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Hyong-Ha Kim 1 Article
Inherently high uncertainty in predicting the time evolution of epidemics
Seung-Nam Park, Hyong-Ha Kim, Kyoung Beom Lee
Epidemiol Health. 2021;43:e2021014.   Published online February 8, 2021
DOI: https://doi.org/10.4178/epih.e2021014
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  • 1 Citations
AbstractAbstract AbstractSummary PDF
Abstract
OBJECTIVES
Amid the spread of coronavirus disease 2019 (COVID-19), with its high infectivity, we have relied on mathematical models to predict the temporal evolution of the disease. This paper aims to show that, due to active behavioral changes of individuals and the inherent nature of infectious diseases, it is complicated and challenging to predict the temporal evolution of epidemics.
METHODS
A modified susceptible-exposed-infectious-hospitalized-removed (SEIHR) compartment model with a discrete feedback-controlled transmission rate was proposed to incorporate individuals’ behavioral changes into the model. To figure out relative uncertainties in the infection peak time and the fraction of the infected population at the peak, a deterministic method and 2 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% using the deterministic method. Incorporating 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 population of 1 million reached 29% and 9%, respectively. Even without feedback, the relative uncertainty of the peak time increased by up to 20% for a population of 100,000.
CONCLUSIONS
It is shown that uncertainty originates from stochastic properties of infections. Without a proper selection of the evolution scenario, active behavioral changes of individuals could serve as an additional source of uncertainty.
Summary
Korean summary
이 논문은 감염병에 대응하는 개인의 능동적 행동 변화와 감염병의 고유한 특성 때문에 그 진행을 예측하는 것은 복잡하고 도전적인 작업이라는 것을 보이기 위한 것이다. 이런 행동 변화를 고려하기 위하여 감염률에 피드백 제어를 줄 수 있는 SEIHR 수정 모델을 제안하였다. 최대 감염까지 경과 시간과 최대 감염률의 상대 불확도를 계산하기 위하여 하나의 결정론적 방법과 두 가지의 확률론적 방법 적용하였다. 감염병 예측의 불확도는 감염의 확률론적 성질에 기인하는 것을 알 수 있었다. 적절한 진행의 시나리오를 설정하지 못할 경우 개인의 능동적 행동 변화가 추가적인 불확도 요인이 될 것이다.
Key Message
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. 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. It is shown that the uncertainty of the prediction 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.

Citations

Citations to this article as recorded by  
  • Evolution and consequences of individual responses during the COVID-19 outbreak
    Wasim Abbas, Masud M. A., Anna Park, Sajida Parveen, Sangil Kim, Siew Ann Cheong
    PLOS ONE.2022; 17(9): e0273964.     CrossRef

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