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Vu DM, Krystosik AR, Ndenga BA, Mutuku FM, Ripp K, Liu E, Bosire CM, Heath C, Chebii P, Maina PW, Jembe Z, Malumbo SL, Amugongo JS, Ronga C, Okuta V, Mutai N, Makenzi NG, Litunda KA, Mukoko D, King CH, LaBeaud AD. Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning algorithms. PLOS Glob Public Health 2023; 3:e0001950. [PMID: 37494331 PMCID: PMC10370704 DOI: 10.1371/journal.pgph.0001950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 06/29/2023] [Indexed: 07/28/2023]
Abstract
Poor access to diagnostic testing in resource limited settings restricts surveillance for emerging infections, such as dengue virus (DENV), to clinician suspicion, based on history and exam observations alone. We investigated the ability of machine learning to detect DENV based solely on data available at the clinic visit. We extracted symptom and physical exam data from 6,208 pediatric febrile illness visits to Kenyan public health clinics from 2014-2019 and created a dataset with 113 clinical features. Malaria testing was available at the clinic site. DENV testing was performed afterwards. We randomly sampled 70% of the dataset to develop DENV and malaria prediction models using boosted logistic regression, decision trees and random forests, support vector machines, naïve Bayes, and neural networks with 10-fold cross validation, tuned to maximize accuracy. 30% of the dataset was reserved to validate the models. 485 subjects (7.8%) had DENV, and 3,145 subjects (50.7%) had malaria. 220 (3.5%) subjects had co-infection with both DENV and malaria. In the validation dataset, clinician accuracy for diagnosis of malaria was high (82% accuracy, 85% sensitivity, 80% specificity). Accuracy of the models for predicting malaria diagnosis ranged from 53-69% (35-94% sensitivity, 11-80% specificity). In contrast, clinicians detected only 21 of 145 cases of DENV (80% accuracy, 14% sensitivity, 85% specificity). Of the six models, only logistic regression identified any DENV case (8 cases, 91% accuracy, 5.5% sensitivity, 98% specificity). Without diagnostic testing, interpretation of clinical findings by humans or machines cannot detect DENV at 8% prevalence. Access to point-of-care diagnostic tests must be prioritized to address global inequities in emerging infections surveillance.
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Affiliation(s)
- David M Vu
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
| | - Amy R Krystosik
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
| | - Bryson A Ndenga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Francis M Mutuku
- Department of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Kelsey Ripp
- University of Global Health Equity, Butaro, Rwanda
| | - Elizabeth Liu
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
| | - Carren M Bosire
- Department of Pure and Applied Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Claire Heath
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
| | - Philip Chebii
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | | | - Zainab Jembe
- Vector-Borne Diseases Unit, Diani Health Center, Ukunda, Kwale, Kenya
| | - Said Lipi Malumbo
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Jael Sagina Amugongo
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Charles Ronga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Victoria Okuta
- Paediatric Department, Obama Children's Hospital, Jaramogi Oginga Odinga Referral Hospital, Kisumu, Kenya
| | - Noah Mutai
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Nzaro G Makenzi
- Department of Pure and Applied Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Kennedy A Litunda
- Department of Pure and Applied Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Dunstan Mukoko
- Vector-Borne Diseases Unit, Ministry of Health, Nairobi, Kenya
| | - Charles H King
- Department of Pathology, Center for Global Health and Diseases, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - A Desiree LaBeaud
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
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