The Puerto Rican Infant Metagenomic and Epidemiologic Study of Respiratory Outcomes (PRIMERO): Design and Baseline Characteristics for a Birth Cohort Study of Early-life Viral Respiratory Illnesses and Airway Dysfunction in Puerto Rican Children.

Epidemiologic studies demonstrate an association between early-life respiratory illnesses (RIs) and the development of childhood asthma. However, it remains uncertain whether these children are predisposed to both conditions or if early-life RIs induce alterations in airway function, immune responses, or other human biology that contribute to the development of asthma. Puerto Rican children experience a disproportionate burden of early-life RIs and asthma, making them an important population for investigating this complex interplay. PRIMERO, the Puerto Rican Infant Metagenomics and Epidemiologic Study of Respiratory Outcomes , recruited pregnant women and their newborns to investigate how the airways develop in early life among infants exposed to different viral RIs, and will thus provide a critical understanding of childhood asthma development. As the first asthma birth cohort in Puerto Rico, PRIMERO will prospectively follow 2,100 term healthy infants. Collected samples include post-term maternal peripheral blood, infant cord blood, the child's peripheral blood at the year two visit, and the child's nasal airway epithelium, collected using minimally invasive nasal swabs, at birth, during RIs over the first two years of life, and at annual healthy visits until age five. Herein, we describe the study's design, population, recruitment strategy, study visits and procedures, and primary outcomes.

Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin.

Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

Risk factors for SARS-CoV-2 infection and transmission in households with children with asthma and allergy: A prospective surveillance study.

BACKGROUND: Whether children and people with asthma and allergic diseases are at increased risk for severe acute respiratory syndrome virus 2 (SARS-CoV-2) infection is unknown. OBJECTIVE: Our aims were to determine the incidence of SARS-CoV-2 infection in households with children and to also determine whether self-reported asthma and/or other allergic diseases are associated with infection and household transmission. METHODS: For 6 months, biweekly nasal swabs and weekly surveys were conducted within 1394 households (N = 4142 participants) to identify incident SARS-CoV-2 infections from May 2020 to February 2021, which was the pandemic period largely before a vaccine and before the emergence of SARS-CoV-2 variants. Participant and household infection and household transmission probabilities were calculated by using time-to-event analyses, and factors associated with infection and transmission risk were determined by using regression analyses. RESULTS: In all, 147 households (261 participants) tested positive for SARS-CoV-2. The household SARS-CoV-2 infection probability was 25.8%; the participant infection probability was similar for children (14.0% [95% CI = 8.0%-19.6%]), teenagers (12.1% [95% CI = 8.2%-15.9%]), and adults (14.0% [95% CI = 9.5%-18.4%]). Infections were symptomatic in 24.5% of children, 41.2% of teenagers, and 62.5% of adults. Self-reported doctor-diagnosed asthma was not a risk factor for infection (adjusted hazard ratio [aHR] = 1.04 [95% CI = 0.73-1.46]), nor was upper respiratory allergy or eczema. Self-reported doctor-diagnosed food allergy was associated with lower infection risk (aHR = 0.50 [95% CI = 0.32-0.81]); higher body mass index was associated with increased infection risk (aHR per 10-point increase = 1.09 [95% CI = 1.03-1.15]). The household secondary attack rate was 57.7%. Asthma was not associated with household transmission, but transmission was lower in households with food allergy (adjusted odds ratio = 0.43 [95% CI = 0.19-0.96]; P = .04). CONCLUSION: Asthma does not increase the risk of SARS-CoV-2 infection. Food allergy is associated with lower infection risk, whereas body mass index is associated with increased infection risk. Understanding how these factors modify infection risk may offer new avenues for preventing infection.

Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network.

Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population. This paradigm has been useful in simplifying the biological reality of epidemics and has allowed the modelling community to focus on the complexity of other factors such as population structure and interventions. However, there is an increasing amount of evidence that the strain diversity of pathogens, and their interplay with the host immune system, can play a large role in shaping the dynamics of epidemics. Here, we introduce a disease model with an underlying genotype network to account for two important mechanisms. One, the disease can mutate along network pathways as it spreads in a host population. Two, the genotype network allows us to define a genetic distance between strains and therefore to model the transcendence of immunity often observed in real world pathogens. We study the emergence of epidemics in this model, through its epidemic phase transitions, and highlight the role of the genotype network in driving cyclicity of diseases, large scale fluctuations, sequential epidemic transitions, as well as localization around specific strains of the associated pathogen. More generally, our model illustrates the richness of behaviours that are possible even in well-mixed host populations once we consider strain diversity and go beyond the "one disease equals one pathogen" paradigm.