journal.pone.0257005.pdf (2.87 MB)
Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study
journal contribution
posted on 2023-06-10, 02:17 authored by Alpha Forna, Ilaria Dorigatti, Pierre NouvelletPierre Nouvellet, Christl A DonnellyBackground Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous. Methods Using simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random—MCAR, missing at random—MAR, or missing not at random—MNAR). Results Across ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%–16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%–11%). Conclusion ML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings—patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes.
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Publication status
- Published
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- Published version
Journal
PLoS ONEISSN
1932-6203Publisher
Public Library of ScienceExternal DOI
Issue
9Volume
16Page range
1-15Article number
a0257005Event location
United StatesDepartment affiliated with
- Evolution, Behaviour and Environment Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-01-14First Open Access (FOA) Date
2022-01-14First Compliant Deposit (FCD) Date
2022-01-14Usage metrics
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