Analysis of the mitigation strategies for COVID-19: from mathematical modelling perspective

Semu Kassa; Hatson John Boscoh Njagarah; Yibeltal Adane Terefe

The parameter values that best fit to the incidence data is given in Table 3 . Figure 2 shows the plot of 288 the reported new-case data together with the incidence of the disease obtained from the model. As 289 we can observe from the graph, the model slightly overestimates the reported data except for the two 290 highest points. In addition, since our model does not assume any control measure at this stage while 291 the reported data after the 31st day may represent the effect of the strict lockdown measure taken by 292 the authorities, the parameters estimated seem to give a good result. When we calculate the value of 293 R 0 from Eq. (3.5) using the estimated parameters given in Table 3 , we obtain R 0 ≈ 2.91, which is 296 We examine the sensitivity of R 0 to variations in parameter values and establish the significance of 297 the sensitivity indices. We used the Latin hypercube Sampling (LHS) scheme, which is an efficient 298 stratified Monte Carlo sampling that allows for simultaneous sampling of the multi-dimensional 299 parameter space [19] . For each run, 1000 simulations were done and Partial Rank Correlation implies that when the value of the input parameter increases, the future number of cases will also 305 12 . 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.