tetano
Editor, Senior Moderator
Epidemiol Health
. 2021 Feb 8;e2021014.
doi: 10.4178/epih.e2021014. Online ahead of print.
Inherently High Uncertainty in Predicting the Time Evolution of Epidemics
Seung-Nam Park[SUP] 1 2 [/SUP], Hyong-Ha Kim[SUP] 1 [/SUP], Kyoung Beom Lee[SUP] 1 2 [/SUP]
Affiliations
Abstract
Objectives: Amid the spread of the coronavirus disease (COVID-19) with high infectivity, we rely on mathematical models to predict temporal evolutions of the disease. This paper is to show that, due to active behavioral changes of individuals and inherent natures of infectious diseases, it is complicated and challenging to predict the temporal evolutions.
Methods: A modified-SEIHR compartment model with a discretely feedback-controlled transmission rate was proposed to incorporate the behavioral changes of individuals into the model. To figure out relative uncertainties in the infection peak times and the fraction of the infected population at the peak, a deterministic method and two stochastic methods were applied.
Results: A relatively small behavioral change of individuals with a feedback constant of 0.02 in the modified SEIHR model resulted in a peak time delay of up to 50 % in the deterministic method. Incorporating the stochastic methods into the modified model with a feedback constant of 0.04 suggested that the relative random uncertainty of the maximum fraction of infections and that of the peak time for a million of population reached 29 % and 9% respectively. Even without the feedback, the relative uncertainty of the peak time increased by up to 20 % for a population of 100,000.
Conclusion: It is shown that the uncertainty originates from stochastic properties of the infections. Without a proper selection of the evolution scenarios, the active behavioral changes of individuals could cause an additional uncertainty.
Keywords: COVID-19; Epidemiology; Mathematical model; Uncertainty.
. 2021 Feb 8;e2021014.
doi: 10.4178/epih.e2021014. Online ahead of print.
Inherently High Uncertainty in Predicting the Time Evolution of Epidemics
Seung-Nam Park[SUP] 1 2 [/SUP], Hyong-Ha Kim[SUP] 1 [/SUP], Kyoung Beom Lee[SUP] 1 2 [/SUP]
Affiliations
- PMID: 33561915
- DOI: 10.4178/epih.e2021014
Abstract
Objectives: Amid the spread of the coronavirus disease (COVID-19) with high infectivity, we rely on mathematical models to predict temporal evolutions of the disease. This paper is to show that, due to active behavioral changes of individuals and inherent natures of infectious diseases, it is complicated and challenging to predict the temporal evolutions.
Methods: A modified-SEIHR compartment model with a discretely feedback-controlled transmission rate was proposed to incorporate the behavioral changes of individuals into the model. To figure out relative uncertainties in the infection peak times and the fraction of the infected population at the peak, a deterministic method and two stochastic methods were applied.
Results: A relatively small behavioral change of individuals with a feedback constant of 0.02 in the modified SEIHR model resulted in a peak time delay of up to 50 % in the deterministic method. Incorporating the stochastic methods into the modified model with a feedback constant of 0.04 suggested that the relative random uncertainty of the maximum fraction of infections and that of the peak time for a million of population reached 29 % and 9% respectively. Even without the feedback, the relative uncertainty of the peak time increased by up to 20 % for a population of 100,000.
Conclusion: It is shown that the uncertainty originates from stochastic properties of the infections. Without a proper selection of the evolution scenarios, the active behavioral changes of individuals could cause an additional uncertainty.
Keywords: COVID-19; Epidemiology; Mathematical model; Uncertainty.