The Importance of Scaling for an Agent Based Model: An Illustrative Case Study with COVID-19 in Zimbabwe
22nd Annual International Conference on Computational Science, ICCS 2022
; 13351 LNCS:259-265, 2022.
Article
in English
| Scopus | ID: covidwho-1958884
ABSTRACT
Agent-based models frequently make use of scaling techniques to render the simulated samples of population more tractable. The degree to which this scaling has implications for model forecasts, however, has yet to be explored;in particular, no research on the spatial implications of this has been done. This work presents a simulation of the spread of Covid-19 among districts in Zimbabwe and assesses the extent to which results vary relative to the samples upon which they are based. It is determined that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for others seeking to use scaled populations in their research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Agent-based modeling; Agent-based modelling; Scaling; Simulation; Synthetic population; Autonomous agents; Computational methods; Population statistics; Simulation platform; Agent-based model; Case-studies; Model forecasts; Scalings; Spread of disease; Synthetic populations; Varying population size; Zimbabwe; COVID-19
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Case report
Language:
English
Journal:
22nd Annual International Conference on Computational Science, ICCS 2022
Year:
2022
Document Type:
Article
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