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Population dynamics and household structures in infectious disease modelling: A demographic perspective
No full textSome population groups are more likely to acquire an infection or to experience a
severe outcome in case of disease due to risk factors that may not be randomly distributed in the population. Some of these factors are related to demographic characteristics and structures (e.g. age, sex, household composition), which typically
are incorporated in the host population in models of infectious disease transmission,
though often in a highly simplified manner. Demographic structures, however, result
from complex demographic processes that tend to change over time. In the context of infectious disease epidemiology, it is not well understood how these underlying
processes shape current and future population structures with relevance for the transmission and burden of infectious diseases. For that reason, the aim of this dissertation
was to explore and improve infectious disease models with dynamic host populations
with the purpose of investigating the impact of demographic structures and changes
on the transmission and burden of infectious diseases transmitted through close contact.
To create an overview of the existing literature, we first carried out a systematic review
of the demographic methods and data used to incorporate dynamic host populations
in models of infectious disease transmission. We found that population-level modelling was more common than individual-based modelling. The advantages of IBMs
emerge when heterogeneity beyond age and sex, for example household structure, is
required in the population or transmission process. The flexibility of IBMs, however,
was rarely used to advance the demographic modelling of the host population. With
the advantages and limitations of the existing literature in mind, we developed a demographic microsimulation for an age- and household-structured population, tailored
for applications in infectious disease modelling. We specifically simulated the Belgian population from 2011 to 2050 and considered the demographic processes of fertility,
mortality, migration and household transitions. The microsimulation was extended
by a disease transmission model to investigate how the spread of an emerging infectious disease was affected by age and household structures in the host population
and how these relationships were influenced by demographic change. The age and
household structures had an impact on the disease transmission dynamics, but the
magnitude of the relationship depended on epidemiological heterogeneity in the population. Moreover, the size and composition of households were crucial for explaining
how the infection spread at the individual, household and population level.
In a second application of the microsimulation, we investigated how population ageing
affects the mortality burden of respiratory infections. The disease transmission model
was modified to resemble the spread of SARS-CoV-2 and novel influenza A virus. We
focused on the living arrangements in the older adult population, as the COVID-19
pandemic, for example, has had a disproportionate impact on those living in LTCFs.
Similarly, we found that this relatively small population group, which is often disregarded in infectious disease modelling, faced a markedly higher risk of infection
in our simulations and accounted for a substantial share of the burden of mortality
associated with the respiratory infections. The burden of future epidemics increased
with the ageing of the population, but the magnitude of this relationship depended
on the living arrangements and general health in the older adult population.
Dynamic microsimulation of an age- and household-structured host population is also
useful for evaluating the long-term dynamics of endemic infectious diseases and the
effectiveness of immunisation programmes. We therefore presented ongoing work involving a demographic microsimulation for the US population from 1960 to 2020,
which was extended by a disease transmission model for VZV and HZ. The demographic methods were similar to those used to develop the microsimulation for Belgium, but with several modifications, as the data and its granularity differed. The host
population, however, was modelled with age and household structures, as household
transmission rarely has been explored explicitly in the existing literature. Declining
fertility rates since the 1960s has led to a changing age composition of the US population, as well as a declining mean household size. These trends are reproduced in
the microsimulation, allowing for an investigating of the implications of the demographic changes for the epidemiology of VZV and HZ at the individual, household
and population level.Sommige bevolkingsgroepen hebben een grotere kans om een infectie op te lopen of
om een ernstige afloop te ervaren in geval van ziekte als gevolg van risicofactoren die
mogelijk niet willekeurig over de bevolking verdeeld zijn. Sommige van deze factoren
houden verband met demografische kenmerken en structuren (bv. leeftijd, geslacht,
samenstelling van het huishouden), die typisch worden opgenomen in modellen van
infectieziekten, zij het vaak op een sterk vereenvoudigde manier. Demografische structuren zijn echter het resultaat van complexe demografische processen die in de loop van
de tijd veranderen. Er is onvoldoende begrip over de manier warop deze onderliggende
processen vorm geven aan huidige en toekomstige bevolkingsstructuren die relevant
zijn voor de transmissie en de last van infectieziekten. Het doel van dit proefschrift is
daarom tweeledig. De eerste doelstelling is het verkennen en verbeteren van modellen
van infectieziekten met dynamische gastheerpopulaties. Daarnaast wordt onderzocht
wat de invloed van demografische structuren en veranderingen is op de transmissie en
last van infectieziekten die via nauw contact worden overgedragen.
Om een overzicht te krijgen van de bestaande literatuur, hebben we eerst een systematische review uitgevoerd van de demografische methoden en gegevens die gebruikt worden om dynamische gastheerpopulaties op te nemen in modellen van infectieziekten.
We ontdekten dat modellering op populatieniveau gebruikelijker was dan modellering
op individueel niveau. De voordelen van IBM’s komen naar voren wanneer heterogeniteit naast leeftijd en geslacht vereist is in de populatie of het transmissieproces. Dit is
bijvoorbeeld het geval bij huishoudens. Deze flexibiliteit van IBM’s werd echter zelden
gebruikt om de demografische modellering van de gastpopulatie te bevorderen. Met de
voordelen en beperkingen van de bestaande literatuur in gedachten, ontwikkelden we
een demografische microsimulatie voor een leeftijds- en huishoudensgestructureerde populatie, op maat gemaakt voor toepassingen in de modellering van infectieziekten.
We simuleren specifiek de Belgische bevolking van 2011 tot 2050 en beschouwen de demografische processen van vruchtbaarheid, sterfte, migratie en huishoudenstransities.
De microsimulatie werd uitgebreid met een transmissiemodel om te onderzoeken hoe
de verspreiding van een opkomende infectieziekte werd beïnvloed door leeftijds- en
huishoudenstructuren in de gastpopulatie en hoe deze relaties werden beïnvloed door
demografische veranderingen. De leeftijds- en huishoudenstructuren hebben een impact op de transmissiedynamiek, maar de grootte van de relatie hangt af van epidemiologische heterogeniteit in de populatie. Bovendien waren de grootte en samenstelling
van huishoudens van cruciaal belang om te verklaren hoe de infectie zich verspreidde
op individueel, huishoud- en populatieniveau.
In een tweede toepassing van de microsimulatie onderzochten we hoe de vergrijzing
van de bevolking de mortaliteitslast van respiratoire infecties beïnvloedt. Het transmissiemodel werd aangepast om verspreiding van SARS-CoV-2 en een nieuwe variant
van het influenza A-virus te simuleren. We richtten ons op de samenstelling van
huishoudens in de populatie van oudere volwassenen, omdat de COVID-19 pandemie
bijvoorbeeld een onevenredig grote impact had op degenen die in zorginstellingen
wonen. Ook ontdekten we dat deze relatief kleine bevolkingsgroep, die vaak buiten
beschouwing wordt gelaten in modellen voor infectieziekten, een duidelijk hoger infectierisico liep in onze simulaties en verantwoordelijk was voor een aanzienlijk deel
van de mortaliteitslast gerelateerd aan respiratoire infecties. De last van toekomstige epidemieën nam toe met de vergrijzing van de bevolking, maar de omvang van
deze samenhang hing af van de samenstelling van huishoudens in de oudere volwassen
bevolking.
Dynamische microsimulatie van een gastpopulatie met leeftijds- en huishoudensstructuur is ook nuttig voor het evalueren van de langetermijndynamiek van endemische
infectieziekten en de effectiviteit van immunisatieprogramma’s. Daarom presenteerden we een demografische microsimulatie voor de Amerikaanse bevolking van 1960
tot 2020, die werd uitgebreid met een transmissiemodel voor VZV en HZ. De demografische methoden waren vergelijkbaar met degenen die gebruikt werden om de
microsimulatie voor België te ontwikkelen, maar met verschillende aanpassingen omdat de gegevens en de granulariteit verschilden. Aangezien huishoudenstransmissie
zelden expliciet is onderzocht in de bestaande literatuur, werd de gastpopulatie evenwel gemodelleerd met leeftijds- en huishoudenstructuren. Dalende vruchtbaarheidscijfers sinds de jaren 1960 hebben geleid tot een veranderende leeftijdsopbouw van
de Amerikaanse bevolking en een afnemende gemiddelde grootte van huishoudens. Deze trends werden gereproduceerd in de microsimulatie. Hierdoor zullen de implicaties van de demografische veranderingen voor de epidemiologie van VZV en HZ op
individueel, huishoud- en populatieniveau kunnen worden onderzoch
Population dynamics and household structures in infectious disease modelling: A demographic perspective
No full textSome population groups are more likely to acquire an infection or to experience a
severe outcome in case of disease due to risk factors that may not be randomly distributed in the population. Some of these factors are related to demographic characteristics and structures (e.g. age, sex, household composition), which typically
are incorporated in the host population in models of infectious disease transmission,
though often in a highly simplified manner. Demographic structures, however, result
from complex demographic processes that tend to change over time. In the context of infectious disease epidemiology, it is not well understood how these underlying
processes shape current and future population structures with relevance for the transmission and burden of infectious diseases. For that reason, the aim of this dissertation
was to explore and improve infectious disease models with dynamic host populations
with the purpose of investigating the impact of demographic structures and changes
on the transmission and burden of infectious diseases transmitted through close contact.
To create an overview of the existing literature, we first carried out a systematic review
of the demographic methods and data used to incorporate dynamic host populations
in models of infectious disease transmission. We found that population-level modelling was more common than individual-based modelling. The advantages of IBMs
emerge when heterogeneity beyond age and sex, for example household structure, is
required in the population or transmission process. The flexibility of IBMs, however,
was rarely used to advance the demographic modelling of the host population. With
the advantages and limitations of the existing literature in mind, we developed a demographic microsimulation for an age- and household-structured population, tailored
for applications in infectious disease modelling. We specifically simulated the Belgian population from 2011 to 2050 and considered the demographic processes of fertility,
mortality, migration and household transitions. The microsimulation was extended
by a disease transmission model to investigate how the spread of an emerging infectious disease was affected by age and household structures in the host population
and how these relationships were influenced by demographic change. The age and
household structures had an impact on the disease transmission dynamics, but the
magnitude of the relationship depended on epidemiological heterogeneity in the population. Moreover, the size and composition of households were crucial for explaining
how the infection spread at the individual, household and population level.
In a second application of the microsimulation, we investigated how population ageing
affects the mortality burden of respiratory infections. The disease transmission model
was modified to resemble the spread of SARS-CoV-2 and novel influenza A virus. We
focused on the living arrangements in the older adult population, as the COVID-19
pandemic, for example, has had a disproportionate impact on those living in LTCFs.
Similarly, we found that this relatively small population group, which is often disregarded in infectious disease modelling, faced a markedly higher risk of infection
in our simulations and accounted for a substantial share of the burden of mortality
associated with the respiratory infections. The burden of future epidemics increased
with the ageing of the population, but the magnitude of this relationship depended
on the living arrangements and general health in the older adult population.
Dynamic microsimulation of an age- and household-structured host population is also
useful for evaluating the long-term dynamics of endemic infectious diseases and the
effectiveness of immunisation programmes. We therefore presented ongoing work involving a demographic microsimulation for the US population from 1960 to 2020,
which was extended by a disease transmission model for VZV and HZ. The demographic methods were similar to those used to develop the microsimulation for Belgium, but with several modifications, as the data and its granularity differed. The host
population, however, was modelled with age and household structures, as household
transmission rarely has been explored explicitly in the existing literature. Declining
fertility rates since the 1960s has led to a changing age composition of the US population, as well as a declining mean household size. These trends are reproduced in
the microsimulation, allowing for an investigating of the implications of the demographic changes for the epidemiology of VZV and HZ at the individual, household
and population level.Sommige bevolkingsgroepen hebben een grotere kans om een infectie op te lopen of
om een ernstige afloop te ervaren in geval van ziekte als gevolg van risicofactoren die
mogelijk niet willekeurig over de bevolking verdeeld zijn. Sommige van deze factoren
houden verband met demografische kenmerken en structuren (bv. leeftijd, geslacht,
samenstelling van het huishouden), die typisch worden opgenomen in modellen van
infectieziekten, zij het vaak op een sterk vereenvoudigde manier. Demografische structuren zijn echter het resultaat van complexe demografische processen die in de loop van
de tijd veranderen. Er is onvoldoende begrip over de manier warop deze onderliggende
processen vorm geven aan huidige en toekomstige bevolkingsstructuren die relevant
zijn voor de transmissie en de last van infectieziekten. Het doel van dit proefschrift is
daarom tweeledig. De eerste doelstelling is het verkennen en verbeteren van modellen
van infectieziekten met dynamische gastheerpopulaties. Daarnaast wordt onderzocht
wat de invloed van demografische structuren en veranderingen is op de transmissie en
last van infectieziekten die via nauw contact worden overgedragen.
Om een overzicht te krijgen van de bestaande literatuur, hebben we eerst een systematische review uitgevoerd van de demografische methoden en gegevens die gebruikt worden om dynamische gastheerpopulaties op te nemen in modellen van infectieziekten.
We ontdekten dat modellering op populatieniveau gebruikelijker was dan modellering
op individueel niveau. De voordelen van IBM’s komen naar voren wanneer heterogeniteit naast leeftijd en geslacht vereist is in de populatie of het transmissieproces. Dit is
bijvoorbeeld het geval bij huishoudens. Deze flexibiliteit van IBM’s werd echter zelden
gebruikt om de demografische modellering van de gastpopulatie te bevorderen. Met de
voordelen en beperkingen van de bestaande literatuur in gedachten, ontwikkelden we
een demografische microsimulatie voor een leeftijds- en huishoudensgestructureerde populatie, op maat gemaakt voor toepassingen in de modellering van infectieziekten.
We simuleren specifiek de Belgische bevolking van 2011 tot 2050 en beschouwen de demografische processen van vruchtbaarheid, sterfte, migratie en huishoudenstransities.
De microsimulatie werd uitgebreid met een transmissiemodel om te onderzoeken hoe
de verspreiding van een opkomende infectieziekte werd beïnvloed door leeftijds- en
huishoudenstructuren in de gastpopulatie en hoe deze relaties werden beïnvloed door
demografische veranderingen. De leeftijds- en huishoudenstructuren hebben een impact op de transmissiedynamiek, maar de grootte van de relatie hangt af van epidemiologische heterogeniteit in de populatie. Bovendien waren de grootte en samenstelling
van huishoudens van cruciaal belang om te verklaren hoe de infectie zich verspreidde
op individueel, huishoud- en populatieniveau.
In een tweede toepassing van de microsimulatie onderzochten we hoe de vergrijzing
van de bevolking de mortaliteitslast van respiratoire infecties beïnvloedt. Het transmissiemodel werd aangepast om verspreiding van SARS-CoV-2 en een nieuwe variant
van het influenza A-virus te simuleren. We richtten ons op de samenstelling van
huishoudens in de populatie van oudere volwassenen, omdat de COVID-19 pandemie
bijvoorbeeld een onevenredig grote impact had op degenen die in zorginstellingen
wonen. Ook ontdekten we dat deze relatief kleine bevolkingsgroep, die vaak buiten
beschouwing wordt gelaten in modellen voor infectieziekten, een duidelijk hoger infectierisico liep in onze simulaties en verantwoordelijk was voor een aanzienlijk deel
van de mortaliteitslast gerelateerd aan respiratoire infecties. De last van toekomstige epidemieën nam toe met de vergrijzing van de bevolking, maar de omvang van
deze samenhang hing af van de samenstelling van huishoudens in de oudere volwassen
bevolking.
Dynamische microsimulatie van een gastpopulatie met leeftijds- en huishoudensstructuur is ook nuttig voor het evalueren van de langetermijndynamiek van endemische
infectieziekten en de effectiviteit van immunisatieprogramma’s. Daarom presenteerden we een demografische microsimulatie voor de Amerikaanse bevolking van 1960
tot 2020, die werd uitgebreid met een transmissiemodel voor VZV en HZ. De demografische methoden waren vergelijkbaar met degenen die gebruikt werden om de
microsimulatie voor België te ontwikkelen, maar met verschillende aanpassingen omdat de gegevens en de granulariteit verschilden. Aangezien huishoudenstransmissie
zelden expliciet is onderzocht in de bestaande literatuur, werd de gastpopulatie evenwel gemodelleerd met leeftijds- en huishoudenstructuren. Dalende vruchtbaarheidscijfers sinds de jaren 1960 hebben geleid tot een veranderende leeftijdsopbouw van
de Amerikaanse bevolking en een afnemende gemiddelde grootte van huishoudens. Deze trends werden gereproduceerd in de microsimulatie. Hierdoor zullen de implicaties van de demografische veranderingen voor de epidemiologie van VZV en HZ op
individueel, huishoud- en populatieniveau kunnen worden onderzoch
Exploring the impact of population ageing on the spread of emerging respiratory infections and the associated burden of mortality
No full textBackgroundIncreasing life expectancy and persistently low fertility levels have led to old population age structures in most high-income countries, and population ageing is expected to continue or even accelerate in the coming decades. While older adults on average have few interactions that potentially could lead to disease transmission, their morbidity and mortality due to infectious diseases, respiratory infections in particular, remain substantial. We aim to explore how population ageing affects the future transmission dynamics and mortality burden of emerging respiratory infections.MethodsUsing longitudinal individual-level data from population registers, we model the Belgian population with evolving age and household structures, and explicitly consider long-term care facilities (LTCFs). Three scenarios are presented for the future proportion of older adults living in LTCFs. For each demographic scenario, we simulate outbreaks of SARS-CoV-2 and a novel influenza A virus in 2020, 2030, 2040 and 2050 and distinguish between household and community transmission. We estimate attack rates by age and household size/type, as well as disease-related deaths and the associated quality-adjusted life-years (QALYs) lost.ResultsAs the population is ageing, small households and LTCFs become more prevalent. Additionally, families with children become smaller (i.e. low fertility, single-parent families). The overall attack rate slightly decreases as the population is ageing, but to a larger degree for influenza than for SARS-CoV-2 due to differential age-specific attack rates. Nevertheless, the number of deaths and QALY losses per 1,000 people is increasing for both infections and at a speed influenced by the share living in LTCFs.ConclusionPopulation ageing is associated with smaller outbreaks of COVID-19 and influenza, but at the same time it is causing a substantially larger burden of mortality, even if the proportion of LTCF residents were to decrease. These relationships are influenced by age patterns in epidemiological parameters. Not only the shift in the age distribution, but also the induced changes in the household structures are important to consider when assessing the potential impact of population ageing on the transmission and burden of emerging respiratory infections.The results reported herein correspond to specifc aims of grant 682540-TransMID to investigator Niel Hens from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program
HOW WILL DEMOGRAPHIC CHANGE AFFECT THE DISEASE BURDEN OF FUTURE EPIDEMICS?
No full texton the cobas SARS-CoV-2 assay (Roche) and the Aptima SARS-CoV-2 assay (Hologic). Findings: We demonstrated comparable sensitivity, specificity, and agreement between self-collected nasal and Rhinoswab samples , compared to HCW-collected samples tested using the cobas SARS-CoV-2 and Aptima SARS-CoV-2 assays. In our study the clinical performance of self-collected specimens was comparable to HCW-collected samples, with both self-collect nasal and Rhi-noswab samples resulting in 90-95% sensitivity, and in most cases > 95% specificity. Discussion: Without the availability of samples for NAAT the ability to perform genomic testing is limited, reducing surveillance and public health investigations. We showed that genomic sequencing from self-collected samples can correctly identify the virus lineage and that the main determination of successful ge-nomic testing is a high viral load rather than collection method. Conclusion: These data support self-collection as an accessible method for community testing for COVID-19 and introduces a novel collection device, the Rhinoswab as an alternative to the standard nasal swab. The testing method of self-collection can be expanded from the widely used RATs to NAAT and genomic testing which may inform the management and public health response to the COVID-19 pandemic
Additional file 1 of Incorporating human dynamic populations in models of infectious disease transmission: a systematic review
No full textAdditional file 1: Protocol, figures and tables
Population age and household structures shape transmission dynamics of emerging infectious diseases: a longitudinal microsimulation approach
No full textHost population demographics and patterns of host-to-host interactions are important drivers of heterogeneity in infectious disease transmission. To improve our understanding of how population structures and changes therein influence disease transmission dynamics at the individual and population level, we model a dynamic age- and household-structured population using longitudinal microdata drawn from Belgian census and population registers. At different points in time, we simulate the spread of a close-contact infectious disease and vary the age profiles of infectiousness and susceptibility to reflect specific infections (e.g. influenza and SARS-CoV-2) using a two-level mixing model, which distinguishes between exposure to infection in the household and exposure in the community. We find that the strong relationship between age and household structures, in combination with social mixing patterns and epidemiological parameters, shape the spread of an emerging infection. Disease transmission in the adult population in particular is to a large degree explained by differential household compositions and not just household size. Moreover, we highlight how demographic processes alter population structures in an ageing population and how these in turn affect disease transmission dynamics across population groups.This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 682540 TransMID). Acknowledgements. We gratefully acknowledge Andrea Torneri for useful discussions of the manuscript and Pietro Coletti and Pavel N. Krivitsky for providing access to the household network model applied in this stud
Chronic disease patients have fewer social contacts: A pilot survey with implications for transmission dynamics
No full textNon-communicable diseases (NCD) are the most important cause of death in the world. The socio-economic costs associated with NCDs makes it imperative to prevent and control them in the 21st century. The severe toll that the COVID-19 pandemic has taken worldwide is an unfortunate illustration of our limited insight into the infectious risk for the global population. Co-incidence between NCD and infection offers an underexplored opportunity to design preventive policies. In a pilot survey, we observed that the NCD population displays a substantial reduction in their social contacting behavior as compared to the general population. This indicates that existing mathematical models based on contact surveys in the general population are not applicable to the NCD population and that the risk of acquiring an infection following a contact is probably underestimated. Our demonstration of reduced social mixing in several chronic conditions, raises the question to what extent the social mixing is influenced by the burden of disease. We advocate the design of disease-specific contact surveys to address how the burden of disease associates with social contact behavior and the risk of infection. The SARS-CoV-2 pandemic offers an unprecedented opportunity to gain insight into the importance of infection in the NCD population and to find ways to improve healthcare procedures
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19
No full textThe COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.sponsorship: The authors are very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano, and from the Vaccine & Infectious Disease Institute (VaxInfectio), University of Antwerp. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. L.W., S.A., P.J.K.L. and N.H. gratefully acknowledge support from the Research Foundation Flanders (FWO) (postdoctoral fellowships 1234620N and 1242021N, 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 programme (P.C., S.A.H. and N.H., grant number 682540-TransMID project; C.F., P.B. and N.H. grant number 101003688-EpiPose project). P.B. and N.H. acknowledge funding from the Antwerp Study Centre for Infectious Diseases (ASCID) and the Methusalem-Centre of Excellence consortium VAX-IDEA. We used computational resources and services provided by the Flemish Supercomputer Centre (VSC), funded by the FWO and the Flemish Government, with special thanks to the CalcUA-team (FB and SB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (Research Foundation Flanders (FWO)|1234620N, Research Foundation Flanders (FWO)|1242021N, Research Foundation Flanders (FWO)|G0G2920N, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme|682540-TransMID, European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme|101003688-EpiPose, Antwerp Study Centre for Infectious Diseases (ASCID), Methusalem-Centre of Excellence consortium VAX-IDEA, FWO, Flemish Government)status: Publishe
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