scholarly journals Projecting social contact matrices to different demographic structures

2018 ◽  
Author(s):  
Sergio Arregui ◽  
Alberto Aleta ◽  
Joaquín Sanz ◽  
Yamir Moreno
Keyword(s):  
Time Evolution ◽  
Large Scale ◽  
Disease Modeling ◽  
Mathematical Framework ◽  
Demographic Structure ◽  
Contact Patterns ◽  
Contact Matrix ◽  
Mixing Patterns ◽  
The Impact ◽  
Demographic Evolution

AbstractThe modeling of large-scale communicable epidemics has greatly benefited in the last years from the increasing availability of highly detailed data. Particularly, in order to achieve quantitative descriptions of the evolution of epidemics, contact networks and mixing patterns are key. These heterogeneous patterns depend on several factors such as location, socioeconomic conditions, time, and age. This last factor has been shown to encapsulate a large fraction of the observed inter-individual variation in contact patterns, an observation validated by different measurements of age-dependent contact matrices. Recently, several works have studied how to project those matrices to areas where empiric data is not available. However, the dependence of contact matrices on demographic structures and their time evolution has been largely neglected. In this work, we tackle the problem of how to transform an empirical contact matrix that has been obtained for a given demographic structure into a different contact matrix that is compatible with a different demography. The methodology discussed here allows extrapolating a contact structure measured in a particular area to any other whose demographic structure is known, as well as to obtain the time evolution of contact matrices as a function of the demographic dynamics of the populations they refer to. To quantify the effect of considering time-dynamics of contact patterns on disease modeling, we implemented a Susceptible-Exposed-Infected-Recovered (SEIR) model on 16 different countries and evaluated the impact of neglecting the temporal evolution of mixing patterns. Our results show that simulated disease incidence rates, both at the aggregated and age-specific levels, are significantly dependent on contact structures variation driven by demographic evolution. The present work opens the path to eliminate technical biases from model-based impact evaluations of future epidemic threats and warns against the use of contact matrices to model diseases without correcting for demographic evolution or geographic variations.Author summaryLarge scale epidemic outbreaks represent an ever increasing threat to humankind. In order to anticipate eventual pandemics, mathematical modeling should not only have the capacity to model in real time an ongoing disease, but also to predict the evolution of potential outbreaks in different locations and times. To this end, computational frameworks need to incorporate, among other ingredients, realistic contact patterns into the models. This not only implies anticipating the demographic structure of the populations under study, but also understanding how demographic evolution reshapes social mixing patterns along time. Here we present a mathematical framework to solve this problem and test our modeling approach on 16 different empirical contact matrices. We also evaluate the impact of an eventual future outbreak by simulating a SEIR scenario in the countries analyzed. Our results show that using outdated or imported contact matrices that do not take into account demographic structure or its evolution can lead to largely misleading conclusions.

scholarly journals Close contact infection dynamics over time: insights from a second large-scale social contact survey in Flanders, Belgium, in 2010-2011

2020 ◽  
Author(s):  
Thang Van Hoang ◽  
Pietro Coletti ◽  
Yimer Wasihun Kiffe ◽  
Kim Van Kerckhove ◽  
Sarah Vercruysse ◽  
...  
Keyword(s):  
Large Scale ◽  
Close Contact ◽  
Social Contact ◽  
Contact Rates ◽  
Contact Patterns ◽  
Time Period ◽  
Social Mixing ◽  
Mixing Patterns ◽  
Contact Survey ◽  
The Impact

AbstractBackgroundIn 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period.MethodsDifferent data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a GAMLSS model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R0) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in gender, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data.ResultsAll estimated contact patterns featured strong homophily in age and gender, especially for small children and adolescents. A 30% (95% BCI [17%; 37%] ) and 29% (95% BCI [14%; 40%] ) reduction in R0 was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study.ConclusionsThe second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.

scholarly journals Inferring high-resolution human mixing patterns for disease modeling

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dina Mistry ◽  
Maria Litvinova ◽  
Ana Pastore y Piontti ◽  
Matteo Chinazzi ◽  
Laura Fumanelli ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
Disease Modeling ◽  
Population Level ◽  
Effective Population ◽  
Human Contact ◽  
Contact Patterns ◽  
Data Driven Approach ◽  
Mixing Patterns ◽  
The Impact

AbstractMathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is, however, calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective population-level contact matrices by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 35 countries, including 277 sub-national administratvie regions of 8 of those countries, covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.

scholarly journals Digital proximity tracing on empirical contact networks for pandemic control

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
G. Cencetti ◽  
G. Santin ◽  
A. Longa ◽  
E. Pigani ◽  
A. Barrat ◽  
...  
Keyword(s):  
Large Scale ◽  
Disease Outbreaks ◽  
Physical Distance ◽  
Contact Tracing ◽  
Reproductive Number ◽  
Modeling Framework ◽  
Infectious Disease Outbreaks ◽  
Contact Patterns ◽  
Contact Data ◽  
The Impact

AbstractDigital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

scholarly journals On the Time Evolution of Limited-Area Ensemble Variance: Case Studies with the Convection-Permitting Ensemble COSMO-E

2018 ◽  
Vol 76 (1) ◽  
pp. 11-26 ◽  
Author(s):  
Christina Klasa ◽  
Marco Arpagaus ◽  
André Walser ◽  
Heini Wernli
Keyword(s):  
Case Studies ◽  
Time Evolution ◽  
Large Scale ◽  
Horizontal Wind ◽  
Limited Area ◽  
Convective Activity ◽  
Moist Convection ◽  
Domain Boundaries ◽  
Large Scale Flow ◽  
The Impact

Abstract Dynamical processes determining the time evolution of difference kinetic energy (DKE) in a limited-area domain are investigated with the convection-permitting ensemble model COSMO-E for a forecasting period of 4 days. DKE is quantified by means of ensemble variance of the irrotational and nondivergent horizontal wind. For three case studies characterized by contrasting predictability levels of precipitation, it is shown that DKE of the irrotational wind strongly increases during periods of solar-forced moist convective activity and decreases when the latter ceases. The response of DKE of the nondivergent wind is also clearly related to the convective activity, but delayed by a few hours, pointing to interactions between both wind components. Apart from the impact of moist convection, DKE of the nondivergent wind is primarily governed by large-scale advection, imposed at the lateral domain boundaries of the limited-area ensemble. This forcing may also sustain or increase DKE of the irrotational wind when moist convection is absent. Consequently, the large-scale flow and diurnal solar forcing, associated with higher spatiotemporal predictability, determines the overall evolution of the limited-area ensemble variance of the horizontal wind, which increases in the presence of moist convective activity or strong synoptic-scale forcing, and stagnates or decreases otherwise, rendering forecasts of convection-permitting ensembles valuable beyond the very short forecast range.

scholarly journals When galaxies align: intrinsic alignments of the progenitors of elliptical galaxies in the Horizon-AGN simulation

2019 ◽  
Author(s):  
James Bate ◽  
Nora Elisa Chisari ◽  
Sandrine Codis ◽  
Garreth Martin ◽  
Yohan Dubois ◽  
...  
Keyword(s):  
Time Evolution ◽  
Large Scale ◽  
Large Scale Structure ◽  
Elliptical Galaxies ◽  
Stellar Mass ◽  
Scale Structure ◽  
Galaxy Dynamics ◽  
Tidal Interactions ◽  
Significant Alignment ◽  
The Impact

Abstract Elliptical galaxies today appear aligned with the large-scale structure of the Universe, but it is still an open question when they acquire this alignment. Observational data is currently insufficient to provide constraints on the time evolution of intrinsic alignments, and hence existing models range from assuming that galaxies gain some primordial alignment at formation, to suggesting that they react instantaneously to tidal interactions with the large-scale structure. Using the cosmological hydrodynamical simulation Horizon-AGN, we measure the relative alignments between the major axes of galaxies and eigenvectors of the tidal field as a function of redshift. We focus on constraining the time evolution of the alignment of the main progenitors of massive z = 0 elliptical galaxies, the main weak lensing contaminant at low redshift. We show that this population, which at z = 0 has a stellar mass above 1010.4 M⊙, transitions from having no alignment with the tidal field at z = 3, to a significant alignment by z = 1. From z = 0.5 they preserve their alignment at an approximately constant level until z = 0. We find a mass-dependence of the alignment signal of elliptical progenitors, whereby ellipticals that are less massive today (1010.4 < M/M⊙ < 1010.7) do not become aligned till later redshifts (z < 2), compared to more massive counterparts. We also present an extended study of progenitor alignments in the parameter space of stellar mass and galaxy dynamics, the impact of shape definition and tidal field smoothing.

scholarly journals Close contact infection dynamics over time: insights from a second large-scale social contact survey in Flanders, Belgium, in 2010-2011

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Thang Van Hoang ◽  
Pietro Coletti ◽  
Yimer Wasihun Kifle ◽  
Kim Van Kerckhove ◽  
Sarah Vercruysse ◽  
...  
Keyword(s):  
Close Contact ◽  
Social Contact ◽  
Additive Models ◽  
Contact Rates ◽  
Contact Patterns ◽  
Time Period ◽  
Social Mixing ◽  
Mixing Patterns ◽  
Contact Survey ◽  
The Impact

Abstract Background In 2010-2011, we conducted a social contact survey in Flanders, Belgium, aimed at improving and extending the design of the first social contact survey conducted in Belgium in 2006. This second social contact survey aimed to enable, for the first time, the estimation of social mixing patterns for an age range of 0 to 99 years and the investigation of whether contact rates remain stable over this 5-year time period. Methods Different data mining techniques are used to explore the data, and the age-specific number of social contacts and the age-specific contact rates are modelled using a generalized additive models for location, scale and shape (GAMLSS) model. We compare different matrices using assortativeness measures. The relative change in the basic reproduction number (R0) and the ratio of relative incidences with 95% bootstrap confidence intervals (BCI) are employed to investigate and quantify the impact on epidemic spread due to differences in sex, day of the week, holiday vs. regular periods and changes in mixing patterns over the 5-year time gap between the 2006 and 2010-2011 surveys. Finally, we compare the fit of the contact matrices in 2006 and 2010-2011 to Varicella serological data. Results All estimated contact patterns featured strong homophily in age and sex, especially for small children and adolescents. A 30% (95% BCI [17%; 37%]) and 29% (95% BCI [14%; 40%]) reduction in R0 was observed for weekend versus weekdays and for holiday versus regular periods, respectively. Significantly more interactions between people aged 60+ years and their grandchildren were observed on holiday and weekend days than on regular weekdays. Comparing contact patterns using different methods did not show any substantial differences over the 5-year time period under study. Conclusions The second social contact survey in Flanders, Belgium, endorses the findings of its 2006 predecessor and adds important information on the social mixing patterns of people older than 60 years of age. Based on this analysis, the mixing patterns of people older than 60 years exhibit considerable heterogeneity, and overall, the comparison of the two surveys shows that social contact rates can be assumed stable in Flanders over a time span of 5 years.

scholarly journals Modelling the SARS-CoV-2 first epidemic wave in Greece: social contact patterns for impact assessment and an exit strategy from social distancing measures

2020 ◽  
Author(s):  
Vana Sypsa ◽  
Sotirios Roussos ◽  
Dimitrios Paraskevis ◽  
Theodore Lytras ◽  
S Sotirios Tsiodras ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Attack Rate ◽  
Social Contact ◽  
Social Distancing ◽  
Relative Contribution ◽  
Contact Patterns ◽  
Social Mixing ◽  
Mixing Patterns ◽  
The Impact ◽  
Epidemic Wave

AbstractIn Greece, a nationwide lockdown to mitigate the transmission of SARS-CoV-2 was imposed on March 23, 2020. As by the end of April the first epidemic wave is waning, it is important to assess the infection attack rate and quantify the impact of physical distancing. We implemented a survey to assess social mixing patterns before the epidemic and during lockdown. We estimated R0 from surveillance data and assessed its decline as a result of physical distancing based on social contacts data. We applied a Susceptible-Exposed-Infectious-Recovered model to estimate the infection attack rate and the infection fatality ratio (IFR). As multiple social distancing measures were implemented simultaneously (schools/work/leisure), we assessed their overall impact as well as their relative contribution. R0 was estimated 2·38 (95%CI: 2·01,2·80). By April 26th, the infection attack rate was 0·12% (95%CrI: 0·06%,0·26%) and the IFR 1·12% (95%CrI: 0·55%,2·31%). During lockdown, daily contacts were reduced by 86·9% and the effective reproduction number reached 0·46 (95%CrI: 0·35,0·57). The reduction in R0 attributed to lockdown was 81·0% (95%CrI: 71·8%,86·0%) whereas the reduction attributed to each measure separately ranged between 10%-24%. We assessed scenarios with less disruptive social distancing measures as well as scenarios where measures are partially lifted after lockdown. This is the first impact assessment of the first wave of SARS-CoV-2 in a European country. It suggests that only multiple measures implemented simultaneously could reduce R0 below 1. Measuring social mixing patterns can be a tool for real-time monitoring of the epidemic potential.

scholarly journals Supermassive black holes in cosmological simulations I: MBH − M⋆ relation and black hole mass function

2021 ◽  
Author(s):  
Mélanie Habouzit ◽  
Yuan Li ◽  
Rachel S Somerville ◽  
Shy Genel ◽  
Annalisa Pillepich ◽  
...  
Keyword(s):  
Black Holes ◽  
Time Evolution ◽  
Galaxy Formation ◽  
Large Scale ◽  
Mass Function ◽  
Supermassive Black Holes ◽  
High Mass ◽  
Cosmological Simulations ◽  
Low Mass ◽  
The Impact

Abstract The past decade has seen significant progress in understanding galaxy formation and evolution using large-scale cosmological simulations. While these simulations produce galaxies in overall good agreement with observations, they employ different sub-grid models for galaxies and supermassive black holes (BHs). We investigate the impact of the sub-grid models on the BH mass properties of the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA simulations, focusing on the MBH − M⋆ relation and the BH mass function. All simulations predict tight MBH − M⋆ relations, and struggle to produce BHs of $M_{\rm BH}\leqslant 10^{7.5}\, \rm M_{\odot }$ in galaxies of $M_{\star }\sim 10^{10.5}-10^{11.5}\, \rm M_{\odot }$. While the time evolution of the mean MBH − M⋆ relation is mild ($\rm \Delta M_{\rm BH}\leqslant 1\, dex$ for 0 ≤ z ≤ 5) for all the simulations, its linearity (shape) and normalization varies from simulation to simulation. The strength of SN feedback has a large impact on the linearity and time evolution for $M_{\star }\leqslant 10^{10.5}\, \rm M_{\odot }$. We find that the low-mass end is a good discriminant of the simulation models, and highlights the need for new observational constraints. At the high-mass end, strong AGN feedback can suppress the time evolution of the relation normalization. Compared with observations of the local Universe, we find an excess of BHs with $M_{\rm BH}\geqslant 10^{9}\, \rm M_{\odot }$ in most of the simulations. The BH mass function is dominated by efficiently accreting BHs (log10 fEdd ≥ −2) at high redshifts, and transitions progressively from the high-mass to the low-mass end to be governed by inactive BHs. The transition time and the contribution of active BHs are different among the simulations, and can be used to evaluate models against observations.

scholarly journals A framework for modeling clustering in natural hazard catastrophe risk management and the implications for re/insurance loss perspectives

2014 ◽  
Vol 2 (8) ◽  
pp. 5247-5285
Author(s):  
S. Khare ◽  
A. Bonazzi ◽  
C. Mitas ◽  
S. Jewson
Keyword(s):  
Large Scale ◽  
Natural Hazard ◽  
Simulation Models ◽  
Mathematical Framework ◽  
Event Correlation ◽  
Poisson Mixtures ◽  
Hazard Risk ◽  
Starting Point ◽  
Catastrophe Risk Management ◽  
The Impact

Abstract. In this paper, we present a novel framework for modelling clustering in natural hazard risk models. The framework we present is founded on physical principles where large-scale oscillations in the physical system is the source of non-Poissonian (clustered) frequency behaviour. We focus on a particular mathematical implementation of the "Super-Cluster" methodology that we introduce. This mathematical framework has a number of advantages including tunability to the problem at hand, as well as the ability to model cross-event correlation. Using European windstorm data as an example, we provide evidence that historical data show strong evidence of clustering. We then develop Poisson and clustered simulation models for the data, demonstrating clearly the superiority of the clustered model which we have implemented using the Poisson-Mixtures approach. We then discuss the implications of including clustering in models of prices on catXL contracts, one of the most commonly used mechanisms for transferring risk between primary insurers and reinsurers. This paper provides a number of new insights into the impact clustering has on modelled catXL contract prices. The simple model presented in this paper provides an insightful starting point for practicioners of natural hazard risk modelling.

scholarly journals Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis

2020 ◽  
Author(s):  
Edwin van Leeuwen ◽  
Frank Sandmann ◽  
Keyword(s):  
Time Use ◽  
Large Scale ◽  
Close Contact ◽  
Public Health Intervention ◽  
Social Contact ◽  
Social Activities ◽  
Social Distancing ◽  
Epidemic Curve ◽  
Mixing Patterns ◽  
The Impact

AbstractBackgroundSocial distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as SARS-CoV-2 during the coronavirus disease 2019 (COVID-19) pandemic. We aimed to explore the impact on the epidemic curve of fewer contacts when individuals reduce the time they spend on selected daily activities.MethodsWe combined the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0-15, 16-24, 25-44, 45-64, 65+) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). We assumed that reductions in time are proportional to reductions in contacts. The derived matrices were then applied in an age-structured dynamic-transmission model of COVID-19 to explore the effects.FindingsThe relative reductions in the derived contact matrices were highest when closing schools (in ages 0-14 years), workplaces (15-64 years), and stopping social visits (65+ years). For COVID-19, the closure of workplaces, schools, and stopping social visits had the largest impact on reducing the epidemic curve and delaying its peak, while the predicted impact of fewer contacts in parks, bars/cafes/restaurants, and non-essential shopping were minimal.InterpretationWe successfully augmented contact matrices with time-use data to predict the highest impact of social distancing measures from reduced contacts when spending less time at work, school, and on social visits. Although the predicted impact from other leisure activities with potential for close physical contact were minimal, changes in mixing patterns and time-use immediately after re-allowing social activities may pose increased short-term transmission risks, especially in potentially crowded environments indoors.Research in contextEvidence before this studyWe searched PubMed for mathematical models using social contact matrices and time-use data to explore the impact of reduced social contacts as seen from social distancing measures adopted during the coronavirus disease 2019 (COVID-19) pandemic with the search string ((social OR physical) AND distancing) OR (contact* OR (contact matri*)) AND (time-use) AND (model OR models OR modeling OR modelling) from inception to May 06, 2020, with no language restrictions. We found several studies that used time-use data to re-create contact matrices based on time spent in similar locations or to calculate the length of exposure. We identified no study that augmented social contact matrices with time-use data to estimate the impact on transmission dynamics of reducing selected social activities and lifting these restrictions again, as seen during the COVID-19 pandemic.Added value of this studyOur study combines the empirical data of two large-scale, representative surveys to derive social contact matrices that enrich the frequency of contacts with the duration of exposure for selected social activities, which allows for more fine-grained mixing patterns and infectious disease modelling. We successfully applied the resulting matrices to estimate reductions in contacts from social distancing measures such as adopted during the COVID-19 pandemic, as well as the effect on the epidemic curve from increased social contacts when lifting such restrictions again.Implications of all the available evidenceSocial distancing measures are an important public health intervention to limit the close-contact transmission of emerging infectious pathogens by reducing the social mixing of individuals. Our model findings suggest a higher fraction of close-contact transmission occurs at work, schools, and social visits than from visits to parks, bars/cafes/restaurants, and non-essential shopping. The minimal predicted impact is suggestive of lifting the restrictions on certain activities and excluding them from the list of social distancing measures, unless required to maintain sufficient healthcare capacity. However, potential replacement effects of activities and in mixing patterns remain unclear, particularly immediately after re-allowing social activities again.

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