Selected article for: "compartmental model and ordinary differential equation"
Author: Shi Chen; Qin Li; Song Gao; Yuhao Kang; Xun Shi
Title: Mitigating COVID-19 outbreak via high testing capacity and strong transmission-intervention in the United States
  • Document date: 2020_4_7
    • ID: c84ybwve_18
    Snippet: The mathematical model that simulates the spatiotemporal dynamics of state-level infections in the United States is a modified compartmental model in epidemiology by taking into account the variation of the 51 administrative units and their interactions (6, 17, 19) . It consists of 51 ordinary differential equation (ODE) systems, with each one characterizing the evolution of susceptible (S), exposed (E), reported (I), unreported (A) and resolved .....
    Document: The mathematical model that simulates the spatiotemporal dynamics of state-level infections in the United States is a modified compartmental model in epidemiology by taking into account the variation of the 51 administrative units and their interactions (6, 17, 19) . It consists of 51 ordinary differential equation (ODE) systems, with each one characterizing the evolution of susceptible (S), exposed (E), reported (I), unreported (A) and resolved (R) cases per state (see more details in the supplementary material and also Fig. S1 ). The 51 ODE systems are then coupled through the state-to-state travel network flows (see Fig. S2 ) that were extracted from the aggregated SafeGraph mobility data and weighted by α t (25, 31) . Unlike most other models, we also incorporate the potential asymptomatic transmission. This makes the derivation of the basic reproduction number R 0 different. Besides, each ODE system also includes two unknown parameters: the transmission rate (b) and the report rate for each state (r). The unknown parameters are inferred based on the number of confirmed cases in each state for the period of March 1-March 20, 2020. The source of infection case data is (10) .

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