Track: Modeling and Simulation

Abstract

Recently there are some mathematical models of COVID-19 transmission have been developed in an attempt to understand the disease. Various approaches of modeling have been devised by many authors which broadly divided into two different methods, the mechanistic modeling method and the empiric modeling method. In empirical modeling process ones usually look at the available COVID-19 data such as the cumulative cases of insidence and the daily cases of incidence. The data of such cases are usually available from legit situs such as the Worldometer website. This website usually  include total confirmed cases, daily new cases, daily active cases, daily death, etc. Ones usually use the time series data of the total confirmed cases to fit with a certain growth model. In this paper we will model the COVID-19 disease transmission by looking at the growth of the confirmed and daily new cases data. The growth model we choose is the Morgan-Mercer-Flodin Equation. We used the equation to model the COVID-19 transmission of Indonesia data starting on 2 February 2020, the official first day of the reported pandemic cases in Indonesia, up to 17 November 2020.  As a comparison, we also  used the classical Verhulst logistic model to fit the time series of the total confirmed cases and the daily new cases. In applying the growth  equations to the pandemic data we  denoted that X(t) is the cumulative of confirmed case at time t. The calculation is done using Solver in the Microsoft Excel application by choosing the GRG Nonlinear (Generalized Reduced Gradient) for the oftimization to find the minimum root of the mean square error as the measure.