Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.
R Soc Open Sci
; 9(9): 220018, 2022 Sep.
Article
in English
| MEDLINE | ID: covidwho-2034608
ABSTRACT
The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Case report
/
Prognostic study
Language:
English
Journal:
R Soc Open Sci
Year:
2022
Document Type:
Article
Affiliation country:
Rsos.220018
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