Concept: Human flu
The avian H7N9 influenza outbreak in 2013 resulted from an unprecedented incidence of influenza transmission to humans from infected poultry. The majority of human H7N9 isolates contained a hemagglutinin (HA) mutation (Q226L) that has previously been associated with a switch in receptor specificity from avian-type (NeuAcα2-3Gal) to human-type (NeuAcα2-6Gal), as documented for the avian progenitors of the 1957 (H2N2) and 1968 (H3N2) human influenza pandemic viruses. While this raised concern that the H7N9 virus was adapting to humans, the mutation was not sufficient to switch the receptor specificity of H7N9, and has not resulted in sustained transmission in humans. To determine if the H7 HA was capable of acquiring human-type receptor specificity, we conducted mutation analyses. Remarkably, three amino acid mutations conferred a switch in specificity for human-type receptors that resembled the specificity of the 2009 human H1 pandemic virus, and promoted binding to human trachea epithelial cells.
Resistance following antiviral therapy is commonly observed in human influenza viruses. Although this evolutionary process is initiated within individual hosts, little is known about the pattern, dynamics, and drivers of antiviral resistance at this scale, including the role played by reassortment. In addition, the short duration of human influenza virus infections limits the available time window in which to examine intrahost evolution. Using single-molecule sequencing, we mapped, in detail, the mutational spectrum of an H3N2 influenza A virus population sampled from an immunocompromised patient who shed virus over a 21-month period. In this unique natural experiment, we were able to document the complex dynamics underlying the evolution of antiviral resistance. Individual resistance mutations appeared weeks before they became dominant, evolved independently on cocirculating lineages, led to a genome-wide reduction in genetic diversity through a selective sweep, and were placed into new combinations by reassortment. Notably, despite frequent reassortment, phylogenetic analysis also provided evidence for specific patterns of segment linkage, with a strong association between the hemagglutinin (HA)- and matrix (M)-encoding segments that matches that previously observed at the epidemiological scale. In sum, we were able to reveal, for the first time, the complex interaction between multiple evolutionary processes as they occur within an individual host.
(See the commentary by Saiman, on pages 257-258 .) Objective. To determine whether well-child visits are a risk factor for subsequent influenza-like illness (ILI) visits within a child’s family. Design. Retrospective cohort. Methods. Using data from the Medical Expenditure Panel Survey from the years 1996-2008, we identified 84,595 families. For each family, we determined those weeks in which a well-child visit or an ILI visit occurred. We identified 23,776 well-child-visit weeks and 97,250 ILI-visit weeks. We fitted a logistic regression model, where the binary dependent variable indicated an ILI clinic visit in a particular week. Independent variables included binary indicators to denote a well-child visit in the concurrent week or one of the previous 2 weeks, the occurrence of the ILI visit during the influenza season, and the presence of children in the family in each of the age groups 0-3, 4-7, and 8-17 years. Socioeconomic variables were also included. We also estimated the overall cost of well-child-exam-related ILI using data from 2008. Results. We found that an ILI office visit by a family member was positively associated with a well-child visit in the same or one of the previous 2 weeks (odds ratio, 1.54). This additional risk translates to potentially 778,974 excess cases of ILI per year in the United States, with a cost of $500 million annually. Conclusions. Our results should encourage ambulatory clinics to strictly enforce infection control recommendations. In addition, clinics could consider time-shifting of well-child visits so as not to coincide with the peak of the influenza season.
Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” - messages that are about influenza but that do not pertain to an actual infection - masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012-2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
Influenza pandemics can emerge unexpectedly and wreak global devastation. However, each of the six pandemics since 1889 emerged in the Northern Hemisphere just after the flu season, suggesting that pandemic timing may be predictable. Using a stochastic model fit to seasonal flu surveillance data from the United States, we find that seasonal flu leaves a transient wake of heterosubtypic immunity that impedes the emergence of novel flu viruses. This refractory period provides a simple explanation for not only the spring-summer timing of historical pandemics, but also early increases in pandemic severity and multiple waves of transmission. Thus, pandemic risk may be seasonal and predictable, with the accuracy of pre-pandemic and real-time risk assessments hinging on reliable seasonal influenza surveillance and precise estimates of the breadth and duration of heterosubtypic immunity.
Infectious disease surveillance systems provide information crucial for protecting populations from influenza epidemics. However, few have reported the nationwide number of patients with influenza-like illness (ILI), detailing virological type. Using data from the infectious disease surveillance system in Japan, we estimated the weekly number of ILI cases by virological type, including pandemic influenza (A(H1)pdm09) and seasonal-type influenza (A(H3) and B) over a four-year period (week 36 of 2010 to week 18 of 2014). We used the reported number of influenza cases from nationwide sentinel surveillance and the proportions of virological types from infectious agents surveillance and estimated the number of cases and their 95% confidence intervals. For the 2010/11 season, influenza type A(H1)pdm09 was dominant: 6.48 million (6.33-6.63), followed by types A(H3): 4.05 million (3.90-4.21) and B: 2.84 million (2.71-2.97). In the 2011/12 season, seasonal influenza type A(H3) was dominant: 10.89 million (10.64-11.14), followed by type B: 5.54 million (5.32-5.75). In conclusion, close monitoring of the estimated number of ILI cases by virological type not only highlights the huge impact of previous influenza epidemics in Japan, it may also aid the prediction of future outbreaks, allowing for implementation of control and prevention measures.
A novel high-speed droplet-polymerase chain reaction can detect human influenza virus in less than 30 min.
- Clinica chimica acta; international journal of clinical chemistry
- Published about 6 years ago
The polymerase chain reaction (PCR) has been widely used for diagnosis of infection. Rapid detection of influenza virus is useful for therapeutic decisions. We attempted to develop a novel assay by real-time droplet-PCR machine for influenza virus.
Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We present a method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.
Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.
Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012-2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.