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bioRxiv2017

Delirium symptoms are associated with decline in cognitive function between ages 53 to 69: findings from a British birth cohort study.

INTRODUCTIONFew population studies have investigated whether longitudinal decline after delirium in mid-to-late life might affect specific cognitive domains.\n\nMETHODSParticipants from a birth cohort completing assessments of search speed, verbal memory and the Addenbrookes Cognitive Examination at age 69 were asked about delirium symptoms between ages 60-69. Linear regression models estimated associations between delirium symptoms and cognitive outcomes.\n\nRESULTSPeriod prevalence of delirium between 60 and 69 was 4% (95% CI 3.2%,4.9%). Self-reported symptoms of delirium over the seventh decade were associated with worse scores in the Addenbrookes Cognitive Examination (-1.7 points, 95% CI -3.2, -0.1, p=0.04). In association with delirium symptoms, verbal memory scores were initially lower, with subsequent decline in search speed by age 69. These effects were independent of other Alzheimers risk factors.\n\nDISCUSSIONDelirium symptoms may be common even at relatively younger ages, and their presence may herald cognitive decline, particularly in search speed, over this time period.

epidemiology↗

bioRxiv2017

Enumerating the Economic Cost of Antimicrobial Resistance Per Antibiotic Consumed to Inform the Evaluation of Interventions Affecting their Use

Background- Antimicrobial resistance (AMR) poses a colossal threat to global health and incurs high economic costs to society. Economic evaluations of antimicrobials and interventions such as diagnostics and vaccines that affect their consumption rarely include the costs of AMR, resulting in sub-optimal policy recommendations. We estimate the economic cost of AMR per antibiotic consumed, stratified by drug class and national income level.\n\nMethods- The model is comprised of three components: correlation coefficients between human antibiotic consumption and subsequent resistance; the economic costs of AMR for five key pathogens; and consumption data for antibiotic classes driving resistance in these organisms. These were used to calculate the economic cost of AMR per antibiotic consumed for different drug classes, using data from Thailand and the United States (US) to represent low/middle and high-income countries.\n\nResults- The correlation coefficients between consumption of antibiotics that drive resistance in S. aureus, E. coli, K. pneumoniae, A. baumanii, and P. aeruginosa and resistance rates were 0.37, 0.27, 0.35, 0.45, and 0.52, respectively. The total economic cost of AMR due to resistance in these five pathogens was $0.5 billion and $2.8 billion in Thailand and the US, respectively. The cost of AMR associated with the consumption of one standard unit (SU) of antibiotics ranged from $0.1 for macrolides to $0.7 for quinolones, cephalosporins and broad-spectrum penicillins in the Thai context. In the US context, the cost of AMR per SU of antibiotic consumed ranged from $0.1 for carbapenems to $0.6 for quinolones, cephalosporins and broad spectrum penicillins.\n\nConclusion- The economic costs of AMR per antibiotic consumed were considerable, often exceeding their purchase cost. Differences between Thailand and the US were apparent, corresponding with variation in the overall burden of AMR and relative prevalence of different pathogens. Notwithstanding their limitations, use of these estimates in economic evaluations can make better-informed policy recommendations regarding interventions that affect antimicrobial consumption and those aimed specifically at reducing the burden of AMR.

epidemiology↗

bioRxiv2017

Agent-based network model predicts strong benefits to youth-centered HIV treatment-as-prevention efforts

We used an agent-based network model to examine the effect of targeting different risk groups with unsuppressed HIV viral load for linkage or re-linkage to HIV-related treatment services in a heterosexual population with annual testing. Our model identifies prevention strategies that can reduce incidence to negligible levels (i.e., less than 0.1 infections per 100 person-years) 20 years after a targeted Treatment-as-Prevention (TasP) campaign. The model assumes that most (default 95%) of the population is reachable (i.e., could, in principle, be linked to effective care) and a modest (default 5% per year) probability of a treated person dropping out of care. Under random allocation or CD4-based targeting, the default version of our model predicts that the TasP campaign would need to suppress viral replication in ~80% of infected people to halt the epidemic. Under age-based strategies, by contrast, this percentage drops to 50% to 60% (for strategies targeting those <30 and <25, respectively). Age-based targeting did not need to be highly exclusive to yield significant benefits; e.g. the scenario that targeted those <25 years old saw ~80% of suppressed individuals fall outside the target group. This advantage to youth-based targeting remained in sensitivity analyses in which key age-related risk factors were eliminated one by one. As testing rates increase in response to UNAIDS 90-90-90 goals, we suggest that efforts to link all young people to effective care could be an effective long-term method for ending the HIV epidemic. Linking greater numbers of young people to effective care will be critical for developing countries in which a demographic \"youth bulge\" is starting to increase the number of young people at risk for HIV infection.

epidemiology↗

bioRxiv2017

Apolipoprotein-E (ApoE) ϵ4 and cognitive decline over the adult life course

We tested the association between APOE-{varepsilon}4 and processing speed and memory between ages 43 and 69 in a population-based birth cohort. Analyses of processing speed (using a timed letter search task) and episodic memory (a 15-item word learning test) were conducted at ages 43, 53, 60-64 and 69 years using linear and multivariable regression, adjusting for gender and childhood cognition. Linear mixed models, with random intercepts and slopes, were conducted to test the association between APOE and the rate of decline in these cognitive scores from age 43 to 69. Model fit was assessed with the Bayesian Information Criterion. A cross-sectional association between APOE-{varepsilon}4 and memory scores was detected at age 69 for both heterozygotes and homozygotes ({beta}=-0.68 & {beta}=-1.38 respectively, p=.03) with stronger associations in homozygotes; no associations were observed before this age. Homozygous carriers of APOE-{varepsilon}4 had a faster rate of decline in memory between ages 43 and 69, when compared to noncarriers, after adjusting for gender and childhood cognition ({beta}=-0.05, p=.04). There were no cross-sectional or longitudinal associations between APOE-{varepsilon}4 and processing speed. We conclude that APOE-{varepsilon}4 is associated with a subtly faster rate of memory decline from midlife to early old age; this may be due to effects of APOE-{varepsilon}4 becoming manifest around the latter stage of life. Continuing follow-up will determine what proportion of this increase will become clinically significant.

epidemiology↗

bioRxiv2017

Frailty index as a predictor of all-cause and cause-specific mortality in a Swedish population-based cohort

BackgroundFrailty is a complex manifestation of aging and associated with increased risk of mortality and poor health outcomes. Younger individuals (under 65 years) typically have low levels of frailty and are less-studied in this respect. Also, the relationship between the Rockwood frailty index (FI) and cause-specific mortality in community settings is understudied.\n\nMethodsWe created and validated a 42-item Rockwood-based FI in The Swedish Adoption/Twin Study of Aging (n=1477; 623 men, 854 women; aged 29-95 years) and analyzed its association with all-cause and cause-specific mortality in up to 30-years of follow-up. Deaths due to cardiovascular disease (CVD), cancer, dementia and other causes were considered as competing risks.\n\nResultsOur FI demonstrated construct validity as its associations with age, sex and mortality were similar to the existing literature. The FI was independently associated with increased risk for all-cause mortality in younger (<65 years; HR per increase in one deficit 1.11, 95%CI 1.07-1.17) and older ([≥]65 years; HR 1.07, 95%CI 1.04-1.10) women and in younger men (HR 1.05, 95%CI 1.01-1.10). In cause-specific mortality analysis, the FI was strongly predictive of CVD mortality in women (HR per increase in one deficit 1.13, 95%CI 1.09-1.17), whereas in men the risk was restricted to deaths from other causes (HR 1.07, 95%CI 1.01-1.13).\n\nConclusionsThe FI showed good predictive value for all-cause mortality especially in the younger group. The FI predicted CVD mortality risk in women, whereas in men it captured vulnerability to death from various causes.

epidemiology↗

bioRxiv2017

Socioeconomic Disparities and Sexual Dimorphism in Neurotoxic Effects of Ambient Fine Particles on Youth IQ: A Longitudinal Analysis

Mounting evidence indicates that early-life exposure to particulate air pollutants pose threats to childrens cognitive development, but studies about the neurotoxic effects associated with exposures during adolescence remain unclear. We examined whether exposure to ambient fine particles (PM2.5) at residential locations affects intelligence quotient (IQ) during pre-/early-adolescence (ages 9-11) and emerging adulthood (ages 18-20) in a demographically-diverse population (N = 1,360) residing in Southern California. Increased ambient PM2.5 levels were associated with decreased IQ scores. This association was more evident for Performance IQ (PIQ), but less for Verbal IQ, assessed by the Wechsler Abbreviated Scale of Intelligence. For each inter-quartile (7.73 g/m3) increase in one-year PM2.5 preceding each assessment, the average PIQ score decreased by 3.08 points (95% confidence interval = [-6.04, -0.12]) accounting for within-family/within-individual correlations, demographic characteristics, family socioeconomic status (SES), parents cognitive abilities, neighborhood characteristics, and other spatial confounders. The adverse effect was 150% greater in low SES families and 89% stronger in males, compared to their counterparts. Better understanding of the social disparities and sexual dimorphism in the adverse PM2.5-IQ effects may help elucidate the underlying mechanisms and shed light on prevention strategies.

epidemiology↗

bioRxiv2017

Delirium, frailty and mortality: interactions in a prospective study of hospitalized older people

BackgroundIt is unknown if the association between delirium and mortality is consistent for individuals across the whole range of health states. A bimodal relationship has been proposed, where delirium is particularly adverse for those with underlying frailty, but may have a smaller effect (perhaps even protective) if it is an early indicator of acute illness in fitter people. We investigated the impact of delirium on mortality in a cohort simultaneously evaluated for frailty.\n\nMethodsWe undertook an exploratory analysis of a cohort of consecutive acute medical admissions aged [≥]70. Delirium on admission was ascertained by psychiatrists. A Frailty Index (FI) was derived according to a standard approach. Deaths were notified from linked national mortality statistics. Cox regression was used to estimate associations between delirium, frailty and their interactions on mortality.\n\nResultsThe sample consisted of 710 individuals. Both delirium and frailty were independently associated with increased mortality rates (delirium: HR 2.4, 95%CI 1.8-3.3, p<0.01; frailty (per SD): HR 3.5, 95%CI 1.2-9.9, p=0.02). Estimating the effect of delirium in tertiles of FI, mortality was greatest in the lowest tertile: tertile 1 HR 3.4 (95%CI 2.1-5.6); tertile 2 HR 2.7 (95%CI 1.5-4.6); tertile 3 HR 1.9 (95% CI 1.2-3.0).\n\nConclusionWhile delirium and frailty contribute to mortality, the overall impact of delirium on admission appears to be greater at lower levels of frailty. In contrast to the hypothesis that there is a bimodal distribution for mortality, delirium appears to be particularly adverse when precipitated in fitter individuals.

epidemiology↗

bioRxiv2017

Use of a scoring strategy to determine clinical risk of progression and risk group-specific treatment adherence in subjects with latent tuberculosis infection

BackgroundAnnual incidence of active tuberculosis (TB) cases has plateaued in the US from 2013-2015. Most cases are from reactivation of latent tuberculosis infection (LTBI). A likely contributor is suboptimal LTBI treatment completion rates in subjects at high risk of developing active TB. It is unknown whether these patients are adequately identified and treated under current standard of care.\n\nMethodsIn this study, we sought to retrospectively assess the utility of an online risk calculator (tstin3d.com) in determining probability of LTBI and defining the characteristics and treatment outcomes of Low: 0-<10%, Intermediate: 10-<50% and High: 50-100% risk groups of asymptomatic subjects with LTBI seen between 2010-2015.\n\nResults51(41%), 46 (37%) and 28 (22%) subjects were in Low, Intermediate and High risk groups respectively. Tstin3d.com was useful in determining the probability of LTBI in tuberculin skin test positive US born subjects. Of 114 subjects with available treatment information, overall completion rate was 61% and rates of completion in Low (60%), Intermediate (63%) and High (57%) risk groups were equivalent. 75% subjects in the 3HP group completed treatment compared to 58% in the INH group. Provider documentation of important clinical risk factors was often incomplete. Logistic regression analysis showed no clear trends of treatment completion being associated with assessment of a risk factor.\n\nConclusionThese findings suggest tstin3d.com could be utilized in the US setting for risk stratification of patients with LTBI and select treatment based on risk. Current standard of care practice leads to subjects in all groups finishing treatment at equivalent rates.

epidemiology↗

bioRxiv2017

A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality

Recent advances uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as from Monoclonal Gammopathy of Undetermined Significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework. We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals, to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence and baseline MM survival. We measured MM-specific mortality and MM prevalence following MGUS detection from simulations and mathematical precition modeling. We showed that our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available progression risk-point estimate of 61% risk, starting screening at age 55 and follow-up screening every 6yrs reduced total MM prevalence by 19%. The same reduction could be achieved with starting age 65 and follow-up every 2yrs. A 40% progression risk reduction per MGUS patient per year would reduce MM-specific mortality by 40%. Generally, age of screening onset and frequency impact disease prevalence, progression risk reduction impacts both prevalence and disease-specific mortality, and screeenign would generally be favorable in high-risk individuals. Screening efforts should focus on specifically identified groups of high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk MGUS individuals would require improved preventions.

epidemiology↗

bioRxiv2017

Longitudinal Analysis of Particulate Air Pollutants and Adolescent Delinquent Behavior in Southern California

Animal experiments and cross-sectional human studies have linked particulate matter (PM) with increased behavioral problems. We conducted a longitudinal study to examine whether the trajectories of delinquent behavior are affected by PM2.5 (PM with aerodynamic diameter [≤]2.5 m) exposures before and during adolescence. We used the parent-reported Child Behavior Checklist at age 9-18 with repeated measures every ~2-3 years (up to 4 behavioral assessments) on 682 children from the Risk Factors for Antisocial Behavior Study conducted in a multi-ethnic cohort of twins born in 1990-1995. Based on prospectively-collected residential addresses and a spatiotemporal model of ambient air concentrations in Southern California, monthly PM2.5 estimates were aggregated to represent long-term (1-, 2-, 3-year average) exposures preceding baseline and cumulative average exposure until the last assessment. Multilevel mixed-effects models were used to examine the association between PM2.5 exposure and individual trajectories of delinquent behavior, adjusting for within-family/within-individual correlations and potential confounders. We also examined whether psychosocial factors modified this association. The results suggest that PM2.5 exposure at baseline and cumulative exposure during follow-up was significantly associated (p<0.05) with increased delinquent behavior. The estimated effect sizes (per interquartile increase of PM2.5 by 3.12-5.18 {micro}g/m3) were equivalent to the difference in delinquency scores between adolescents who are 3.5-4 years apart in age. The adverse effect was stronger in families with unfavorable parent-to-child relationships, increased parental stress or maternal depressive symptoms. Overall, these findings suggest long-term PM2.5 exposure may increase delinquent behavior of urban-dwelling adolescents, with the resulting neurotoxic effect aggravated by psychosocial adversities.

epidemiology↗

bioRxiv2017

A modeling of a Diphtheria epidemic in the refugees camps

BackgroundDiphtheria has a big mortality rate. Vaccination practically eradicated it in industrialized countries. A decrease in vaccine coverage and public health deterioration cause a reemergence in the Soviet Union in 1990. These circumstances seem to be being reproduced in refugee camps with a potential risk of new outbreak.\n\nMethodsWe constructed a mathematical model that describes the evolution of the Soviet Union epidemic outbreak. We use it to evaluate how the epidemic would be modified by changing the rate of vaccination, and improving public health conditions.\n\nResultsWe observe that a small decrease of 15% in vaccine coverage, translates an ascent of 47% in infected people. A coverage increase of 15% and 25% decreases a 44% and 66% respectively of infected people. Just improving health care measures a 5%, infected people decreases a 11.31%. Combining high coverage with public health measures produces a bigger reduction in the amount of infected people compare to amelioration of coverage rate or health measures alone.\n\nConclusionsOur model estimates the evolution of a diphtheria epidemic outbreak. Small increases in vaccination rates and in public health measures can translate into large differences in the evolution of a possible epidemic. These estimates can be helpful in socioeconomic instability, to prevent and control a disease spread.

epidemiology↗

bioRxiv2017

Exploring variation in human papillomavirus vaccination uptake: multi-level spatial analysis

BackgroundUnderstanding the factors that influence human papillomavirus (HPV) vaccination uptake is critically important to design effective vaccination programmes. In Switzerland, completed HPV vaccination by age 16 years among women ranges from 30 to 79% across 26 cantons (states). Our objective was to identify factors that are associated with the spatial variation in HPV vaccination uptake.\n\nMethods and findingsWe used data from the Swiss National Vaccination Coverage Survey 2009-2016 on HPV vaccination status ([≥]1 dose) of 14-17 year old girls, their municipality of residence and their nationality for 21 of 26 cantons (N=8,965). We examined covariates at municipality level: language, degree of urbanisation, socio-economic position, religious denomination, results of a vote about vaccination laws; and, at cantonal level, availability of school-based vaccination and survey period. We used a series of conditional auto regressive (CAR) models to assess the effects of covariates while accounting for variability between cantons and municipal-level spatial autocorrelation. In the best-fit model, school-based vaccination (adjusted odds ratio, OR: 2.51, 95% credible interval, CI: 1.77-3.56) was associated with increased uptake, while lower acceptance of vaccination laws was associated with lower HPV vaccination uptake (OR 0.61, 95% CI: 0.50-0.73). Overall, the covariates explained 88% of the municipal-level variation in uptake.\n\nConclusionsIn Switzerland, cantons play a prominent role in the variation in HPV vaccination uptake, especially through the provision of school-based vaccination delivery. HPV vaccination uptake is also strongly associated with inhabitants attitudes towards vaccination. To increase uptake, efforts should be made both to mitigate vaccination scepticism and to encourage school-based vaccination.

epidemiology↗

bioRxiv2017

The Trauma Severity Model: An Ensemble Learning Approach to Risk Prediction

Statistical theory indicates that a flexible model can attain a lower generalization error than an inflexible model, provided that the setting is appropriate. This is highly relevant in the context of mortality risk prediction for trauma patients, as researchers have focused exclusively on the use of generalized linear models for risk prediction, and generalized linear models may be too inflexible to capture the potentially complex relationships in trauma data. Due to this, we propose a machine learning model, the Trauma Severity Model (TSM), for risk prediction. In order to validate TSMs performance, this study compares TSM to three established risk prediction models: the Bayesian Logistic Injury Severity Score, the Harborview Assessment for Risk of Mortality, and the Trauma Mortality Prediction Model. Our results indicate that TSM has superior performance, and thereby provides improved risk prediction.\n\nHighlightsO_LIWe propose an ensemble machine learning model for trauma risk prediction.\nC_LIO_LIA hyper-parameter search scheme is proposed for model development.\nC_LIO_LIWe compare our model to established models for trauma risk prediction.\nC_LIO_LIOur model improves over established models for each performance metric considered.\nC_LI

epidemiology↗

bioRxiv2017

Trends, geographic variation, and factors associated with prescribing of gluten-free foods in English primary care: a cross sectional study

BackgroundThere is substantial disagreement about whether gluten-free foods should be prescribed on the NHS. We aim to describe time trends, variation and factors associated with prescribing gluten-free foods in England.\n\nMethodsWe described long-term national trends in gluten-free prescribing, and practice and Clinical Commissioning Group (CCG) level monthly variation in the rate of gluten-free prescribing (per 1000 patients) over time. We used a mixed effect poisson regression model to determine factors associated with gluten-free prescribing rate.\n\nResultsThere were 1.3 million gluten-free prescriptions between July 2016 and June 2017, down from 1.8 million in 2012/13, with a corresponding cost reduction from {pound}25.4m to {pound}18.7m. There was substantial variation in prescribing rates among practices (range 0 to 148 prescriptions per 1000 patients, interquartile range 7.3 to 31.8), driven in part by substantial variation at the CCG level, likely due to differences in prescribing policy. Practices in the most deprived quintile of deprivation score had a lower prescribing rate than those in the highest quintile (incidence rate ratio 0.89, 95% confidence interval 0.87-0.91). This is potentially a reflection of the lower rate of diagnosed coeliac disease in more deprived populations.\n\nConclusionGluten-free prescribing is in a state of flux, with substantial clinically unwarranted variation between practices and CCGs.\n\nStrengths and weaknesses of the studyO_LIWe were able to measure the prescribing of gluten-free foods across all prescribing in England, eliminating bias. We also removed seasonal variation by aggregating savings over 12 months.\nC_LIO_LIAs well as gluten-free prescribing variation at practice and CCG level, we have described long-term prescribing trends at national level, back to 1998.\nC_LIO_LIUsing the available data, we were unable to look at gluten-free prescribing at prescriber level, or investigate factors associated with prescribing to individual patients\nC_LI

epidemiology↗

bioRxiv2017

Trends and variation in Prescribing of Low-Priority Medicines Identified by NHS England: A Cross-Sectional Study and Interactive Data Tool in English Primary Care

BackgroundRoutine accessible audit of prescribing data presents significant opportunities to identify cost-saving opportunities. NHS England recently announced a consultation seeking to discourage use of medicines it considers to be low-value. We set out to produce an interactive data resource to show savings in each NHS general practice, and to assess the current use of these medicines, their change in use over time, and the extent and reasons for variation in such prescribing.\n\nResultsThe total NHS spend on all low-value medicines identified by NHS England was {pound}153.5m in the last year, across 5.8m prescriptions (mean {pound}26 per prescription). Among individual medications, liothyronine had the highest prescribing cost at {pound}29.6m, followed by trimipramine ({pound}20.2m) and gluten-free foods ({pound}18.7m). Over time, the overall total number of low-value prescriptions decreased, but the cost increased, although this varied greatly between medications. Annual practice level spending varied widely (median, {pound}2,262 per thousand patients, IQR {pound}1,439 to {pound}3,298). The proportion of patients over 65 showed the strongest association with low-value prescribing; CCG was also strongly associated. Our interactive data tool was deployed to OpenPrescribing.net where monthly updated figures and graphs can be viewed.\n\nConclusionsPrescribing of low-value medications is extensive but varies widely by medication, geographic area and individual practice. Despite a fall in prescription numbers, the overall cost of prescribing for low-value items has risen. Prescribing behaviour is clustered by CCG, which may represent variation in medicines optimisation efficiency, or in some cases access inequality.\n\nAbbreviations

epidemiology↗

bioRxiv2017

The frailty index is associated with the need for care in an aging Swedish population

BackgroundThe Rockwood frailty index (FI) has proven a valid predictor of mortality, institutionalization and requirement for health services. However, little is known about the relationship between the FI and the need for care - an indication of dependency. To this end, we ascertained the associations between the FI and the need for current and future care.\n\nMethodsA Rockwood-based FI was tested for association with the current need for care and care needs in the future during a 23-year follow-up in the Swedish Adoption/Twin Study of Aging (n=1477; 623 men, 854 women; aged 29-95 years at baseline). Need for care was defined as receiving help at least once a week in daily routines. Age, sex, education, living alone, smoking status and body mass index were considered as covariates.\n\nResultsThe FI was independently associated with current need for care (OR=1.27 for accumulation of one deficit, 95%CI 1.20-1.34) and future need for care (HR=1.12 for accumulation of one deficit, 95%CI 1.08-1.15). Co-twin control analyses confirmed the results; the pair member currently needing care had higher median FI levels compared to their co-twin not needing care, and the pair member having higher baseline FI had shorter median time to the onset of future care need compared to their co-twin with lower FI.\n\nConclusionsThe FI is a determinant of current care needs and predictive of care needs in the future. The FI may thus represent a risk indicator for dependency and offer an amenable target for preventive measures.

epidemiology↗

bioRxiv2017

Identification of an epitope of limited variability under strong immune selection in the haemagglutinin head domain of H1N1 influenza

Antigenic targets of influenza vaccination are currently seen to be polarised between (i) highly immunogenic (and protective) epitopes of high variability, and (ii) conserved epitopes of low immunogenicity. This requires vaccines directed against the variable sites to be continuously updated, with the only other alternative being seen as the artificial boosting of immunity to invariant epitopes of low natural efficacy. However, theoretical models suggest that the antigenic evolution of influenza is best explained by postulating the existence of highly immunogenic epitopes of limited variability. Here we report the identification of such an epitope of limited variability in the head domain of the H1 haemagglutinin protein. We show that the epitope mediates immunity to historical influenza strains not previously seen by a cohort of young children. Furthermore, vaccinating mice with these epitope conformations can induce immunity to all the human H1N1 influenza strains that have circulated since 1918. The identification of epitopes of limited variability offers a mechanism by which a universal influenza vaccine can be created; these vaccines would also have the potential to protect against newly emerging influenza strains.

epidemiology↗

bioRxiv2017

EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. This framework integrates recent advances in statistical methods for network analysis, temporal exponential random graph models, which allows the epidemic modeling to be firmly grounded in empirical data on the contacts and persistent partnerships that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers.

epidemiology↗