Project description:It is assumed that a timely mass administration of antiviral drugs, backed by quarantines and social distancing, could contain a nascent influenza epidemic at its source, provided that the first clusters of cases were localized within a short time. However, effective routine surveillance may be impossible in countries lacking basic public health resources. For a global containment strategy to be successful, low-cost, easy-to-use handheld units that permit decentralized testing would be vital. Here we present a microfluidic platform that can detect the highly pathogenic avian influenza virus H5N1 in a throat swab sample by using magnetic forces to manipulate a free droplet containing superparamagnetic particles. In a sequential process, the viral RNA is isolated, purified, preconcentrated by 50,000% and subjected to ultrafast real-time RT-PCR. Compared to commercially available tests, the bioassay is equally sensitive and is 440% faster and 2,000-5,000% cheaper.
Project description:In 1918, a strain of influenza A virus caused a human pandemic resulting in the deaths of 50 million people. A century later, with the advent of sequencing technology and corresponding phylogenetic methods, we know much more about the origins, evolution and epidemiology of influenza epidemics. Here we review the history of avian influenza viruses through the lens of their genetic makeup: from their relationship to human pandemic viruses, starting with the 1918 H1N1 strain, through to the highly pathogenic epidemics in birds and zoonoses up to 2018. We describe the genesis of novel influenza A virus strains by reassortment and evolution in wild and domestic bird populations, as well as the role of wild bird migration in their long-range spread. The emergence of highly pathogenic avian influenza viruses, and the zoonotic incursions of avian H5 and H7 viruses into humans over the last couple of decades are also described. The threat of a new avian influenza virus causing a human pandemic is still present today, although control in domestic avian populations can minimize the risk to human health. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.
Project description:UnlabelledInfluenza A virus subtype H5N1, also known as "bird flu" has been documented to cause an outbreak of respiratory diseases in humans. The unprecedented spread of highly pathogenic avian influenza type A is a threat to veterinary and human health. The BFluenza is a relational database which is solely devoted to proteomic information of H5N1 subtype. Bfluenza has novel features including computed physico-chemical properties data of H5N1 viral proteins, modeled structures of viral proteins, data of protein coordinates, experimental details, molecular description and bibliographic reference. The database also contains nucleotide and their decoded protein sequences data. The database can be searched in various modes by setting search options. The structure of viral protein could be visualized by JMol viewer or by Discovery Studio.AvailabilityThe database is available for free at http://www.bfluenza.info.
Project description:The epidemic of H7N9 bird flu in eastern China in early 2013 has caused much attention from researchers as well as public health workers. The issue on modeling the transmission risks is very interesting topic. In this article, this issue is debated and discussed in order to promote further researches on prediction and prevention of avian influenza viruses supported by better interdisciplinary datasets from the surveillance and response system.
Project description:The exponential growth in database of bio-molecular sequences have spawned many approaches towards storage, retrieval, classification and analyses requirements. Alignment-free techniques such as graphical representations and numerical characterisation (GRANCH) methods have enabled some detailed analyses of large sequences and found a number of different applications in the eukaryotic and prokaryotic domain. In particular, recalling the history of pandemic influenza in brief, we have followed the progress of viral infections such as bird flu of 1997 onwards and determined that the virus can spread conserved over space and time, that influenza virus can undergo fairly conspicuous recombination-like events in segmented genes, that certain segments of the neuraminidase and hemagglutinin surface proteins remain conserved and can be targeted for peptide vaccines. We recount in some detail a few of the representative GRANCH techniques to provide a glimpse of how these methods are used in formulating quantitative sequence descriptors to analyse DNA, RNA and protein sequences to derive meaningful results. Finally, we survey the surveillance techniques with a special reference to how the GRANCH techniques can be used for the purpose and recount the forecasts made of possible metamorphosis of pandemic bird flu to pandemic human infecting agents.
Project description:The United States opioid epidemic is among this century's most profound threats to public health and demands that all physicians consider their role in reversing its trajectory. Previous literature demonstrated that plastic surgery trainees lack vital practices that promote opioid stewardship. However, it is not understood why this practice gap exists. This is a national survey-based study evaluating the availability and effectiveness of opioid education in US plastic surgery programs. A total of 91 residents completed the survey. Our study found that there is an unmet need for practical and comprehensive training regarding safe opioid prescribing among plastic surgery trainees. "Informal training," defined as the "learn as you go" method, was found to be more common than formal training and considerably more valuable according to trainees. Trainees cited real-world applicability of informal training and that it comes from teachers whom they know and trust as valuable attributes of this type of education. Furthermore, the severity of the opioid epidemic has not translated into improved trainee education, as there was no significant difference in knowledge on safe opioid prescribing practices between junior and senior residents. To change the course of the epidemic, plastic surgery programs need to better train younger generations who believe they are critical stakeholders. This study lays the framework for the "formalization of informal training," and the creation of practical and efficacious educational initiatives.
Project description:Accurate prediction of flu activity enables health officials to plan disease prevention and allocate treatment resources. A promising forecasting approach is to adapt the well-established endemic-epidemic modeling framework to time series of infectious disease proportions. Using U.S. influenza-like illness surveillance data over 18 seasons, we assessed probabilistic forecasts of this new beta autoregressive model with proper scoring rules. Other readily available forecasting tools were used for comparison, including Prophet, (S)ARIMA and kernel conditional density estimation (KCDE). Short-term flu activity was equally well predicted up to four weeks ahead by the beta model with four autoregressive lags and by KCDE; however, the beta model runs much faster. Non-dynamic Prophet scored worst. Relative performance differed for seasonal peak prediction. Prophet produced the best peak intensity forecasts in seasons with standard epidemic curves; otherwise, KCDE outperformed all other methods. Peak timing was best predicted by SARIMA, KCDE or the beta model, depending on the season. The best overall performance when predicting peak timing and intensity was achieved by KCDE. Only KCDE and naive historical forecasts consistently outperformed the equal-bin reference approach for all test seasons. We conclude that the endemic-epidemic beta model is a performant and easy-to-implement tool to forecast flu activity a few weeks ahead. Real-time forecasting of the seasonal peak, however, should consider outputs of multiple models simultaneously, weighing their usefulness as the season progresses.