Automated monitoring of tweets for early detection of the 2014 Ebola epidemic

First reported in March 2014, an Ebola epidemic impacted West Africa, most notably Liberia, Guinea and Sierra Leone. We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic. We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately and then aggregates the set of alerts generated by the architecture. Using a dataset of tweets posted from the affected region from 2011 to 2014, we obtain alerts in December 2013, which is three months prior to the official announcement of the epidemic. Among the two variations, the second, which produces a restricted but useful set of alerts, can potentially be applied to other infectious disease surveillance and alert systems

Erratum to “Estimating the burden of COVID-19 on the Australian healthcare workers and health system during the first six months of the pandemic” [International Journal of Nursing Studies, 114 (2021), 103811] (International Journal of Nursing Studies (2021) 114, (S0020748920302972), (10.1016/j.ijnurstu.2020.103811))

The publisher regrets to inform that due to a typesetter error during the correction of the proofs, the following errors were missed: In Table 2: National HCW should be 2.69 with a 95% Cl of 2.48-2.93, as indicated in the text. On page 9, the duplicate word “suggests” was not removed. The Publisher apologises for these errors and any inconvenience caused

Epidemiology of Influenza-like Illness during Pandemic (H1N1) 2009, New South Wales, Australia

To rapidly describe the epidemiology of influenza-like illness (ILI) during the 2009 winter epidemic of pandemic (H1N1) 2009 virus in New South Wales, Australia, we used results of a continuous population health survey. During July–September 2009, ILI was experienced by 23% of the population. Among these persons, 51% were unable to undertake normal duties for <3 days, 55% sought care at a general practice, and 5% went to a hospital. Factors independently associated with ILI were younger age, daily smoking, and obesity. Effectiveness of prepandemic seasonal vaccine was ≈20%. The high prevalence of risk factors associated with a substantially increased risk for ILI deserves greater recognition

The impact of COVID-19 and COVID vaccination on cardiovascular outcomes

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

Association of influenza infection and vaccination with cardiac biomarkers and left ventricular ejection fraction in patients with acute myocardial infarction

Aims: The aim of this study was to examine the association of influenza infection and vaccination with extent of cardiac damage during acute myocardial infarctions (AMIs) as measured by serum biomarkers and left ventricular ejection function (LVEF) in patients. Methods: Post-hoc analysis was performed on data from a prospective case-control study of influenza and AMI, conducted in a tertiary care hospital in Sydney, Australia. We included 275 cases of AMI, aged ≥ 40 years admitted to the cardiology during the study period. Results: Mean and median CK-MB levels were significantly higher among unvaccinated group compared to vaccinated group (p value < 0.05). Troponin levels were also higher among unvaccinated group compared to vaccinated group; although not statistically significant. Troponin and CKMB values were not statistically different among influenza positive cases and influenza negative cases. Large size infarcts were less frequent among vaccinated cases compared to unvaccinated cases (25% vs 35.5%) and were more frequent among influenza positive cases compared to influenza negative cases (35.3% vs 31.5%), however differences were not statistically significant. LVEF was lower among vaccinated cases compared to unvaccinated cases (62.5% vs. 52.8%) and influenza positive cases compared to influenza negative cases (58.8% vs 55.4), however differences were not significant. Conclusion: Lower CKMB levels among vaccinated groups showed that influenza vaccine may have a protective effect against large infarcts, therefore influenza vaccination should be recommended for high risk groups. The study suggests an association of larger infarcts with influenza infection, but larger studies are required to confirm this