Network Modeling of the Spread of Disease

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081758%3A_____%2F24%3A00578901" target="_blank" >RIV/68081758:_____/24:00578901 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1093/oxfordhb/9780198854265.013.29" target="_blank" >http://dx.doi.org/10.1093/oxfordhb/9780198854265.013.29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/oxfordhb/9780198854265.013.29" target="_blank" >10.1093/oxfordhb/9780198854265.013.29</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Network Modeling of the Spread of Disease

Popis výsledku v původním jazyce

The presence of various epidemic diseases can be expected within past human populations. They are well attested through vivid narratives of literary-rich civilizations such as the Roman empire as well as the 2020 pandemic. Traditionally, much of the study of such phenomena has been anchored in paleopathological evidence from skeletal remains. Nevertheless, like the integration of methodological tools such as social network analysis in archaeological studies, network concepts have become important for modeling in epidemiology. Epidemiological modeling has developed various methodological approaches after nearly a century of development. Early approaches were dominated by so-called compartmental models that used various forms and concepts of population structure, which have been gradually complemented with analyses of more complex structures through network analyses. Heterogeneous contact patterns of connections have already proven that the structure of communication networks significantly conditions the resulting epidemic dynamics and its impact. Therefore, methodological intersections between network analyses and epidemiological models render great potential for future studies of past epidemics. Formalization of the featuring entities (e.g. individuals, communities, or entire cities) through their position within a multilevel social network provides a framework to analyze our qualitative and quantitative assumptions about disease transmission. Despite the presence of empirical paleopathological datasets, independent validation of network models using this data is still scarce. New possibilities in pathogen identification—e.g. genomics—could help to bridge future gaps between our theoretical models and empirical data.

Název v anglickém jazyce

Network Modeling of the Spread of Disease

Popis výsledku anglicky

The presence of various epidemic diseases can be expected within past human populations. They are well attested through vivid narratives of literary-rich civilizations such as the Roman empire as well as the 2020 pandemic. Traditionally, much of the study of such phenomena has been anchored in paleopathological evidence from skeletal remains. Nevertheless, like the integration of methodological tools such as social network analysis in archaeological studies, network concepts have become important for modeling in epidemiology. Epidemiological modeling has developed various methodological approaches after nearly a century of development. Early approaches were dominated by so-called compartmental models that used various forms and concepts of population structure, which have been gradually complemented with analyses of more complex structures through network analyses. Heterogeneous contact patterns of connections have already proven that the structure of communication networks significantly conditions the resulting epidemic dynamics and its impact. Therefore, methodological intersections between network analyses and epidemiological models render great potential for future studies of past epidemics. Formalization of the featuring entities (e.g. individuals, communities, or entire cities) through their position within a multilevel social network provides a framework to analyze our qualitative and quantitative assumptions about disease transmission. Despite the presence of empirical paleopathological datasets, independent validation of network models using this data is still scarce. New possibilities in pathogen identification—e.g. genomics—could help to bridge future gaps between our theoretical models and empirical data.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

60102 - Archaeology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

The Oxford handbook of archaeological Network research

ISBN

978-0-19-885426-5

Počet stran výsledku

16

Strana od-do

512-527

Počet stran knihy

699

Název nakladatele

Oxford University Press

Místo vydání

Oxford

Kód UT WoS kapitoly

—