Zika Virus: Medical Countermeasure Development Challenges

Introduction: Reports of high rates of primary microcephaly and Guillain–Barré syndrome associated with Zika virus infection in French Polynesia and Brazil have raised concerns that the virus circulating in these regions is a rapidly developing neuropathic, teratogenic, emerging infectious public health threat. There are no licensed medical countermeasures (vaccines, therapies or preventive drugs) available for Zika virus infection and disease. The Pan American Health Organization (PAHO) predicts that Zika virus will continue to spread and eventually reach all countries and territories in the Americas with endemic Aedes mosquitoes. This paper reviews the status of the Zika virus outbreak, including medical countermeasure options, with a focus on how the epidemiology, insect vectors, neuropathology, virology and immunology inform options and strategies available for medical countermeasure development and deployment. Methods: Multiple information sources were employed to support the review. These included publically available literature, patents, official communications, English and Lusophone lay press. Online surveys were distributed to physicians in the US, Mexico and Argentina and responses analyzed. Computational epitope analysis as well as infectious disease outbreak modeling and forecasting were implemented. Field observations in Brazil were compiled and interviews conducted with public health officials

Spread of Middle East Respiratory Coronavirus: Genetic versus Epidemiological Data

ObjectiveHere we use novel methods of phylogenetic transmission graphanalysis to reconstruct the geographic spread of MERS-CoV.We compare these results to those derived from text mining andvisualization of the World Health Organization’s (WHO) DiseaseOutbreak News.IntroductionMERS-CoV was discovered in 2012 in the Middle East and humancases around the world have been carefully reported by the WHO.MERS-CoV virus is a novel betacoronavirus closely related to a virus(NeoCov) hosted by a bat, Neoromicia capensis. MERS-CoV infectshumans and camels. In 2015, MERS-CoV spread from the MiddleEast to South Korea which sustained an outbreak. Thus, it is clearthat the virus can spread among humans in areas in which camels arenot husbanded.MethodsPhylogenetic analysesWe calculated a phylogenetic tree from 100 genomic sequencesof MERS-CoV hosted by humans and camels using NeoCov as theoutgroup. In order to evaluate the relative order and significance ofgeographic places in spread of the virus, we generated a transmissiongraph (Figure 1) based on methods described in 1.The graph indicates places as nodes and transmission events asedges. Transmission direction and frequency are depicted withdirected and weighted edges. Betweenness centrality, representedby node size, measures the number of shortest paths from all nodesto others that pass through the corresponding node. Places withhigh betweenness represent key hubs for the spread of the disease.In contrast, smaller nodes at the periphery of the network are lessimportant for the spread of the disease.Web scraping and mappingDue to the journalistic style of the WHO data, it had to be structuredsuch that mapping software can ingest the data. We used Import.io tobuild the API. We provided the software a sample page, selected thedata that is pertinent, then provided a list of all URLs for the software.We used Tableau to map the information both geographically andtemporally.ResultsGeographic spread of Mers-CoV based on transmissions identifiedin phylogenetic dataMost important among the places in the MERS-CoV epidemicis Saudi Arabia as measured by the betweenness metric applied toa changes in place mapped to a phylogenetic tree. In figure 1, thecircle representing Saudi Arabia is slightly larger compared to otherlocation indicating its high importance in the epidemic. Saudi Arabiais the source of virus for Jordan, England, Qatar, South Korea, UAE,Indiana, and Egypt. The United Arab Emirates has a bidirectionalconnection with Saudi Arabia indicating the virus has spreadbetween the two countries. The United Arab Emirates also has highbetweenness. The United Arab Emirates is between Saudi Arabia andOman and Between Saudi Arabia and France. South Korea, and Qatarhave mild betweeness. South Korea is between Saudi Arabia andChina. Qatar is between Saudi Arabia and Florida. Other locations(Jordan, England, Indiana, and Egypt) have low betweenness as theyhave no outbound connections.Visualization of geographical transmissions in WHO DataCertain articles include the infected individuals’ countries oforigin. ln constrast, many reports are in a lean format that includes asingle paragraph that only summarizes the total number of cases forthat country. If we build the API in a manner that recognizes featuresin the detailed reports, we can generate a map that draws lines fromorigin to reporting country and create visualizations. However, sinceonly some of the articles contain this extra information, mapping inthis manner will miss many of the cases that are reported in the leanformat.ConclusionsOur goal is to develop methods for understanding syndromicand pathogen genetic data on the spread of diseases. Drawingparallels between the transmissions events in the WHO data and thegenetic data has shown to be challenging. Analyses of the geneticinformation can be used to imply a transmission pathway but it ishard to find epidemiological data in the public domain to corroboratethe transmission pathway. There are rare cases in the WHO data thatinclude travel history (e.g. “The patient is from Riyadh and flew to theUK”). We conclude that epidemiological data combined with geneticdata and metadata have strong potential to understand the geographicprogression of an infectious disease. However, reporting standardsneed to be improved where travel history does not impinge on privacy.A transmission graph for MERS-CoV based on viral genomes and place ofisolation metadata. The direction of transmission is represented by the arrow.The frequency of transmission is indicated by the number. The size of the nodesindicates betweenness

Spread of Middle East Respiratory Coronavirus: Genetic versus Epidemiological Data

ObjectiveHere we use novel methods of phylogenetic transmission graphanalysis to reconstruct the geographic spread of MERS-CoV.We compare these results to those derived from text mining andvisualization of the World Health Organization’s (WHO) DiseaseOutbreak News.IntroductionMERS-CoV was discovered in 2012 in the Middle East and humancases around the world have been carefully reported by the WHO.MERS-CoV virus is a novel betacoronavirus closely related to a virus(NeoCov) hosted by a bat, Neoromicia capensis. MERS-CoV infectshumans and camels. In 2015, MERS-CoV spread from the MiddleEast to South Korea which sustained an outbreak. Thus, it is clearthat the virus can spread among humans in areas in which camels arenot husbanded.MethodsPhylogenetic analysesWe calculated a phylogenetic tree from 100 genomic sequencesof MERS-CoV hosted by humans and camels using NeoCov as theoutgroup. In order to evaluate the relative order and significance ofgeographic places in spread of the virus, we generated a transmissiongraph (Figure 1) based on methods described in 1.The graph indicates places as nodes and transmission events asedges. Transmission direction and frequency are depicted withdirected and weighted edges. Betweenness centrality, representedby node size, measures the number of shortest paths from all nodesto others that pass through the corresponding node. Places withhigh betweenness represent key hubs for the spread of the disease.In contrast, smaller nodes at the periphery of the network are lessimportant for the spread of the disease.Web scraping and mappingDue to the journalistic style of the WHO data, it had to be structuredsuch that mapping software can ingest the data. We used Import.io tobuild the API. We provided the software a sample page, selected thedata that is pertinent, then provided a list of all URLs for the software.We used Tableau to map the information both geographically andtemporally.ResultsGeographic spread of Mers-CoV based on transmissions identifiedin phylogenetic dataMost important among the places in the MERS-CoV epidemicis Saudi Arabia as measured by the betweenness metric applied toa changes in place mapped to a phylogenetic tree. In figure 1, thecircle representing Saudi Arabia is slightly larger compared to otherlocation indicating its high importance in the epidemic. Saudi Arabiais the source of virus for Jordan, England, Qatar, South Korea, UAE,Indiana, and Egypt. The United Arab Emirates has a bidirectionalconnection with Saudi Arabia indicating the virus has spreadbetween the two countries. The United Arab Emirates also has highbetweenness. The United Arab Emirates is between Saudi Arabia andOman and Between Saudi Arabia and France. South Korea, and Qatarhave mild betweeness. South Korea is between Saudi Arabia andChina. Qatar is between Saudi Arabia and Florida. Other locations(Jordan, England, Indiana, and Egypt) have low betweenness as theyhave no outbound connections.Visualization of geographical transmissions in WHO DataCertain articles include the infected individuals’ countries oforigin. ln constrast, many reports are in a lean format that includes asingle paragraph that only summarizes the total number of cases forthat country. If we build the API in a manner that recognizes featuresin the detailed reports, we can generate a map that draws lines fromorigin to reporting country and create visualizations. However, sinceonly some of the articles contain this extra information, mapping inthis manner will miss many of the cases that are reported in the leanformat.ConclusionsOur goal is to develop methods for understanding syndromicand pathogen genetic data on the spread of diseases. Drawingparallels between the transmissions events in the WHO data and thegenetic data has shown to be challenging. Analyses of the geneticinformation can be used to imply a transmission pathway but it ishard to find epidemiological data in the public domain to corroboratethe transmission pathway. There are rare cases in the WHO data thatinclude travel history (e.g. “The patient is from Riyadh and flew to theUK”). We conclude that epidemiological data combined with geneticdata and metadata have strong potential to understand the geographicprogression of an infectious disease. However, reporting standardsneed to be improved where travel history does not impinge on privacy.A transmission graph for MERS-CoV based on viral genomes and place ofisolation metadata. The direction of transmission is represented by the arrow.The frequency of transmission is indicated by the number. The size of the nodesindicates betweenness

Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission dynamics. In this study, we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance data. The impact of the choice of tree-priors, informative epidemiological priors, and evolutionary parameters on the transmission potential estimation is evaluated. North American Influenza A(H1N1)pdm09 hemagglutinin (HA) gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number (R0). Epidemiological priors gathered from published literature are used to simulate the birth-death skyline models. Path-sampling marginal likelihood estimation is conducted to assess model fit. A bibliographic search to gather surveillance-based R0 values were consistently lower (mean ≤ 1.2) when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness (mean ≥ 1.3 to ≤2.88 days). The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative estimates. While there was no certain impact of clock rate and tree height on the R0 estimation, an opposite relationship was observed between coalescent and birth-death tree priors. There was no significant difference (p = 0.46) between the birth-death model and surveillance R0 estimates. This study concludes that tree-prior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary parameters. The study also reports a consensus between the sequence-based R0 estimation and surveillance-based R0 estimates. Altogether, these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance and epidemiological activities to better assess and respond to emerging infectious diseases

Methodological synthesis of Bayesian phylodynamics, HIV-TRACE, and GEE: HIV-1 transmission epidemiology in a racially/ethnically diverse Southern U.S. context

This study introduces an innovative methodological approach to identify potential drivers of structuring HIV-1 transmission clustering patterns between different subpopulations in the culturally and racially/ethnically diverse context of Houston, TX, the largest city in the Southern United States. Using 6332 HIV-1 pol sequences from persons newly diagnosed with HIV during the period 2010-2018, we reconstructed HIV-1 transmission clusters, using the HIV-TRAnsmission Cluster Engine (HIV-TRACE); inferred demographic and risk parameters on HIV-1 transmission dynamics by jointly estimating viral transmission rates across racial/ethnic, age, and transmission risk groups; and modeled the degree of network connectivity by using generalized estimating equations (GEE). Our results indicate that Hispanics/Latinos are most vulnerable to the structure of transmission clusters and serve as a bridge population, acting as recipients of transmissions from Whites (3.0 state changes/year) and from Blacks (2.6 state changes/year) as well as sources of transmissions to Whites (1.8 state changes/year) and to Blacks (1.2 state changes/year). There were high rates of transmission and high network connectivity between younger and older Hispanics/Latinos as well as between younger and older Blacks. Prevention and intervention efforts are needed for transmission clusters that involve younger racial/ethnic minorities, in particular Hispanic/Latino youth, to reduce onward transmission of HIV in Houston

Summary of Brazilian States (Federated units), current Zika circulation patterns, and increased incidence of primary microcephaly.

<p>Summary of Brazilian States (Federated units), current Zika circulation patterns, and increased incidence of primary microcephaly.</p

Zika virus, past and current distribution.

<p>Source: Centers for Disease Control and Prevention [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004530#pntd.0004530.ref083" target="_blank">83</a>].</p

Phylogeographic analyses illustrating the lineage of the Zika virus currently circulating in Brazil.

<p>Phylogeographic analysis based on the envelope gene of Zika virus. This analysis illustrates the path of travel of Zika virus from Africa, Asia, and across the Pacific to South America. This analysis was created with Supramap [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004530#pntd.0004530.ref068" target="_blank">68</a>]. Yellow circles and branches are associated with common ancestors. Red pins and black lines are associated with observed viral isolates. The root of the tree is indicated with a green circle. Data analyzed included all envelope variants of Zika virus available in the public domain as of January 18, 2016. Nucleotide sequence data were aligned using MAFFT v7.215 under default settings. A dataset for the envelope gene was created resulting in a matrix of 56 taxa and 753 aligned positions. A phylogenetic tree search was conducted for each dataset using RAxML v8.1.16 for 100 replicates under the GTRCAT model of nucleotide substitution. The outgroup was set to HQ234498. Supramap to project the phylogenetic tree into the earth [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004530#pntd.0004530.ref068" target="_blank">68</a>].</p

Countries and territories with active Zika virus transmission.

<p>Countries and territories with active Zika virus transmission.</p

Projection of Zika virus infections in states with laboratory confirmation of Zika virus circulation during 2015 (18 of 27 Brazilian states or federated units).

<p>Projection of Zika virus infections in states with laboratory confirmation of Zika virus circulation during 2015 (18 of 27 Brazilian states or federated units).</p