An epidemic simulation with a delayed stochastic SIR model based on international socioeconomic-technological databases

This study proposes an epidemic model for Ebola virus disease (EVD) based on a combination between a delayed stochastic SIR model and a metapopulation network model. Our proposed model consists of a set of stochastic differential equations of state variables, such as the number of susceptible persons, the number of infected patients both inside and outside of isolation wards, and the number of recovered persons both inside and outside of isolation wards, the number of deaths. We collected socioeconomic, technological-environmental data, such as OAG aviation timetable data, grid statistics on world population estimates provided by the Socioeconomic Data and Applications Center (SEDAC), the number of cases and deaths by EVD reported in 2014 by the World Health Organization (WHO) and economic statistics provided from the World DataBank by the World Bank. Linking these databases, we calibrated model parameters. We then conducted a numerical simulation by using aviation timetables and calculated the potential numbers of infectious persons and deaths. We found that the pandemic would not be completely prevented in industrialized countries by medical efforts alone. We conducted sensitivity analysis for the numbers of cases and deaths in terms of possible scenarios for medical, socioeconomic, and transport dimensions. We conclude that the medical efforts in industrialized countries can control the pandemic rate and that international aviation transport can reduce the number of passengers from places where epidemic outbreaks occur, and delay the beginning of the pandemic. The numerical simulation model can be extended to epidemic diseases other than EVD.