Time lines of infection and disease in human influenza: a review of volunteer challenge studies

The Etiological Role of Common Respiratory Viruses in Acute Respiratory Infections in Older Adults: A Systematic Review and Meta-analysis

Acute respiratory tract infections (ARI) constitute a substantial disease burden in adults and elderly individuals. We aimed to identify all case-control studies investigating the potential role of respiratory viruses in the etiology of ARI in older adults aged ≥65 years. We conducted a systematic literature review (across 7 databases) of case-control studies published from 1996 to 2017 that investigated the viral profile of older adults with and those without ARI. We then computed a pooled odds ratio (OR) with a 95% confidence interval and virus-specific attributable fraction among the exposed (AFE) for 8 common viruses: respiratory syncytial virus (RSV), influenza virus (Flu), parainfluenza virus (PIV), human metapneumovirus (HMPV), adenovirus (AdV), rhinovirus (RV), bocavirus (BoV), and coronavirus (CoV). From the 16 studies included, there was strong evidence of possible causal attribution for RSV (OR, 8.5 [95% CI, 3.9-18.5]; AFE, 88%), Flu (OR, 8.3 [95% CI, 4.4-15.9]; AFE, 88%), PIV (OR, not available; AFE, approximately 100%), HMPV (OR, 9.8 [95% CI, 2.3-41.0]; AFE, 90%), AdV (OR, not available; AFE, approximately 100%), RV (OR, 7.1 [95% CI, 3.7-13.6]; AFE, 86%) and CoV (OR, 2.8 [95% CI, 2.0-4.1]; AFE, 65%) in older adults presenting with ARI, compared with those without respiratory symptoms (ie, asymptomatic individuals) or healthy older adults. However, there was no significant difference in the detection of BoV in cases and controls. This review supports RSV, Flu, PIV, HMPV, AdV, RV, and CoV as important causes of ARI in older adults and provides quantitative estimates of the absolute proportion of virus-associated ARI cases to which a viral cause can be attributed. Disease burden estimates should take into account the appropriate AFE estimates (for older adults) that we report.

Optimising age coverage of seasonal influenza vaccination in England: A mathematical and health economic evaluation

For infectious disease prevention, policy-makers are typically required to base policy decisions in light of operational and monetary restrictions, prohibiting implementation of all candidate interventions. To inform the evidence-base underpinning policy decision making, mathematical and health economic modelling can be a valuable constituent.

Applied to England, this study aims to identify the optimal target age groups when extending a seasonal influenza vaccination programme of at-risk individuals to those individuals at low risk of developing complications following infection. To perform this analysis, we utilise an age- and strain-structured transmission model that includes immunity propagation mechanisms which link prior season epidemiological outcomes to immunity at the beginning of the following season. Making use of surveillance data from the past decade in conjunction with our dynamic model, we simulate transmission dynamics of seasonal influenza in England from 2012 to 2018. We infer that modified susceptibility due to natural infection in the previous influenza season is the only immunity propagation mechanism to deliver a non-negligible impact on the transmission dynamics. Further, we discerned case ascertainment to be higher for young infants compared to adults under 65 years old, and uncovered a decrease in case ascertainment as age increased from 65 to 85 years of age.

Our health economic appraisal sweeps vaccination age space to determine threshold vaccine dose prices achieving cost-effectiveness under differing paired strategies. In particular, we model offering vaccination to all those low-risk individuals younger than a given age (but no younger than two years old) and all low-risk individuals older than a given age, while maintaining vaccination of at-risk individuals of any age. All posited strategies were deemed cost-effective. In general, the addition of low-risk vaccination programmes whose coverage encompassed children and young adults (aged 20 and below) were highly cost-effective. The inclusion of elder age-groups to the low-risk programme typically lessened the cost-effectiveness. Notably, elderly-centric programmes vaccinating from 65-75 years and above had the least permitted expense per vaccine.

Vaccination is an established method to provide protection against seasonal influenza and its complications. Yet, a need to administer an updated vaccine on an annual basis presents significant operational challenges and sizeable costs. Consequently, policy makers typically have to decide how to deploy a finite amount of resource in a cost-effective manner. A combination of mathematical and health economic modelling can be used to address such a question. Here, we developed an age- and strain-structured mathematical model for seasonal influenza transmission dynamics that incorporates mechanisms for immunity propagation, which we used to reconstruct transmission dynamics of seasonal influenza in England from 2012 to 2018.

We then performed a health economic evaluation assessing the cost-effectiveness of extending a seasonal influenza vaccination programme of at-risk individuals to also include, for targeted age groups, those individuals at low risk of developing complications following infection. The findings suggest the inclusion of low-risk vaccination programmes whose coverage encompassed children and young adults (aged 20 and below) to be highly cost-effective. In contrast, the inclusion of elder age-groups to the low-risk programme typically lessened the cost-effectiveness.

Convening on the influenza human viral challenge model for universal influenza vaccines, Part 2: Methodologic considerations

In response to global interest in the development of a universal influenza vaccine, the Bill & Melinda Gates Foundation, PATH, and the Global Funders Consortium for Universal Influenza Vaccine Development convened a meeting of experts (London, UK, May 2018) to assess the role of a standardized controlled human influenza virus infection model (CHIVIM) towards the development of novel influenza vaccine candidates. This report (in two parts) summarizes those discussions and offers consensus recommendations. Part 1 covers challenge virus selection, regulatory and ethical considerations, and issues concerning standardization, access, and capacity. This article (Part 2) summarizes the discussion and recommendations concerning CHIVIM methods. The panelists identified an overall need for increased standardization of CHIVIM trials, in order to produce comparable results that can support universal vaccine licensure. Areas of discussion included study participant selection and screening, route of exposure and dose, devices for administering challenge, rescue therapy, protection of participants and institutions, clinical outcome measures, and other considerations. The panelists agreed upon specific recommendations to improve the standardization and usefulness of the model for vaccine development. Experts agreed that a research network of institutions working with a standardized CHIVIM could contribute important data to support more rapid development and licensure of novel vaccines capable of providing long-lasting protection against seasonal and pandemic influenza strains.

Meeting report: Convening on the influenza human viral challenge model for universal influenza vaccines, Part 1: Value; challenge virus selection; regulatory, industry and ethical considerations; increasing standardization, access and capacity

In response to global interest in the development of a universal influenza vaccine, the Bill & Melinda Gates Foundation, PATH, and the Global Funders Consortium for Universal Influenza Vaccine Development convened a meeting of experts (London, UK, May 2018) to assess the role of a standardized controlled human influenza virus infection model (CHIVIM) towards the development of novel influenza vaccine candidates. This report (in two parts) summarizes those discussions and offers consensus recommendations. This article (Part 1) covers challenge virus selection, regulatory and ethical considerations, and issues concerning standardization, access, and capacity. Part 2 covers specific methodologic considerations.

Current methods for influenza vaccine development and licensure require large costly field trials. The CHIVIM requires fewer subjects and the controlled setting allows for better understanding of influenza transmission and host immunogenicity. The CHIVIM can be used to identify immune predictors of disease for at-risk populations and to measure efficacy of potential vaccines for further development.

Limitations to the CHIVIM include lack of standardization, limited access to challenge viruses and assays, lack of consensus regarding role of the CHIVIM in vaccine development pathway, and concerns regarding risk to study participants and community. To address these issues, the panel of experts recommended that WHO and other key stakeholders provide guidance on standardization, challenge virus selection, and risk management. A common repository of well-characterized challenge viruses, harmonized protocols, and standardized assays should be made available to researchers. A network of research institutions performing CHIVIM trials should be created, and more study sites are needed to increase capacity.

Experts agreed that a research network of institutions working with a standardized CHIVIM could contribute important data to support more rapid development and licensure of novel vaccines capable of providing long-lasting protection against seasonal and pandemic influenza strains.

Why crowding matters in the time of COVID-19 pandemic? - a lesson from the carnival effect on the 2017/2018 influenza epidemic in the Netherlands

Background

To evaluate the association between crowding and transmission of viral respiratory infectious diseases, we investigated the change in transmission patterns of influenza and COVID-19 before and after a mass gathering event (i.e., carnival) in the Netherlands.

Methods

Information on individual hospitalizations related to the 2017/2018 influenza epidemic were accessed from Statistics Netherlands. The influenza cases were stratified between non-carnival and carnival regions. Distributions of influenza cases were plotted with time and compared between regions. A similar investigation in the early outbreak of COVID-19 was also conducted using open data from the Dutch National Institute for Public Health and the Environment.

Results

Baseline characteristics between non-carnival and carnival regions were broadly similar. There were 13,836 influenza-related hospitalizations in the 2017/2018 influenza epidemic, and carnival fell about 1 week before the peak of these hospitalizations. The distributions of new influenza-related hospitalizations per 100,000 inhabitants with time between regions followed the same pattern with a surge of new cases in the carnival region about 1 week after carnival, which did not occur in the non-carnival region. The increase of new cases for COVID-19 in the carnival region exceeded that in the non-carnival region about 1 week after the first case was reported, but these results warrant caution as for COVID-19 there were no cases reported before the carnival and social measures were introduced shortly after carnival.

Conclusion

In this study, a mass gathering event (carnival) was associated with aggravating the spread of viral respiratory infectious diseases.

Behavioral Immune Trade-Offs: Interpersonal Value Relaxes Social Pathogen Avoidance

Behavioral-immune-system research has illuminated how people detect and avoid signs of infectious disease. But how do we regulate exposure to pathogens that produce no symptoms in their hosts? This research tested the proposition that estimates of interpersonal value are used for this task. The results of three studies (N = 1,694), each conducted using U.S. samples, are consistent with this proposition: People are less averse to engaging in infection-risky acts not only with friends relative to foes but also with honest and agreeable strangers relative to dishonest and disagreeable ones. Further, a continuous measure of how much a person values a target covaries with comfort with infection-risky acts with that target, even within relationship categories. Findings indicate that social prophylactic motivations arise not only from cues to infectiousness but also from interpersonal value. Consequently, pathogen transmission within social networks might be exacerbated by relaxed contamination aversions with highly valued social partners.

Modelling within-host macrophage dynamics in influenza virus infection

Human respiratory disease associated with influenza virus infection is of significant public health concern. Macrophages, as part of the front line of host innate cellular defence, have been shown to play an important role in controlling viral replication. However, fatal outcomes of infection, as evidenced in patients infected with highly pathogenic viral strains, are often associated with prompt activation and excessive accumulation of macrophages. Activated macrophages can produce a large amount of pro-inflammatory cytokines, which leads to severe symptoms and at times death. However, the mechanism for rapid activation and excessive accumulation of macrophages during infection remains unclear. It has been suggested that the phenomena may arise from complex interactions between macrophages and influenza virus. In this work, we develop a novel mathematical model to study the relationship between the level of macrophage activation and the level of viral load in influenza infection. Our model combines a dynamic model of viral infection, a dynamic model of macrophages and the essential interactions between the virus and macrophages. Our model predicts that the level of macrophage activation can be negatively correlated with the level of viral load when viral infectivity is sufficiently high. We further identify that temporary depletion of resting macrophages in response to viral infection is a major driver in our model for the negative relationship between the level of macrophage activation and viral load, providing new insight into the mechanisms that regulate macrophage activation. Our model serves as a framework to study the complex dynamics of virus-macrophage interactions and provides a mechanistic explanation for existing experimental observations, contributing to an enhanced understanding of the role of macrophages in influenza viral infection.

Uba C, Rivero-Perez N, Zaragoza-Bastida A, Shah MA, Behl T, Batiha GES. Factors Associated with Increased Morbidity and Mortality of Obese and Overweight COVID-19 Patients

Overweight and obesity are defined as an unnecessary accumulation of fat, which poses a risk to health. It is a well-identified risk factor for increased mortality due to heightened rates of heart disease, certain cancers, musculoskeletal disorders, and bacterial, protozoan and viral infections. The increasing prevalence of obesity is of concern, as conventional pathogenesis may indeed be increased in obese hosts rather than healthy hosts, especially during this COVID-19 pandemic. COVID-19 is a new disease and we do not have the luxury of cumulative data. Obesity activates the development of gene induced hypoxia and adipogenesis in obese animals. Several factors can influence obesity, for example, stress can increase the body weight by allowing people to consume high amounts of food with a higher propensity to consume palatable food. Obesity is a risk factor for the development of immune-mediated and some inflammatory-mediated diseases, including atherosclerosis and psoriasis, leading to a dampened immune response to infectious agents, leading to weaker post-infection impacts. Moreover, the obese host creates a special microenvironment for disease pathogenesis, marked by persistent low-grade inflammation. Therefore, it is advisable to sustain healthy eating habits by increasing the consumption of various plant-based and low-fat foods to protect our bodies and decrease the risk of infectious diseases, especially COVID-19.

Burden of influenza in Romania. A retrospective analysis of 2014/15 - 2018/19 seasons in Romania

Introduction

Influenza is a seasonal epidemic with a heavy negative impact both on population health, and healthcare system utilization; until now, there are only two burden of disease studies in the Romanian context. This study aims to quantify the burden of influenza for the Romanian population for the seasons 2014/15 to 2018/19, using health administrative databases.

Methods

Incidence, hospitalization and mortality rates attributable to influenza as well as total number of influenza cases and deaths were estimated, for each season in the analyzed period, by combining the new cases reported by General Practitioners, Emergency Department presentations, hospitalizations, number of deaths, positivity rate of influenza, and probability to be consulted by a physician. Years of life lost due to premature death attributable to influenza complications were also computed.

Results

On average, 591,151 cases/season attributable to influenza were estimated during the period 2014/15 - 2018/19. The highest rates for incidence, hospitalization and presentation to emergency department were found in the age groups 0-4 years and 65 years and above. Influenza mortality rate was estimated at 3 per 100,000 persons and the 65 and above age group had the highest rate.

Conclusions

About 3% of the total Romanian population is estimated to develop an influenza attributable disease in a non-pandemic season. An overall increasing trend of the mortality rate attributable to influenza may be also underlined. On average, a person loses 12 years due to premature death caused by complications of influenza.

Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data

BACKGROUND

Deciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.

RESULTS

We propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection.

CONCLUSIONS

The biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.

Seasonal influenza vaccination in Kenya: an economic evaluation using dynamic transmission modelling

BACKGROUND

There is substantial burden of seasonal influenza in Kenya, which led the government to consider introducing a national influenza vaccination programme. Given the cost implications of a nationwide programme, local economic evaluation data are needed to inform policy on the design and benefits of influenza vaccination. We set out to estimate the cost-effectiveness of seasonal influenza vaccination in Kenya.

METHODS

We fitted an age-stratified dynamic transmission model to active surveillance data from patients with influenza from 2010 to 2018. Using a societal perspective, we developed a decision tree cost-effectiveness model and estimated the incremental cost-effectiveness ratio (ICER) per disability-adjusted life year (DALY) averted for three vaccine target groups: children 6-23 months (strategy I), 2-5 years (strategy II) and 6-14 years (strategy III) with either the Southern Hemisphere influenza vaccine (Strategy A) or Northern Hemisphere vaccine (Strategy B) or both (Strategy C: twice yearly vaccination campaigns, or Strategy D: year-round vaccination campaigns). We assessed cost-effectiveness by calculating incremental net monetary benefits (INMB) using a willingness-to-pay (WTP) threshold of 1-51% of the annual gross domestic product per capita ($17-$872).

RESULTS

The mean number of infections across all ages was 2-15 million per year. When vaccination was well timed to influenza activity, the annual mean ICER per DALY averted for vaccinating children 6-23 months ranged between $749 and $1385 for strategy IA, $442 and $1877 for strategy IB, $678 and $4106 for strategy IC and $1147 and $7933 for strategy ID. For children 2-5 years, it ranged between $945 and $1573 for strategy IIA, $563 and $1869 for strategy IIB, $662 and $4085 for strategy IIC, and $1169 and $7897 for strategy IID. For children 6-14 years, it ranged between $923 and $3116 for strategy IIIA, $1005 and $2223 for strategy IIIB, $883 and $4727 for strategy IIIC and $1467 and $6813 for strategy IIID. Overall, no vaccination strategy was cost-effective at the minimum ($17) and median ($445) WTP thresholds. Vaccinating children 6-23 months once a year had the highest mean INMB value at $872 (WTP threshold upper limit); however, this strategy had very low probability of the highest net benefit.

CONCLUSION

Vaccinating children 6-23 months once a year was the most favourable vaccination option; however, the strategy is unlikely to be cost-effective given the current WTP thresholds.

Simulating the effect of school closure during COVID-19 outbreaks in Ontario, Canada

BACKGROUND

The province of Ontario, Canada, has instituted indefinite school closures (SC) as well as other social distancing measures to mitigate the impact of the novel coronavirus disease 2019 (COVID-19) pandemic. We sought to evaluate the effect of SC on reducing attack rate and the need for critical care during COVID-19 outbreaks, while considering scenarios with concurrent implementation of self-isolation (SI) of symptomatic cases.

METHODS

We developed an age-structured agent-based simulation model and parameterized it with the demographics of Ontario stratified by age and the latest estimates of COVID-19 epidemiologic characteristics. Disease transmission was simulated within and between different age groups by considering inter- and intra-group contact patterns. The effect of SC of varying durations on the overall attack rate, magnitude and peak time of the outbreak, and requirement for intensive care unit (ICU) admission in the population was estimated. Secondly, the effect of concurrent community-based voluntary SI of symptomatic COVID-19 cases was assessed.

RESULTS

SC reduced attack rates in the range of 7.2-12.7% when the duration of SC increased from 3 to 16 weeks, when contacts among school children were restricted by 60-80%, and in the absence of SI by mildly symptomatic persons. Depending on the scenario, the overall reduction in ICU admissions attributed to SC throughout the outbreak ranged from 3.3 to 6.7%. When SI of mildly symptomatic persons was included and practiced by 20%, the reduction of attack rate and ICU admissions exceeded 6.3% and 9.1% (on average), respectively, in the corresponding scenarios.

CONCLUSION

Our results indicate that SC may have limited impact on reducing the burden of COVID-19 without measures to interrupt the chain of transmission during both pre-symptomatic and symptomatic stages. While highlighting the importance of SI, our findings indicate the need for better understanding of the epidemiologic characteristics of emerging diseases on the effectiveness of social distancing measures.

Timelines of infection and transmission dynamics of H1N1pdm09 in swine

Influenza is a major cause of mortality and morbidity worldwide. Despite numerous studies of the pathogenesis of influenza in humans and animal models the dynamics of infection and transmission in individual hosts remain poorly characterized. In this study, we experimentally modelled transmission using the H1N1pdm09 influenza A virus in pigs, which are considered a good model for influenza infection in humans. Using an experimental design that allowed us to observe individual transmission events occurring within an 18-hr period, we quantified the relationships between infectiousness, shed virus titre and antibody titre. Transmission event was observed on 60% of occasions when virus was detected in donor pig nasal swabs and transmission was more likely when donor pigs shed more virus. This led to the true infectious period (mean 3.9 days) being slightly shorter than that predicted by detection of virus (mean 4.5 days). The generation time of infection (which determines the rate of epidemic spread) was estimated for the first time in pigs at a mean of 4.6 days. We also found that the latent period of the contact pig was longer when they had been exposed to smaller amount of shed virus. Our study provides quantitative information on the time lines of infection and the dynamics of transmission that are key parts of the evidence base needed to understand the spread of influenza viruses though animal populations and, potentially, in humans.

Influenza is a major cause of mortality and morbidity worldwide. The relationship between the time course of influenza infection and virus shedding and onward transmission of the virus remains poorly characterized. Pigs are a natural host for influenza infection with shedding patterns similar to humans. Therefore we experimentally infected pigs with the H1N1pdm09 influenza A virus using direct contact challenge and then mixed the infected pigs with a different naïve pig each day to understand when transmission occurred. Using mathematical modeling, we found that transmission events occurred on 60% of occasions when the infected pigs were shedding virus and that the risk of transmission increased with the quantity of virus shed. Also it was clear the incontact pigs started to shed virus later after exposure when the infected pigs were shedding low quantities of virus. Our study therefore provides quantitative information on the time lines of influenza virus infection and the dynamics of transmission. This is important to understand the spread of influenza viruses through animal populations and, potentially, in humans.

Minimal transmission in an influenza A (H3N2) human challenge-transmission model within a controlled exposure environment

Uncertainty about the importance of influenza transmission by airborne droplet nuclei generates controversy for infection control. Human challenge-transmission studies have been supported as the most promising approach to fill this knowledge gap. Healthy, seronegative volunteer ‘Donors’ (n = 52) were randomly selected for intranasal challenge with influenza A/Wisconsin/67/2005 (H3N2). ‘Recipients’ randomized to Intervention (IR, n = 40) or Control (CR, n = 35) groups were exposed to Donors for four days. IRs wore face shields and hand sanitized frequently to limit large droplet and contact transmission. One transmitted infection was confirmed by serology in a CR, yielding a secondary attack rate of 2.9% among CR, 0% in IR (p = 0.47 for group difference), and 1.3% overall, significantly less than 16% (p<0.001) expected based on a proof-of-concept study secondary attack rate and considering that there were twice as many Donors and days of exposure. The main difference between these studies was mechanical building ventilation in the follow-on study, suggesting a possible role for aerosols.

Understanding the relative importance of influenza modes of transmission informs strategic use of preventive measures to reduce influenza risk in high-risk settings such as hospitals and is important for pandemic preparedness. Given the increasing evidence from epidemiological modelling, exhaled viral aerosol, and aerobiological survival studies supporting a role for airborne transmission and the potential benefit of respirators (and other precautions designed to prevent inhalation of aerosols) versus surgical masks (mainly effective for reducing exposure to large droplets) to protect healthcare workers, more studies are needed to evaluate the extent of risk posed airborne versus contact and large droplet spray transmission modes. New human challenge-transmission studies should be carefully designed to overcome limitations encountered in the current study. The low secondary attack rate reported herein also suggests that the current challenge-transmission model may no longer be a more promising approach to resolving questions about transmission modes than community-based studies employing environmental monitoring and newer, state-of-the-art deep sequencing-based molecular epidemiological methods.

Global Geographical and Temporal Patterns of Seasonal Influenza and Associated Climatic Factors

Understanding geographical and temporal patterns of seasonal influenza can help strengthen influenza surveillance to early detect epidemics and inform influenza prevention and control programs. We examined variations in spatiotemporal patterns of seasonal influenza in different global regions and explored climatic factors that influence differences in influenza seasonality, through a systematic review of peer-reviewed publications. The literature search was conducted to identify original studies published between January 2005 and November 2016. Studies were selected using predetermined inclusion and exclusion criteria. The primary outcome was influenza cases; additional outcomes included seasonal or temporal patterns of influenza seasonality, study regions (temperate or tropical), and associated climatic factors. Of the 2,160 records identified in the selection process, 36 eligible studies were included. There were significant differences in influenza seasonality in terms of the time of onset, duration, number of peaks, and amplitude of epidemics between temperate and tropical/subtropical regions. Different viral types, cocirculation of influenza viruses, and climatic factors, especially temperature and absolute humidity, contributed to the variations in spatiotemporal patterns of seasonal influenza. The findings reported in this review could inform global surveillance of seasonal influenza and influenza prevention and control measures such as vaccination recommendations for different regions.

Potential Fast COVID-19 Containment With Trehalose

Countries worldwide have confirmed a staggering number of COVID-19 cases, and it is now clear that no country is immune to the SARS-CoV-2 infection. Resource-poor countries with weaker health systems are struggling with epidemics of their own and are now in a more uncertain situation with this rapidly spreading infection. Frontline healthcare workers are succumbing to the infection in their efforts to save lives. There is an urgency to develop treatments for COVID-19, yet there is limited clinical data on the efficacy of potential drug treatments. Countries worldwide implemented a stay-at-home order to “flatten the curve” and relieve the pressure on the health system, but it is uncertain how this will unfold after the economy reopens. Trehalose, a natural glucose disaccharide, is known to impair viral function through the autophagy system. Here, we propose trehalose as a potential preventative treatment for SARS-CoV-2 infection and transmission.

Variations in Seasonal Influenza Vaccine Effectiveness due to Study Characteristics: A Systematic Review and Meta-analysis of Test-Negative Design Studies

BACKGROUND

Study characteristics influence vaccine effectiveness (VE) estimation. We examined the influence of some of these on seasonal influenza VE estimates from test-negative design (TND) studies.

METHODS

We systematically searched bibliographic databases and websites for full-text publications of TND studies on VE against laboratory-confirmed seasonal influenza in outpatients after the 2009 pandemic influenza. We followed the Cochrane Handbook for Systematic Reviews of Interventions guidelines. We examined influence of source of vaccination information, respiratory specimen swab time, and covariate adjustment on VE. We calculated pooled adjusted VE against H1N1 and H3N2 influenza subtypes, influenza B, and all influenza using an inverse-variance random-effects model.

RESULTS

We included 70 full-text articles. Pooled VE against H1N1 and H3N2 influenza subtypes, influenza B, and all influenza was higher for studies that used self-reported vaccination than for those that used medical records. Pooled VE was higher with respiratory specimen collection within ≤7 days vs ≤4 days of symptom onset, but the opposite was observed for H1N1. Pooled VE was higher for studies that adjusted for age but not for medical conditions compared with those that adjusted for both. There was, however, a lack of statistical significance in almost all differences in pooled VE between compared groups.

CONCLUSIONS

The available evidence is not strong enough to conclude that influenza VE from TND studies varies by source of vaccination information, respiratory specimen swab time, or adjustment for age/medical conditions. The evidence is, however, indicative that these factors ought to be considered while designing or evaluating TND studies of influenza VE.

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

BACKGROUND

As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations.

METHOD AND FINDINGS

We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.

CONCLUSIONS

We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

BACKGROUND

As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations.

METHOD AND FINDINGS

We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.

CONCLUSIONS

We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

Impact of influenza virus during pregnancy: from disease severity to vaccine efficacy

Pregnant women are among the individuals at the highest risk for severe influenza virus infection. Infection of the mother during pregnancy increases the probability of adverse fetal outcomes such as small for gestational age, preterm birth and fetal death. Animal models of syngeneic and allogeneic mating can recapitulate the increased disease severity observed in pregnant women and are used to define the mechanism(s) of that increased severity. This review focuses on influenza A virus pathogenesis, the unique immunological landscape during pregnancy, the impact of maternal influenza virus infection on the fetus and the immune responses at the maternal–fetal interface. Finally, we summarize the importance of immunization and antiviral treatment in this population and highlight issues that warrant further investigation.

One hundred years of (influenza) immunopathology

It has been over 100 years since the 1918 influenza pandemic, one of the most infamous examples of viral immunopathology. Since that time, there has been an inevitable repetition of influenza pandemics every few decades and yearly influenza seasons, which have a significant impact on human health. Recently, noteworthy progress has been made in defining the cellular and molecular mechanisms underlying pathology induced by an exuberant host response to influenza virus infection. Infection with influenza viruses is associated with a wide spectrum of disease, from mild symptoms to severe complications including respiratory failure, and the severity of influenza disease is driven by a complex interplay of viral and host factors. This chapter will discuss mechanisms of infection severity using concepts of disease resistance and tolerance as a framework for understanding the balance between viral clearance and immunopathology. We review mechanistic studies in animal models of infection and correlational studies in humans that have begun to define these factors and discuss promising host therapeutic targets to improve outcomes from severe influenza disease.

Dynamic interactions of influenza viruses in Hong Kong during 1998-2018

Influenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.

Development of an RNA Strand-Specific Hybridization Assay To Differentiate Replicating versus Nonreplicating Influenza A Viruses

Replication of influenza A virus (IAV) from negative-sense viral RNA (vRNA) requires the generation of positive-sense RNA (+RNA). Most molecular assays, such as conventional real-time reverse transcriptase PCR (rRT-PCR), detect total RNA in a sample without differentiating vRNA from +RNA. These assays are not designed to distinguish IAV infection versus exposure of an individual to an environment enriched with IAVs but wherein no viral replication occurs. We therefore developed a strand-specific hybridization (SSH) assay that differentiates between vRNA and +RNA and quantifies relative levels of each RNA species. The SSH assay exhibited a linearity of 7 logs with a lower limit of detection of 6.0 × 10² copies of molecules per reaction. No signal was detected in samples with a high load of nontarget template or influenza B virus, demonstrating assay specificity. IAV +RNA was detected 2 to 4 h postinoculation of MDCK cells, whereas synthesis of cold-adapted IAV +RNA was significantly impaired at 37°C. The SSH assay was then used to test IAV rRT-PCR positive nasopharyngeal specimens collected from individuals exposed to IAV at swine exhibitions (n = 7) or while working at live bird markets (n = 2). The SSH assay was able to differentiate vRNA and +RNA in samples collected from infected, symptomatic individuals versus individuals who were exposed to IAV in the environment but had no active viral replication. Data generated with this technique, especially when coupled with clinical data and assessment of seroconversion, will facilitate differentiation of actual IAV infection with replicating virus versus individuals exposed to high levels of environmental contamination but without virus infection.

Rapid Detection of Influenza Virus Subtypes Based on an Integrated Centrifugal Disc

Influenza is a zoonotic disease, infecting a wide variety of warm-blooded animals. It is caused by an influenza virus, which has been found with hundreds of subtypes. These subtypes are often associated with different sources of infection and possess complex courses of infection. In the early stage of influenza infection, rapid subtype detection is very practicable to prevent the disease from getting worse. Herein, we presented a high-throughput microfluidic centrifugal disc for rapid detection of influenza virus subtypes. The disc realized detection reagent preloads, automated reagent control, and RT-LAMP detections. Six kinds of highly pathogenic influenza viruses could be simultaneously identified, including influenza A subtypes H1, H3, H5, H7, and H9 and influenza B virus. Two different fluorescent dyes could be used on the disc for real-time detection or read by the naked eye. The performance of the disc was demonstrated by testing the clinical samples. The integrated centrifugal disc was expected for rapid detection of influenza virus subtypes to facilitate accurate drug usage in resource-constrained settings and contribute to reduce the risk of the influenza pandemic.

From trivalent to quadrivalent influenza vaccines: Public health and economic burden for different immunization strategies in Spain

PURPOSE

Quadrivalent influenza vaccine (QIV) includes the same strains as trivalent influenza vaccine (TIV) plus an additional B strain of the other B lineage. The aim of the study was to analyse the public health and economic impact of replacing TIV with QIV in different scenarios in Spain.

METHODS

A dynamic transmission model was developed to estimate the number of influenza B cases prevented under TIV and QIV strategies (<65 years (high risk) and ≥65 years). This model considers cross-protective immunity induced by different lineages of influenza B. The output of the transmission model was used as input for a decision tree model that estimated the economic impact of switching TIV to QIV. The models were populated with Spanish data whenever possible. Deterministic univariate and probabilistic multivariate sensitivity analyses were performed.

RESULTS

Replacing TIV with QIV in all eligible patients with current vaccine coverage in Spain may have prevented 138,707 influenza B cases per season and, therefore avoided 10,748 outpatient visits, 3,179 hospitalizations and 192 deaths. The replacement could save €532,768 in outpatient visit costs, €13 million in hospitalization costs, and €3 million in costs of influenza-related deaths per year. An additional €5 million costs associated with productivity loss could be saved per year, from the societal perspective. The budget impact from societal perspective would be €6.5 million, and the incremental cost-effectiveness ratio (ICER) €1,527 per quality-adjusted life year (QALY). Sensitivity analyses showed robust results. In additional scenarios, QIV also showed an impact at public health level reducing influenza B related cases, outpatient visits, hospitalizations and deaths.

CONCLUSIONS

Our results show public health and economic benefits for influenza prevention with QIV. It would be an efficient intervention for the Spanish National Health Service with major health benefits especially in the population ≥65-year.

A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study

Multi-scale epidemic forecasting models have been used to inform population-scale predictions with within-host models and/or infection data collected in longitudinal cohort studies. However, most multi-scale models are complex and require significant modelling expertise to run. We formulate an alternative multi-scale modelling framework using a compartmental model with multiple infected stages. In the large-compartment limit, our easy-to-use framework generates identical results compared to previous more complicated approaches. We apply our framework to the case study of influenza A in humans. By using a viral dynamics model to generate synthetic patient-level data, we explore the effects of limited and inaccurate patient data on the accuracy of population-scale forecasts. If infection data are collected daily, we find that a cohort of at least 40 patients is required for a mean population-scale forecasting error below 10%. Forecasting errors may be reduced by including more patients in future cohort studies or by increasing the frequency of observations for each patient. Our work, therefore, provides not only an accessible epidemiological modelling framework but also an insight into the data required for accurate forecasting using multi-scale models.

Virulence-mediated infectiousness and activity trade-offs and their impact on transmission potential of influenza patients

Communicable diseases are often virulent, i.e. they cause morbidity symptoms in those infected. While some symptoms may be transmission-enhancing, other symptoms are likely to reduce transmission potential. For human diseases, the reduction in transmission opportunities is commonly caused by reduced activity. There is limited data regarding the potential impact of virulence on transmission potential. We performed an exploratory data analysis of 324 influenza patients at a university health centre during the 2016/2017 influenza season. We classified symptoms as infectiousness-related or morbidity-related and calculated two scores. The scores were used to explore the relationship between infectiousness, morbidity (virulence), and activity level. We found a decrease in the activity level with increasing morbidity scores. There was no consistent pattern between an activity level and an infectiousness score. We also found a positive correlation between morbidity and infectiousness scores. Overall, we find that increasing virulence leads to increased infectiousness and reduced activity, suggesting a trade-off that can impact overall transmission potential. Our findings indicate that a reduction of systemic symptoms may increase host activity without reducing infectiousness. Therefore, interventions should target both systemic- and infectiousness-related symptoms to reduce overall transmission potential. Our findings can also inform simulation models that investigate the impact of different interventions on transmission.

Impact of influenza vaccine-modified infectivity on attack rate, case fatality ratio and mortality

Generally, vaccines are designed to provide protection against infection (susceptibility), disease (symptoms and transmissibility), and/or complications. In a recent study of influenza vaccination, it was observed that vaccinated yet infected individuals experienced increased transmission levels. In this paper, using a mathematical model of infection and transmission, we study the impact of vaccine-modified effects, including susceptibility and infectivity, on important epidemiological outcomes of an immunization program. The balance between vaccine-modified susceptibility, infectivity and recovery needed in preventing an influenza outbreak, or in mitigating the health outcomes of the outbreak is studied using the SIRV-type of disease transmission model. We also investigate the impact of influenza vaccination program on the infection risk of vaccinated and non-vaccinated individuals.

The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model

The spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been unprecedented in its speed and effects. Interruption of its transmission to prevent widespread community transmission is critical because its effects go beyond the number of COVID-19 cases and deaths and affect the health system capacity to provide other essential services. Highlighting the implications of such a situation, the predictions presented here are derived using a Markov chain model, with the transition states and country specific probabilities derived based on currently available knowledge. A risk of exposure, and vulnerability index are used to make the probabilities country specific. The results predict a high risk of exposure in states of small size, together with Algeria, South Africa and Cameroon. Nigeria will have the largest number of infections, followed by Algeria and South Africa. Mauritania would have the fewest cases, followed by Seychelles and Eritrea. Per capita, Mauritius, Seychelles and Equatorial Guinea would have the highest proportion of their population affected, while Niger, Mauritania and Chad would have the lowest. Of the World Health Organization's 1 billion population in Africa, 22% (16%-26%) will be infected in the first year, with 37 (29 - 44) million symptomatic cases and 150 078 (82 735-189 579) deaths. There will be an estimated 4.6 (3.6-5.5) million COVID-19 hospitalisations, of which 139 521 (81 876-167 044) would be severe cases requiring oxygen, and 89 043 (52 253-106 599) critical cases requiring breathing support. The needed mitigation measures would significantly strain health system capacities, particularly for secondary and tertiary services, while many cases may pass undetected in primary care facilities due to weak diagnostic capacity and non-specific symptoms. The effect of avoiding widespread and sustained community transmission of SARS-CoV-2 is significant, and most likely outweighs any costs of preventing such a scenario. Effective containment measures should be promoted in all countries to best manage the COVID-19 pandemic.

Modeling Favipiravir Antiviral Efficacy Against Emerging Viruses: From Animal Studies to Clinical Trials

In 2014, our research network was involved in the evaluation of favipiravir, an anti-influenza polymerase inhibitor, against Ebola virus. In this review, we discuss how mathematical modeling was used, first to propose a relevant dosing regimen in humans, and then to optimize its antiviral efficacy in a nonhuman primate (NHP) model. The data collected in NHPs were finally used to develop a model of Ebola pathogenesis integrating the interactions among the virus, the innate and adaptive immune response, and the action of favipiravir. We conclude the review of this work by discussing how these results are of relevance for future human studies in the context of Ebola virus, but also for other emerging viral diseases for which no therapeutics are available.

Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)

A total of 565 Japanese citizens were evacuated from Wuhan, China to Japan. All passengers were screened for symptoms and also undertook reverse transcription polymerase chain reaction testing, identifying 5 asymptomatic and 7 symptomatic passengers testing positive for 2019-nCoV. We show that the screening result is suggestive of the asymptomatic ratio at 41.6%.

Stochastic modeling of influenza spread dynamics with recurrences

We present results of a study of a simple, stochastic, agent-based model of influenza A infection, simulating its dynamics over the course of one flu season. Building on an early work of Bartlett, we define a model with a limited number of parameters and rates that have clear epidemiological interpretation and can be constrained by data. We demonstrate the occurrence of recurrent behavior in the infected number [more than one peak in a season], which is observed in data, in our simulations for populations consisting of cohorts with strong intra- and weak inter-cohort transmissibility. We examine the dependence of the results on epidemiological and population characteristics by investigating their dependence on a range of parameter values. Finally, we study infection with two strains of influenza, inspired by observations, and show a counter-intuitive result for the effect of inoculation against the strain that leads to the first wave of infection.

A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract

Within the human respiratory tract (HRT), virus diffuses through the periciliary fluid (PCF) bathing the epithelium. But virus also undergoes advection: as the mucus layer sitting atop the PCF is pushed along by the ciliated cell's beating cilia, the PCF and its virus content are also pushed along, upwards towards the nose and mouth. While many mathematical models (MMs) have described the course of influenza A virus (IAV) infections in vivo, none have considered the impact of both diffusion and advection on the kinetics and localization of the infection. The MM herein represents the HRT as a one-dimensional track extending from the nose down towards the lower HRT, wherein stationary cells interact with IAV which moves within (diffusion) and along with (advection) the PCF. Diffusion was found to be negligible in the presence of advection which effectively sweeps away IAV, preventing infection from disseminating below the depth at which virus first deposits. Higher virus production rates (10-fold) are required at higher advection speeds (40 μm/s) to maintain equivalent infection severity and timing. Because virus is entrained upwards, upper parts of the HRT see more virus than lower parts. As such, infection peaks and resolves faster in the upper than in the lower HRT, making it appear as though infection progresses from the upper towards the lower HRT, as reported in mice. When the spatial MM is expanded to include cellular regeneration and an immune response, it reproduces tissue damage levels reported in patients. It also captures the kinetics of seasonal and avian IAV infections, via parameter changes consistent with reported differences between these strains, enabling comparison of their treatment with antivirals. This new MM offers a convenient and unique platform from which to study the localization and spread of respiratory viral infections within the HRT.

Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections

It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.

Influenza and Bacterial Coinfection in Adults With Community-Acquired Pneumonia Admitted to Conventional Wards: Risk Factors, Clinical Features, and Outcomes

Background

Relevance of viral and bacterial coinfection (VBC) in non-intensive care unit (ICU) hospitalized adults with community-acquired pneumonia (CAP) is poorly characterized. We aim to determine risk factors, features, and outcomes of VBC-CAP in this setting.

Methods

This is a prospective cohort of adults admitted to conventional wards with CAP. Patients were divided into VBC-CAP, viral CAP (V-CAP), and bacterial CAP (B-CAP) groups. Independent risk and prognostic factors for VBC-CAP were identified.

Results

We documented 1123 episodes: 57 (5.1%) VBC-CAP, 98 (8.7%) V-CAP, and 968 (86.1%) B-CAP. Patients with VBC-CAP were younger than those with B-CAP (54 vs 71 years; P < .001). Chronic respiratory disease was more frequent in patients with VBC-CAP than in those with V-CAP (26.3% vs 14.3%%; P = .001). Among those with influenza (n = 153), the VBC-CAP group received empirical oseltamivir less often (56.1% vs 73.5%; P < .001). Patients with VBC-CAP also had more respiratory distress (21.1% VBC-CAP; 19.4% V-CAP, and 9.8% B-CAP; P < .001) and required ICU admission more often (31.6% VBC-CAP, 31.6% V-CAP, and 12.8% B-CAP; P < .001). The 30-day case-fatality rate was 3.5% in the VBC-CAP group, 3.1% in the V-CAP group, and 6.3% in the B-CAP group (P = .232). Furthermore, VBC-CAP was associated with severity criteria (odds ratio [OR], 5.219; P < .001) and lack of empirical oseltamivir therapy in influenza cases (OR, 0.401; P < .043).

Conclusions

Viral and bacterial coinfection-CAP involved younger patients with comorbidities and with poor influenza vaccination rate. Patients with VBC-CAP presented more respiratory complications and more often required ICU admission. Nevertheless, 30-day mortality rate was low and related either to severity criteria or to delayed initiation of oseltamivir therapy.

Favipiravir, an anti-influenza drug against life-threatening RNA virus infections

Favipiravir has been developed as an anti-influenza drug and licensed as an anti-influenza drug in Japan. Additionally, favipiravir is being stockpiled for 2 million people as a countermeasure for novel influenza strains. This drug functions as a chain terminator at the site of incorporation of the viral RNA and reduces the viral load. Favipiravir cures all mice in a lethal influenza infection model, while oseltamivir fails to cure the animals. Thus, favipiravir contributes to curing animals with lethal infection. In addition to influenza, favipiravir has a broad spectrum of anti-RNA virus activities in vitro and efficacies in animal models with lethal RNA viruses and has been used for treatment of human infection with life-threatening Ebola virus, Lassa virus, rabies, and severe fever with thrombocytopenia syndrome. The best feature of favipiravir as an antiviral agent is the apparent lack of generation of favipiravir-resistant viruses. Favipiravir alone maintains its therapeutic efficacy from the first to the last patient in an influenza pandemic or an epidemic lethal RNA virus infection. Favipiravir is expected to be an important therapeutic agent for severe influenza, the next pandemic influenza strain, and other severe RNA virus infections for which standard treatments are not available.

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.

Linking influenza virus evolution within and between human hosts

Influenza viruses rapidly diversify within individual human infections. Several recent studies have deep-sequenced clinical influenza infections to identify viral variation within hosts, but it remains unclear how within-host mutations fare at the between-host scale. Here, we compare the genetic variation of H3N2 influenza within and between hosts to link viral evolutionary dynamics across scales. Synonymous sites evolve at similar rates at both scales, indicating that global evolution at these putatively neutral sites results from the accumulation of within-host variation. However, nonsynonymous mutations are depleted between hosts compared to within hosts, suggesting that selection purges many of the protein-altering changes that arise within hosts. The exception is at antigenic sites, where selection detectably favors nonsynonymous mutations at the global scale, but not within hosts. These results suggest that selection against deleterious mutations and selection for antigenic change are the main forces that act on within-host variants of influenza virus as they transmit and circulate between hosts.

Inter- Versus Intra-Host Sequence Diversity of pH1N1 and Associated Clinical Outcomes

The diversity of RNA viruses dictates their evolution in a particular host, community or environment. Here, we reported within- and between-host pH1N1virus diversity at consensus and sub-consensus levels over a three-year period (2015-2017) and its implications on disease severity. A total of 90 nasal samples positive for the pH1N1 virus were deep-sequenced and analyzed to detect low-frequency variants (LFVs) and haplotypes. Parallel evolution of LFVs was seen in the hemagglutinin (HA) gene across three scales: among patients (33%), across years (22%), and at global scale. Remarkably, investigating the emergence of LFVs at the consensus level demonstrated that within-host virus evolution recapitulates evolutionary dynamics seen at the global scale. Analysis of virus diversity at the HA haplotype level revealed the clustering of low-frequency haplotypes from early 2015 with dominant strains of 2016, indicating rapid haplotype evolution. Haplotype sharing was also noticed in all years, strongly suggesting haplotype transmission among patients infected during a specific influenza season. Finally, more than half of patients with severe symptoms harbored a larger number of haplotypes, mostly in patients under the age of five. Therefore, patient age, haplotype diversity, and the presence of certain LFVs should be considered when interpreting illness severity. In addition to its importance in understanding virus evolution, sub-consensus virus diversity together with whole genome sequencing is essential to explain variabilities in clinical outcomes that cannot be explained by either analysis alone.

Vaccinating children against influenza: overall cost-effective with potential for undesirable outcomes

BACKGROUND

The present study aims to assess the cost-effectiveness of an influenza vaccination program for children in the Netherlands. This requires an evaluation of the long-term impact of such a program on the burden of influenza across all age groups, using a transmission model that accounts for the seasonal variability in vaccine effectiveness and the shorter duration of protection following vaccination as compared to natural infection.

METHODS

We performed a cost-effectiveness analysis based on a stochastic dynamic transmission model that has been calibrated to reported GP visits with influenza-like illness in the Netherlands over 11 seasons (2003/2004 to 2014/2015). We analyzed the costs and effects of extending the current program with vaccination of children aged 2-16 years at 50% coverage over 20 consecutive seasons. We measured the effects in quality-adjusted life-years (QALYs) and we adopted a societal perspective.

RESULTS

The childhood vaccination program is estimated to have an average incremental cost-effectiveness ratio (ICER) of €3944 per QALY gained and is cost-effective in the general population (across 1000 simulations; conventional Dutch threshold of €20,000 per QALY gained). The childhood vaccination program is not estimated to be cost-effective for the target-group itself with an average ICER of €57,054 per QALY gained. Uncertainty analyses reveal that these ICERs hide a wide range of outcomes. Even though introduction of a childhood vaccination program decreases the number of infections, it tends to lead to larger epidemics: in 23.3% of 1000 simulations, the childhood vaccination program results in an increase in seasons with a symptomatic attack rate larger than 5%, which is expected to cause serious strain on the health care system. In 6.4% of 1000 simulations, the childhood vaccination program leads to a net loss of QALYs. These findings are robust across different targeted age groups and vaccination coverages.

CONCLUSIONS

Modeling indicates that childhood influenza vaccination is cost-effective in the Netherlands. However, childhood influenza vaccination is not cost-effective when only outcomes for the children themselves are considered. In approximately a quarter of the simulations, the introduction of a childhood vaccination program increases the frequency of seasons with a symptomatic attack rate larger than 5%. The possibility of an overall health loss cannot be excluded.

Discrete time forecasting of epidemics

Forecasting in the domain of infectious diseases aims at estimating the number of cases ahead of time during an epidemic, hence fundamentally requires understanding its dynamics. In fact, estimates about the dynamics help to predict the number of cases in an epidemic, which will depend on determining a few of defining factors such as its starting point, the turning point, growth factor, and the size of the epidemic in total number of cases. In this work a phenomenological model deals with a practical aspect often disregarded in such studies, namely that health surveillance produces counts in batches when aggregated over discrete time, such as days, weeks, months, or other time units. This model enables derivation of equations that permit both estimating key dynamics parameters and forecasting. Results using both severe acute respiratory illness data and synthetic data show that the forecasting follows very well over time the dynamics and is resilient with statistical noise, but has a delay effect due to the discrete time.

Comparative Pathogenicity and Transmissibility of Pandemic H1N1, Avian H5N1, and Human H7N9 Influenza Viruses in Tree Shrews

Influenza A viruses (IAVs) continuously challenge the poultry industry and human health. Studies of IAVs are still hampered by the availability of suitable animal models. Chinese tree shrews (Tupaia belangeri chinensis) are closely related to primates physiologically and genetically, which make them a potential animal model for human diseases. In this study, we comprehensively evaluated infectivity and transmissibility in Chinese tree shrews by using pandemic H1N1 (A/Sichuan/1/2009, pdmH1N1), avian-origin H5N1 (A/Chicken/Gansu/2/2012, H5N1) and early human-origin H7N9 (A/Suzhou/SZ19/2014, H7N9) IAVs. We found that these viruses replicated efficiently in primary tree shrew cells and tree shrews without prior adaption. Pathological lesions in the lungs of the infected tree shrews were severe on day 3 post-inoculation, although clinic symptoms were self-limiting. The pdmH1N1 and H7N9 viruses, but not the H5N1 virus, transmitted among tree shrews by direct contact. Interestingly, we also observed that unadapted H7N9 virus could transmit from tree shrews to naïve guinea pigs. Virus-inoculated tree shrews generated a strong humoral immune response and were protected from challenge with homologous virus. Taken together, our findings suggest the Chinese tree shrew would be a useful mammalian model to study the pathogenesis and transmission of IAVs.

Assessing direct and indirect effects of pediatric influenza vaccination in Germany by individual-based simulations

Children have a high burden of influenza and play a central role in spreading influenza. Routinely vaccinating children against influenza may, thus, not only reduce their disease burden, but also that of the general population, including the elderly who frequently suffer severe complications. Using the published individual-based tool 4Flu, we simulated how pediatric vaccination would change infection incidence in Germany. Transmission of four influenza strains was simulated in 100,000 individuals with German demography and contact structure. After initialization with the recorded trivalent influenza vaccination coverage for 20 years (1997-2016), all vaccinations were switched to quadrivalent influenza vaccine (QIV). Scenarios where vaccination coverage of children (0.5-17-year-old) was increased from the current value (4.3%) to a maximum of 10-60% were compared to baseline with unchanged coverage, averaging results of 1,000 pairs of simulations over a 20-year evaluation period (2017-2036). Pediatric vaccination coverage of 10-60% annually prevented 218-1,732 (6.3-50.5%) infections in children, 204-1,961 (2.9-28.2%) in young adults and 95-868 (3.1-28.9%) in the elderly in a population of 100,000 inhabitants; overall, 34.1% of infections in the total population (3.7 million infections per year in Germany) can be prevented if 60% of all children are vaccinated annually. 4.4-4.6 vaccinations were needed to prevent one infection among children; 1.7-1.8 were needed to prevent one in the population. Enhanced pediatric vaccination prevents many infections in children and even more in young adults and the elderly.

More than a “touch of the flu”: a response to Mayrhuber et al’s ““with fever it’s the real flu I would say”: laypersons’ perception of common cold and influenza and their differences - a qualitative study in Austria, Belgium and Croatia”

This short reply contests two assumptions made by the authors of Mayrhuber et al’s. “With fever it’s the real flu I would say.” The first is that there is influenza can be reliably defined by a medical case definition. The second is that this small qualitative study can be generalisable. However, it does underline the important point that technical diagnostic terms may be used on different registers by a variety of actors in the medical setting.

Modelling the optimal target age group for seasonal influenza vaccination in Japan

BACKGROUND

In Japan, the current influenza vaccination programme is targeting older individuals. On the other hand, epidemics of influenza are likely to be mainly driven by children. In this study, we consider the most cost-effective target age group for a seasonal influenza vaccination programme in Japan.

METHODS

We constructed a deterministic compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) model with data from the 2012/13 to 2014/15 influenza seasons in Japan. Bayesian inference with Markov Chain Monte Carlo method was used for parameter estimation. Cost-effectiveness analyses were conducted from public health care payer's perspective.

RESULTS

A scenario targeting children under 15 was expected to reduce the number of cases 6,382,345 compared to the current strategy. A scenario targeting elderly population (age over 49 years) was expected to reduce the number of cases 693,206. The children targeted scenario demonstrated negative ICER (incremental cost-effectiveness ratio) value. On the other hand, elderly targeted scenario demonstrated higher ICER value than the willingness to pay (50,000 USD/QALY).

CONCLUSIONS

A vaccination programme which targets children under 15 is predicted to have much larger epidemiological impact than those targeting elderly.

Risk Factors and Attack Rates of Seasonal Influenza Infection: Results of the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) Seroepidemiologic Cohort Study

Background

Understanding the attack rate of influenza infection and the proportion who become ill by risk group is key to implementing prevention measures. While population-based studies of antihemagglutinin antibody responses have been described previously, studies examining both antihemagglutinin and antineuraminidase antibodies are lacking.

Methods

In 2015, we conducted a seroepidemiologic cohort study of individuals randomly selected from a population in New Zealand. We tested paired sera for hemagglutination inhibition (HAI) or neuraminidase inhibition (NAI) titers for seroconversion. We followed participants weekly and performed influenza polymerase chain reaction (PCR) for those reporting influenza-like illness (ILI).

Results

Influenza infection (either HAI or NAI seroconversion) was found in 321 (35% [95% confidence interval, 32%-38%]) of 911 unvaccinated participants, of whom 100 (31%) seroconverted to NAI alone. Young children and Pacific peoples experienced the highest influenza infection attack rates, but overall only a quarter of all infected reported influenza PCR-confirmed ILI, and one-quarter of these sought medical attention. Seroconversion to NAI alone was higher among children aged <5 years vs those aged ≥5 years (14% vs 4%; P < .001) and among those with influenza B vs A(H3N2) virus infections (7% vs 0.3%; P < .001).

Conclusions

Measurement of antineuraminidase antibodies in addition to antihemagglutinin antibodies may be important in capturing the true influenza infection rates.

A Framework for Discovering Health Disparities among Cohorts in an Influenza Epidemic

Infectious diseases such as Influenza and Ebola pose a serious threat to everyone but certain demographics and cohorts face a higher risk of infection than others. This research provides a computational framework for studying health disparities among cohorts based on individual level features, such as age, gender, income, etc. We apply this framework to find health disparities among subpopulations in an influenza epidemic and evaluate vaccination prioritization strategies to achieve specific objectives. We explore the heterogeneities in individuals' demographic and socioeconomic attributes as the potential cause of health disparities. An agent-based model is used to simulate an influenza epidemic over a synthetic social contact network of the Montgomery County in Southwest Virginia to identify infected cases which are then labeled with a specific clinical outcome by using a predefined probability distribution based on age and risk level. We divide the population into age and income based cohorts and measure the direct and indirect economic impact of vaccination for each cohort. Simulation-based results find strong health disparities across age and income groups. Various vaccine distribution strategies are considered and outcomes are measured through metrics such as death count, total number of infections, net return per capita, net return per dollar spent and net return per vaccinated person. The results, framework, and methodology developed here can assist public health policy makers in efficiently allocating limited pharmaceutical resources.

Improving immunological insights into the ferret model of human viral infectious disease

Ferrets are a well-established model for studying both the pathogenesis and transmission of human respiratory viruses and evaluation of antiviral vaccines. Advanced immunological studies would add substantial value to the ferret models of disease but are hindered by the low number of ferret-reactive reagents available for flow cytometry and immunohistochemistry. Nevertheless, progress has been made to understand immune responses in the ferret model with a limited set of ferret-specific reagents and assays. This review examines current immunological insights gained from the ferret model across relevant human respiratory diseases, with a focus on influenza viruses. We highlight key knowledge gaps that need to be bridged to advance the utility of ferrets for immunological studies.