Inferring risks of coronavirus transmission from community household data

A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England

We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.

COVID-19 infections in English schools and the households of students and staff 2020-21: a self-controlled case-series analysis

BACKGROUND

The role of children and staff in SARS-CoV-2 transmission outside and within households is still not fully understood when large numbers are in regular, frequent contact in schools.

METHODS

We used the self-controlled case-series method during the alpha- and delta-dominant periods to explore the incidence of infection in periods around a household member infection, relative to periods without household infection, in a cohort of primary and secondary English schoolchildren and staff from November 2020 to July 2021.

RESULTS

We found the relative incidence of infection in students and staff was highest in the 1-7 days following household infection, remaining high up to 14 days after, with risk also elevated in the 6--12 days before household infection. Younger students had a higher relative incidence following household infection, suggesting household transmission may play a more prominent role compared with older students. The relative incidence was also higher among students in the alpha variant dominant period.

CONCLUSIONS

This analysis suggests SARS-CoV2 infection in children, young people and staff at English schools were more likely to be associated with within-household transmission than from outside the household, but that a small increased risk of seeding from outside is observed.

Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study

Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of susceptibility and infectivity by person-type. A main inclusion criterion in such studies is usually the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we estimate age- and time-dependent household introduction hazards together with within household transmission rates using data from a prospective household-based study in the Netherlands. A total of 307 households containing 1,209 persons were included from August 2020 until March 2021. Follow-up of households took place between August 2020 and August 2021 with maximal follow-up per household mostly limited to 161 days. Almost 1 out of 5 households (59/307) had evidence of an introduction of SARS-CoV-2. We estimate introduction hazards and within-household transmission rates in our study population with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. Best fitting transmission models included increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses indicate that vaccination of adults can strongly reduce household infection attack rates and that adding adolescent vaccination offers limited added benefit.

Saline nasal irrigation and gargling in COVID-19: a multidisciplinary review of effects on viral load, mucosal dynamics, and patient outcomes

With unrelenting SARS-CoV-2 variants, additional COVID-19 mitigation strategies are needed. Oral and nasal saline irrigation (SI) is a traditional approach for respiratory infections/diseases. As a multidisciplinary network with expertise/experience with saline, we conducted a narrative review to examine mechanisms of action and clinical outcomes associated with nasal SI, gargling, spray, or nebulization in COVID-19. SI was found to reduce SARS-CoV-2 nasopharyngeal loads and hasten viral clearance. Other mechanisms may involve inhibition of viral replication, bioaerosol reduction, improved mucociliary clearance, modulation of ENaC, and neutrophil responses. Prophylaxis was documented adjunctive to personal protective equipment. COVID-19 patients experienced significant symptom relief, while overall data suggest lower hospitalization risk. We found no harm and hence recommend SI use, as safe, inexpensive, and easy-to-use hygiene measure, complementary to hand washing or mask-wearing. In view of mainly small studies, large well-controlled or surveillance studies can help to further validate the outcomes and to implement its use.

Estimation of introduction and transmission rates of SARS-CoV-2 in a prospective household study

Household studies provide an efficient means to study transmission of infectious diseases, enabling estimation of individual susceptibility and infectivity. A main inclusion criterion in such studies is often the presence of an infected person. This precludes estimation of the hazards of pathogen introduction into the household. Here we use data from a prospective household-based study to estimate SARS-CoV-2 age- and time-dependent household introduction hazards together with within household transmission rates in the Netherlands from August 2020 to August 2021. Introduction hazards and within-household transmission rates are estimated with penalized splines and stochastic epidemic models, respectively. The estimated hazard of introduction of SARS-CoV-2 in the households was lower for children (0-12 years) than for adults (relative hazard: 0.62; 95%CrI: 0.34-1.0). Estimated introduction hazards peaked in mid October 2020, mid December 2020, and mid April 2021, preceding peaks in hospital admissions by 1-2 weeks. The best fitting transmission models include increased infectivity of children relative to adults and adolescents, such that the estimated child-to-child transmission probability (0.62; 95%CrI: 0.40-0.81) was considerably higher than the adult-to-adult transmission probability (0.12; 95%CrI: 0.057-0.19). Scenario analyses show that vaccination of adults could have strongly reduced infection attack rates in households and that adding adolescent vaccination would have offered limited added benefit.

Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic

Importance

Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing.

Objective

To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19.

Design, Setting, and Participants

This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing.

Main Outcomes and Measures

Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data.

Results

A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years.

Conclusions and Relevance

In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.

Risk factors for SARS-CoV-2 infection in primary and secondary school students and staff in England in the 2020/2021 school year: a longitudinal study

OBJECTIVES

Investigate risk factors for SARS-CoV-2 infections in school students and staff.

METHODS

In the 2020/2021 school year, we administered polymerase chain reaction, antibody tests, and questionnaires to a sample of primary and secondary school students and staff, with data linkage to COVID-19 surveillance. We fitted logistic regression models to identify the factors associated with infection.

RESULTS

We included 6799 students and 5090 staff in the autumn and 11,952 students and 4569 staff in the spring/summer terms. Infections in students in autumn 2020 were related to the percentage of students eligible for free school meals. We found no statistical association between infection risk in primary and secondary schools and reported contact patterns between students and staff in either period in our study. Using public transports was associated with increased risk in autumn in students (adjusted odds ratio = 1.72; 95% confidence interval 1.31-2.25) and staff. One or more infections in the same household during either period was the strongest risk factor for infection in students and more so among staff.

CONCLUSION

Deprivation, community, and household factors were more strongly associated with infection than contacts patterns at school; this suggests that the additional school-based mitigation measures in England in 2020/2021 likely helped reduce transmission risk in schools.

Kombe I, Ojal J, Parisi A, Keeling MJ, Nokes DJ, Manson-Sawko R, House T. A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.