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How many can you infect? Simple (and naive) methods of estimating the reproduction number

  • H. Susanto
  • , V. R. Tjahjono
  • , A. Hasan
  • , M. F. Kasim
  • , N. Nuraini
  • , E. R.M. Putri
  • , R. Kusdiantara
  • , H. Kurniawan
  • Institut Teknologi Sepuluh Nopember
  • University of Southern Denmark
  • University of Oxford
  • Institut Teknologi Bandung

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

This is a pedagogical paper on estimating the number of people that can be infected by one infectious person during an epidemic outbreak, known as the reproduction number. Knowing the number is crucial for developing policy responses. There are generally two types of such a number, i.e., basic and effective (or instantaneous). While basic reproduction number is the average expected number of cases directly generated by one case in a population where all individuals are susceptible, effective reproduction number is the number of cases generated in the current state of a population. In this paper, we exploit the deterministic susceptible-infected-removed (SIR) model to estimate them through three different numerical approximations. We apply the methods to the pandemic COVID-19 in Italy to provide insights into the spread of the disease in the country. We see that the effect of the national lockdown in slowing down the disease exponential growth appeared about two weeks after the implementation date. We also discuss available improvements to the simple (and naive) methods that have been made by researchers in the field. Authors of this paper are members of the SimcovID (Simulasi dan Pemodelan COVID-19 Indonesia) collaboration.

Original languageBritish English
Pages (from-to)28-36
Number of pages9
JournalCommunication in Biomathematical Sciences
Volume3
Issue number1
DOIs
StatePublished - 8 Apr 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • compartment model
  • COVID-19
  • infectious disease
  • Reproduction number

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