Modeling COVID-19: Forecasting and analyzing the dynamics of the outbreak in Hubei and Turkey

ibrahim Halil Aslan; Mahir Demir; Michael Morgan Wise; Suzanne Lenhart

Several parameters play important roles in the model (1). These parameters were estimated with existing data as of (Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE, 2020). In order to determine the set of parameters that are statistically significant regarding the number of cumulative infected cases, we conduct a sensitivity analysis of the model. We utilized a Latin Hypercube Sampling (LHS) and the Partial Rank Correlation Coefficients (PRCC) method (Marino, Hogue, Ray, & Kirschner, 2008) . We use a range given in Table 2 to sample parameters from a uniform distribution, then use these samples as input variables when we run the system (1) with initial conditions S(0) = 1000, S q (0) = 0, E(0) = 10, I(0) = 3, I q (0) = 0, R(0) = 0 for 90 days. The number of cumulative infected cases is the output variables in sensitivity analysis. Table 2 shows PRCC values, p-values and the range for each corresponding parameters. The sensitivity analysis indicates that β, s q , i q , and r are statistically more significant parameters depending on the . CC-BY-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.