Detection of dengue virus type 2 of Indian origin in acute febrile patients in rural Kenya

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Abstract

Dengue virus (DENV) has caused recent outbreaks in coastal cities of Kenya, but the epidemiological situation in other areas of Kenya is largely unknown. We investigated the role of DENV infection as a cause of acute febrile disease in non-epidemic settings in rural and urban study areas in Kenya. Altogether, 560 patients were sampled in 2016–2017 in rural Taita–Taveta County ( n = 327) and urban slums of Kibera, Nairobi ( n = 233). The samples were studied for DENV IgM, IgG, NS1 antigen and flaviviral RNA. IgG seroprevalence was found to be higher in Taita–Taveta (14%) than in Nairobi (3%). Five Taita–Taveta patients were positive for flaviviral RNA, all identified as DENV-2, cosmopolitan genotype. Local transmission in Taita–Taveta was suspected in a patient without travel history. The sequence analysis suggested that DENV-2 strains circulating in coastal and southern Kenya likely arose from a single introduction from India. The molecular clock analyses dated the most recent ancestor to the Kenyan strains a year before the large 2013 outbreak in Mombasa. After this, the virus has been detected in Kilifi in 2014, from our patients in Taita–Taveta in 2016, and in an outbreak in Malindi in 2017. The results highlight that silent transmission occurs between epidemics and also affects rural areas. More information is needed to understand the local epidemiological characteristics and future risks of dengue in Kenya.

Author summary

Dengue virus (DENV) is an emerging mosquito-borne global health threat in the tropics and subtropics. The majority of the world’s population live in areas at risk of dengue that can cause a wide variety of symptoms from febrile illness to haemorrhagic fever. Information of DENV in Africa is limited and fragmented. In Kenya, dengue is a recognized disease in coastal cities that have experienced recent outbreaks. We investigated the role of DENV infection as a cause of acute febrile disease in non-epidemic settings in rural and urban study areas in Kenya. We found DENV-2 in five febrile patients from rural Taita–Taveta, where no dengue has been reported before. Genetic analysis of the virus suggests it to be most likely of Indian origin. This Indian origin DENV-2 was detected in the Mombasa outbreak in 2013, in Kilifi in 2014, in Taita–Taveta in 2016 (our study samples) and again in the Malindi outbreak in 2017. The results suggest that dengue is unrecognized in rural Kenya and more studies are needed for local risk assessment. Our findings of virus transmission between epidemics contribute to better understanding of the epidemiological situation and origins of DENV in Kenya.

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Most cited references 38

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A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

Heng Li (2011)

Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.

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Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Guy Baele, Philippe Lemey, Trevor Bedford … (2012)

Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.

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Asymptomatic humans transmit dengue virus to mosquitoes.

Veasna Duong, Louis Lambrechts, Richard Paul … (2015)

Three-quarters of the estimated 390 million dengue virus (DENV) infections each year are clinically inapparent. People with inapparent dengue virus infections are generally considered dead-end hosts for transmission because they do not reach sufficiently high viremia levels to infect mosquitoes. Here, we show that, despite their lower average level of viremia, asymptomatic people can be infectious to mosquitoes. Moreover, at a given level of viremia, DENV-infected people with no detectable symptoms or before the onset of symptoms are significantly more infectious to mosquitoes than people with symptomatic infections. Because DENV viremic people without clinical symptoms may be exposed to more mosquitoes through their undisrupted daily routines than sick people and represent the bulk of DENV infections, our data indicate that they have the potential to contribute significantly more to virus transmission to mosquitoes than previously recognized.

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Author and article information

Contributors

Moses Muia Masika: Role: Formal analysisRole: InvestigationRole: Writing – original draftRole: Writing – review & editing

Essi M. Korhonen:

ORCID: http://orcid.org/0000-0003-4730-7715

Role: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing

Teemu Smura: Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing

Ruut Uusitalo: Role: InvestigationRole: VisualizationRole: Writing – review & editing

Katariina Vapalahti: Role: Data curationRole: InvestigationRole: Writing – review & editing

Dufton Mwaengo: Role: Project administrationRole: ResourcesRole: Writing – original draftRole: Writing – review & editing

Anne J. Jääskeläinen: Role: InvestigationRole: MethodologyRole: Writing – review & editing

Omu Anzala: Role: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing

Olli Vapalahti: Role: Data curationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing

Eili Huhtamo: Role: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing

Joao Trindade Marques: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Negl Trop Dis

Journal ID (iso-abbrev): PLoS Negl Trop Dis

Journal ID (publisher-id): plos

Journal ID (pmc): plosntds

Title: PLoS Neglected Tropical Diseases

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1935-2727

ISSN (Electronic): 1935-2735

Publication date (Electronic): 3 March 2020

Publication date Collection: March 2020

Volume: 14

Issue: 3

Electronic Location Identifier: e0008099

Affiliations

[1 ] Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya

[2 ] KAVI Institute of Clinical Research, University of Nairobi, Nairobi, Kenya

[3 ] Department of Virology, Medicum, University of Helsinki, Helsinki, Finland

[4 ] Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland

[5 ] Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland

[6 ] Institute of Tropical and Infectious Diseases, University of Nairobi, Nairobi, Kenya

[7 ] Department of Virology and Immunology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

Universidade Federal de Minas Gerais, BRAZIL

Author notes

The authors have declared that no competing interests exist.

* E-mail: essi.m.korhonen@ 123456helsinki.fi

Author information

Essi M. Korhonen http://orcid.org/0000-0003-4730-7715

Article

Publisher ID: PNTD-D-19-01011

DOI: 10.1371/journal.pntd.0008099

PMC ID: 7069648

PubMed ID: 32126086

SO-VID: d8bf38c7-515d-4459-ad5d-18395e597857

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 18 June 2019

Date accepted : 29 January 2020

Page count

Figures: 3, Tables: 3, Pages: 17

Funding

Funded by: Academy of Finland

Award Recipient : Olli Vapalahti

Funded by: funder-id http://dx.doi.org/10.13039/100008376, Helsingin ja Uudenmaan Sairaanhoitopiiri;

Award ID: HUSLAB; TYH2017257

Award Recipient : Olli Vapalahti

This work was funded by The Centre for International Mobility (CIMO) Finland to MM, the Paulo Foundation to EH, Academy of Finland to OV, Helsinki University to OV, Hospital (HUSLAB; TYH2017257), Jane and Aatos Erkko Foundation, Finland to OV, and Finnish Cultural Foundation, Kymenlaakso Foundation to EMK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Custom metadata

PLOS Publication Stage vor-update-to-uncorrected-proof

Publication Update 2020-03-13

Data Availability All relevant data are within the manuscript and its Supporting Information files.

Detection of dengue virus type 2 of Indian origin in acute febrile patients in rural Kenya

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Most cited references 38

A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

Asymptomatic humans transmit dengue virus to mosquitoes.

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