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COVID-19 is an independent risk factor for cardiovascular disease. COVID-19 vaccination may prevent this, but in some cases, COVID-19 vaccination may cause myocarditis or pericarditis. Patients with COVID-19 may present with non-specific symptoms that have a cardiac origin. This review examines the cardiovascular complications of COVID-19 infection and the impact of COVID-19 vaccination. COVID-19 cardiovascular complications include myocardial injury, pericarditis, coagulopathy, myocardial infarction, heart failure, arrhythmias, and persistent post-acute risk of adverse cardiovascular outcomes. Diagnostic and referral pathways for non-specific symptoms, such as dyspnoea and fatigue, remain unclear. COVID-19 vaccination is cardioprotective overall but is associated with myopericarditis in young males, though at a lower rate than following SARS-CoV-2 infection. Increased awareness among primary care physicians of potential cardiovascular causes of non-specific post-COVID-19 symptoms, including in younger adults, such as fatigue, dyspnoea, and chest pain, is essential. We recommend full vaccination with scheduled booster doses, optimal management of cardiovascular risk factors, rapid treatment of COVID-19, and clear diagnostic, referral, and management pathways for patients presenting with non-specific symptoms to rule out cardiac complications.

Cardiac arrest is the least preventable burden of cardiovascular disease, as treatment depends on timely resuscitation. The incidence of sudden cardiac arrest (SCA) is high, contributing 10-20% of cardiovascular mortality globally. The influenza vaccine reduces the risk of acute cardiovascular events. Little is known about the relationship of influenza infection to cardiac arrest.This study aimed to determine the estimated rate of SCA hospitalisations attributable to influenza in Australian adults. A generalised-additive statistical model was applied in the study. Weekly counts of laboratory-confirmed influenza notifications were used as independent variables in the model.Our estimates showed that the yearly rate of SCA hospitalisations varied, and a significant association with influenza was observed in some years in older adults aged 65 years and over. On average, the annual estimated SCA hospitalisations rate due to influenza in adults aged 50-64 years and ≥ 65 years were 0.7 (95%CI: 0.4, 1.1) and 5.3 (95%CI: 4.4, 6.2) per 100,000 population, respectively.The association between influenza and SCA is evident in adults and the disease burden is significant in older people. Prevention of influenza by vaccination may reduce SCA.

Background Residential aged-care facilities in Australia emerged as the high-risk setting during 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 assess 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. 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 merely 3.14 per 100,000 population despite having the highest COVID-19 cases in the state. Excluding South Australia, all states followed 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. We recommend immediate hospital transfer of infected residents to hospitals as healthcare settings follow necessary infection control measures and ensure staff are trained in personal protective equipment use.

Background Estimation of influenza disease burden is necessary to monitor the impact of intervention programmes. This study aims to estimate the attributable fraction of respiratory and circulatory disease due to influenza among Australian adults 50–64 and ≥65 years of age. Methods A semi‐parametric generalised‐additive model was used to estimate annual and average rate of influenza‐attributable hospitalisation and death per 100,000 population under the principal diagnosis of influenza/pneumonia, respiratory, circulatory and myocardial infarction (MI) from 2001 through 2017. Results Over the study period, seasonal influenza accounted for an estimated annual average respiratory hospitalisation rate of 78.9 (95%CI: 76.3, 81.4) and 287.5 (95%CI: 279.8, 295.3) per 100,000 population in adults aged 50–64 and ≥65 years, respectively. The corresponding respiratory mortality rates were 0.9 (95%CI: 0.7, 1.2) and 18.2 (95%CI: 16.9, 19.4) per 100,000 population. The 2017 season had the highest influenza‐attributable respiratory hospitalisations in both age groups, and respiratory complications were estimated approximately 2.5 times higher than the average annual estimate in adults aged ≥65 years in 2017. For mortality, on average, influenza attributed 1,080 circulatory and 361 MI deaths in adults aged ≥65 years per year. Influenza accounted for 1% and 2.8% of total MI deaths in adults aged 50–64 and ≥65 years, respectively. Conclusion Rates of cardiorespiratory morbidity and mortality were high in older adults, whilst the younger age group contributed a lower disease burden. Extension of influenza vaccination programme beyond the targeted population could be an alternative strategy to reduce the burden of influenza.

Objective: Open-source data from online news reports and informal sources may provide information about outbreaks before official notification. This study aims to evaluate the use of open-source data from the epidemic observatory, EpiWATCH, to identify the early signals of pneumonia of unknown cause as a proxy for COVID-19 in Indonesia. Methods: Using open-source data on pneumonia of unknown cause in Indonesia between 1 November 2019 and 31 March 2020 (extracted from EpiWATCH, an open-source epidemic observatory), a descriptive analysis was performed to identify the trend of pneumonia of unknown cause in Indonesia before official notification of COVID-19 cases. Results: A rise in reports of pneumonia of unknown cause was identified in Indonesia, starting from late January 2020. There were 304 reported cases of pneumonia of unknown cause, 30 of which occurred before the identification of the first COVID-19 cases on 2 March 2020. The early signals of pneumonia of unknown cause in Indonesia may indicate possible unrecognized circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before official detection. Discussion: Open-source data may provide rapid, unvalidated information for early detection of outbreaks. Although unvalidated, such information may be used to supplement or trigger investigation and testing. As EpiWATCH sources global information, this methodology can be repeated for other countries within the Western Pacific Region, and for other diseases.

Background The 2017 A/H3N2 influenza season was the most severe season since the 2009 influenza pandemic. There were over 591 influenza outbreaks in institutions across the state of New South Wales (NSW) in Australia. Aim To describe the epidemiology of influenza outbreaks in nine Sydney aged care facilities in 2017. Methods Study data were collected from nine Sydney aged care facilities for 2017 influenza season. Descriptive epidemiological analysis was conducted. Results From the nine sites included, with a total of 716 residents, four sites reported laboratory-confirmed influenza outbreaks during the study period, with an attack rate in residents ranging from 6% to 29%. The outbreaks resulted in lockdowns in two facilities and hospitalisation of seven residents. No deaths were reported as a result of influenza infection. Influenza A was the most common influenza type reported across the facilities. The duration of outbreak lasted for 1-4 weeks varied by site. Conclusion The 2017 season was a severe influenza season recorded in Australia. About half of the facilities studied experienced outbreaks of influenza, with a high attack rate among residents. Infection prevention and control measures and outbreak management plans are crucial for aged care facilities, including vaccination of staff and visitors to prevent outbreaks among the vulnerable residents.

An influenza outbreak occurred during summer (February 2019) in an aged-care facility in Sydney, Australia. Residents had not received the annual 2019 influenza vaccine while 76.7% had received 2018 influenza vaccines about 9 months prior. Overall, 2018 influenza vaccine effectiveness during this outbreak was high (93.6%). The effectiveness of the high-dose trivalent vaccine (HD-TIV) and adjuvanted trivalent (a-TIV) vaccine were 89.8% (95% confidence interval: 18.8%-98.7%) and 72.5% (95% confidence interval: -106.7%-96.3%) respectively. The differences in effectiveness between HD-TIV, a-TIV and SD-QIV, during the summer outbreak were not significant.

Background The 2009 Influenza A(H1N1) pandemic prompted one of the largest public health responses in history. The continuous emergence of new and deadly pathogens has highlighted the need to reflect upon past experiences to improve pandemic preparedness. The aim of this study was to examine the development and rollout of 2009 influenza A(H1N1) pandemic vaccine and knowledge challenges for the effective implementation of vaccination programs for COVID-19 and future influenza pandemics. Methods A systematic review was conducted searching EMBASE (inception to current date) and PUBMED (from January 2009 to current date) databases for relevant published studies about influenza A(H1N1) pandemic vaccines. A Google search was conducted to identify relevant documents from grey literature. Selected Studies were reviewed and summarized. Results A total of 22, comprising of 12 original studies and 10 relevant documents met the inclusion criteria. Fourteen papers reported an initial high demand that outweighed production capacity and caused vaccine shortages. Vaccine procurement and supply were skewed towards high-income countries. Low vaccination rates of about 5% to 50% were reported in all studies mainly due to a low-risk perception of getting infected, safety concerns and the fear of adverse effects. Conclusion Safety concerns about the approved H1N1 vaccines resulted in many unsuccessful vaccination campaigns worldwide. Understanding the factors that influence people's decision to accept or refuse vaccination, effective risk communication strategies, adequate resources for vaccine deployment initiatives and building local capacities through shared knowledge and technology transfer may help to improve COVID-19 vaccine uptake and accelerate pandemic control.

Background Human Papillomavirus (HPV) causes significant burden of HPV-related diseases, which are more prevalent in immunosuppressed compared to immunocompetent people. We conducted a multi-centre clinical trial to determine the immunogenicity and reactogenicity of HPV vaccine in immunocompromised children. Here we present the immunogenicity results 5 years post vaccination. Methods We followed up immunocompromised children (5–18 years) with a range of specified underlying conditions who were previously recruited from three Australian paediatric hospitals. Participants received three doses of quadrivalent HPV vaccine (Gardasil Quadrivalent HPV Types 6, 11, 16, 18) and were followed up between 2007 and 2016 (60 months post-vaccination). The immunogenicity primary outcome was seroconversion and geometric mean titres (GMT) of the quadrivalent HPV vaccine serotypes in the study. Results Of the 59 original participants, 37 were followed up at 60 months. The proportion of participants who seroconverted were: 86.5%, 89.2%, 89.2%, 91.9% by competitive Luminex immunoassay (cLIA) and 83.8%, 83.8%, 94.6%, 78.4% by total immunoglobulin G assays (IgG) for serotypes 6, 11, 16 and 18 respectively. GMT values ranged from 118 (95%CI: 79–177) for serotype 11, to 373 (95%CI: 215–649) for serotype 16 by cLIA. For IgG, serotype 16 had the highest GMT of 261 (95%CI: 143–477) and serotype 18 had the lowest value of 37 (95%CI: 21–68). All antibody titres were lower in females compared to males but the difference was not statistically significant except for serotype 16. No serious adverse event was reported during this follow-up period. Conclusion Our evidence, although limited by small numbers, is reassuring that a three dose schedule of HPV vaccine remains immunogenic in immunocompromised children to five years post vaccination. Large scale studies are required to determine long term protection in immunocompromised children. Clinical trial registration: NCT02263703 (ClinicalTrials.gov).

'Background: 'The outbreak of novel coronavirus (COVID-19) has turned into a public health emergency of international concern. With no antiviral drugs nor vaccine, presence of carriers without obvious symptoms and varied clinical characteristics, the traditional public health measures are certainly less effective. The early signs of COVID-19 and epidemiological data are essential to strengthen the overwhelmed healthcare systems. 'Aim:' To detect, assess and analyse the early signs of COVID-19 in Pakistan before the official reporting of cases in the country. 'Methods:' The study uses the EpiWATCH observatory to extract data from 1 November 2019 to 1 April 2020. The trend of pneumonia of unknown origin cases in Pakistan was observed to assess if cases of COVID-19 could have been detected before the first official case was reported. A descriptive analysis of the obtained data was achieved on the basis of geographic and demographic features. 'Results:' A total of 151 entries were included in the study. Before the identification of the first official case of COVID-19 in Pakistan, 54 reports of cases of pneumonia of unknown origin were recorded. After the first case of COVID-19 was reported, 97 reports of cases of COVID-19 were recorded. The study findings suggest that there were early signs of COVID-19 in Pakistan since the second week of January 2020, a month and a half prior to the first case being reported in the country.

During late July to August 2019, Zhongmu Lanzhou biopharmaceutical plant of Gansu province was producing brucella vaccine for animals and reportedly used an expired disinfectant for cleaning, which failed to kill all the bacteria. At the same time a leak occurred in the plant, which allowed the contaminated waste in the form of aerosol to be leaked in the air. This caused the first outbreak in the Lanzhou animal research institute as the contaminated aerosols were carried and spread there. Community transmission took place after this event. The infection has so far been confirmed in 10,528 people after testing - 68571 people up to November 30, 2020.

Hepatitis A (Hep A) can cause sporadic or epidemic disease and has been frequently linked to contamination of the global food chain. Global surveillance data on Hep A are unavailable, and in some countries, reporting is incomplete or not timely, either because of lack of human resources or sensitivities around reporting. The use of vast open-source data such as news-feeds and social media however can overcome barriers to surveillance and provide timely data on global epidemics. In this study we use EpiWATCH, semi-automated outbreak scanning service to review the global epidemiology of Hep A reports from 2016-2018. We reviewed the EpiWATCH Outbreak Alerts database for reports on the Hep A dated between August 1, 2016, to April 31, 2018 which was the analysed by outbreak clusters, location, and time. Of 5098 total entries in the database a total of 169 non-duplicate Hep A outbreak reports were found and included for descriptive analysis. The majority of outbreak reports (68.6%%; N=116/169) originated from the United States of America (USA). The largest Hep A outbreaks were multi-country outbreaks in the European region, and multistate outbreaks in the USA and Australia. Homelessness (mainly in US outbreaks) was the predominant risk factor (40.2%), followed by foodborne outbreaks (26.6%) and outbreaks in men who have sex with men (6.5%). Using EpiWATCH we found that the emergence of outbreaks in homeless people has dominated the epidemiology of Hep A in the U.S and this appears a relatively new phenomenon. Epidemic intelligence systems such as EpiWATCH are a useful proxy for global surveillance of Hep A outbreaks and using open-source data can provide epidemic intelligence and outbreak alerts where global data is unavailable.

Background: The epidemic of novel coronavirus disease (COVID-19) started in Wuhan, China, in December 2019, has been progressed to a pandemic level with high morbidity and mortality affecting many countries globally. Due to varied clinical characteristics of the disease, diagnosis can be missed easily among the affected population. The epidemiological data and early signals of COVID-19 infection are crucial for the disease investigation. Aim: To assess the presence of early signals of COVID-19 in India before official reporting of cases in the country and to compare epidemiological characters using different surveillance sources in the study. Methods: This study uses Epiwatch outbreaks surveillance data from November 2019 to March 2020 was used to determine the trend of pneumonia of unknown causes in India in order to assess if the COVID-19 could have been detected before the first case was reported.The search was carried out using the search terms defined in the study to identify the early signals of COVID-19.The COVID-19 line list extracted from the crowdsourced database (https://www.covid19india.org/) was additionally used to determine the demographic characteristics of cases. A descriptive analysis was conducted. Results: A total of 379 reports was extracted from Epiwatch. In 2019, there were only six reports of pneumonia of unknown causes in India, and the majority of the reports were focused on swine flu or undetected influenza cases. In 2020, there were 312 news reports of unknown pneumonia and COVID-19. The early signals of COVID-19 in India were observed from 24th January 2020 as the trend of pneumonia news had moderately increased. The trend of pneumonia reporting was low before the first confirmed case was reported in India. The study findings also suggested that there was under-reporting of COVID-19 cases in India by the World Health Organization (WHO) and the Ministry of Health, India when compared to data reported by Epiwatch and the line list. Conclusion: The study findings suggested that there were early signals of COVID-19 in India around 24th January when there was a slight increase in the trend of pneumonia reports in the country. The number of COVID-19 cases in India is under-reported by the Ministry of Health and the WHO. The use of online media surveillance reports such as Epiwatch and the line list could provide real-time assessment and timely investigation of emerging diseases globally.

Introduction: Pertussis is a highly infectious disease that remains endemic despite a rising vaccination coverage rates globally. Due to the lack of global surveillance data for pertussis, the unconventional use of open-source data gives a glimpse into global outbreaks, compensating for the lack of national reporting systems in some countries. The objective of the study is to describe global reporting of pertussis through open source data. Methods: An open-source database, EpiWATCH was used to analyse global outbreaks of pertussis. Data was retrieved on pertussis and analysed on multiple epidemiological factors from 2016 to 2019. In addition, prevalence rates were calculated for each country and compared to the World Health Organization (WHO) public domain data on global reported cases. Results: A total of 96 reports were collected globally between the years 2016 to 2019. Of those reports, 95.8% (92/96) were from high-income countries. Data from the United States comprised 59.3% (57/96) of the total reports. . In addition, prevalence rates were calculated for each country and compared to the World Health Organization (WHO) public domain data on global reported cases. An outbreak report was identified in Papua New Guinea, which was not reported in WHO surveillance. Discussion: Open-source data gives insight and analysis on pertussis outbreaks globally, given there is no formal global surveillance system for pertussis. There is a bias toward reports from high income countries in open source data. However, the timeliness of reporting coupled with assisting countries with lacking national reporting systems are benefits of open source data.

BACKGROUND Indonesia claimed zero cases of COVID-19 up until 2 March 2020. Given high travel volume between China and Indonesia, there were doubts over the lack of reported cases. OBJECTIVE This study aims to identify the early signals of pneumonia of unknown cause as a proxy for COVID-19 in Indonesia METHODS Using open-source data of pneumonia of unknown cause in Indonesia between 1 November 2019 to 31 March 2020 obtained from EpiWATCH, an open-source epidemic observatory, a descriptive analysis was performed to identify the trend of pneumonia of unknown cause in Indonesia prior to official notification of COVID-19 cases. RESULTS A rise in reports of pneumonia of unknown cause is identified in Indonesia starting from late January 2020. There were 304 reported cases of pneumonia of unknown cause, 30 of which occurred prior to the identification of the first COVID-19 cases on 2 March 2020. The early signals of pneumonia of unknown cause in Indonesia may indicate possible unrecognised circulation of SARS-CoV-2 prior to official detection CONCLUSIONS Open source data can provide early signals of epidemics, especially in countries where surveillance, testing or reporting is low

Anti-Microbial Resistance (AMR) is a significant public health threat in many countries around the world, endangering the control of infections and infectious disease. AMR related illness has been responsible for annual global mortality of 700,000. Besides health outcomes, they bear an adverse impact on economic development. However, the lack of comprehensive global AMR surveillance data and an over-reliance on indicator-based surveillance system has limited the early detection of emerging AMR threats and trends. Methods; EpiWATCH outbreak database has been used to retrieve AMR outbreak reports between August 2016-March 2020 using keywords such as resistance, resistant, superbug, bugs, MRSA and VRE. Cases were grouped according to geolocation and time to conduct a descriptive epidemiologic analysis of the outbreak. Results; A total of 60 reports on AMR outbreak were detected. Majority of the infections were caused by gram-negative bacteria. Drug-resistant Klebsiella. Acinetobacter and New Delhi Metallo-β-lactamase-1 (NDM-1) Carbapenemase-producing Enterobacteriaceae acquired in hospitals were strongly associated with mortality. As dense places, schools and universities were at-risk locations in the community for Methicillin-resistant Staphylococcus Aureus (MRSA) outbreaks and in hospital settings, the Neonatal units were at risk. Conclusion; EpiWATCH detected AMR outbreaks quickly and timely compared to an indicator-based surveillance system. It detected outbreaks by pathogens not monitored by the World Health Organisation. Also, it presented information on both colonised and infected cases from the population. Thus, EpiWATCH could be complemented with an indicator-based surveillance system for strengthening surveillance and identifying action areas.

Background Influenza continues to cause seasonal epidemics and pandemics in humans. The burden of influenza is underestimated by traditional laboratory-based surveillance, and modelled estimates are required for influenza-attributable morbidity and mortality. We aimed to estimate the influenza-attributable hospitalisation in Australia, by influenza type. Methods A generalised-additive regression model was used to estimate type- and age-specific influenza-attributable hospitalisation rates per 100,000 population by principal diagnosis in Australia, from 2001 through 2013. Weekly counts of laboratory-confirmed influenza notifications and by type, influenza A and B were used as covariates in the model. Main principal diagnosis categories of interest were influenza and pneumonia and respiratory admissions. A smoothing spline was used to control for unmeasured time varying factors. Results for 2009, in which the pandemic influenza A(H1N1)pdm09 virus circulated, were not included in annual averages and are reported separately. Results During the study period, the estimated annual average, all-age, annual respiratory hospitalisation rates attributable to seasonal influenza type A, B and total influenza were 45.4 (95% CI: 34.9, 55.9), 32.6 (95% CI: 22.8, 42.4), and 76.9 (95% CI: 73.6, 80.2) per 100,000 population, respectively. During 2009, the estimated total pandemic influenza-attributable, all-age, respiratory hospitalisation rate was 56.1 (95% CI: 47.4, 64.9) per 100,000. Older adults (≥85 years of age) experienced the highest influenza-attributable hospitalisation rates for both seasonal and 2009 pandemic influenza. Collinearity between influenza A and B time series in some years limited the ability of the model to resolve differences in influenza attribution between the two virus types. Conclusion Both seasonal and pandemic influenza caused considerable morbidity in Australia during the years studied, particularly among older adults. The pandemic hospitalisation rate in 2009 was lower than the average overall annual rate for seasonal influenza, but young to middle aged adults experience a hospitalisation rate similar to that of severe seasonal influenza.

Background Over the last two decades, Australia has experienced four severe influenza seasons caused by a predominance of influenza A (A/H3N2): 2003, 2007, 2012, and 2017. Methods We compared the epidemiology, genetics, and transmission dynamics of severe A/H3N2 seasons in Australia from 2003 to 2017. Results Since 2003, the proportion of notifications in 0‐4 years old has decreased, while it has increased in the age group >80 years old (P

Background It appears inter‐seasonal influenza notifications have been increasing in summer months in Australia. This study aims to determine changes in inter‐seasonal influenza activity in Australia over time. Methods Routine influenza surveillance data and hospitalisations data were analysed to study the epidemiology of inter‐seasonal influenza and to examine the impact of inter‐seasonal influenza on morbidity in Australia at a national level. To adjust for changes in testing over time, we calculated a ratio of summer‐to‐winter notifications for each year in the study. A P‐value of

Background Influenza B is characterised by two antigenic lineages; B/Victoria and B/Yamagata. These lineages circulate together with influenza A during influenza seasons, with varying incidence from year to year and by geographic region. Objective To determine the epidemiology of influenza B relative to influenza A in Australia. Methods Laboratory-confirmed influenza notifications between 2001 and 2014 in Australia were obtained from the Australian National Notifiable Diseases Surveillance System. Results A total of 278,485 laboratory-confirmed influenza cases were notified during the study period; comprising influenza A (82.2%), B (17.1%), and ‘other and untyped’ (0.7%). The proportion of notifications that were influenza B was highest in 5-9 year-olds (27.5%) and lowest in persons aged 85 years and over (11.5%). Of all B notifications with lineage determined, 77.1% were B/Victoria and 22.9% were B/Yamagata infections. Mismatches between the dominant B lineage in a season and the trivalent vaccine B lineage occurred in over one-third of seasons during the study years. In general, influenza B notifications peaked later than influenza A notifications. Conclusion The proportion of circulating influenza B in Australia during 2001 to 2014 was slightly lower than the global average and was dominated by B Victoria. Compared with influenza A, influenza B infection was more common among older children and young adults and less common in the very elderly. Influenza B lineage mismatch with the trivalent vaccine occurred about one third of the time. This article is protected by copyright. All rights reserved.

The aim of this study was to determine the immunogenicity and reactogenicity of HPV vaccine in immunocompromised children.A multi-centre clinical trial was conducted in three paediatric hospitals in Australia. Unvaccinated children 5-18years of age attending one of three paediatric hospitals with a range of specified conditions associated with immunosuppression were included. Quadrivalent HPV vaccine (Gardasil) was given to the participants and serum anti-HPV antibody levels were measured at baseline (before first dose), 7 and 24months after the first dose of vaccine.Fifty-nine participants were enrolled across the three paediatric hospitals and among those one was seropositive to types 6, 11 and 16 at baseline. Seven months after the first dose, seroconversion rates were 93.3%, 100%, 100% and 88.9% for type 6, 11, 16 and 18 respectively. The corresponding rates at 24month follow up were 82.2%, 91.1%, 91.1% and 68.9%. The greatest increase in geometric mean titre (GMT) was for type 16, followed by type 11. GMTs declined over the following months, but remained more than fourfold higher for all serotypes compared to baseline titres at 24months post vaccination. Injection site erythema, pain and swelling were commonly reported local adverse events and were less common after each dose. Few participants reported systemic adverse events, and minor disease flare occurred in two participants. One child developed a squamous cell oral carcinoma during follow up, but tissue was unable to be tested for HPV.Immunosuppressed children had an adequate immunogenic response to Quadrivalent HPV vaccine regardless of age and the cause of immunosuppression. HPV related cancers occur at higher frequency and earlier in immunosuppressed patients, so early vaccination and optimal scheduling should be further studied in such children.NCT02263703 (ClinicalTrials.gov).

Background: Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza. Objective: This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. Methods: Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season. Results: The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. Conclusions: The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.

BACKGROUND Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicated a preference of forecasting tools that are easy to use and understand, to assist with surge capacity planning for influenza. OBJECTIVE This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. METHODS Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports, Department of Health, Australia. We tested Flucast using retrospective seasonal data for eleven Australian influenza seasons. We compared five different models, using parameters known early in the season and which may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. A model with highest predictive accuracy against retrospective seasonal activity was chosen as a best fit model to develop the Flucast tool. The tool was prospectively tested against the emerging 2018 influenza season. RESULTS The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. For 2018, the tool provided a reliable early prediction of severe seasonal influenza with the use of real-time data. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. CONCLUSIONS The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity and intervention programs in real time and can be adapted for different settings and geographic locations. CLINICALTRIAL NA

We analysed Australian plans issued by the public sector and current at the time of the last human pandemic in 2009. They came from various levels of governance, and offered guidance in key domains. Using 13 established criteria, we rated 10 plans (national, state, and territorial) for their usefulness to guide health and medical intervention, business continuity, and crisis communication, plus consideration of at-risk populations. The intended end-user of most plans was not clear, whether hospital manager, health worker, or policy maker. Scores ranged from 8 to 29 of a maximum possible of 39, with many inconsistencies between plans. Health system-related issues were better addressed than critical infrastructure and essential systems resilience. The needs of Indigenous populations and use of pneumococcal vaccination and antibiotics were rarely considered in plans. Pandemic response would be more effective if plans were standardised, clear, and were to include overlooked dimensions of a pandemic's impact as well as guidance for specified end-users.

Objective We compared the efficacy of medical masks (MM) and N95 respirators (N95) in preventing bacterial colonization/infection in healthcare workers (HCWs). Methods A cluster randomized clinical trial (RCT) of 1441 hospital HCWs randomized to medical masks or N95 respirators, and compared to 481 control HCWs, was performed in Beijing, China, during the winter season of 2008–2009. Participants were followed for development of clinical respiratory illness (CRI). Symptomatic subjects were tested for Streptococcus pneumoniae, Bordetella pertussis, Chlamydia pneumoniae, Mycoplasma pneumoniae or Haemophilus influenza type B by multiplex polymerase chain reaction (PCR). Results The rate of bacterial colonization was 2.8% in the N95 group (p = 0.02), 5.3% among medical mask users (p, Highlights • N95 respirators had a significant protective efficacy against bacterial colonization. • Increased respiratory protection decreases bacterial colonization and co-infections. • Co-infections with viruses and bacteria are frequent in symptomatic health workers.