Prediction of the peak Covid-19 pandemic in Indonesia using SIR model

Abstract

This research implements the Susceptible, Infected, and Removed (SIR) model to predict the Covid-19 outbreak in Indonesia. The government official data, consisting of infected, dead, and recovered, are used as actual data to interpolate the model through matching data with minimum mean squared error (MSE). The study uses one of the Quasi-Newton search methods, the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) algorithm, to determine the interaction coefficient's optimal value in the model with the minimum MSE value. Based on data as of July 18, 2020, it predicts that the peak of the infected number will be in October 2020 with around 14 % of the total population infected, and the MSE of 18.42 is relative to the period of the actual data. Meanwhile, the basic reproduction rate is calculated to be 2.035 from the model, where it is underestimated about 29 % compared to the relative basic reproduction rate from the provided actual data.

Analisis Penyebaran Covid 19 Menggunakan Model SIR (Susceptible, Infected, Recovered) Di Provinsi Bali

“…Model epidemiologi SIR dibagi menjadi tiga kompartemen, yaitu kelompok individu yang rentan penyakit (susceptible), kelompok individu yang terinfeksi (infected), kelompok individu yang telah sembuh dan kebal dari penyakit (recovered) [7]. Walaupun SIR memiliki bentuk model yang sederhana, model SIR dinilai paling sesuai untuk prediksi berbasis data dibandingkan dengan model lainnya yang lebih kompleks [8].…”

Nutrition control in nutrient film technique hydroponic system using fuzzy method

“…The fuzzy model for controlling hydroponic cultivation has been implemented by [3,4]. Whether the DFT system controlled its water pH was developed by [5] by using the Internet of Things system. [6] was developed as a hydroponic tower with a nutrient control system.…”

Infectious Disease Modeling with Socio-Viral Behavioral Aspects—Lessons Learned from the Spread of SARS-CoV-2 in a University

“…Moreover, setting constant rates of transmission and recovery results in a high number of projected infected cases once the model is applied to a vast and highly populated community; this could be at the scale of entire nations [ 12 ]. According to recent studies, SIR-based predictions using early data for COVID-19 cases have shown an enormous figure for predicted cases, with the peak reaching up to 15–30% of the total population [ 13 , 14 ]. Nevertheless, an absence of the psychological behavior of the population could overestimate the prediction figure [ 15 ].…”