The Oxford handbook of archaeological Network research

0578901 - ARÚB 2024 RIV GB eng M - Monography Chapter
Vlach, Marek
Network Modeling of the Spread of Disease.
The Oxford handbook of archaeological Network research. Oxford: Oxford University Press, 2024 - (Brughmans, T.; Mills, B.; Munson, J.; Peeples, M.), s. 512-527. ISBN 978-0-19-885426-5
Institutional support: RVO:68081758
Keywords : paleoepidemiology * epidemic diseases * epidemiological modeling * archaeology * network science
OECD category: Archaeology

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.
Permanent Link: https://hdl.handle.net/11104/0350050