Network Modeling of the Spread of Disease

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Network Modeling of the Spread of Disease

Original language description

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.

Czech name

—

Czech description

—

Type

C - Chapter in a specialist book

CEP classification

—

OECD FORD branch

60102 - Archaeology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Book/collection name

The Oxford handbook of archaeological Network research

ISBN

978-0-19-885426-5

Number of pages of the result

16

Pages from-to

512-527

Number of pages of the book

699

Publisher name

Oxford University Press

Place of publication

Oxford

UT code for WoS chapter

—