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1.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.02.09.23285703

ABSTRACT

Background: Severity of SARS-CoV-2 infection may vary over time. Here, we estimate age-specific risks of hospitalization, ICU admission and death given infection in the Netherlands from February 2020 - June 2021. Methods: A nationwide longitudinal serology study was used to estimate numbers of infections in three epidemic periods (February 2020 - June 2020, July 2020 - February 2021, March 2021 - June 2021). We accounted for reinfections and, as vaccination started in January 2021, breakthrough infections among vaccinated persons. Severity estimates were inferred by combining numbers of infections with aligned numbers of hospitalizations and ICU admissions from a national hospital-based registry, and aligned numbers of deaths based on national excess all-cause mortality estimates. Results: In each period there was a nearly consistent pattern of accelerating, almost exponential, increase in severity of infection with age. The rate of increase with age was highest for death and lowest for hospitalization. In the first period, the overall risk of hospitalization, ICU admission and death were 1.5% (95%-confidence interval [CI] 1.3-1.8%), 0.36% (95%-CI: 0.31-0.42%) and 1.2% (95%-CI: 1.0-1.4), respectively. The risk of hospitalization was higher in the following periods, while the risk of ICU admission remained stable. The risk of death decreased over time, with a substantial drop among [≥]70-years-olds in February 2021 - June 2021. Conclusion: The accelerating increase in severity of SARS-CoV-2 with age remained intact during the first three epidemic periods in the Netherlands. The substantial drop in risk of death among elderly in the third period coincided with the introduction of COVID-19 vaccination.


Subject(s)
Death , Breakthrough Pain , COVID-19
2.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.10.19.22281248

ABSTRACT

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.


Subject(s)
COVID-19
3.
researchsquare; 2022.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-1940453.v1

ABSTRACT

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We investigated incubation period and serial interval distributions in data on 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.


Subject(s)
COVID-19
4.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.08.05.22278461

ABSTRACT

Background The generation time distribution, reflecting the time between successive infections in transmission chains, is one of the fundamental epidemiological parameters for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution, reflecting the time between illness onsets of infector and infectee. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. Methods We analyzed data on observed incubation period and serial interval distributions in China, during January and February 2020, under different sampling approaches, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. Results We analyzed data on a total of 2989 confirmed cases for COVID-19 during January 1 to February 29, 2020 in Mainland China. During the study period, the empirical forward serial interval decreased from a mean of 8.90 days to 2.68 days. The estimated mean backward incubation period of infectors increased from 3.77 days to 9.61 days, and the mean forward incubation period of infectees also increased from 5.39 days to 7.21 days. The estimated mean forward generation time decreased from 7.27 days (95% confidence interval: 6.42, 8.07) to 4.21 days (95% confidence interval: 3.70, 4.74) days by January 29. We used simulations to examine the sensitivity of our modelling approach to a number of assumptions and alternative dynamics. Conclusions The proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.


Subject(s)
COVID-19
5.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.07.02.22277186

ABSTRACT

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.

6.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.04.07.22273549

ABSTRACT

We propose a mathematical framework to analyze and interpret the outcomes of human challenge trials. We present plausible infection risks with HCoV-229E and SARS-CoV-2 over a wide range of infectious dose, and suggest ways to improve the design of future trials and to translate its outcomes to the general population.

7.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.03.21.22272611

ABSTRACT

Variants of concern (VOCs) of SARS-CoV-2 have caused resurging waves of infections worldwide. In the Netherlands, Alpha, Beta, Gamma and Delta variants circulated widely between September 2020 and August 2021. To understand how various control measures had impacted the spread of these VOCs, we analyzed 39,844 SARS-CoV-2 genomes collected under the Dutch national surveillance program. We found that all four VOCs were introduced before targeted flight restrictions were imposed on countries where the VOCs first emerged. Importantly, foreign introductions, predominantly from other European countries, continued during these restrictions. Our findings show that flight restrictions had limited effectiveness in deterring VOC introductions due to the strength of regional land travel importation risks. We also found that the Alpha and Delta variants largely circulated more populous regions with international connections after their respective introduction before asymmetric bidirectional transmissions occurred with the rest of the country and the variant dominated infections in the Netherlands. As countries consider scaling down SARS-CoV-2 surveillance efforts in the post-crisis phase of the pandemic, our results highlight that robust surveillance in regions of early spread is important for providing timely information for variant detection and outbreak control.

8.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.02.12.22270851

ABSTRACT

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.


Subject(s)
Respiratory Tract Infections , Communicable Diseases
9.
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.01.18.22269217

ABSTRACT

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.

10.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.12.02.21267189

ABSTRACT

In infectious disease epidemiology, the instantaneous reproduction number R ( t ) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at time t . It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R ( t ) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R ( t ) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R ( t ) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France. Author summary The instantaneous reproduction number R ( t ) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve and R ( t ). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available ( https://github.com/oswaldogressani ).


Subject(s)
Communicable Diseases
11.
researchsquare; 2021.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-1026794.v2

ABSTRACT

Background. The impact of COVID-19 on population health is recognised as being substantial, yet few studies have attempted to quantify to what extent infection causes mild or moderate symptoms only, requires hospital and/or ICU admission, results in prolonged and chronic illness, or leads to premature death. We aimed to quantify the total disease burden of acute COVID-19 in the Netherlands in 2020 using the disability-adjusted life-years (DALY) measure, and to investigate how burden varies between age-groups and occupations.Methods. Using standard methods and diverse data sources (mandatory notifications, population-level seroprevalence, hospital and ICU admissions, registered COVID-19 deaths, and the literature), we estimated years of life lost (YLL), years lived with disability, DALY and DALY per 100,000 population due to COVID-19, excluding post-acute sequelae, stratified by 5-year age-group and occupation category.Results. The total disease burden due to acute COVID-19 was 286,100 (95% CI:281,700–290,500) DALY, and the per-capita burden was 1640 (95% CI:1620–1670) DALY/100,000, of which 99.4% consisted of YLL. The per-capita burden increased steeply with age, starting from 60–64 years, with relatively little burden estimated for persons under 50 years old.Conclusions. SARS-CoV-2 infection and associated premature mortality was responsible for a considerable direct health burden in the Netherlands, despite extensive public health measures. DALY were much higher than for other high-burden infectious diseases, but lower than estimated for coronary heart disease. These findings are valuable for informing public health decision-makers regarding the expected COVID-19 health burden among population subgroups, and the possible gains from targeted preventative interventions.


Subject(s)
COVID-19
12.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.10.21.21265318

ABSTRACT

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.


Subject(s)
Infections , Severe Acute Respiratory Syndrome , COVID-19
13.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.07.20.21260889

ABSTRACT

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest, such as new infections, due to vaccination that fully immunizes a single individual. We express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. The principle of allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.


Subject(s)
Death , COVID-19
14.
researchsquare; 2021.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-688708.v1

ABSTRACT

BackgroundVoluntary testing for SARS-CoV-2 infection is an integral component of an effective response to the COVID-19 pandemic. It is essential to identify populations at a high risk for infection but who are less likely to present for testing. Here, we use internet-based participatory surveillance data from the Netherlands to identify sociodemographic and household factors that are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.MethodsMultivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted odds ratios of testing and of positivity associated with participant and household characteristics.ResultsBased on five months (17 November 2020 to 18 April 2021) of weekly surveys obtained from 12,026 participants, males (adjusted odds ratio for testing (ORt): 0.92; adjusted odds ratio for positivity (ORp): 1.30, age-groups <20 (ORt: 0.89; ORp: 1.27) 50-64 years (ORt: 0.94; ORp: 1.06) and 65+ years (ORt: 0.78; ORp: 1.24), diabetics (ORt: 0.97; ORp: 1.06), and sales/administrative employees (ORt: 0.93; ORp: 1.90) were distinguished as lower propensity/higher positivity factors.ConclusionsThe factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.


Subject(s)
COVID-19
15.
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.03.29.21254334

ABSTRACT

BackgroundThe proportion of SARS-CoV-2 positive persons who are asymptomatic - and whether this proportion is age-dependent - are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or crude proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. MethodsBased on a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May 2020 in the Netherlands (n=3147), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. ResultsUsing age-aggregated data, the estimated AP was 70% (95% CI: 65-77%). The estimated AP decreased with age, from 80% (95% CI: 67-100%) for the <20 years age-group, to 55% (95% CI: 48-68%) for the 70+ years age-group. ConclusionWhereas the crude AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.


Subject(s)
COVID-19
16.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.08.21.20167965

ABSTRACT

Background School closures are a well-established non-pharmaceutical intervention in the event of infectious disease outbreaks, and have been implemented in many countries across the world, including the UK, to slow down the spread of SARS-CoV-2. As governments begin to relax restrictions on public life there is a need to understand the potential impact that reopening schools may have on transmission. Methods We used data provided by the UK Department for Education to construct a network of English schools, connected through pairs of pupils resident at the same address. We used the network to evaluate the potential for transmission between schools, and for long range propagation across the network, under different reopening scenarios. Results Amongst the options evaluated we found that reopening only Reception, Year 1 and Year 6 (4-6 and 10-11 year olds) resulted in the lowest risk of transmission between schools, with outbreaks within a single school unlikely to result in outbreaks in adjacent schools in the network. The additional reopening of Years 10 and 12 (14-15 and 16-17 year olds) resulted in an increase in the risk of transmission between schools comparable to reopening all primary school years (4-11 year olds). However, the majority of schools presented low risk of initiating widespread transmission through the school system. Reopening all secondary school years (11-18 year olds) resulted in large potential outbreak clusters putting up to 50% of households connected to schools at risk of infection if sustained transmission within schools was possible. Conclusions Reopening secondary school years is likely to have a greater impact on community transmission than reopening primary schools in England. Keeping transmission within schools limited is essential for reducing the risk of large outbreaks amongst school-aged children and their household members.

17.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.06.18.20134858

ABSTRACT

Estimation of the effective reproductive number, Rt, is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make methodological recommendations. For near real-time estimation of Rt, we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for some retrospective analyses. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. A challenge common to all approaches is reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.


Subject(s)
COVID-19
18.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.05.18.20101501

ABSTRACT

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.


Subject(s)
COVID-19
19.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.03.05.20031815

ABSTRACT

Background: Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions: Estimating generation and serial interval distributions from outbreak data requires careful investigation of the underlying transmission network. Detailed contact tracing information is essential for correctly estimating these quantities.


Subject(s)
COVID-19
20.
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.03.03.20029983

ABSTRACT

Background: As the COVID-19 epidemic is spreading, incoming data allows us to quantify values of key variables that determine the transmission and the effort required to control the epidemic. We determine the incubation period and serial interval distribution for transmission clusters in Singapore and in Tianjin. We infer the basic reproduction number and identify the extent of pre-symptomatic transmission. Methods: We collected outbreak information from Singapore and Tianjin, China, reported from Jan.19-Feb.26 and Jan.21-Feb.27, respectively. We estimated incubation periods and serial intervals in both populations. Results: The mean incubation period was 7.1 (6.13, 8.25) days for Singapore and 9 (7.92, 10.2)days for Tianjin. Both datasets had shorter incubation periods for earlier-occurring cases. The mean serial interval was 4.56 (2.69, 6.42) days for Singapore and 4.22 (3.43, 5.01) for Tianjin. We inferred that early in the outbreaks, infection was transmitted on average 2.55 and 2.89days before symptom onset (Singapore, Tianjin). The estimated basic reproduction number for Singapore was 1.97 (1.45, 2.48) secondary cases per infective; for Tianjin it was 1.87 (1.65,2.09) secondary cases per infective. Conclusions: Estimated serial intervals are shorter than incubation periods in both Singapore and Tianjin, suggesting that pre-symptomatic transmission is occurring. Shorter serial intervals lead to lower estimates of R0, which suggest that half of all secondary infections should be prevented to control spread.


Subject(s)
COVID-19
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