 2011-2016. METHODS: Stool specimens of outpatient children under five years old were collected from three children's hospitals on a weekly basis. Group A rotavirus antigens were detected using enzyme-linked immunosorbent assay (ELISA) kit. The partial VP4 genes and VP7 genes of rotavirus were both amplified and sequenced. Genotyping and phylogenetic analyses were performed. Logistic regression and Chi-square tests were performed to determine differences across age groups, districts and years in rotavirus prevalence and genotype distribution. RESULTS: A total of 3668 stool specimens from children with acute diarrhea identified through hospital-based surveillance were collected from 2011 to 2016 in Beijing. A total of 762 (20.8%) specimens tested positive for rotavirus. The rotavirus-positive rate was highest among the 1-2 years old age group (29.0%, 310/1070). November, December and January were the highest rotavirus-positive rate months each year. G9 was the most common G genotype (64.4%, 461/716), and P [8] was the most common P genotype (87.0%, 623/716) among the 716 rotavirus-positive specimens. G9P [8], G3P [8] and G2P [4] were the most common strains. The rotavirus-positive rates of samples in 2012 and 2013 were higher than that in 2011, and the dominant genotype changed from G3P [8] to G9P [8] in 2012 and 2013. VP7 gene sequences of G9 strains in this study clustered into two main lineages. Most of the G9 strains exhibited the highest nucleotide similarity (99.1%~ 100.0%) to the strain found in Japan (MI1128). VP4 gene sequences of P [8] strains were almost P[8]b. CONCLUSIONS: Rotavirus accounted for more than one fifth of childhood diarrhea in Beijing during the study period. Targeted measures such as immunization with effective rotavirus vaccines should be carried out to reduce the morbidity and mortality due to rotavirus.

Alcopops, taxation and harm: a segmented time series analysis of emergency department presentations.

BACKGROUND: In Australia, a Goods and Services Tax (GST) introduced in 2000 led to a decline in the price of ready-to-drink (RTD) beverages relative to other alcohol products. The 2008 RTD ("alcopops") tax increased RTD prices. The objective of this study was to estimate the change in incidence of Emergency Department (ED) presentations for acute alcohol problems associated with each tax. METHODS: Segmented regression analyses were performed on age and sex-specific time series of monthly presentation rates for acute alcohol problems to 39 hospital emergency departments across New South Wales, Australia over 15 years, 1997 to 2011. Indicator variables represented the introduction of each tax. Retail liquor turnover controlled for large-scale economic factors such as the global financial crisis that may have influenced demand. Under-age (15-17 years) and legal age (18 years and over) drinkers were included. RESULTS: The GST was associated with a statistically significant increase in ED presentations for acute alcohol problems among 18-24 year old females (0 · 14/100,000/month, 95% CI 0 · 05-0 · 22). The subsequent alcopops tax was associated with a statistically significant decrease in males 15-50 years, and females 15-65 years, particularly in 18-24 year old females (-0 · 37/100,000/month, 95% CI -0 · 45 to -0 · 29). An increase in retail turnover of liquor was positively and statistically significantly associated with ED presentations for acute alcohol problems across all age and sex strata. CONCLUSIONS: Reduced tax on RTDs was associated with increasing ED presentations for acute alcohol problems among young women. The alcopops tax was associated with declining presentations in young to middle-aged persons of both sexes, including under-age drinkers.

A machine learning-based universal outbreak risk prediction tool.

In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targeting only one specific disease and one country. Differences between countries and diseases (e.g., different economic conditions, different modes of transmission, etc.) pose challenges for building models with cross-country and cross-disease prediction capabilities. The limitation of universality affects domestic and international efforts to control and prevent pandemic outbreaks. To address this problem, we used outbreak data from 43 diseases in 206 countries to develop a universal risk prediction system that can be used across countries and diseases. This system used five machine learning models (including Neural Network XGBoost, Logistic Boost, Random Forest and Kernel SVM) to predict and vote together to make ensemble predictions. It can make predictions with around 80%-90 % accuracy from economic, cultural, social, and epidemiological factors. Three different datasets were designed to test the performance of ML models under different realistic situations. This prediction system has strong predictive ability, adaptability, and generality. It can give universal outbreak risk assessment that are not limited by border or disease type, facilitate rapid response to pandemic outbreaks, government decision-making and international cooperation.

Modeling on the Effects of Deliberate Release of Aerosolized Inhalational Bacillus anthracis (Anthrax) on an Australian Population.

This study aimed to determine optimal mitigation strategies in the event of an aerosolized attack with Bacillus anthracis, a category A bioterrorism agent with a case fatality rate of nearly 100% if inhaled and untreated. To simulate the effect of an anthrax attack, we used a plume dispersion model for Sydney, Australia, accounting for weather conditions. We determined the radius of exposure in different sizes of attack scenarios by spore quantity released per second. Estimations of different spore concentrations were then used to calculate the exposed population to inform a Susceptible-Exposed-Infected-Recovered (SEIR) deterministic mathematical model. Results are shown as estimates of the total number of exposed and infected people, along with the burden of disease, to quantify the amount of vaccination and antibiotics doses needed for stockpiles. For the worst-case scenario, over 500,000 people could be exposed and over 300,000 infected. The number of deaths depends closely on timing to start postexposure prophylaxis. Vaccination used as a postexposure prophylaxis in conjunction with antibiotics is the most effective mitigation strategy to reduce deaths after an aerosolized attack and is more effective when the response starts early (2 days after release) and has high adherence, while it makes only a small difference when started late (after 10 days).

Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Uptake of influenza, pneumococcal and herpes zoster vaccines among people with heart failure and atrial fibrillation.

BACKGROUND: Cardiovascular diseases are the major cause of hospitalisation and death globally. Infections exacerbate cardiovascular events among cardiac patients, contributing to all-cause mortality. Vaccination is a cheap and effective intervention that can prevent infection. In Australia, influenza, pneumococcal and herpes zoster vaccines are recommended and funded for high-risk adults such as cardiac patients. There is high prevalence of high-risk adults in Western Sydney. OBJECTIVES: This study investigates the uptake of influenza, pneumococcal and herpes zoster vaccines in patients admitted with heart failure and atrial fibrillation in a tertiary hospital in Western Sydney and factors associated with the uptake of the vaccines. METHODS: Consecutive patients' hospitalised between 2014 and 2018 with heart failure or atrial fibrillation as principal diagnoses were identified. Information on patients' social demographic, clinical and vaccination status was collected and described using descriptive analysis. Univariate and multivariate analyses were conducted to determine factors associated with the uptake of the vaccines. RESULTS: Low uptake for pneumococcal (40-45 %) and herpes zoster (15 %) vaccines were found. Prevalence of influenza vaccination was lower among participants younger than 65 (51-72 %) than in older ones (78-96 %). Australia-born participants were more likely to receive pneumococcal vaccine than those born overseas (OR 2.02, 95 % CI 1.05-3.89). Participants 65 years or older and those with comorbidities such as hypertension, COPD and chronic renal impairment were more likely to receive the vaccines. CONCLUSION: Multidisciplinary strategies are needed to improve access to vaccination, community knowledge, community engagement, and healthcare provider support to provide appropriate care to migrants and younger cardiac patients and reduce morbidity and mortality in this high-risk group.

Adapt or die: how the pandemic made the shift from EBM to EBM+ more urgent.

Evidence-based medicine (EBM's) traditional methods, especially randomised controlled trials (RCTs) and meta-analyses, along with risk-of-bias tools and checklists, have contributed significantly to the science of COVID-19. But these methods and tools were designed primarily to answer simple, focused questions in a stable context where yesterday's research can be mapped more or less unproblematically onto today's clinical and policy questions. They have significant limitations when extended to complex questions about a novel pathogen causing chaos across multiple sectors in a fast-changing global context. Non-pharmaceutical interventions which combine material artefacts, human behaviour, organisational directives, occupational health and safety, and the built environment are a case in point: EBM's experimental, intervention-focused, checklist-driven, effect-size-oriented and deductive approach has sometimes confused rather than informed debate. While RCTs are important, exclusion of other study designs and evidence sources has been particularly problematic in a context where rapid decision making is needed in order to save lives and protect health. It is time to bring in a wider range of evidence and a more pluralist approach to defining what counts as 'high-quality' evidence. We introduce some conceptual tools and quality frameworks from various fields involving what is known as mechanistic research, including complexity science, engineering and the social sciences. We propose that the tools and frameworks of mechanistic evidence, sometimes known as 'EBM+' when combined with traditional EBM, might be used to develop and evaluate the interdisciplinary evidence base needed to take us out of this protracted pandemic. Further articles in this series will apply pluralistic methods to specific research questions.

Influenza cases in nine aged care facilities in Sydney, Australia over a three-year surveillance period, 2018-2020.

BACKGROUND: Influenza outbreaks in aged care facilities are a major public health concern. In response to the severe 2017 influenza season in Australia, enhanced influenza vaccines were introduced from 2018 onwards for those over 65 and more emphasis was placed on improving vaccination rates among aged care staff. During the COVID-19 pandemic, these efforts were then further escalated to reduce the additional burden that influenza could pose to facilities. METHODS: An observational epidemiological study was conducted from 2018 to 2020 in nine Sydney (Australia) aged care facilities of the same provider. De-identified vaccination data and physical layout data were collected from participating facility managers from 2018 to 2020. Active surveillance of influenza-like illness was carried out from 2018 to 2020 influenza seasons. Correlation and Poisson regression analyses were carried out to explore the relationship between physical layout variables to occurrence of influenza cases. RESULTS: Influenza cases were low in 2018 and 2019, and there were no confirmed influenza cases identified in 2020. Vaccination rates increased among staff by 50.5% and residents by 16.8% over the three-year period of surveillance from 2018 to 2020. For each unit increase in total number of beds, common areas, single rooms, all types of rooms (including double occupancy rooms), the influenza cases increased by 1.02 (95% confidence interval:1.018-1.025), 1.04 (95% confidence interval: 1.019-1.073), 1.03 (95% confidence interval: 1.016-1 0.038) and 1.02 (95% confidence interval:1.005-1.026) times which were found to be statistically significant. For each unit increase in the proportion of shared rooms, influenza cases increased by 1.004 (95% confidence interval:1.0001-1.207) which was found to be statistically significant. CONCLUSIONS: There is a relationship between influenza case counts and aspects of the physical layout such as facility size, and this should be considered in assessing risk of outbreaks in aged care facilities. Increased vaccination rates in staff and COVID-19 prevention and control measures may have eliminated influenza in the studied facilities in 2020.

Global epidemiology of vaccine-derived poliovirus 2016-2021: A descriptive analysis and retrospective case-control study.

Background: Vaccine derived poliovirus (VDPV) remains a major barrier to polio eradication, and recent growing emergences are concerning. This paper presents the global epidemiology of circulating VDPV (cVDPV) by exploring associations between demographic and socioeconomic factors with its recent rise. Methods: Data on reported cVDPV cases and isolates between January 1 2016 and June 30 2021 were compiled from EPIWATCH, an open-source observatory for outbreak scanning and analysis, the World Health Organisation (WHO) and ProMed, and analysed descriptively. Reports containing cVDPV case information were included while duplicates and defective links were excluded. Data collection occurred from April 5 2021 to July 16 2021. To identify factors associated with cVDPV, a retrospective case-control study comparing socioeconomic profiles of countries which reported cVDPV with those that did not was undertaken with weighted logistic regression analysis. Findings: cVDPV caused by serotype 2 poliovirus was the predominant strain (95%) of 1818 total human cVDPV cases reported. Of 40 countries reporting cVDPV cases or isolates, 22 (55%) had polio vaccination coverages below 80%. Low vaccination coverage (Adjusted OR = 83·41, 95% CI: [5·01, 1387·71], p = 0·0020) was found to be associated with increased odds of reporting cVDPV after adjusting for confounding effects of GDP per capita, female adult literacy rates, maternal mortality rate, and Global Peace Index. Interpretation: Our findings reinforce the importance of maintaining high levels of vaccination, as risk of re-emergence rises when immunity wanes. Interventions to increase vaccination and standards of living in developing countries, coupled with robust surveillance are required if humanity hopes to eradicate polio in the near future. Funding: This research was supported by the MRFF 2021 Frontier Health and Medical Research Grant (ID RFRHPI000280), Department of Health, the Australian Government.