Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study

Forna, Alpha, Dorigatti, Ilaria, Nouvellet, Pierre and Donnelly, Christl A (2021) Comparison of machine learning methods for estimating case fatality ratios: an Ebola outbreak simulation study. PLoS ONE, 16 (9). a0257005 1-15. ISSN 1932-6203

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Abstract

Background
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.

Item Type: Article
Keywords: Computer Simulation, Data Interpretation, Statistical, Datasets as Topic, Disease Outbreaks, Hemorrhagic Fever, Ebola, Humans, Machine Learning, Models, Statistical, Research Design, Survival Analysis
Schools and Departments: School of Life Sciences > Evolution, Behaviour and Environment
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 14 Jan 2022 11:50
Last Modified: 14 Jan 2022 12:00
URI: http://sro.sussex.ac.uk/id/eprint/103824

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