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Indonesian COVID-19 Case Modeling Using Gaussian Equation

Originally published: Jurnal Sains dan Teknologi Mitigasi Bencana, 2020 Author: Luthfi Yufajjiru Surya Dharma

Abstract

This research aimed to construct a mathematical model using the Gaussian equation to predict the trajectory of COVID-19 cases in Indonesia during the early stages of the pandemic. By analyzing data scraped from official sources, the study produced models for daily and cumulative cases, recoveries, and deaths, providing early projections for public health awareness.

Methodology

The modeling used the Gaussian function to represent the distribution of cases over time. Data was collected by scraping information from the Ministry of Health (KEMENKES RI) via official communication channels. The model parameters were evaluated and assessed in April 2020 to generate early-stage predictions.

Key Insights

  • Multi-faceted Modeling: The study modeled eight different parameters, including daily active cases and cumulative deaths.
  • Predictive Scope: Early projections suggested that cases could surpass 33,000 with deaths exceeding 4,000, based on the data available at the time.
  • Precision and Variables: The research highlighted that Root Mean Square Error (RMSE) was significantly influenced by community behavior and government policy shifts, which "reshaped" the theoretical curve.
  • Data Dependency: A critical finding was the dependency of positive case numbers on testing capacity, suggesting that reported cumulative cases were a subset of reality.

This is a summary of the academic publication as part of Luthfi Yufajjiru's research archive.