Estimating social contact rates for the COVID-19 pandemic using Google mobility and pre-pandemic contact surveys (preprint)

During the COVID-19 pandemic, aggregated mobility data was frequently used to estimate changing social contact rates. By taking contact matrices estimated pre-pandemic, and transforming these using pandemic-era mobility data, epidemiologists attempted to predict the number of contacts individuals were expected to have during large-scale restrictions. This study explores the most effective method for this transformation, comparing it to the accuracy of pandemic-era contact surveys. We compared four methods for scaling synthetic contact matrices: two using fitted regression models and two using "naive" mobility or mobility squared models. The regression models were fitted using CoMix contact survey and Google mobility data from the UK over March 2020 - March 2021. The four models were then used to scale synthetic contact matrices--a representation of pre-pandemic behaviour--using mobility data from the UK, Belgium and the Netherlands to predict the number of contacts expected in "work" and "other" settings for a given mobility level. We then compared partial reproduction numbers estimated from the four models with those calculated directly from CoMix contact matrices across the three countries. The accuracy of each model was assessed using root mean squared error. The fitted regression models had substantially more accurate predictions than the naive models, even when the regression models were applied to Belgium and the Netherlands. Across all countries investigated, the naive model using mobility alone was the least accurate, followed by the naive model using mobility squared. When attempting to estimate social contact rates during a pandemic without the resources available to conduct contact surveys, using a model fitted to data from another pandemic context is likely to be an improvement over using a "naive" model based on raw mobility data. If a naive model is to be used, mobility squared may be a better predictor of contact rates than mobility per se.

Social contact patterns following the COVID-19 pandemic: a snapshot of post-pandemic behaviour from the CoMix study (preprint)

The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted a final new round of the CoMix survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R0. Data collection occurred from 17 November to 7 December 2022. 7,477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4% of all participants reported wearing a facemask on the previous day, varying between 6.7% in NL to 17.8% in CH. Self-reported vaccination rates in adults were similar for each country at around 86%. Trimmed mean recorded contacts were highest in NL with 9.9 (95% confidence interval [CI] 9.0 to 10.8) contacts per person per day and lowest in CH at 6.0 (95% CI 5.4 to 6.6). The number of contacts at home were similar between the countries. Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95% CI 1.4 to 1.9) and highest in NL at 3.4 recorded per person per day (95% CI 4.0 to 4.0). Using the next-generation approach suggests that R0 for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80% in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns that would be expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding.

Projecting COVID-19 intensive care admissions in the Netherlands for policy advice: February 2020 to January 2021 (preprint)

0.IntroductionModel projections of COVID-19 incidence into the future help policy makers about decisions to implement or lift control measures. During 2020, policy makers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions. Here we present the model and the procedure by which it was updated. Methodsthe projections were produced using an age-structured transmission model. A consistent, incremental update procedure that integrated all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a change point analysis guided by Akaikes Information Criterion. ResultsThe model and update procedure allowed us to make mostly accurate weekly projections, accounting for recent and future policy changes, and to adapt the estimated effectiveness of the policy changes based only on the natural accumulation of incoming data. DiscussionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation, as it would develop after 2020.

Contact patterns of frail and non-frail elderly persons in the Netherlands during the COVID-19 pandemic (preprint)

Background During the COVID-19 pandemic social distancing measures were imposed to protect the population from exposure, especially elderly and frail persons who have the highest risk for severe outcomes. These restrictions greatly reduced contacts in the general population, but little is known about behaviour changes among elderly and frail persons themselves. Our aim was to quantify how COVID-19 measures affected contact behaviour of elderly and how this differed between frail and non-frail elderly. Methods In 2021 a contact survey was carried out among persons aged 70 years and older in the Netherlands. A random sample of persons per age group (70-74, 75-79, 80-84, 85-89, 90+) and gender was invited to participate, either during a period with stringent (April 2021) or moderate (October 2021) measures. Participants provided general information on themselves including their frailty, and reported characteristics of all persons with whom they had face-to-face contact on a given day, over the course of a full week. Results In total 720 community-dwelling elderly persons were included (overall response rate of 15%), who reported 16,505 contacts. During the survey period with moderate measures, non-frail participants had significantly more contacts outside their household than frail participants. Especially for women, frailty was a more informative predictor for number of contacts than age. During the survey period with stringent measures, frail and non-frail participants had significantly lower numbers of contacts compared to the survey period with moderate measures. The reduction of number of contacts was largest for the eldest non-frail participants. As they likely interact closely with highly aged and highly frail persons, this reduction of number of contacts indirectly protects frail elderly from SARS-CoV-2 exposure. Conclusions The results of this study reveal that social distancing measures during the COVID-19 pandemic differentially affected the contact patterns of frail and non-frail elderly. The reduction of contacts may have led to direct protection of elderly persons in general but also to indirect protection of frail elderly.

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands (preprint)

The COVID-19 pandemic was in 2020 and 2021 for a large part mitigated by reducing contacts in the general population. To monitor how these contacts changed over the course of the pandemic in the Netherlands, a longitudinal survey was conducted where participants reported on their at-risk contacts every two weeks, as part of the European CoMix survey. The survey included 1659 participants from April to August 2020 and 2514 participants from December 2020 to September 2021. We categorized the number of unique contacted persons excluding household members, reported per participant per day into six activity levels, defined as 0, 1, 2, 3-4, 5-9 and 10 or more reported contacts. After correcting for age, vaccination status, risk status for severe outcome of infection, and frequency of participation, activity levels increased over time, coinciding with relaxation of COVID-19 control measures.

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2 (preprint)

Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission and control. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection and transmission---for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we re-analyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same data set reported shorter mean observed incubation period (3.2 days vs 4.4 days) and serial interval (3.5 days vs 4.1 days) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8--4.5 days) for both variants but a shorter mean generation interval for the Omicron variant (3.0 days; 95\% CI: 2.7--3.2 days) than for the Delta variant (3.8 days; 95\% CI: 3.7--4.0 days). We further note that the differences in estimated generation intervals may be driven by the "network effect"---higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant.

Empirical evidence of transmission over a school-household network for SARS-CoV-2; exploration of transmission pairs stratified by primary and secondary school. (preprint)

Background Children play a key role in the transmission of many infectious diseases. They have many of their close social encounters at home or at school. We hypothesized that most of the transmission of respiratory infections among children occur in these two settings and that transmission patterns can be predicted by a bipartite network of schools and households. Aim and methods To confirm transmission over a school-household network, SARS-CoV-2 transmission pairs in children aged 4-17 years were analyzed by study year and primary/secondary school. Cases with symptom onset between the 1st of March 2021 and the 4th of April 2021 identified by source and contact-tracing in the Netherlands were included. In this period, primary schools were open and secondary school students attended class at least once per week. Within pairs, spatial distance between the postcodes was calculated as the Euclidean distance. Results A total of 4,059 transmission pairs were identified; 51.9% between primary schoolers; 19.6% between primary and secondary schoolers; 28.5% between secondary schoolers. Most (68.5%) of the transmission for children in the same study year occurred at school. In contrast, most of the transmission of children from different study years (64.3%) and most primary-secondary transmission (81.7%) occurred at home. The average spatial distance between infections was 1.2km (median 0.4) for primary school pairs, 1.6km (median 0) for primary-secondary school pairs and 4.1km (median 1.2) for secondary school pairs. Conclusion The results provide evidence of transmission on a bipartite school-household network. Schools play an important role in transmission within study years, and households play an important role in transmission between study years and between primary and secondary schools. Spatial distance between infections in a transmission pair reflects the smaller school catchment area of primary schools versus secondary schools. Many of these observed patterns likely hold for other respiratory pathogens.

Shorter serial intervals in SARS-CoV-2 cases with Omicron variant compared to Delta variant in the Netherlands, 13 - 19 December 2021 (preprint)

The SARS-CoV-2 Omicron variant has a growth advantage over the Delta variant, due to higher transmissibility, immune evasion, or a shorter serial interval. Using S-gene target failure (SGTF) as indication for Omicron, we identify 220 SGTF and 869 non-SGTF serial intervals in the same week. Within households, we find a mean serial interval of 3.4 days for SGTF and 3.9 days for non-SGTF cases. This suggests that the growth advantage of Omicron is partly due to a shorter serial interval.

The impact of vaccinating adolescents and children on COVID-19 disease outcomes (preprint)

Introduction: Despite the high COVID-19 vaccination coverage among adults, there is concern over a peak in SARS-CoV-2 infections in the coming months. To help ensure that healthcare systems are not overwhelmed in the event of a new wave of SARS-CoV-2 infections, many countries have extended vaccination to adolescents (those aged 12-17 years) and may consider further extending to children aged 5-11 years. However, there is considerable debate about whether or not to vaccinate healthy adolescents and children against SARS-CoV-2 because, while vaccination of children and adolescents may limit transmission from these groups to other, more vulnerable groups, adolescents and children themselves have limited risk of severe disease if infected and may experience adverse events from vaccination. To quantify the benefits of extending COVID-19 vaccination beyond adults we compare daily cases, hospital admissions, and intensive care (IC) admissions for vaccination in adults only, those 12 years and above, and those 5 years and above. Methods and Findings: We developed a deterministic, age-structured susceptible-exposed-infectious-recovered (SEIR) model to simulate disease outcomes (e.g., cases, hospital admissions, IC admissions) under different vaccination scenarios. The model is partitioned into 10-year age bands (0-9, 10-19, ..., 70-79, 80+) and accounts for differences in susceptibility and infectiousness by age group, seasonality in transmission rate, modes of vaccine protection (e.g., infection, transmission), and vaccine characteristics (e.g., vaccine effectiveness). Model parameters are estimated by fitting the model piecewise to daily cases from the Dutch notification database Osiris from 01 January 2020 to 22 June 2021. Forward simulations are performed from 22 June 2021 to 31 March 2022. We performed sensitivity analyses in which vaccine-induced immunity waned. We found that upon relaxation of all non-pharmaceutical control measures a large wave occurred regardless of vaccination strategy. We found overall reductions of 5.7% (4.4%, 6.9%) of cases, 2.0% (0.7%, 3.2%) of hospital admissions, and 1.7% (0.6%, 2.8%) of IC admissions when those 12 years and above were vaccinated compared to vaccinating only adults. When those 5 years and above were vaccinated we observed reductions of 8.7% (7.5%, 9.9%) of cases, 3.2% (2.0%, 4.5%) of hospital admissions, and 2.4% (1.2%, 3.5%) of IC admissions compared to vaccination in adults only. Benefits of extending vaccination were larger within the age groups included in the vaccination program extension than in other age groups. The benefits of vaccinating adolescents and children were smaller if vaccine protection against infection, hospitalization, and transmission (once infected) wanes. Discussion: Our results highlight the benefits of extending COVID-19 vaccination programs beyond adults to reduce infections and severe outcomes in adolescents and children and in the wider population. A reduction of infections in school-aged children/adolescents may have the added benefit of reducing the need for school closures during a new wave. Additional control measures may be required in future to prevent a large wave despite vaccination program extensions. While the results presented here are based on population characteristics and the COVID-19 vaccination program in The Netherlands, they may provide valuable insights for other countries who are considering COVID-19 vaccination program extensions.

Vaccine effectiveness against SARS-CoV-2 transmission to household contacts during dominanceof Delta variant (B.1.617.2), August-September 2021, the Netherlands (preprint)

We estimated vaccine effectiveness against onward transmission by comparing secondary attack rates among household members between vaccinated and unvaccinated index cases, based on source and contact tracing data collected when Delta variant was dominant. Effectiveness of full vaccination of the index against transmission to fully vaccinated household contacts was 40% (95% confidence interval (CI) 20-54%), which is in addition to the direct protection of vaccination of contacts against infection. Effectiveness of full vaccination of the index against transmission to unvaccinated household contacts was 63% (95%CI 46-75%). We previously reported effectiveness of 73% (95%CI 65-79%) against transmission to unvaccinated household contacts for the Alpha variant.

The impact of physical distancing measures against COVID-19 transmission on contacts and mixing patterns in the Netherlands: repeated cross-sectional surveys (preprint)

BackgroundDuring the current pandemic of coronavirus (COVID-19) many countries have taken drastic measures to reduce transmission of SARS-CoV2. The measures often include physical distancing that aims to reduce the number of contacts in the population. Little is known about the actual reduction in number of contacts as a consequence of physical distancing measures. MethodsIn the Netherlands, a cross-sectional survey was carried out in 2016/2017 in which 8179 participants retrospectively reported the number, age and gender of different persons they had contacted (spoken to in person or touched) during the previous day. The survey was repeated among 2830 of the original participants, using the same questionnaire, in March and April 2020 after physical distancing measures had been implemented. ResultsThe average number of contacts in the community was reduced from on average 12.5 (interquartile range: 2-17) to 3.7 (interquartile range: 0-4) different persons per participant, a reduction of 71% (95% confidence interval: 71-71). The reduction in the number of community contacts was highest for children and adolescents (between 5 and 20 years) and smallest for elderly persons of 80 years and older. The reduction in the effective number of total contacts, measured as the largest eigenvalue of the matrix with community and household contacts, was 62% (95% confidence interval: 48 - 72). ConclusionThe substantial reduction in contacts has contributed greatly in halting the COVID-19 epidemic. This reduction was unevenly distributed over age groups, household sizes and occupations. These findings offer guidance for the lifting of age-group targeted measures.

The incubation period of 2019-nCoV infections among travellers from Wuhan, China (preprint)

Currently, a novel coronavirus 2019-nCoV causes an outbreak of viral pneumonia in Wuhan, China. Little is known about its epidemiological characteristics. Using the travel history and symptom onset of 88 confirmed cases that were detected outside Wuhan, we estimate the mean incubation period to be 6.4 (5.6 - 7.7, 95% CI) days, ranging from 2.1 to 11.1 days (2.5th to 97.5th percentile). These values help to inform case definitions for 2019-nCoV and appropriate durations for quarantine.