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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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Khan A, Bisanzio D, Mutuku F, Ndenga B, Grossi-Soyster EN, Jembe Z, Maina PW, Chebii PK, Ronga CO, Okuta V, LaBeaud AD. Spatiotemporal overlapping of dengue, chikungunya, and malaria infections in children in Kenya. BMC Infect Dis 2023; 23:183. [PMID: 36991340 PMCID: PMC10053720 DOI: 10.1186/s12879-023-08157-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
Malaria, chikungunya virus (CHIKV), and dengue virus (DENV) are endemic causes of fever among children in Kenya. The risks of infection are multifactorial and may be influenced by built and social environments. The high resolution overlapping of these diseases and factors affecting their spatial heterogeneity has not been investigated in Kenya. From 2014-2018, we prospectively followed a cohort of children from four communities in both coastal and western Kenya. Overall, 9.8% were CHIKV seropositive, 5.5% were DENV seropositive, and 39.1% were malaria positive (3521 children tested). The spatial analysis identified hot-spots for all three diseases in each site and in multiple years. The results of the model showed that the risk of exposure was linked to demographics with common factors for the three diseases including the presence of litter, crowded households, and higher wealth in these communities. These insights are of high importance to improve surveillance and targeted control of mosquito-borne diseases in Kenya.
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Affiliation(s)
- Aslam Khan
- Stanford University School of Medicine, Stanford, CA, USA.
- Center for Academic Medicine, 453 Quarry Road, Palo Alto, CA, 94304, USA.
| | | | | | | | | | - Zainab Jembe
- Msambweni County Referral hospital, Msambweni, Kenya
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Khan A, Ndenga B, Mutuku F, Bosire CM, Okuta V, Ronga CO, Mutai NK, Musaki SK, Chebii PK, Maina PW, Jembe Z, Amugongo JS, Malumbo SL, Ng'ang'a CM, LaBeaud D. Majority of pediatric dengue virus infections in Kenya do not meet 2009 WHO criteria for dengue diagnosis. PLOS Glob Public Health 2022; 2:e0000175. [PMID: 36962138 PMCID: PMC10021889 DOI: 10.1371/journal.pgph.0000175] [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: 08/25/2021] [Accepted: 03/09/2022] [Indexed: 11/19/2022]
Abstract
From 1975-2009, the WHO guidelines classified symptomatic dengue virus infections as dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. In 2009 the case definition was changed to a clinical classification after concern the original criteria was challenging to apply in resource-limited settings and not inclusive of a substantial proportion of severe dengue cases. Our goal was to examine how well the current WHO definition identified new dengue cases at our febrile surveillance sites in Kenya. Between 2014 and 2019 as part of a child cohort study of febrile illness in our four clinical study sites (Ukunda, Kisumu, Msambweni, Chulaimbo) we identified 369 dengue PCR positive symptomatic cases and characterized whether they met the 2009 revised WHO diagnostic criteria for dengue with and without warning signs and severe dengue. We found 62% of our PCR-confirmed dengue cases did not meet criteria per the guidelines. Our findings also correlate with our experience that dengue disease in children in Kenya is less severe as reported in other parts of the world. Although the 2009 clinical classification has recently been criticized for being overly inclusive and non-specific, our findings suggest the 2009 WHO dengue case definition may miss more than 50% of symptomatic infections in Kenya and may require further modification to include the African experience.
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Affiliation(s)
- Aslam Khan
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
| | - Bryson Ndenga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Francis Mutuku
- Department of Environment and Health Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Carren M Bosire
- Department of Pure and Applied Sciences, Technical University of Mombasa, Mombasa, Kenya
| | - Victoria Okuta
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Charles O Ronga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Noah K Mutai
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Sandra K Musaki
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Philip K Chebii
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Priscilla W Maina
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Zainab Jembe
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Jael S Amugongo
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Said L Malumbo
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Charles M Ng'ang'a
- Vector-Borne Diseases Unit, Msambweni County Referral Hospital, Msambweni, Kwale, Kenya
| | - Desiree LaBeaud
- Department of Pediatrics, Division of Infectious Diseases, Stanford University School of Medicine, Stanford, California, United States of America
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Shah MM, Ndenga BA, Mutuku FM, Vu DM, Grossi-Soyster EN, Okuta V, Ronga CO, Chebii PK, Maina P, Jembe Z, Bosire CM, Amugongo JS, Sahoo MK, Huang C, Weber J, Edgerton SV, Hortion J, Bennett SN, Pinsky BA, LaBeaud AD. High Dengue Burden and Circulation of 4 Virus Serotypes among Children with Undifferentiated Fever, Kenya, 2014-2017. Emerg Infect Dis 2021; 26:2638-2650. [PMID: 33079035 PMCID: PMC7588514 DOI: 10.3201/eid2611.200960] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Little is known about the extent and serotypes of dengue viruses circulating in Africa. We evaluated the presence of dengue viremia during 4 years of surveillance (2014–2017) among children with febrile illness in Kenya. Acutely ill febrile children were recruited from 4 clinical sites in western and coastal Kenya, and 1,022 participant samples were tested by using a highly sensitive real-time reverse transcription PCR. A complete case analysis with genomic sequencing and phylogenetic analyses was conducted to characterize the presence of dengue viremia among participants during 2014–2017. Dengue viremia was detected in 41.9% (361/862) of outpatient children who had undifferentiated febrile illness in Kenya. Of children with confirmed dengue viremia, 51.5% (150/291) had malaria parasitemia. All 4 dengue virus serotypes were detected, and phylogenetic analyses showed several viruses from novel lineages. Our results suggests high levels of dengue virus infection among children with undifferentiated febrile illness in Kenya.
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Shah MM, Ndenga BA, Mutuku FM, Okuta V, Ronga CO, Chebii PK, Maina P, Jembe Z, Sahoo MK, Huang C, Weber J, Pinsky BA, LaBeaud AD. No Evidence of O'nyong-nyong Viremia among Children with Febrile Illness in Kenya (2015-2018). Am J Trop Med Hyg 2021; 104:1435-1437. [PMID: 33617476 PMCID: PMC8045621 DOI: 10.4269/ajtmh.20-0580] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/10/2021] [Indexed: 11/07/2022] Open
Abstract
O'nyong-nyong virus (ONNV) is a little-known arbovirus causing intermittent, yet explosive, outbreaks in Africa. It is closely related to chikungunya virus, an emerging infectious disease. O'nyong-nyong virus causes a self-limited illness characterized by bilateral polyarthritis, rash, low-grade fever, and lymphadenopathy. In 1959, an extensive outbreak of ONNV occurred in East Africa, and decades later, another large outbreak was documented in Uganda in 1996. Limited evidence for interepidemic transmission is available, although serologic studies indicate a high prevalence of exposure. 1,045 febrile child participants in western and coastal Kenya were tested for the presence of ONNV using a multiplexed real-time reverse transcriptase-PCR assay. More than half of the participants had malaria parasitemia, and there was no evidence of active ONNV viremia in these participants. Further work is required to better understand the interepidemic circulation of ONNV and to reconcile evidence of high serologic exposure to ONNV among individuals in East Africa.
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Affiliation(s)
- Melisa M. Shah
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - 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
| | - Victoria Okuta
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Charles O. Ronga
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | | | | | | | - Malaya K. Sahoo
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - ChunHong Huang
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Jenna Weber
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Benjamin A. Pinsky
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Department of Pathology, Stanford University School of Medicine, Stanford, California
| | - Angelle Desiree LaBeaud
- Division of Infectious Disease, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
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Liu E, Vu D, Boothroyd D, Ndenga B, Onyango W, Okuta V, Labeaud AD. Evaluation of the Health-Related Quality of Life of Children With Dengue and Malaria in Western Kenya. Open Forum Infect Dis 2016. [DOI: 10.1093/ofid/ofw172.454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
| | - David Vu
- Stanford University, Stanford, California
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