Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark
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Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks : a case study using population data from Denmark. / Heltberg, Mathias Spliid; Michelsen, Christian; Martiny, Emil S.; Christensen, Lasse Engbo; Jensen, Mogens H.; Halasa, Tariq; Petersen, Troels C.
I: Royal Society Open Science, Bind 9, Nr. 9, 220018, 14.09.2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks
T2 - a case study using population data from Denmark
AU - Heltberg, Mathias Spliid
AU - Michelsen, Christian
AU - Martiny, Emil S.
AU - Christensen, Lasse Engbo
AU - Jensen, Mogens H.
AU - Halasa, Tariq
AU - Petersen, Troels C.
PY - 2022/9/14
Y1 - 2022/9/14
N2 - 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.
AB - 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.
KW - pandemics
KW - agent-based modelling
KW - spatial heterogenity
KW - fitting
KW - COVID-19
U2 - 10.1098/rsos.220018
DO - 10.1098/rsos.220018
M3 - Journal article
C2 - 36117868
VL - 9
JO - Royal Society Open Science
JF - Royal Society Open Science
SN - 2054-5703
IS - 9
M1 - 220018
ER -
ID: 321271663