In this presentation, we present the intervention strategies on the infectious diseases, especially Ebola virus disease (EVD) epidemics in West Africa and 2009 A/H1N1 influenza panemic in Korea.
First, the epidemics of EVD threaten a public health with a high fatality rate and an absence of licensed treatment and vaccines. On August 8, 2014, the World Health Organization declared the epidemic a "Public Health Emergency of International Concern''. The control interventions of public health measures are crucial factors for stopping Ebola transmission. In this work, we propose optimal intervention strategy for preventing the EVD epidemic by a mathematical model. The SEIR type model incorporated with time-dependent control measures was developed and optimal control theory has been applied to this controlled Ebola model. In West Africa, the dead people but not yet buried is one of major sources of infection transmission. Families or friends touch and kiss the dead body during traditional funeral ceremony. One burial control and three distancing controls in the community, at the hospital and during burial were considered to investigate optimal intervention strategies for minimizing the infectious individuals while keeping the cost implementing the controls. Simulation results show that although we do controlling only the dead people but not yet buried, optimal control strategies show dramatically reducing the EVD transmission. Finally, we also show the estimation of the number of maximum sickbeds using the controlled EVD model under the various scenarios.
Second, we present a spatial-temporal model of 2009 A/H1N1 influenza in Korea and the intervention strategies for preventing the spread of influenza during early period. A novel influenza A/H1N1 is characterized by high transmissibility and low fatality. Population mobility is considered as a key factor in the spread of pandemic influenza. In this study, we propose a multipatch SEIAR model based on daily data of the confirmed A/H1N1 influenza cases collected by the Korea Center for Disease Control (KCDC) from April 27 to September 15, 2009. Population movements estimated from census collection dataset on 33 administrative regions in Seoul metropolitan area are used. The transmission rate on each region is estimated by a least squares fit to the KCDC data. We also analyze the correlation between the basic reproductive numbers and spatial factor for spread of influenza, and visualize how the influenza spreads out in Seoul metropolitan area by Geographic Information System (GIS). The effect of non-pharmaceutical intervention such as quarantine and isolation during early spread of the influenza is performed and containment polices for outbreak is suggested.