Long-term effects of non-pharmaceutical interventions on total disease burden in parsimonious epidemiological models

The recent global COVID-19 pandemic resulted in governments enacting non-pharmaceutical interventions (NPIs) targeted at reducing transmission of SARS-CoV-2. But the NPIs also affected the transmission of viruses causing non-target seasonal respiratory diseases, including influenza and respiratory syncytial virus (RSV). In many countries, the NPIs were found to reduce cases of such seasonal respiratory diseases, but there is also evidence that subsequent relaxation of NPIs led to outbreaks of these diseases that were larger than pre-pandemic ones, due to the accumulation of susceptible individuals prior to relaxation. Therefore, the net long-term effects of NPIs on the total disease burden of non-target diseases remain unclear. Knowledge of this is important for infectious disease management and maintenance of public health. In this study, we shed light on this issue for the simplified scenario of a set of NPIs that prevent or reduce transmission of a seasonal respiratory disease for about a year and are then removed, using mathematical analyses and numerical simulations of a suite of four epidemiological models with varying complexity and generality. The model parameters were estimated using empirical data pertaining to seasonal respiratory diseases and covered a wide range. Our results showed that NPIs reduced the total disease burden of a non-target seasonal respiratory disease in the long-term. Expressed as a percentage of population size, the reduction was greater for larger values of the basic reproduction number and the immunity loss rate, reflecting larger outbreaks and hence more infections averted by imposition of NPIs. Our study provides a foundation for exploring the effects of NPIs on total disease burden in more-complex scenarios.

Incidence and transmission associated with respiratory viruses in an acute care facility: An observational study

We estimated the extent of respiratory virus transmission over three pre-COVID-19 seasons. Of 16,273 assays, 22.9% (3,726) detected ≥1 respiratory virus. The frequency of putatively hospital-acquired infection ranged from 6.9% (influenza A/B) to 24.7% (adenovirus). The 176 clusters were most commonly associated with rhinovirus/enterovirus (70) and influenza A/B (62).

Risk assessment of a highly pathogenic H5N1 influenza virus from mink

Outbreaks of highly pathogenic H5N1 clade 2.3.4.4b viruses in farmed mink and seals combined with isolated human infections suggest these viruses pose a pandemic threat. To assess this threat, using the ferret model, we show an H5N1 isolate derived from mink transmits by direct contact to 75% of exposed ferrets and, in airborne transmission studies, the virus transmits to 37.5% of contacts. Sequence analyses show no mutations were associated with transmission. The H5N1 virus also has a low infectious dose and remains virulent at low doses. This isolate carries the adaptive mutation, PB2 T271A, and reversing this mutation reduces mortality and airborne transmission. This is the first report of a H5N1 clade 2.3.4.4b virus exhibiting direct contact and airborne transmissibility in ferrets. These data indicate heightened pandemic potential of the panzootic H5N1 viruses and emphasize the need for continued efforts to control outbreaks and monitor viral evolution.

Diet-induced obesity affects influenza disease severity and transmission dynamics in ferrets

Obesity, and the associated metabolic syndrome, is a risk factor for increased disease severity with a variety of infectious agents, including influenza virus. Yet, the mechanisms are only partially understood. As the number of people, particularly children, living with obesity continues to rise, it is critical to understand the role of host status on disease pathogenesis. In these studies, we use a diet-induced obese ferret model and tools to demonstrate that, like humans, obesity resulted in notable changes to the lung microenvironment, leading to increased clinical disease and viral spread to the lower respiratory tract. The decreased antiviral responses also resulted in obese animals shedding higher infectious virus for a longer period, making them more likely to transmit to contacts. These data suggest that the obese ferret model may be crucial to understanding obesity's impact on influenza disease severity and community transmission and a key tool for therapeutic and intervention development for this high-risk population.

Ozone as an environmental driver of influenza

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O3) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010-2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O3 on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O3 commonly known as air pollutant, the novel findings here on the inhibition effect of O3 on influenza activity warrant further investigations to inform environmental management and public health protection.

Evaluating Seasonal Variations in Human Contact Patterns and Their Impact on the Transmission of Respiratory Infectious Diseases

BACKGROUND

Human contact patterns are a key determinant driving the spread of respiratory infectious diseases. However, the relationship between contact patterns and seasonality as well as their possible association with the seasonality of respiratory diseases is yet to be clarified.

METHODS

We investigated the association between temperature and human contact patterns using data collected through a cross-sectional diary-based contact survey in Shanghai, China, between December 24, 2017, and May 30, 2018. We then developed a compartmental model of influenza transmission informed by the derived seasonal trends in the number of contacts and validated it against A(H1N1)pdm09 influenza data collected in Shanghai during the same period.

RESULTS

We identified a significant inverse relationship between the number of contacts and the seasonal temperature trend defined as a spline interpolation of temperature data (p = 0.003). We estimated an average of 16.4 (95% PrI: 15.1-17.5) contacts per day in December 2017 that increased to an average of 17.6 contacts (95% PrI: 16.5-19.3) in January 2018 and then declined to an average of 10.3 (95% PrI: 9.4-10.8) in May 2018. Estimates of influenza incidence obtained by the compartmental model comply with the observed epidemiological data. The reproduction number was estimated to increase from 1.24 (95% CI: 1.21-1.27) in December to a peak of 1.34 (95% CI: 1.31-1.37) in January. The estimated median infection attack rate at the end of the season was 27.4% (95% CI: 23.7-30.5%).

CONCLUSIONS

Our findings support a relationship between temperature and contact patterns, which can contribute to deepen the understanding of the relationship between social interactions and the epidemiology of respiratory infectious diseases.

Comparative Effectiveness of Baloxavir Marboxil and Oseltamivir Treatment in Reducing Household Transmission of Influenza: A Post Hoc Analysis of the BLOCKSTONE Trial

BACKGROUND

The transmission of influenza virus in households, especially by children, is a major route of infection. Prior studies suggest that timely antiviral treatment of ill cases may reduce infection in household contacts. The aim of the study was to compare the effects of oseltamivir (OTV) and baloxavir marboxil (BXM) treatment of index cases on the secondary attack rate (SAR) of influenza within household.

METHODS

A post hoc analysis was done in BLOCKSTONE trial-a placebo-controlled, double-blinded post-exposure prophylaxis of BXM. Data were derived from the laboratory-confirmed index cases' household contacts who received placebo in the trial and also from household members who did not participate in the trial but completed illness questionnaires. To assess the SAR of household members, multivariate analyses adjusted for factors including age, vaccination status, and household size were performed and compared between contacts of index cases treated with BXM or OTV.

RESULTS

In total, 185 index cases (116 treated with BXM and 69 treated with OTV) and 410 household contacts (201 from trial, 209 by questionnaire) were included. The Poisson regression modeling showed that the SAR in household contacts of index cases treated with BXM and OTV was 10.8% and 18.5%, respectively; the adjusted relative reduction in SAR was 41.8% (95% confidence interval: 1.0%-65.7%, p = 0.0456) greater with BXM than OTV. Similar reductions were found in contacts from the trial and those included by questionnaire.

CONCLUSION

BXM treatment of index cases appeared to result in a greater reduction in secondary household transmission than OTV treatment.

The airborne transmission of viruses causes tight transmission bottlenecks

The transmission bottleneck describes the number of viral particles that initiate an infection in a new host. Previous studies have used genome sequence data to suggest that transmission bottlenecks for influenza and SARS-CoV-2 involve few viral particles, but the general principles of virus transmission are not fully understood. Here we show that, across a broad range of circumstances, tight transmission bottlenecks are a simple consequence of the physical process of airborne viral transmission. We use mathematical modelling to describe the physical process of the emission and inhalation of infectious particles, deriving the result that that the great majority of transmission bottlenecks involve few viral particles. While exceptions to this rule exist, the circumstances needed to create these exceptions are likely very rare. We thus provide a physical explanation for previous inferences of bottleneck size, while predicting that tight transmission bottlenecks prevail more generally in respiratory virus transmission.

Bat-borne H9N2 influenza virus evades MxA restriction and exhibits efficient replication and transmission in ferrets

Influenza A viruses (IAVs) of subtype H9N2 have reached an endemic stage in poultry farms in the Middle East and Asia. As a result, human infections with avian H9N2 viruses have been increasingly reported. In 2017, an H9N2 virus was isolated for the first time from Egyptian fruit bats (Rousettus aegyptiacus). Phylogenetic analyses revealed that bat H9N2 is descended from a common ancestor dating back centuries ago. However, the H9 and N2 sequences appear to be genetically similar to current avian IAVs, suggesting recent reassortment events. These observations raise the question of the zoonotic potential of the mammal-adapted bat H9N2. Here, we investigate the infection and transmission potential of bat H9N2 in vitro and in vivo, the ability to overcome the antiviral activity of the human MxA protein, and the presence of N2-specific cross-reactive antibodies in human sera. We show that bat H9N2 has high replication and transmission potential in ferrets, efficiently infects human lung explant cultures, and is able to evade antiviral inhibition by MxA in transgenic B6 mice. Together with its low antigenic similarity to the N2 of seasonal human strains, bat H9N2 fulfils key criteria for pre-pandemic IAVs.

Cross-species spill-over potential of the H9N2 bat influenza A virus

In 2017, a novel influenza A virus (IAV) was isolated from an Egyptian fruit bat. In contrast to other bat influenza viruses, the virus was related to avian A(H9N2) viruses and was probably the result of a bird-to-bat transmission event. To determine the cross-species spill-over potential, we biologically characterize features of A/bat/Egypt/381OP/2017(H9N2). The virus has a pH inactivation profile and neuraminidase activity similar to those of human-adapted IAVs. Despite the virus having an avian virus-like preference for α2,3 sialic acid receptors, it is unable to replicate in male mallard ducks; however, it readily infects ex-vivo human respiratory cell cultures and replicates in the lungs of female mice. A/bat/Egypt/381OP/2017 replicates in the upper respiratory tract of experimentally-infected male ferrets featuring direct-contact and airborne transmission. These data suggest that the bat A(H9N2) virus has features associated with increased risk to humans without a shift to a preference for α2,6 sialic acid receptors.

The evolution, pathogenicity and transmissibility of quadruple reassortant H1N2 swine influenza virus in China: A potential threat to public health

Swine are regarded as "intermediate hosts" or "mixing vessels" of influenza viruses, capable of generating strains with pandemic potential. From 2020 to 2021, we conducted surveillance on swine H1N2 influenza (swH1N2) viruses in swine farms located in Guangdong, Yunnan, and Guizhou provinces in southern China, as well as Henan and Shandong provinces in northern China. We systematically analyzed the evolution and pathogenicity of swH1N2 isolates, and characterized their replication and transmission abilities. The isolated viruses are quadruple reassortant H1N2 viruses containing genes from pdm/09 H1N1 (PB2, PB1, PA and NP genes), triple-reassortant swine (NS gene), Eurasian Avian-like (HA and M genes), and recent human H3N2 (NA gene) lineages. The NA, PB2, and NP of SW/188/20 and SW/198/20 show high gene similarities to A/Guangdong/Yue Fang277/2017 (H3N2). The HA gene of swH1N2 exhibits a high evolutionary rate. The five swH1N2 isolates replicate efficiently in human, canine, and swine cells, as well as in the turbinate, trachea, and lungs of mice. A/swine/Shandong/198/2020 strain efficiently replicates in the respiratory tract of pigs and effectively transmitted among them. Collectively, these current swH1N2 viruses possess zoonotic potential, highlighting the need for strengthened surveillance of swH1N2 viruses.

The effect of nonpharmaceutical interventions on influenza virus transmission

Background

The use of nonpharmaceutical interventions (NPIs) during severe acute respiratory syndrome 2019 (COVID-19) outbreaks may influence the spread of influenza viruses. This study aimed to evaluate the impact of NPIs against SARS-CoV-2 on the epidemiological features of the influenza season in China.

Methods

We conducted a retrospective observational study analyzing influenza monitoring data obtained from the China National Influenza Center between 2011 and 2023. We compared the changes in influenza-positive patients in the pre-COVID-19 epidemic, during the COVID-19 epidemic, and post-COVID-19 epidemic phases to evaluate the effect of NPIs on influenza virus transmission.

Results

NPIs targeting COVID-19 significantly suppressed influenza activity in China from 2019 to 2022. In the seventh week after the implementation of the NPIs, the number of influenza-positive patients decreased by 97.46% in southern regions of China and 90.31% in northern regions of China. However, the lifting of these policies in December 2022 led to an unprecedented surge in influenza-positive cases in autumn and winter from 2022 to 2023. The percentage of positive influenza cases increased by 206.41% (p < 0.001), with high positivity rates reported in both the northern and southern regions of China.

Conclusion

Our findings suggest that NPIs against SARS-CoV-2 are effective at controlling influenza epidemics but may compromise individuals' immunity to the virus.

A naturally occurring HA-stabilizing amino acid (HA1-Y17) in an A(H9N2) low-pathogenic influenza virus contributes to airborne transmission

IMPORTANCE

Despite the accumulation of evidence showing that airborne transmissible influenza A virus (IAV) typically has a lower pH threshold for hemagglutinin (HA) fusion activation, the underlying mechanism for such a link remains unclear. In our study, by using a pair of isogenic recombinant A(H9N2) viruses with a phenotypical difference in virus airborne transmission in a ferret model due to an acid-destabilizing mutation (HA1-Y17H) in the HA, we demonstrate that an acid-stable A(H9N2) virus possesses a multitude of advantages over its less stable counterpart, including better fitness in the ferret respiratory tract, more effective aerosol emission from infected animals, and improved host susceptibility. Our study provides supporting evidence for the requirement of acid stability in efficient airborne transmission of IAV and sheds light on fundamental mechanisms for virus airborne transmission.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland

BACKGROUND

Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system.

METHODS

Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.

RESULTS

All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.

CONCLUSIONS

Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Progress on viral and host determinants of influenza A virus species specificity

Influenza A viruses have a wide range of hosts and are highly infectious, which can cause zoonotic diseases and pose a serious public health threat to human safety. An influenza pandemic could outbreak if new strains gain the ability of human-to-human transmission, either by genetic mutation or by gene reassortment. It is an urgent issue for the scientific community to reveal the genetic basis and molecular mechanisms underlying the interspecies transmission of influenza viruses, which will provide important implications for the effective monitoring and prevention of potential influenza pandemics. In this review, we summarize the molecular determinants of influenza viruses for host adaptation, and highlight the advances in the gene mutations of the virus itself and the interaction between virus and host factors. This will help to make a theoretical reserve for the next influenza pandemic and find new strategies to fight against influenza.

BTN3A3 evasion promotes the zoonotic potential of influenza A viruses

Spillover events of avian influenza A viruses (IAVs) to humans could represent the first step in a future pandemic1. Several factors that limit the transmission and replication of avian IAVs in mammals have been identified. There are several gaps in our understanding to predict which virus lineages are more likely to cross the species barrier and cause disease in humans1. Here, we identified human BTN3A3 (butyrophilin subfamily 3 member A3)2 as a potent inhibitor of avian IAVs but not human IAVs. We determined that BTN3A3 is expressed in human airways and its antiviral activity evolved in primates. We show that BTN3A3 restriction acts primarily at the early stages of the virus life cycle by inhibiting avian IAV RNA replication. We identified residue 313 in the viral nucleoprotein (NP) as the genetic determinant of BTN3A3 sensitivity (313F or, rarely, 313L in avian viruses) or evasion (313Y or 313V in human viruses). However, avian IAV serotypes, such as H7 and H9, that spilled over into humans also evade BTN3A3 restriction. In these cases, BTN3A3 evasion is due to substitutions (N, H or Q) in NP residue 52 that is adjacent to residue 313 in the NP structure3. Thus, sensitivity or resistance to BTN3A3 is another factor to consider in the risk assessment of the zoonotic potential of avian influenza viruses.

Assessing intervention effects in a randomized trial within a social network

Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.

Household Transmission of Influenza A Viruses in 2021-2022

IMPORTANCE

Influenza virus infections declined globally during the COVID-19 pandemic. Loss of natural immunity from lower rates of influenza infection and documented antigenic changes in circulating viruses may have resulted in increased susceptibility to influenza virus infection during the 2021-2022 influenza season.

OBJECTIVE

To compare the risk of influenza virus infection among household contacts of patients with influenza during the 2021-2022 influenza season with risk of influenza virus infection among household contacts during influenza seasons before the COVID-19 pandemic in the US.

DESIGN, SETTING, AND PARTICIPANTS

This prospective study of influenza transmission enrolled households in 2 states before the COVID-19 pandemic (2017-2020) and in 4 US states during the 2021-2022 influenza season. Primary cases were individuals with the earliest laboratory-confirmed influenza A(H3N2) virus infection in a household. Household contacts were people living with the primary cases who self-collected nasal swabs daily for influenza molecular testing and completed symptom diaries daily for 5 to 10 days after enrollment.

EXPOSURES

Household contacts living with a primary case.

MAIN OUTCOMES AND MEASURES

Relative risk of laboratory-confirmed influenza A(H3N2) virus infection in household contacts during the 2021-2022 season compared with prepandemic seasons. Risk estimates were adjusted for age, vaccination status, frequency of interaction with the primary case, and household density. Subgroup analyses by age, vaccination status, and frequency of interaction with the primary case were also conducted.

RESULTS

During the prepandemic seasons, 152 primary cases (median age, 13 years; 3.9% Black; 52.0% female) and 353 household contacts (median age, 33 years; 2.8% Black; 54.1% female) were included and during the 2021-2022 influenza season, 84 primary cases (median age, 10 years; 13.1% Black; 52.4% female) and 186 household contacts (median age, 28.5 years; 14.0% Black; 63.4% female) were included in the analysis. During the prepandemic influenza seasons, 20.1% (71/353) of household contacts were infected with influenza A(H3N2) viruses compared with 50.0% (93/186) of household contacts in 2021-2022. The adjusted relative risk of A(H3N2) virus infection in 2021-2022 was 2.31 (95% CI, 1.86-2.86) compared with prepandemic seasons.

CONCLUSIONS AND RELEVANCE

Among cohorts in 5 US states, there was a significantly increased risk of household transmission of influenza A(H3N2) in 2021-2022 compared with prepandemic seasons. Additional research is needed to understand reasons for this association.

Fair warning

"Swientists" hunt for influenza virus at hog shows, hoping to cut off the next pandemic at the pen.

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society's responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak.

Influenza viruses and coronaviruses: Knowns, unknowns, and common research challenges

The development of safe and effective vaccines in a record time after the emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a remarkable achievement, partly based on the experience gained from multiple viral outbreaks in the past decades. However, the Coronavirus Disease 2019 (COVID-19) crisis also revealed weaknesses in the global pandemic response and large gaps that remain in our knowledge of the biology of coronaviruses (CoVs) and influenza viruses, the 2 major respiratory viruses with pandemic potential. Here, we review current knowns and unknowns of influenza viruses and CoVs, and we highlight common research challenges they pose in 3 areas: the mechanisms of viral emergence and adaptation to humans, the physiological and molecular determinants of disease severity, and the development of control strategies. We outline multidisciplinary approaches and technological innovations that need to be harnessed in order to improve preparedeness to the next pandemic.

Frequency and patterns of exposure to live poultry and the potential risk of avian influenza transmission to humans in urban Bangladesh

Avian influenza is endemic in Bangladesh, where greater than 90% of poultry are marketed through live poultry markets (LPMs). We conducted a population-based cross-sectional mobile telephone survey in urban Dhaka, Bangladesh to investigate the frequency and patterns of human exposure to live poultry in LPMs and at home. Among 1047 urban residents surveyed, 74.2% (95% CI 70.9-77.2) reported exposure to live poultry in the past year, with the majority of exposure occurring on a weekly basis. While visiting LPMs was less common amongst females (40.3%, 95% CI 35.0-45.8) than males (58.9%, 95% CI 54.0-63.5), females reported greater poultry exposure through food preparation, including defeathering (13.2%, 95% CI 9.5-17.9) and eviscerating (14.8%, 95% CI 11.2-19.4) (p < 0.001). A large proportion of the urban population is frequently exposed to live poultry in a setting where avian influenza viruses are endemic in LPMs. There is thus not only ample opportunity for spillover of avian influenza infections into humans in Dhaka, Bangladesh, but also greater potential for viral reassortment which could generate novel strains with pandemic potential.

Airborne Transmission of Avian Origin H9N2 Influenza A Viruses in Mammals

Influenza A viruses (IAV) are widespread viruses affecting avian and mammalian species worldwide. IAVs from avian species can be transmitted to mammals including humans and, thus, they are of inherent pandemic concern. Most of the efforts to understand the pathogenicity and transmission of avian origin IAVs have been focused on H5 and H7 subtypes due to their highly pathogenic phenotype in poultry. However, IAV of the H9 subtype, which circulate endemically in poultry flocks in some regions of the world, have also been associated with cases of zoonotic infections. In this review, we discuss the mammalian transmission of H9N2 and the molecular factors that are thought relevant for this spillover, focusing on the HA segment. Additionally, we discuss factors that have been associated with the ability of these viruses to transmit through the respiratory route in mammalian species. The summarized information shows that minimal amino acid changes in the HA and/or the combination of H9N2 surface genes with internal genes of human influenza viruses are enough for the generation of H9N2 viruses with the ability to transmit via aerosol.

Influenza Virus Infections in Cats

In the past, cats were considered resistant to influenza. Today, we know that they are susceptible to some influenza A viruses (IAVs) originating in other species. Usually, the outcome is only subclinical infection or a mild fever. However, outbreaks of feline disease caused by canine H3N2 IAV with fever, tachypnoea, sneezing, coughing, dyspnoea and lethargy are occasionally noted in shelters. In one such outbreak, the morbidity rate was 100% and the mortality rate was 40%. Recently, avian H7N2 IAV infection occurred in cats in some shelters in the USA, inducing mostly mild respiratory disease. Furthermore, cats are susceptible to experimental infection with the human H3N2 IAV that caused the pandemic in 1968. Several studies indicated that cats worldwide could be infected by H1N1 IAV during the subsequent human pandemic in 2009. In one shelter, severe cases with fatalities were noted. Finally, the highly pathogenic avian H5N1 IAV can induce a severe, fatal disease in cats, and can spread via cat-to-cat contact. In this review, the Advisory Board on Cat Diseases (ABCD), a scientifically independent board of experts in feline medicine from 11 European countries, summarises current data regarding the aetiology, epidemiology, pathogenesis, clinical picture, diagnostics, and control of feline IAV infections, as well as the zoonotic risks.

Asymptomatic transmission and high community burden of seasonal influenza in an urban and a rural community in South Africa, 2017-18 (PHIRST): a population cohort study

Background

Data on influenza community burden and transmission are important to plan interventions especially in resource-limited settings. However, data are limited, particularly from low-income and middle-income countries. We aimed to evaluate the community burden and transmission of influenza in a rural and an urban setting in South Africa.

Methods

In this prospective cohort study approximately 50 households were selected sequentially from both a rural setting (Agincourt, Mpumalanga Province, South Africa; with a health and sociodemographic surveillance system) and an urban setting (Klerksdorp, Northwest Province, South Africa; using global positioning system data), enrolled, and followed up for 10 months in 2017 and 2018. Different households were enrolled in each year. Households of more than two individuals in which 80% or more of the occupants agreed to participate were included in the study. Nasopharyngeal swabs were collected twice per week from participating household members irrespective of symptoms and tested for influenza using real-time RT-PCR. The primary outcome was the incidence of influenza infection, defined as the number of real-time RT-PCR-positive episodes divided by the person-time under observation. Household cumulative infection risk (HCIR) was defined as the number of subsequent infections within a household following influenza introduction.

Findings

81 430 nasopharyngeal samples were collected from 1116 participants in 225 households (follow-up rate 88%). 917 (1%) tested positive for influenza; 178 (79%) of 225 households had one or more influenza-positive individual. The incidence of influenza infection was 43·6 (95% CI 39·8–47·7) per 100 person-seasons. 69 (17%) of 408 individuals who had one influenza infection had a repeat influenza infection during the same season. The incidence (67·4 per 100 person-seasons) and proportion with repeat infections (22 [23%] of 97 children) were highest in children younger than 5 years and decreased with increasing age (p<0·0001). Overall, 268 (56%) of 478 infections were symptomatic and 66 (14%) of 478 infections were medically attended. The overall HCIR was 10% (109 of 1088 exposed household members infected [95% CI 9–13%). Transmission (HCIR) from index cases was highest in participants aged 1–4 years (16%; 40 of 252 exposed household members) and individuals with two or more symptoms (17%; 68 of 396 exposed household members). Individuals with asymptomatic influenza transmitted infection to 29 (6%) of 509 household contacts. HIV infection, affecting 167 (16%) of 1075 individuals, was not associated with increased incidence or HCIR.

Interpretation

Approximately half of influenza infections were symptomatic, with asymptomatic individuals transmitting influenza to 6% of household contacts. This suggests that strategies, such as quarantine and isolation, might be ineffective to control influenza. Vaccination of children, with the aim of reducing influenza transmission might be effective in African settings given the young population and high influenza burden.

Accounting for the spread of vaccination behavior to optimize influenza vaccination programs

Vaccination is the most efficient means of preventing influenza infection and its complications. While previous studies have considered the externalities of vaccination that arise from indirect protection against influenza infection, they have often neglected another key factor-the spread of vaccination behavior among social contacts. We modeled influenza vaccination as a socially contagious process. Our model uses a contact network that we developed based on aggregated and anonymized mobility data from the cellphone devices of ~1.8 million users in Israel. We calibrated the model to high-quality longitudinal data of weekly influenza vaccination uptake and influenza diagnoses over seven years. We demonstrate how a simple coupled-transmission model accurately captures the spatiotemporal patterns of both influenza vaccination uptake and influenza incidence. Taking the identified complex underlying dynamics of these two processes into account, our model determined the optimal timing of influenza vaccination programs. Our simulation shows that in regions where high vaccination coverage is anticipated, vaccination uptake would be more rapid. Thus, our model suggests that vaccination programs should be initiated later in the season, to mitigate the effect of waning immunity from the vaccine. Our simulations further show that optimally timed vaccination programs can substantially reduce disease transmission without increasing vaccination uptake.

Impact of COVID-19 outbreaks and interventions on influenza in China and the United States

Coronavirus disease 2019 (COVID-19) was detected in China during the 2019-2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019-2020 seasonal influenza activity through March 29, 2020 with the 2011-2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%-87.2%), 79.4% (44.9%-87.4%) and 67.2% (11.5%-80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019-2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.

Animal Models for Influenza Research: Strengths and Weaknesses

Influenza remains one of the most significant public health threats due to its ability to cause high morbidity and mortality worldwide. Although understanding of influenza viruses has greatly increased in recent years, shortcomings remain. Additionally, the continuous mutation of influenza viruses through genetic reassortment and selection of variants that escape host immune responses can render current influenza vaccines ineffective at controlling seasonal epidemics and potential pandemics. Thus, there is a knowledge gap in the understanding of influenza viruses and a corresponding need to develop novel universal vaccines and therapeutic treatments. Investigation of viral pathogenesis, transmission mechanisms, and efficacy of influenza vaccine candidates requires animal models that can recapitulate the disease. Furthermore, the choice of animal model for each research question is crucial in order for researchers to acquire a better knowledge of influenza viruses. Herein, we reviewed the advantages and limitations of each animal model-including mice, ferrets, guinea pigs, swine, felines, canines, and non-human primates-for elucidating influenza viral pathogenesis and transmission and for evaluating therapeutic agents and vaccine efficacy.

Ingestion of Okinawa Island Vegetables Increases IgA Levels and Prevents the Spread of Influenza RNA Viruses

Background: It has been hypothesized that flavonoid ingestion stimulates immunity, promotes health, and prevents human illness. The aim of this analysis was to evaluate the association of the levels of immunoglobulin A (IgA) with the prevention of influenza infections and with the polyphenols contained in Okinawan vegetables. Methods: IgA, immunoglobulin G (IgG), immunoglobulin M (IgM), and soluble interleukin-2 receptor (sIL-2R) levels were measured in 44 outpatients who regularly ingested vegetables grown on Okinawa Island (200–300 g/day for ≥ 300 days/year) with no history of influenza infection and in 73 patients who ingested the vegetables irregularly or not at all with a history of influenza infection. Results: The patients who regularly ate Okinawan vegetables had higher IgA, IgG, and IgM levels than those who did not. On the other hand, patients who did not consume Okinawan vegetables and had influenza had lower IgA, IgG, and IgM levels. In addition, the IgA and IgG levels showed significant positive correlations with the sIL-2R levels in both groups. Conclusions: It may be beneficial to eat vegetables abundant in polyphenols every day. Secretory IgA antibodies are an important part of the immune defense against viral diseases. People who ingest Okinawan vegetables have high IgA levels and might be more likely to develop immunity against influenza RNA viruses.

[Proposal for territorial analysis to face the SARS-CoV-2 pandemic based on the Profile of pneumonia and Influenza in Ecuador 2016-2018]

OBJECTIVE

The present study seeks to analyse sociodemographic determinants related to severe acute respiratory infections (SARI) and calculate the priorization index in the cantons of Ecuador to identify areas probably most vulnerable to COVID-19 transmission.

DESIGN

This descriptive ecological observational study.

SETTING

224 cantons (geographical area) of Ecuador with secondary data sources of hospital information.

PARTICIPANTS

The unit of measurement was 224 cantons of Ecuador, in which analysed morbidity and lethality rates for SARI using hospital release data (2016-2018).

MAIN MEASUREMENTS

Eight sociodemographic indicators were structuralized, and correlation tests applied for a multiple regression model. The priorization index was created with criteria of efficiency, efficacy, effect size (IRR) and equity. Using the sum of the index for each indicator, the priorization score was calculated and localized in a territorial map.

RESULTS

Morbidity associated factors where: school attendance years, urbanization and population density; for mortality resulted: school attendance and ethnics (indigenous) IRR: 1.09 (IC95%:1.06-1.15) and IRR: 1.024 (IC95%:102-1.03) respectively. With lethality where related cantons, with population older than 60 years, IRR: 1.049 (IC95%: 1.03-1.07); 87 cantons had high priority mostly localized in the mountain region and the Morona Santiago Province.

CONCLUSIONS

Morbidity and mortality of SARI in Ecuador are associated to social and demographic factors. Priorization exercises considering these factors permit the identification of vulnerable territories facing respiratory disease propagation. The social determinants characteristic for each territory should be added to known individual factors to analyse the risk and vulnerability for COVID in the population.

Hemagglutinin Stability and Its Impact on Influenza A Virus Infectivity, Pathogenicity, and Transmissibility in Avians, Mice, Swine, Seals, Ferrets, and Humans

Genetically diverse influenza A viruses (IAVs) circulate in wild aquatic birds. From this reservoir, IAVs sporadically cause outbreaks, epidemics, and pandemics in wild and domestic avians, wild land and sea mammals, horses, canines, felines, swine, humans, and other species. One molecular trait shown to modulate IAV host range is the stability of the hemagglutinin (HA) surface glycoprotein. The HA protein is the major antigen and during virus entry, this trimeric envelope glycoprotein binds sialic acid-containing receptors before being triggered by endosomal low pH to undergo irreversible structural changes that cause membrane fusion. The HA proteins from different IAV isolates can vary in the pH at which HA protein structural changes are triggered, the protein causes membrane fusion, or outside the cell the virion becomes inactivated. HA activation pH values generally range from pH 4.8 to 6.2. Human-adapted HA proteins tend to have relatively stable HA proteins activated at pH 5.5 or below. Here, studies are reviewed that report HA stability values and investigate the biological impact of variations in HA stability on replication, pathogenicity, and transmissibility in experimental animal models. Overall, a stabilized HA protein appears to be necessary for human pandemic potential and should be considered when assessing human pandemic risk.

Heterogeneity in transmissibility and shedding SARS-CoV-2 via droplets and aerosols

Background

Which virological factors mediate overdispersion in the transmissibility of emerging viruses remains a long-standing question in infectious disease epidemiology.

Methods

Here, we use systematic review to develop a comprehensive dataset of respiratory viral loads (rVLs) of SARS-CoV-2, SARS-CoV-1 and influenza A(H1N1)pdm09. We then comparatively meta-analyze the data and model individual infectiousness by shedding viable virus via respiratory droplets and aerosols.

Results

The analyses indicate heterogeneity in rVL as an intrinsic virological factor facilitating greater overdispersion for SARS-CoV-2 in the COVID-19 pandemic than A(H1N1)pdm09 in the 2009 influenza pandemic. For COVID-19, case heterogeneity remains broad throughout the infectious period, including for pediatric and asymptomatic infections. Hence, many COVID-19 cases inherently present minimal transmission risk, whereas highly infectious individuals shed tens to thousands of SARS-CoV-2 virions/min via droplets and aerosols while breathing, talking and singing. Coughing increases the contagiousness, especially in close contact, of symptomatic cases relative to asymptomatic ones. Infectiousness tends to be elevated between 1 and 5 days post-symptom onset.

Conclusions

Intrinsic case variation in rVL facilitates overdispersion in the transmissibility of emerging respiratory viruses. Our findings present considerations for disease control in the COVID-19 pandemic as well as future outbreaks of novel viruses.

Funding

Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant program, NSERC Senior Industrial Research Chair program and the Toronto COVID-19 Action Fund.

The origins of viral infection sleuth

The events of the past year have underscored the serious and rapid threat that emerging viruses pose to global health. However, much of the rapid progress in understanding and combating SARS-CoV-2 was made possible because of the decades of important groundwork laid from researchers studying other emergent infectious diseases. The 2021 John Dirks Canada Gairdner Global Health award recognizes the contributions of Joseph Sriyal Malik Peiris and Yi Guan toward understanding the origins and options for control of newly emerging infectious disease outbreaks in Asia, notably zoonotic influenza and severe acute respiratory syndrome (SARS). Cell's Nicole Neuman corresponded with Yi Guan about his path to becoming a viral infection sleuth and the challenges of understanding emerging pathogens and their origins. Excerpts of their exchange are included here.

Health inequities in influenza transmission and surveillance

The lower an individual’s socioeconomic position, the higher their risk of poor health in low-, middle-, and high-income settings alike. As health inequities grow, it is imperative that we develop an empirically-driven mechanistic understanding of the determinants of health disparities, and capture disease burden in at-risk populations to prevent exacerbation of disparities. Past work has been limited in data or scope and has thus fallen short of generalizable insights. Here, we integrate empirical data from observational studies and large-scale healthcare data with models to characterize the dynamics and spatial heterogeneity of health disparities in an infectious disease case study: influenza. We find that variation in social and healthcare-based determinants exacerbates influenza epidemics, and that low socioeconomic status (SES) individuals disproportionately bear the burden of infection. We also identify geographical hotspots of influenza burden in low SES populations, much of which is overlooked in traditional influenza surveillance, and find that these differences are most predicted by variation in susceptibility and access to sickness absenteeism. Our results highlight that the effect of overlapping factors is synergistic and that reducing this intersectionality can significantly reduce inequities. Additionally, health disparities are expressed geographically, and targeting public health efforts spatially may be an efficient use of resources to abate inequities. The association between health and socioeconomic prosperity has a long history in the epidemiological literature; addressing health inequities in respiratory-transmitted infectious disease burden is an important step towards social justice in public health, and ignoring them promises to pose a serious threat.

Health inequities, or increased morbidity and mortality due to social factors, have been demonstrated for respiratory-transmitted infectious diseases, most recently highlighted by disparities in COVID-19 severe cases and deaths. Many potential causes of these inequities have been proposed, but they have not been compared, and we do not understand their population-scale impacts. Our understanding of these issues is further hindered by epidemiological surveillance, which has been shown to overlook areas of low socioeconomic status. Here, we combine mechanistic and statistical modeling with high volume datasets to disentangle the drivers of respiratory-transmitted disease disparities, and to estimate locations where these health inequities are most severe, using influenza as a case study. We show that low socioeconomic status individuals disproportionately bear the burden of influenza infection, and that all proposed factors are synergistic in their effect. Additionally we identify geographical hotspots of poor disease surveillance among populations of low socioeconomic status, which contribute to an underestimation of health disparities. As the divide in health inequities, driven by income inequality and systemic racism, grows wider across the United States, we highlight the need to understand the mechanisms that may be at the root of disparities, and we advocate for the prioritization of capabilities to monitor outbreaks in at-risk populations so that we may prevent exacerbation of inequities.

Facemask use in community settings to prevent respiratory infection transmission: A rapid review and meta-analysis

INTRODUCTION

Synthesis of the available evidence on the effectiveness of medical and cloth facemask use by the general public in community settings is required to learn lessons for future respiratory epidemics/pandemics.

METHOD

Search terms relating to facemasks, infection and community settings were used for PubMed, the Cochrane Library Database and Google Scholar. A meta-analysis was conducted using a random-effects model.

RESULTS

The review included 12 primary studies on the effectiveness of medical facemask use to prevent influenza, influenza-like illness, SARS-CoV, and SARS-CoV-2 transmission. The meta-analysis demonstrated that facemask use significantly reduces the risk of transmitting these respiratory infections (pooled OR = 0.66, 95% CI 0.54-0.81). Of the 12 studies, 10 clinical trials suggested that respiratory infection incidence is lower with high medical facemask compliance, early use and use in combination with intensive hand hygiene. One cohort study conducted during the SARS-CoV-2 pandemic demonstrated that facemasks are effective in reducing SARS-CoV-2 transmission when used before those who are infected develop symptoms. One case-control study reported that controls used medical facemasks more often than cases infected with SARS-CoV (p <  0.05). No primary study on cloth facemask effectiveness to prevent respiratory infection transmission was found.

CONCLUSION

Based on the available evidence, medical facemask use by healthy and sick individuals is recommended for preventing respiratory infection transmission in community settings. Medical facemask effectiveness is dependent on compliance and utilization in combination with preventive measures such as intensive hand hygiene. No direct evidence is currently available in humans supporting the recommendation of cloth facemask use to prevent respiratory infection transmission.

Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events

SARS-CoV-2 is difficult to contain because many transmissions occur during pre-symptomatic infection. Unlike influenza, most SARS-CoV-2-infected people do not transmit while a small percentage infect large numbers of people. We designed mathematical models which link observed viral loads with epidemiologic features of each virus, including distribution of transmissions attributed to each infected person and duration between symptom onset in the transmitter and secondarily infected person. We identify that people infected with SARS-CoV-2 or influenza can be highly contagious for less than 1 day, congruent with peak viral load. SARS-CoV-2 super-spreader events occur when an infected person is shedding at a very high viral load and has a high number of exposed contacts. The higher predisposition of SARS-CoV-2 toward super-spreading events cannot be attributed to additional weeks of shedding relative to influenza. Rather, a person infected with SARS-CoV-2 exposes more people within equivalent physical contact networks, likely due to aerosolization.

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

BACKGROUND

Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.

METHODS

The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.

RESULTS

The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.

CONCLUSIONS

This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors' incidence and the transmission relationships.

Absence of nosocomial influenza and respiratory syncytial virus infection in the coronavirus disease 2019 (COVID-19) era: Implication of universal masking in hospitals

Universal masking for healthcare workers and patients in hospitals was adopted to combat coronavirus disease 2019 (COVID-19), with compliance rates of 100% and 75.9%, respectively. Zero rates of nosocomial influenza A, influenza B, and respiratory syncytial virus infection were achieved from February to April 2020, which was significantly lower than the corresponding months in 2017-2019.

Adenoviral Vectors as Vaccines for Emerging Avian Influenza Viruses

It is evident that the emergence of infectious diseases, which have the potential for spillover from animal reservoirs, pose an ongoing threat to global health. Zoonotic transmission events have increased in frequency in recent decades due to changes in human behavior, including increased international travel, the wildlife trade, deforestation, and the intensification of farming practices to meet demand for meat consumption. Influenza A viruses (IAV) possess a number of features which make them a pandemic threat and a major concern for human health. Their segmented genome and error-prone process of replication can lead to the emergence of novel reassortant viruses, for which the human population are immunologically naïve. In addition, the ability for IAVs to infect aquatic birds and domestic animals, as well as humans, increases the likelihood for reassortment and the subsequent emergence of novel viruses. Sporadic spillover events in the past few decades have resulted in human infections with highly pathogenic avian influenza (HPAI) viruses, with high mortality. The application of conventional vaccine platforms used for the prevention of seasonal influenza viruses, such as inactivated influenza vaccines (IIVs) or live-attenuated influenza vaccines (LAIVs), in the development of vaccines for HPAI viruses is fraught with challenges. These issues are associated with manufacturing under enhanced biosafety containment, and difficulties in propagating HPAI viruses in embryonated eggs, due to their propensity for lethality in eggs. Overcoming manufacturing hurdles through the use of safer backbones, such as low pathogenicity avian influenza viruses (LPAI), can also be a challenge if incompatible with master strain viruses. Non-replicating adenoviral (Ad) vectors offer a number of advantages for the development of vaccines against HPAI viruses. Their genome is stable and permits the insertion of HPAI virus antigens (Ag), which are expressed in vivo following vaccination. Therefore, their manufacture does not require enhanced biosafety facilities or procedures and is egg-independent. Importantly, Ad vaccines have an exemplary safety and immunogenicity profile in numerous human clinical trials, and can be thermostabilized for stockpiling and pandemic preparedness. This review will discuss the status of Ad-based vaccines designed to protect against avian influenza viruses with pandemic potential.

Estimating and interpreting secondary attack risk: Binomial considered biased

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. The most common statistical models used to estimate the SAR are binomial models such as logistic regression, which implicitly assume that all secondary infections in a household are infected by the primary case. Here, we use analytical calculations and simulations to show that estimation of the SAR must account for multiple generations of transmission within households. As an example, we show that binomial models and statistical models that account for multiple generations of within-household transmission reach different conclusions about the household SAR for 2009 influenza A (H1N1) in Los Angeles County, with the latter models fitting the data better. In an epidemic, accurate estimation of the SAR allows rigorous evaluation of the effectiveness of public health interventions such as social distancing, prophylaxis or treatment, and vaccination.

Gaps in Serologic Immunity against Contemporary Swine-Origin Influenza A Viruses among Healthy Individuals in the United States

Influenza A Viruses (IAV) in domestic swine (IAV-S) are associated with sporadic zoonotic transmission at the human–animal interface. Previous pandemic IAVs originated from animals, which emphasizes the importance of characterizing human immunity against the increasingly diverse IAV-S. We analyzed serum samples from healthy human donors (n = 153) using hemagglutination-inhibition (HAI) assay to assess existing serologic protection against a panel of contemporary IAV-S isolated from swine in the United States (n = 11). Age-specific seroprotection rates (SPR), which are the proportion of individuals with HAI ≥ 1:40, corresponded with lower or moderate pandemic risk classifications for the multiple IAV-S examined (one H1-δ1, one H1-δ2, three H3-IVA, one H3-IVB, one H3-IVF). Individuals born between 2004 and 2013 had SPRs of 0% for the five classified H3 subtype IAV-S, indicating youth may be particularly predisposed to infection with these viruses. Expansion of existing immunologic gaps over time could increase likelihood of future IAV-S spillover to humans and facilitate subsequent sustained human-to-human transmission resulting in disease outbreaks with pandemic potential.

Inferring high-resolution human mixing patterns for disease modeling

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

Surface texture limits transfer of S. aureus, T4 bacteriophage, influenza B virus and human coronavirus

Spread of pathogens on contaminated surfaces plays a key role in disease transmission. Surface technologies that control pathogen transfer can help control fomite transmission and are of great interest to public health. Here, we report a novel bead transfer method for evaluating fomite transmission in common laboratory settings. We show that this method meets several important criteria for quantitative test methods, including reasonableness, relevancy, resemblance, responsiveness, and repeatability, and therefore may be adaptable for standardization. In addition, this method can be applied to a wide variety of pathogens including bacteria, phage, and human viruses. Using the bead transfer method, we demonstrate that an engineered micropattern limits transfer of Staphylococcus aureus by 97.8% and T4 bacteriophage by 93.0% on silicone surfaces. Furthermore, the micropattern significantly reduces transfer of influenza B virus and human coronavirus on silicone and polypropylene surfaces. Our results highlight the potential of using surface texture as a valuable new strategy in combating infectious diseases.

Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study

With the COVID-19 pandemic infecting millions of people, large-scale isolation policies have been enacted across the globe. To assess the impact of isolation measures on deaths, hospitalizations, and economic output, we create a mathematical model to simulate the spread of COVID-19, incorporating effects of restrictive measures and segmenting the population based on health risk and economic vulnerability. Policymakers make isolation policy decisions based on current levels of disease spread and economic damage. For 76 weeks in a population of 330 million, we simulate a baseline scenario leaving strong isolation restrictions in place, rapidly reducing isolation restrictions for non-seniors shortly after outbreak containment, and gradually relaxing isolation restrictions for non-seniors. We use 76 weeks as an approximation of the time at which a vaccine will be available. In the baseline scenario, there are 235,724 deaths and the economy shrinks by 34.0%. With a rapid relaxation, a second outbreak takes place, with 525,558 deaths, and the economy shrinks by 32.3%. With a gradual relaxation, there are 262,917 deaths, and the economy shrinks by 29.8%. We also show that hospitalizations, deaths, and economic output are quite sensitive to disease spread by asymptomatic people. Strict restrictions on seniors with very gradual lifting of isolation for non-seniors results in a limited number of deaths and lesser economic damage. Therefore, we recommend this strategy and measures that reduce non-isolated disease spread to control the pandemic while making isolation economically viable.

Public transportation and transmission of viral respiratory disease: Evidence from influenza deaths in 121 cities in the United States

One important concern around the spread of respiratory infectious diseases has been the contribution of public transportation, a space where people are in close contact with one another and with high-use surfaces. While disease clearly spreads along transportation routes, there is limited evidence about whether public transportation use itself is associated with the overall prevalence of contagious respiratory illnesses at the local level. We examine the extent of the association between public transportation and influenza mortality, a proxy for disease prevalence, using city-level data on influenza and pneumonia mortality and public transit use from 121 large cities in the United States (US) between 2006 and 2015. We find no evidence of a positive relationship between city-level transit ridership and influenza/pneumonia mortality rates, suggesting that population level rates of transit use are not a singularly important factor in the transmission of influenza.