A new computational algorithm to estimate case fatality rate

Kidane Desta Gebreyesus, Chuan Hsiung Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Case fatality rate, an indicator of disease severity, is one of the most important parameters in medical and epidemiological studies. Simple naive estimate method is often used to estimate case fatality rate. However, the naive method can lead to biased estimates for a number of reasons including, for example, ascertainment error and incomplete data. Here, we developed a new model called NELM (derived from Naive Estimate and Linear Model), which incorporates both the naive method and the regression models, for the regression model takes Gaussian random error into account. We illustrated the algorithm using Ebola epidemic data of three West African countries. Our algorithm is robust and effective. And it provides insight into the ongoing Ebola outbreak and other emerging infectious diseases.

Original languageEnglish
Title of host publicationWCE 2016 - World Congress on Engineering 2016
EditorsS. I. Ao, S. I. Ao, Len Gelman, S. I. Ao, Len Gelman, David W.L. Hukins, Andrew Hunter, Alexander M. Korsunsky
PublisherNewswood Limited
Number of pages3
ISBN (Electronic)9789881404800
StatePublished - 2016
EventWorld Congress on Engineering 2016, WCE 2016 - London, United Kingdom
Duration: 29 Jun 20161 Jul 2016

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958


ConferenceWorld Congress on Engineering 2016, WCE 2016
Country/TerritoryUnited Kingdom


  • Case fatality rate
  • Computational algorithm
  • Disease control
  • Linear model
  • Naive method


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