Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19

Kerem Senel*, Mesut Ozdinc, Selcen Ozturkcan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic. K-means algorithm is used to perform a cluster analysis of the top ten countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness. As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited. We propose a SPE (Single Parameter Estimation) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy and decision-makers in shedding light on the next phases of the pandemic.

Original languageEnglish
JournalDisaster Medicine and Public Health Preparedness
DOIs
Publication statusE-pub ahead of print - 25 Jun 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • coronavirus
  • COVID-19
  • epidemic models
  • robust estimation
  • SIR

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