Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.
【저자키워드】 Infectious diseases, Socioeconomic scenarios, Nonlinear phenomena, 【초록키워드】 social distancing, COVID-19 pandemic, Variation, Infection, virus, outbreak, modeling, Research, Travel, Isolation, compartmental model, United States, information, New York, resource, Interaction, MOST, SEIR, self-quarantine, widespread, Prevent, effective, produced, example, the United State, the disease, determine, reveal, susceptible-exposed-infectious-removed, mathematical, 【제목키워드】 COVID-19 infection, United States, measure, the United State,