The role of human immunity and social behavior in shaping influenza evolution

Using contact tracing from interlocking diaries to map mood contagion along network chains

Both viruses and moods are transmitted through interpersonal contacts, but it has been extremely difficult to track each unique chain of contacts through which particular moods diffuse. By analyzing 56,060 contact records from 113 interlocking, yearlong diaries collected through a web-based platform in Taiwan, we traced mood states before and after each specific contact along a triplet of persons where B contacts C and subsequently contacts A. Multilevel analyses show that both positive and negative emotions are contagious, but the two paths diverge markedly in how the diffusion stops. Positive contact between C and B (which leads to improved mood for B) spreads to A through B’s contact with A, making A feel better afterward, regardless of whether B’s mood deteriorated between the two interactions. Negative contact between C and B (which leads to worsened mood for B) also spreads to A, making A feel worse after the contact with B. However, the spread of a negative mood discontinues if B’s mood improved between the two contacts. The different patterns of diffusion suggest that a negative mood is harder to disperse, probably because people generally make efforts to keep their negative emotions from spreading to others.

Can children's oral hygiene and sleep routines be compromised during the COVID-19 pandemic?

BACKGROUND

During COVID-19 pandemic, children are confined at home, with changes in family routines.

AIM

Evaluate sleep disorders among Brazilian and Portuguese children during social distancing, and its association with parental perception of child's oral hygiene.

DESIGN

In this cross-sectional study, Portuguese and Brazilian parents/caregivers of 3- to 15-year-old children, practicing social distancing due to COVID-19 pandemic, answered an online questionnaire, from April 24-26, 2020, evaluating sociodemographic characteristics, child's school activities online, child's sleep quality during social distancing. Two questions from the questionnaire, developed based on previous studies, evaluated the parental perception of child's oral hygiene quality and routine changes during social distancing. Parents/caregivers answered five domains of the Portuguese-language version of the Sleep Disturbances Scale for Children, evaluating sleep-breathing disorders, disorders of arousal, sleep-wake transition disorders, disorders of excessive somnolence and sleep hyperhidrosis. Descriptive, Linear-by-linear association, Kruskal-Wallis and post hoc analysis were performed (P ≤ .05).

RESULTS

Participated in the study 253 parents/caregivers, 50.2% from Brazil. Most parents (72.2%) reported changes in child's routine during social distancing. Sleep breathing disorders (P = .019), sleep-wake transition disorders (P = .022), and disorders of excessive somnolence (P < .001) were associated with poor oral hygiene during social distancing.

CONCLUSION

Sleep disorders are associated with poor oral hygiene during social distancing.

Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study

Background

Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested.

Methods

Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored.

Results

The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates for R₀ were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R₀ across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity.

Conclusions

This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

Seasonal dynamics of influenza viruses and age distribution of infected individuals across nine seasons covering 2009-2018 in Taiwan

BACKGROUND/PURPOSE

A swine-origin influenza A/H1N1 virus (termed A/H1N1pdm) caused a pandemic in 2009 and has continuously circulated in the human population. To investigate its possible ecological effects on circulating influenza strains, the seasonal patterns of influenza viruses and the respective age distribution of infected patients were studies.

METHODS

The data obtained from national influenza surveillance systems in Taiwan from July 2009 to June 2018 were analyzed.

RESULTS

The A/H1N1pdm and A/H3N2 strains usually caused a higher ratio of severe to mild cases than influenza B. New variants of A/H1N1pdm and A/H3N2 emerged accompanied by a large epidemic peak. However, the new influenza B variants intended to circulate for several seasons before causing a large epidemic. The major group of outpatients affected by A/H1N1pdm were aged 13-23 years in the pandemic wave, and the age range of infected individuals gradually shifted to 24-49 and 0-6 years across seasons; A/H1N1pdm-infected inpatients were aged 24-49 years in 2009-2011, and the age range gradually switched to older groups aged 50-65 and >65 years. Individuals aged 0-6 or 24-49 years accounted for the majority of A/H3N2-infected outpatients across seasons, whereas most of the inpatients affected by A/H3N2 were aged >65 years.

CONCLUSION

Understanding the effects of new variants and changes in dominant circulating viral strains on the age distribution of the affected human population, disease severity and epidemic levels is useful for the establishment of fine-tuned strategies for further improvement of influenza control.

Timescales of influenza A/H3N2 antibody dynamics

Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants' histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.

fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks

Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the United Kingdom to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources.