Incidence and Reproduction Numbers of Pertussis: Estimates from Serological and Social Contact Data in Five European Countries

Background Large measles and mumps outbreaks recently occurred throughout Europe and the United States. Aim: Our aim was to estimate and map the risk of resurgence for measles, mumps and rubella in France. Methods: We used a multi-cohort model combining seroprevalence information, vaccine coverage and social contact data. Results: The overall outbreak risk for France in 2018 was highest for mumps, remained significant for measles despite a recent measles outbreak and was low for rubella. Outbreak risks were heterogeneous between departments, as the effective reproduction numbers for 2018 ranged from 1.08 to 3.66. The seroprevalence, and therefore the risk of measles and rubella infection, differed significantly between males and females. There was a lower seroprevalence, and therefore a higher risk, for males. Infants of less than 1 year would be seriously affected in a future outbreak of measles, mumps or rubella, but the highest overall caseload contribution would come from teenagers and young adults (10–25 years old). Conclusions: The high risk for teenagers and young adults is of concern in view of their vulnerability to more severe measles, mumps and rubella disease and complications.

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SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries

BMC Medicine ◽

10.1186/s12916-021-02133-y ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Frederik Verelst ◽

Lisa Hermans ◽

Sarah Vercruysse ◽

Amy Gimma ◽

Pietro Coletti ◽

...

Keyword(s):

Close Contact ◽

Social Contact ◽

European Countries ◽

Distance Measures ◽

Essential Information ◽

Health And Wellbeing ◽

Contact Data ◽

And Gender ◽

Transmission Models ◽

Pharmaceutical Interventions

Abstract Background SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. Methods Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. Conclusion These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.

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A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

BMC Infectious Diseases ◽

10.1186/s12879-021-06092-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pietro Coletti ◽

Pieter Libin ◽

Oana Petrof ◽

Lander Willem ◽

Steven Abrams ◽

...

Keyword(s):

Phase 1 ◽

Leisure Activities ◽

National Level ◽

Social Contact ◽

Exit Strategy ◽

Initial Wave ◽

Contact Data ◽

Stochastic Data ◽

The Impact ◽

Second Wave

Abstract Background In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. Methods We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Results Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Conclusions Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

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Rapid review of available evidence on the serial interval and generation time of COVID-19

BMJ Open ◽

10.1136/bmjopen-2020-040263 ◽

2020 ◽

Vol 10 (11) ◽

pp. e040263

Author(s):

John Griffin ◽

Miriam Casey ◽

Áine Collins ◽

Kevin Hunt ◽

David McEvoy ◽

...

Keyword(s):

Generation Time ◽

Social Contact ◽

Careful Consideration ◽

Rapid Review ◽

Eligibility Criteria ◽

Reproduction Numbers ◽

Serial Interval ◽

Scientific Papers ◽

The Mean ◽

Parameter Values

The serial interval is the time between symptom onsets in an infector–infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector–infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. Objective Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. Methods We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. Results The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. Discussion Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

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SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

10.1101/2020.03.03.20030627 ◽

2020 ◽

Cited By ~ 5

Author(s):

Lander Willem ◽

Thang Van Hoang ◽

Sebastian Funk ◽

Pietro Coletti ◽

Philippe Beutels ◽

...

Keyword(s):

Data Sharing ◽

Social Contact ◽

School Closure ◽

Mitigation Strategies ◽

Online Application ◽

Online Tool ◽

R Shiny ◽

Contact Data ◽

Considerable Impact ◽

Disease Specific

AbstractObjectiveEstablishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19.ResultsWe organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R0. We incorporated location-specific isolation measures (e.g. school closure or telework) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that social distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s).

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Modeling Infectious Disease Parameters Based on Serological and Social Contact Data

10.1007/978-1-4614-4072-7 ◽

2012 ◽

Cited By ~ 61

Author(s):

Niel Hens ◽

Ziv Shkedy ◽

Marc Aerts ◽

Christel Faes ◽

Pierre Van Damme ◽

...

Keyword(s):

Infectious Disease ◽

Social Contact ◽

Contact Data ◽

Disease Parameters

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Individual social contact data and population mobility data as early markers of SARS-CoV-2 transmission dynamics during the first wave in Germany—an analysis based on the COVIMOD study

BMC Medicine ◽

10.1186/s12916-021-02139-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Damilola Victoria Tomori ◽

Nicole Rübsamen ◽

Tom Berger ◽

Stefan Scholz ◽

Jasmin Walde ◽

...

Keyword(s):

Survey Data ◽

Social Contact ◽

Transmission Dynamics ◽

Relative Reduction ◽

Population Mobility ◽

Mobility Data ◽

Infection Dynamics ◽

Contact Data ◽

The One ◽

Contact Survey

Abstract Background The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. The aim of this study is to compare social contact data and population mobility data with respect to their ability to reflect transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany. Methods We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020 to 06/2020 (compared to the pre-pandemic period from previous studies) and estimated the percentage mean reduction over time. We compared these results as well as the percentage mean reduction in population mobility data (corrected for pre-pandemic mobility) with and without the introduction of scaling factors and specific weights for different types of contacts and mobility to the relative reduction in transmission dynamics measured by changes in R values provided by the German Public Health Institute. Results We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contact reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. Relative reduction of infection dynamics derived from contact survey data underestimated the one based on reported R values in the time of strictest contact reduction measures but reflected it well thereafter. Relative reduction of infection dynamics derived from mobility data overestimated the one based on reported R values considerably throughout the study. After the introduction of a scaling factor, specific weights for different types of contacts and mobility reduced the mean absolute percentage error considerably; in all analyses, estimates based on contact data reflected measured R values better than those based on mobility. Conclusions Contact survey data reflected infection dynamics better than population mobility data, indicating that both data sources cover different dimensions of infection dynamics. The use of contact type-specific weights reduced the mean absolute percentage errors to less than 1%. Measuring the changes in mobility alone is not sufficient for understanding the changes in transmission dynamics triggered by public health measures.

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Combining fine-scale social contact data with epidemic modelling reveals interactions between contact tracing, quarantine, testing and physical distancing for controlling COVID-19

10.1101/2020.05.26.20113720 ◽

2020 ◽

Cited By ~ 2

Author(s):

Josh A. Firth ◽

Joel Hellewell ◽

Petra Klepac ◽

Stephen Kissler ◽

Adam J. Kucharski ◽

...

Keyword(s):

Real World ◽

Social Dynamics ◽

Control Strategies ◽

Single Point ◽

Local Population ◽

Social Contact ◽

Contact Tracing ◽

Contact Rates ◽

Contact Data ◽

Potential Impact

AbstractCase isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 in a real-world social network generated from high resolution GPS data3,4. We found that tracing contacts-of-contacts reduced the size of simulated outbreaks more than tracing of only contacts, but resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals led to increases in outbreak size, suggesting that contact tracing and quarantine may be most effective when it acts as a ‘local lockdown’ when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our approach highlights the importance of network structure and social dynamics in evaluating the potential impact of SARS-CoV-2 control.

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Comment on Flaxman et al. (2020): The illusory effects of non-pharmaceutical interventions on COVID-19 in Europe

10.31124/advance.12479987.v1 ◽

2020 ◽

Author(s):

Stefan Homburg ◽

Christof Kuhbandner

Keyword(s):

United Kingdom ◽

European Countries ◽

The United Kingdom ◽

Reproduction Numbers ◽

Pharmaceutical Interventions

Flaxman et al. (Nature, 8 June 2020, https://doi.org/10.1038/s41586-020-2405-7, 2020) infer that non-pharmaceutical interventions conducted by several European countries considerably reduced effective reproduction numbers and saved millions of lives. We show that their method is ill-conceived and that the alleged effects are artefacts. Moreover, we demonstrate that the United Kingdom?s lockdown was both superfluous and ineffective.<br>

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Individual social contact data reflected SARS-CoV-2 transmission dynamics during the first wave in Germany better than population mobility data - an analysis based on the COVIMOD study

10.1101/2021.03.24.21254194 ◽

2021 ◽

Author(s):

Damilola Victoria Tomori ◽

Nicole Ruebsamen ◽

Tom Berger ◽

Stefan Scholz ◽

Jasmin Walde ◽

...

Keyword(s):

Public Health ◽

Survey Data ◽

Social Contact ◽

Transmission Dynamics ◽

Population Mobility ◽

Social Contacts ◽

Mobility Data ◽

Contact Data ◽

Contact Survey ◽

T Values

Background The effect of contact reduction measures on infectious disease transmission can only be assessed indirectly and with considerable delay. However, individual social contact data and population mobility data can offer near real-time proxy information. Aim To compare social contact data and population mobility data with respect to their ability to predict transmission dynamics during the first wave of the SARS-CoV-2 pandemic in Germany. Methods We quantified the change in social contact patterns derived from self-reported contact survey data collected by the German COVIMOD study from 04/2020-06/2020 (compared to the pre-pandemic period), and estimated the percentage mean reduction in the effective reproduction number R(t) over time. We compared these results to the ones based on R(t) estimates from open-source mobility data and to R(t) values provided by the German Public Health Institute. Results We observed the largest reduction in social contacts (90%, compared to pre-pandemic data) in late April corresponding to the strictest contacts reduction measures. Thereafter, the reduction in contacts dropped continuously to a minimum of 73% in late June. R(t) estimates based on social contacts underestimated measured R(t) values slightly in the time of strictest contact reduction measures but predicted R(t) well thereafter. R(t) estimates based on mobility data overestimated R(t) considerably throughout the study. Conclusions R(t) prediction accuracy based on contact survey data was superior to the one based on population mobility data, indicating that measuring changes in mobility alone is not sufficient for understanding changes in transmission dynamics triggered by public health measures.

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