수학적 모델을 이용한 신종인플루엔자 환자 예측 및 대응 전략 평가
Mathematical Modeling of the Novel Influenza A (H1N1) Virus and Evaluation of the Epidemic Response Strategies in the Republic of Korea
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초록
Objectives: The pandemic of novel influenza A (H1N1) virus has required decision-makers to act in the face of the substantial uncertainties. In this study, we evaluated the potential impact of the pandemic response strategies in the Republic of Korea using a mathematical model. Methods: We developed a deterministic model of a pandemic (H1N1) 2009 in a structured population using the demographic data from the Korean population and the epidemiological feature of the pandemic (H1N1) 2009. To estimate the parameter values for the deterministic model, we used the available data from the previous studies on pandemic influenza. The pandemic response strategies of the Republic of Korea for novel influenza A (H1N1) virus such as school closure, mass vaccination (70%of population in 30 days), and a policy for anti-viral drug (treatment or prophylaxis) were applied to the deterministic model. Results: The effect of two-week school closure on the attack rate was low regardless of the timing of the intervention. The earlier vaccination showed the effect of greater delays in reaching the peak of outbreaks. When it was no vaccination,vaccination at initiation of outbreak, vaccination 90 days after the initiation of outbreak and vaccination at the epidemic peak point, the total number of clinical cases for 400 days were 20.8 million, 4.4 million, 4.7 million and 12.6 million, respectively. The pandemic response strategies of the Republic of Korea delayed the peak of outbreaks (about 40 days) and decreased the number of cumulative clinical cases (8 million). Conclusions: Rapid vaccination was the most important factor to control the spread of pandemic influenza, and the response strategies of the Republic of Korea were shown to delay the spread of pandemic influenza in this deterministic model.
Abstract
Objectives: The pandemic of novel influenza A (H1N1) virus has required decision-makers to act in the face of the substantial uncertainties. In this study, we evaluated the potential impact of the pandemic response strategies in the Republic of Korea using a mathematical model. Methods: We developed a deterministic model of a pandemic (H1N1) 2009 in a structured population using the demographic data from the Korean population and the epidemiological feature of the pandemic (H1N1) 2009. To estimate the parameter values for the deterministic model, we used the available data from the previous studies on pandemic influenza. The pandemic response strategies of the Republic of Korea for novel influenza A (H1N1) virus such as school closure, mass vaccination (70%of population in 30 days), and a policy for anti-viral drug (treatment or prophylaxis) were applied to the deterministic model. Results: The effect of two-week school closure on the attack rate was low regardless of the timing of the intervention. The earlier vaccination showed the effect of greater delays in reaching the peak of outbreaks. When it was no vaccination,vaccination at initiation of outbreak, vaccination 90 days after the initiation of outbreak and vaccination at the epidemic peak point, the total number of clinical cases for 400 days were 20.8 million, 4.4 million, 4.7 million and 12.6 million, respectively. The pandemic response strategies of the Republic of Korea delayed the peak of outbreaks (about 40 days) and decreased the number of cumulative clinical cases (8 million). Conclusions: Rapid vaccination was the most important factor to control the spread of pandemic influenza, and the response strategies of the Republic of Korea were shown to delay the spread of pandemic influenza in this deterministic model.
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