Dataset Information

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico.

ABSTRACT: The Zika virus (ZIKV) epidemic in the Americas established ZIKV as a major public health threat and uncovered its association with severe diseases, including microcephaly. However, genetic epidemiology in some at-risk regions, particularly Central America and Mexico, remains limited. We report 61 ZIKV genomes from this region, generated using metagenomic sequencing with ZIKV-specific enrichment, and combine phylogenetic, epidemiological, and environmental data to reconstruct ZIKV transmission. These analyses revealed multiple independent ZIKV introductions to Central America and Mexico. One introduction, likely from Brazil via Honduras, led to most infections and the undetected spread of ZIKV through the region from late 2014. Multiple lines of evidence indicate biannual peaks of ZIKV transmission in the region, likely driven by varying local environmental conditions for mosquito vectors and herd immunity. The spatial and temporal heterogeneity of ZIKV transmission in Central America and Mexico challenges arbovirus surveillance and disease control measures.

SUBMITTER: Theze J

PROVIDER: S-EPMC6006413 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico.

Thézé Julien J Li Tony T du Plessis Louis L Bouquet Jerome J Kraemer Moritz U G MUG Somasekar Sneha S Yu Guixia G de Cesare Mariateresa M Balmaseda Angel A Kuan Guillermina G Harris Eva E Wu Chieh-Hsi CH Ansari M Azim MA Bowden Rory R Faria Nuno R NR Yagi Shigeo S Messenger Sharon S Brooks Trevor T Stone Mars M Bloch Evan M EM Busch Michael M Muñoz-Medina José E JE González-Bonilla Cesar R CR Wolinsky Steven S López Susana S Arias Carlos F CF Bonsall David D Chiu Charles Y CY Pybus Oliver G OG

Cell host & microbe 20180524 6

The Zika virus (ZIKV) epidemic in the Americas established ZIKV as a major public health threat and uncovered its association with severe diseases, including microcephaly. However, genetic epidemiology in some at-risk regions, particularly Central America and Mexico, remains limited. We report 61 ZIKV genomes from this region, generated using metagenomic sequencing with ZIKV-specific enrichment, and combine phylogenetic, epidemiological, and environmental data to reconstruct ZIKV transmission. T ...[more]

PMID: 29805095

Similar Datasets

Project description:BackgroundZika is of great medical relevance due to its rapid geographical spread in 2015 and 2016 in South America and its serious implications, for example, certain birth defects. Recent epidemics urgently require a better understanding of geographic patterns of the Zika virus transmission risk. This study aims to map the Zika virus transmission risk in South and Central America. We applied the maximum entropy approach, which is common for species distribution modelling, but is now also widely in use for estimating the geographical distribution of infectious diseases.MethodsAs predictor variables we used a set of variables considered to be potential drivers of both direct and indirect effects on the emergence of Zika. Specifically, we considered (a) the modelled habitat suitability for the two main vector species Aedes aegypti and Ae. albopictus as a proxy of vector species distributions; (b) temperature, as it has a great influence on virus transmission; (c) commonly called evidence consensus maps (ECM) of human Zika virus infections on a regional scale as a proxy for virus distribution; (d) ECM of human dengue virus infections and, (e) as possibly relevant socio-economic factors, population density and the gross domestic product.ResultsThe highest values for the Zika transmission risk were modelled for the eastern coast of Brazil as well as in Central America, moderate values for the Amazon basin and low values for southern parts of South America. The following countries were modelled to be particularly affected: Brazil, Colombia, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Puerto Rico and Venezuela. While modelled vector habitat suitability as predictor variable showed the highest contribution to the transmission risk model, temperature of the warmest quarter contributed only comparatively little. Areas with optimal temperature conditions for virus transmission overlapped only little with areas of suitable habitat conditions for the two main vector species. Instead, areas with the highest transmission risk were characterised as areas with temperatures below the optimum of the virus, but high habitat suitability modelled for the two main vector species.ConclusionModelling approaches can help estimating the spatial and temporal dynamics of a disease. We focused on the key drivers relevant in the Zika transmission cycle (vector, pathogen, and hosts) and integrated each single component into the model. Despite the uncertainties generally associated with modelling, the approach applied in this study can be used as a tool and assist decision making and managing the spread of Zika.

Project description:BACKGROUND:Colombia was the second most affected country during the American Zika virus (ZIKV) epidemic, with over 109,000 reported cases. Despite the scale of the outbreak, limited genomic sequence data were available from Colombia. We sought to sequence additional samples and use genomic epidemiology to describe ZIKV dynamics in Colombia. METHODS:We sequenced ZIKV genomes directly from clinical diagnostic specimens and infected Aedes aegypti samples selected to cover the temporal and geographic breadth of the Colombian outbreak. We performed phylogeographic analysis of these genomes, along with other publicly-available ZIKV genomes from the Americas, to estimate the frequency and timing of ZIKV introductions to Colombia. RESULTS:We attempted PCR amplification on 184 samples; 19 samples amplified sufficiently to perform sequencing. Of these, 8 samples yielded sequences with at least 50% coverage. Our phylogeographic reconstruction indicates two separate introductions of ZIKV to Colombia, one of which was previously unrecognized. We find that ZIKV was first introduced to Colombia in February 2015 (95%CI: Jan 2015 - Apr 2015), corresponding to 5 to 8 months of cryptic ZIKV transmission prior to confirmation in September 2015. Despite the presence of multiple introductions, we find that the majority of Colombian ZIKV diversity descends from a single introduction. We find evidence for movement of ZIKV from Colombia into bordering countries, including Peru, Ecuador, Panama, and Venezuela. CONCLUSIONS:Similarly to genomic epidemiological studies of ZIKV dynamics in other countries, we find that ZIKV circulated cryptically in Colombia. More accurately dating when ZIKV was circulating refines our definition of the population at risk. Additionally, our finding that the majority of ZIKV transmission within Colombia was attributable to transmission between individuals, rather than repeated travel-related importations, indicates that improved detection and control might have succeeded in limiting the scale of the outbreak within Colombia.

Project description:BackgroundZika virus (ZIKV) transmission has been reported in 67 countries/territories in the Oceania region and the Americas since 2015, prompting the World Health Organization (WHO) to declare ZIKV as a Public Health Emergency of International Concern in February 2016, due to its strong association with medical complications such as microcephaly and Guillain-Barré Syndrome (GBS). However, a substantial gap in knowledge still exists regarding differing temporal pattern and potential of transmission of ZIKV in different regions of the world.MethodsWe use a phenomenological model to ascertain the temporal patterns and transmission potential of ZIKV in various countries/territories, by fitting the model to Zika case data from Yap Island and French Polynesia in the Oceania region and 11 countries/territories with confirmed case data, namely, Colombia, Ecuador, French Guiana, Guadeloupe, Guatemala, Mexico, Nicaragua, Panama, Puerto Rico, Saint Martin, and Suriname, to pinpoint the waves of infections in each country/territory and to estimate the respective basic reproduction number R0.ResultsSix of these time series datasets resulted in statistically significant model fit of at least one wave of reported cases, namely that of French Polynesia, Colombia, Puerto Rico, Guatemala, Suriname and Saint Martin. However, only Colombia and Guatemala exhibited two waves of cases while the others had only one wave. Temporal patterns of the second wave in Colombia and the single wave in Suriname are very similar, with the respective turning points separated by merely a week. Moreover, the mean estimates of R0 for Colombia, Guatemala and Suriname, all land-based populations, range between 1.05 and 1.75, while the corresponding mean estimates for R0 of island populations in French Polynesia, Puerto Rico and Saint Martin are significantly lower with a range of 5.70-6.89. We also fit the Richards model to Zika case data from six main archipelagos in French Polynesia, suggesting the outbreak in all six island populations occurred during the same time, albeit with different peak time, with mean R0 range of 3.09-5.05.DiscussionUsing the same modeling methodology, in this study we found a significant difference between transmissibility (as quantified by R0) in island populations as opposed to land-based countries/territories, possibly suggesting an important role of geographic heterogeneity in the spread of vector-borne diseases and its future course, which requires further monitoring. Our result has potential implications for planning respective intervention and control policies targeted for island and land-based populations.

Dataset Information

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico.

Publications

Genomic Epidemiology Reconstructs the Introduction and Spread of Zika Virus in Central America and Mexico.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets