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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.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 22nd Annual International Conference on Computational Science, ICCS 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 22nd Annual International Conference on Computational Science, ICCS 2022 Year: 2022 Document Type: Article