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

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.

Controlling the pandemic during the SARS-CoV-2 vaccination rollout: a modeling study (preprint)

There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point control of COVID-19 would be achieved for each scenario.

Estimating the strength of selection for new SARS-CoV-2 variants (preprint)

A challenge to controlling the SARS-CoV-2 pandemic is the ability of the virus to adapt to its new human hosts, with novel and more transmissible strains of the virus being continually identified. Yet there are no generally accepted methods to consistently estimate the relative magnitude of the change in transmissiblity of newly emerging variants. In this paper we consider three methods for examining and quantifying positive selection of new and emerging strains of SARS-CoV-2 over an existing wild-type strain. We consider replication at the level of countries and allow for the action of other processes that can change variants' frequencies, specifically migration and drift. We apply these methods to the D614G spike mutation and the variant designated B.1.1.7, in every country where there is sufficient sequence data. For each of D614G and B.1.1.7, we find evidence for strong selection (greater than 25% increased contagiousness) in more than half of countries analyzed. Our results also shows that the selective advantages of these strains are highly heterogeneous at the country level, suggesting the need for a truly global perspective on the molecular epidemiology of SARS-CoV-2.

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic (preprint)

BackgroundIn autumn 2020, many countries, including the Netherlands, are experiencing a second wave of the COVID-19 pandemic. Health policymakers are struggling with choosing the right mix of measures to keep the COVID-19 case numbers under control, but still allow a minimum of social and economic activity. The priority to keep schools open is high, but the role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We used a transmission model to estimate the impact of school contacts on transmission of SARS-CoV-2 and to assess the effects of school-based measures, including school closure, on controlling the pandemic at different time points during the pandemic. Methods and FindingsThe age-structured model was fitted to age-specific seroprevalence and hospital admission data from the Netherlands during spring 2020. Compared to adults older than 60 years, the estimated susceptibility was 23% (95%CrI 20--28%) for children aged 0 to 20 years and 61% (95%CrI 50%--72%) for the age group of 20 to 60 years. The time points considered in the analyses were (i) August 2020 when the effective reproduction number (Re) was estimated to be 1.31 (95%CrI 1.15--2.07), schools just opened after the summer holidays and measures were reinforced with the aim to reduce Re to a value below 1, and (ii) November 2020 when measures had reduced Re to 1.00 (95%CrI 0.94--1.33). In this period schools remained open. Our model predicts that keeping schools closed after the summer holidays, in the absence of other measures, would have reduced Re by 10% (from 1.31 to 1.18 (95%CrI 1.04--1.83)) and thus would not have prevented the second wave in autumn 2020. Reducing non-school-based contacts in August 2020 to the level observed during the first wave of the pandemic would have reduced Re to 0.83 (95%CrI 0.75--1.10). Yet, this reduction was not achieved and the observed Re in November was 1.00. Our model predicts that closing schools in November 2020 could reduce Re from the observed value of 1.00 to 0.84 (95%CrI 0.81--0.90), with unchanged non-school based contacts. Reductions in Re due to closing schools in November 2020 were 8% for 10 to 20 years old children, 5% for 5 to 10 years old children and negligible for 0 to 5 years old children. ConclusionsThe impact of measures reducing school-based contacts, including school closure, depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce Re with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among the older school children.