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 -