Influenza and the implications of a pandemic for Malta

An influenza pandemic is inevitable and recent reports from Southeast Asia on avian influenza viruses infecting humans have served to fuel worries that a new pandemic is near. The purpose of this article is to provide an overview of the epidemiological and public health aspects of seasonal, avian and pandemic influenza through a literature review and to describe the possible effects of an Influenza pandemic on Malta using the FluAid model. The results of the model indicate that between 158 and 454 deaths would be expected for a 12-week pandemic causing clinical symptoms in 25% of the population. There would be between 432 and 1,488 hospitalisations and between 40,483 and 74,704 general practice consultations. Although the results of the model show a wide range of estimates and are limited by a lack of local parameters, the data presented in this article shows the severe effect of a pandemic on the Maltese health care system and will be useful for pandemic planning. Further research needs to be undertaken to determine local parameters to improve the model estimates and local health authorities need to ensure that adequate resources are provided to implement an effective pandemic preparedness plan.peer-reviewe

Estimating the value of containment strategies in delaying the arrival time of an influenza pandemic: A case study of travel restriction and patient isolation

With a simple phenomenological metapopulation model, which characterizes the invasion process of an influenza pandemic from a source to a subpopulation at risk, we compare the efficiency of inter- and intra-population interventions in delaying the arrival of an influenza pandemic. We take travel restriction and patient isolation as examples, since in reality they are typical control measures implemented at the inter- and intra-population levels, respectively. We find that the intra-population interventions, e.g., patient isolation, perform better than the inter-population strategies such as travel restriction if the response time is small. However, intra-population strategies are sensitive to the increase of the response time, which might be inevitable due to socioeconomic reasons in practice and will largely discount the efficiency.Comment: 5 pages,3 figure

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Abstract Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread

Plan your pandemic: A guide for GPs

Background Influenza A virus has a range of subtypes characterised by the display of particular surface structures and is associated with significant symptoms and a tendency to cause epidemics and pandemics. Objective This article presents a checklist to assist general practitioners in preparing for an influenza pandemic. Discussion The Australian Federal Government launched ‘Exercise Cumpston’ in October 2006 to assess Australian pandemic preparedness. The report of the outcomes recommends the integration of general practice into the planning process at a national and jurisdictional level. General practitioners are enthusiastic about receiving further information and training in pandemic preparedness but preparing a general practice to deal with an influenza pandemic is a complex task

Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009-2011

Knowledge of the severity of an influenza outbreak is crucial for informing and monitoring appropriate public health responses, both during and after an epidemic. However, case-fatality, case-intensive care admission and case-hospitalisation risks are difficult to measure directly. Bayesian evidence synthesis methods have previously been employed to combine fragmented, under-ascertained and biased surveillance data coherently and consistently, to estimate case-severity risks in the first two waves of the 2009 A/H1N1 influenza pandemic experienced in England. We present in detail the complex probabilistic model underlying this evidence synthesis, and extend the analysis to also estimate severity in the third wave of the pandemic strain during the 2010/2011 influenza season. We adapt the model to account for changes in the surveillance data available over the three waves. We consider two approaches: (a) a two-stage approach using posterior distributions from the model for the first two waves to inform priors for the third wave model; and (b) a one-stage approach modelling all three waves simultaneously. Both approaches result in the same key conclusions: (1) that the age-distribution of the case-severity risks is "u"-shaped, with children and older adults having the highest severity; (2) that the age-distribution of the infection attack rate changes over waves, school-age children being most affected in the first two waves and the attack rate in adults over 25 increasing from the second to third waves; and (3) that when averaged over all age groups, case-severity appears to increase over the three waves. The extent to which the final conclusion is driven by the change in age-distribution of those infected over time is subject to discussion.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS775 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org