40 research outputs found
The role of frailty in shaping social contact patterns in Belgium, 2022-2023
No full textSocial contact data are essential for understanding the spread of respiratory infectious diseases and designing effective prevention strategies. However, many studies often overlook the heterogeneity in mixing patterns among older age groups and individual frailty levels, assuming homogeneity across these sub-populations. This shortcoming may undermine non-pharmaceutical interventions by not targeting specific contact behaviours, potentially reducing their effectiveness in controlling disease. To address this gap, we conducted a contact survey in Flanders, Belgium (June 2022-June 2023). We collected data from 5995 participants (overall response rates of 19.34%) who recorded 31,375 contacts with distinct individuals. Within this cohort, 14.50% were classified as frail, and 46.85% were classified as non-frail. On average, participants report 5.48 contacts daily, with a median of 4 contacts (IQR: 2-7). These contacts vary based on participants' age and frailty levels, influenced by the locations of their interactions. Using the collected data, we reconstructed frailty-dependent contact matrices and developed a contact-based mathematical model that integrates participants' and contactees' frailty levels to investigate how frailty levels affect transmission dynamics. Incorporating frailty levels into the mathematical model substantially alters the shape of epidemic curves and peak incidences. Such insights might provide useful insights for informing non-pharmaceutical interventions, indicating the potential benefit of similar data collection in different countries.Funding
Funding for this study [study number: 215366] was provided by GSK (GlaxoSmithKline). GSK was provided the opportunity to review a preliminary version of this publication for factual accuracy, but the authors are solely
responsible for final content and interpretation.
Acknowledgements
The authors gratefully acknowledge the IMI VITAL project for their valuable input and feedback during the development of the study protocol. We extend our sincere thanks to the Ipsos team for conducting the survey, collecting data, and facilitating the rapid progress of this study. We especially appreciate the exceptional project management support provided by Sarah Vercruysse. All important findings will be informed to the IMI VITAL WP3
STRIDE v1.2.0 (superspreading)
No full textThe STRIDE acronym stands for Simulate Transmission of Infectious DisEases and the model is developed to study the transmission of Influenza, Measles and COVID-19. This version contains extensions to investigate both infectiousness-related and contact-related superspreading. Reference: Kuylen EJ, Torneri A, Willem L, Libin PJK, Abrams S, Coletti P, Franco N, Verelst F, Beutels P, Liesenborgs J, Hens N: Different forms of superspreading lead to different outcomes: heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2. medRxiv (2022). https://doi.org/10.1101/2022.03.03.22271824 Population data is available here: https://zenodo.org/record/4485995#.YH1KtxMzbYUFonds Wetenschappelijk Onderzoek (FWO)Vlaamse OverheidFlemish Supercomputer Centre (VSC
STRIDE v1.2.0 (superspreading)
No full textThe STRIDE acronym stands for Simulate Transmission of Infectious DisEases and the model is developed to study the transmission of Influenza, Measles and COVID-19. This version contains extensions to investigate both infectiousness-related and contact-related superspreading. Reference: Kuylen EJ, Torneri A, Willem L, Libin PJK, Abrams S, Coletti P, Franco N, Verelst F, Beutels P, Liesenborgs J, Hens N: Different forms of superspreading lead to different outcomes: heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2. medRxiv (2022). https://doi.org/10.1101/2022.03.03.22271824 Population data is available here: https://zenodo.org/record/4485995#.YH1KtxMzbYUFonds Wetenschappelijk Onderzoek (FWO)Vlaamse OverheidFlemish Supercomputer Centre (VSC
Repetition in social contacts: implications in modelling the transmission of respiratory infectious diseases in pre-pandemic and pandemic settings
No full textThe spread of viral respiratory infections is intricately linked to human interactions, and this relationship can be characterized and modelled using social contact data. However, many analyses tend to overlook the recurrent nature of these contacts. To bridge this gap, we undertake the task of describing individuals' contact patterns over time by characterizing the interactions made with distinct individuals during a week. Moreover, we gauge the implications of this temporal reconstruction on disease transmission by juxtaposing it with the assumption of random mixing over time. This involves the development of an age-structured individual-based model, using social contact data from a pre-pandemic scenario (the POLYMOD study) and a pandemic setting (the Belgian CoMix study), respectively. We found that accounting for the frequency of contacts impacts the number of new, distinct, contacts, revealing a lower total count than a naive approach, where contact repetition is neglected. As a consequence, failing to account for the repetition of contacts can result in an underestimation of the transmission probability given a contact, potentially leading to inaccurate conclusions when using mathematical models for disease control. We, therefore, underscore the necessity of acknowledging contact repetition when formulating effective public health strategies
Flexible Bayesian estimation of incubation times
No full textThe incubation period is of paramount importance in infectious disease epidemiology as it informs about the transmission potential of a pathogenic organism and helps the planning of public health strategies to keep an epidemic outbreak under control. Estimation of the incubation period distribution from reported exposure times and symptom onset times is challenging as the underlying data is coarse. We developed a new Bayesian methodology using Laplacian-P-splines that provides a semiparametric estimation of the incubation density based on a Langevinized Gibbs sampler. A finite mixture density smoother informs a set of parametric distributions via moment matching and an information criterion arbitrates between competing candidates. Algorithms underlying our method find a natural nest within the EpiLPS package, which has been extended to cover estimation of incubation times. Various simulation scenarios accounting for different levels of data coarseness are considered with encouraging results. Applications to real data on coronavirus disease 2019, Middle East respiratory syndrome, and Mpox reveal results that are in alignment with what has been obtained in recent studies. The proposed flexible approach is an interesting alternative to classic Bayesian parametric methods for estimation of the incubation distribution.Funding
This work was supported by the ESCAPE project (101095619) and the VERDI project (101045989), funded by the European Union.
Acknowledgments
We thank Jantien Backer and Jacco Wallinga from the National Institute for Public Health and the Environment (RIVM) for discussing their results on the COVID-19 incubation period estimation based on confirmed cases with Wuhan travel histor
Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics
No full textOutbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one hand pool isolation, where we isolate all individuals that belong to a positive PCR test pool, and on the other hand individual isolation, where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in the STRIDE individual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus.sponsorship: PJKL, LW, TV and NH gratefully acknowledge support from the Fonds voor Wetenschappelijk Onderzoek (FWO) (PJKL: post-doctoral fellowship 1242021N, LW: post-doctoral fellowship 1234620N, TV: doctoral fellowship 1S47617N, and RESTORE project - G0G2920N). This work also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (NH, AT: grant number 682540 TransMID project; NH, PL: grant number 101003688 - EpiPose project). AT acknowledges support from the special research fund of the University of Antwerp. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (Fonds voor Wetenschappelijk Onderzoek (FWO)|1242021N, Fonds voor Wetenschappelijk Onderzoek (FWO)|1234620N, Fonds voor Wetenschappelijk Onderzoek (FWO)|1S47617N, Fonds voor Wetenschappelijk Onderzoek (FWO)|G0G2920N, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|682540, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|101003688, special research fund of the University of Antwerp, Research Foundation - Flanders (FWO), Flemish Government)status: Publishe
Serial Intervals for SARS-CoV-2 Omicron and Delta Variants, Belgium, November 19–December 31, 2021
No full textWe investigated the serial interval for SARS-CoV-2 Omicron BA.1 and Delta variants and observed a shorter serial interval for Omicron, suggesting faster transmission. Results indicate a relationship between empirical serial interval and vaccination status for both variants. Further assessment of the causes and extent of Omicron dominance over Delta is warranted
On realized serial and generation intervals given control measures: The COVID-19 pandemic case
No full textThe SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.sponsorship: A.T. acknowledges support from the special research fund of the University of Antwerp. This work also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (NH, AT: grant number 682540 - TransMID project; NH, PL: grant number 101003688 - EpiPose project). P.L. gratefully acknowledges support from the Fonds voor Wetenschappelijk Onderzoek (FWO) via postdoctoral fellowship 1242021N. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (University of Antwerp, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|682540 - TransMID, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program|101003688 - EpiPose, Fonds voor Wetenschappelijk Onderzoek (FWO)|1242021N)status: Publishe
Authors' response: Estimating the generation interval for COVID-19 based on symptom onset data
No full textDevelopment and application of statistical models for medical scientific researc
Quantifying superspreading for COVID-19 using Poisson mixture distributions
No full textAbstract The number of secondary cases, i.e. the number of new infections generated by an infectious individual, is an important parameter for the control of infectious diseases. When individual variation in disease transmission is present, like for COVID-19, the distribution of the number of secondary cases is skewed and often modeled using a negative binomial distribution. However, this may not always be the best distribution to describe the underlying transmission process. We propose the use of three other offspring distributions to quantify heterogeneity in transmission, and we assess the possible bias in estimates of the mean and variance of this distribution when the data generating distribution is different from the one used for inference. We also analyze COVID-19 data from Hong Kong, India, and Rwanda, and quantify the proportion of cases responsible for 80% of transmission, p 80 % , while acknowledging the variation arising from the assumed offspring distribution. In a simulation study, we find that variance estimates may be biased when there is a substantial amount of heterogeneity, and that selection of the most accurate distribution from a set of distributions is important. In addition we find that the number of secondary cases for two of the three COVID-19 datasets is better described by a Poisson-lognormal distribution
