Article ; Online: Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.
2021 Volume 16, Issue 9, Page(s) e0257005
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.: ... ...
| 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. |
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| MeSH term(s) | Computer Simulation ; Data Interpretation, Statistical ; Datasets as Topic ; Disease Outbreaks ; Hemorrhagic Fever, Ebola/epidemiology ; Hemorrhagic Fever, Ebola/mortality ; Humans ; Machine Learning ; Models, Statistical ; Research Design ; Survival Analysis |
| Language | English |
| Publishing date | 2021-09-15 |
| Publishing country | United States |
| Document type | Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2267670-3 |
| ISSN | 1932-6203 ; 1932-6203 |
| ISSN (online) | 1932-6203 |
| ISSN | 1932-6203 |
| DOI | 10.1371/journal.pone.0257005 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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