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Background: Children are important in community‐level influenza transmission. School‐based monitoring may inform influenza surveillance. Methods: We used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010‐2015 influenza seasons using Pennsylvania's Allegheny County Health Department all‐age influenza cases from health facilities, and all‐cause and influenza‐like illness (ILI)‐specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all‐cause and illness‐specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross‐validations. Results: School districts reported 2 184 220 all‐cause absences (2010‐2015). Three one‐season studies reported 19 577 all‐cause and 3012 ILI‐related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K‐5 grade‐specific absence models had lowest mean absolute errors (MAE) in cross‐validations. ILI‐specific absences performed marginally better than all‐cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions: Our findings suggest seasonal models including K‐5th grade absences predict all‐age‐confirmed influenza and may serve as a useful surveillance tool. [ABSTRACT FROM AUTHOR]

Background: Influenza infection is often measured by a fourfold antibody titer increase over an influenza season (ie seroconversion). However, this approach may fail when influenza seasons are less distinct as it does not account for transient effects from recent infections. Here, we present a method to determine seroconversion for non‐paired sera, adjusting for changes in individuals' antibody titers to influenza due to the transient impact of recent exposures, varied sampling times, and laboratory processes. Methods: We applied our method using data for five H3N2 strains collected from 942 individuals, aged 2‐90 years, during the first two study visits of the Fluscape cohort study (2009‐2012) in Guangzhou, China. Results: After adjustment, apparent seroconversion rates for non‐circulating strains decreased while we observed a 20% increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain (A/Brisbane/20/2007) in our study, participants aged under 18, and over 64 had the highest seroconversion rates compared to other age groups. Conclusions: Our results highlight the need for improved methods when using antibody titers as an endpoint in settings where there is no clear influenza "off" season. Methods, like those presented here, that use titers from circulating and non‐circulating strains may be key. [ABSTRACT FROM AUTHOR]

Health planners from global to local levels must anticipate year‐to‐year and week‐to‐week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real‐time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic. [ABSTRACT FROM AUTHOR]

Background: Year‐round transmission of influenza has been detected in Vietnam through both national surveillance and other epidemiological studies. Understanding the demographic and clinical features of influenza‐like illness (ILI) presenting to primary care in urban Vietnam is vital to understand these transmission dynamics. Methods: An observational study of patients with ILI in Ho Chi Minh City, Vietnam, was conducted between August 2013 and November 2015 in a mix of public and private primary care settings. Molecular testing for influenza A and influenza B and 12 other respiratory viruses was performed. Results: A total of 1152 ILI patients were recruited. 322 and 136 subjects tested positive for influenza A and influenza B, respectively. 193 subjects tested positive for another respiratory virus; most commonly rhinovirus and parainfluenza virus 3. Influenza was detected in 81% of the 116 study weeks. Three peaks of influenza activity were detected; an H3N2 peak April‐June 2014, an influenza B peak July‐December 2014, and a mixed H3N2 and H1N1 peak March‐September 2015. Subjects recruited from private clinics were more likely to have higher income and to have reported previous influenza vaccination. Antibiotic use was common (50.3%) despite limited evidence of bacterial infection. Conclusion: Influenza in southern Vietnam has complex transmission dynamics including periods of intense influenza activity of alternating types and subtypes. Broadening surveillance from hospital to the community in tropical settings is feasible and a valuable for improving our understanding of the global spread and evolution of the virus. [ABSTRACT FROM AUTHOR]

Background Serum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay ( HI) and the viral neutralization assay ( NT), which have not been systematically compared for a large number of influenza viruses. Methods A total of 151 study participants from near Guangzhou, China, were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results. Results We observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, ρ=.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log( NT) on log( HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve ( AUC) indicated good accuracy (>80%) for predicting NT with HI. Conclusions While we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, although attention should be given to the effect of age on these assays. [ABSTRACT FROM AUTHOR]

Infectious disease introduced by non-native species is increasingly cited as a facilitator of native population declines, but direct evidence may be lacking due to inadequate population and disease prevalence data surrounding an outbreak. Previous indirect evidence and theoretical models support squirrelpox virus ( SQPV) as being potentially involved in the decline of red squirrels ( Sciurus vulgaris) following the introduction of the non-native gray squirrel ( Sciurus carolinensis) to the United Kingdom. The red squirrel is a major UK conservation concern and understanding its continuing decline is important for any attempt to mitigate the decline. The red squirrel-gray squirrel system is also exemplary of the interplay between infectious disease (apparent competition) and direct competition in driving the replacement of a native by an invasive species. Time series data from Merseyside are presented on squirrel abundance and squirrelpox disease ( SQPx) incidence, to determine the effect of the pathogen and the non-native species on the native red squirrel populations. Analysis indicates that SQPx in red squirrels has a significant negative impact on squirrel densities and their population growth rate ( PGR). There is little evidence for a direct gray squirrel impact; only gray squirrel presence (but not density) proved to influence red squirrel density, but not red squirrel PGR. The dynamics of red SQPx cases are largely determined by previous red SQPx cases, although previous infection of local gray squirrels also feature, and thus, SQPV-infected gray squirrels are identified as potentially initiating outbreaks of SQPx in red squirrels. Retrospective serology indicates that approximately 8% of red squirrels exposed to SQPV may survive infection during an epidemic. This study further highlights the UK red squirrel - gray squirrel system as a classic example of a native species population decline strongly facilitated by infectious disease introduced by a non-native species. It is therefore paramount that disease prevention and control measures are integral in attempts to conserve red squirrels in the United Kingdom. [ABSTRACT FROM AUTHOR]

Traditional models of disease evolution are based upon the deterministic competition between strains that confer complete cross-immunity, and predict the selection of strains with higher basic reproductive ratios ( R0). In contrast, evolution in a stochastic setting is determined by a complex mixture of influences. Here, to isolate the impact of stochasticity, we constrain all competing strains to have an equal basic reproductive ratio – thereby eliminating deterministic selection. The resulting stochastic models predict an evolutionary unstable strategy, which separates a region favouring the evolution of rapid-transmission (acute) strains from one favouring persistent (chronic) strains. We find this to be a generic phenomenon with strain evolution consistently driven towards extremes of epidemiological behaviour. Even in the absence of an equal R0 constraint, such stochastic selective pressures operate in addition to standard deterministic selection and will therefore influence the evolutionary behaviour of disease in all scenarios. [ABSTRACT FROM AUTHOR]

Investigates time series dynamics and explores the density-dependent structure of blowfly, Lucilia cuprina, populations using nonparametric additive modeling. Larva-to-adult survival rate and adult survival rate of cadmium populations compared with control populations; Nonlinear dependence of the survival from larva to adult on larval density; Effect of increased larval density on larval survival.