Transmission Dynamics of COVID-19 and Impact on Public Health Policy

B Shayak; Mohit Manoj Sharma; Richard H Rand; Awadhesh Kumar Singh; Anoop Misra

is the author/funder, who has granted medRxiv a license to display the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03. 29.20047035 doi: medRxiv preprint this period. The benchmark μ1 comes from Reference [50] . The parameters τ3, k3, k4 and μ2 on the other hand are region-specific and they come from records collected at the testing facilities in the region. Here we have gone with average, plausible values -when modeling a specific region they will need to be determined from the data. The most important parameter in the model is k0 which has both virological and region-dependent contributions -here we have chosen it so as to generate a peak in reported cases at about 35-40 days after initiation. We have found that the results which we shall claim in the subsequent pages are to a large degree independent of the exact numerical values of the parameters and hold true whenever there is a general spreading profile of a sharp peak surrounded by flats on both sides. Later in this Section, we shall give an example of how to estimate the parameters by fitting the model to an available data set. We note that for the benchmark run, the fraction of healthy at large people remaining at the end of the 100day period is 31 percent of the initial value. This number will be useful in comparing the disease trajectories as the parameters are varied.