Objective

Here we use novel methods of phylogenetic transmission graph

analysis to reconstruct the geographic spread of MERS-CoV.

We compare these results to those derived from text mining and

visualization of the World Health Organization’s (WHO) Disease

Outbreak News.

Introduction

MERS-CoV was discovered in 2012 in the Middle East and human

cases 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 infects

humans and camels. In 2015, MERS-CoV spread from the Middle

East to South Korea which sustained an outbreak. Thus, it is clear

that the virus can spread among humans in areas in which camels are

not husbanded.

Methods

Phylogenetic analyses

We calculated a phylogenetic tree from 100 genomic sequences

of MERS-CoV hosted by humans and camels using NeoCov as the

outgroup. In order to evaluate the relative order and significance of

geographic places in spread of the virus, we generated a transmission

graph (Figure 1) based on methods described in 1.

The graph indicates places as nodes and transmission events as

edges. Transmission direction and frequency are depicted with

directed and weighted edges. Betweenness centrality, represented

by node size, measures the number of shortest paths from all nodes

to others that pass through the corresponding node. Places with

high betweenness represent key hubs for the spread of the disease.

In contrast, smaller nodes at the periphery of the network are less

important for the spread of the disease.

Web scraping and mapping

Due to the journalistic style of the WHO data, it had to be structured

such that mapping software can ingest the data. We used Import.io to

build the API. We provided the software a sample page, selected the

data that is pertinent, then provided a list of all URLs for the software.

We used Tableau to map the information both geographically and

temporally.

Results

Geographic spread of Mers-CoV based on transmissions identified

in phylogenetic data

Most important among the places in the MERS-CoV epidemic

is Saudi Arabia as measured by the betweenness metric applied to

a changes in place mapped to a phylogenetic tree. In figure 1, the

circle representing Saudi Arabia is slightly larger compared to other

location indicating its high importance in the epidemic. Saudi Arabia

is the source of virus for Jordan, England, Qatar, South Korea, UAE,

Indiana, and Egypt. The United Arab Emirates has a bidirectional

connection with Saudi Arabia indicating the virus has spread

between the two countries. The United Arab Emirates also has high

betweenness. The United Arab Emirates is between Saudi Arabia and

Oman and Between Saudi Arabia and France. South Korea, and Qatar

have mild betweeness. South Korea is between Saudi Arabia and

China. Qatar is between Saudi Arabia and Florida. Other locations

(Jordan, England, Indiana, and Egypt) have low betweenness as they

have no outbound connections.

Visualization of geographical transmissions in WHO Data

Certain articles include the infected individuals’ countries of

origin. ln constrast, many reports are in a lean format that includes a

single paragraph that only summarizes the total number of cases for

that country. If we build the API in a manner that recognizes features

in the detailed reports, we can generate a map that draws lines from

origin to reporting country and create visualizations. However, since

only some of the articles contain this extra information, mapping in

this manner will miss many of the cases that are reported in the lean

format.

Conclusions

Our goal is to develop methods for understanding syndromic

and pathogen genetic data on the spread of diseases. Drawing

parallels between the transmissions events in the WHO data and the

genetic data has shown to be challenging. Analyses of the genetic

information can be used to imply a transmission pathway but it is

hard to find epidemiological data in the public domain to corroborate

the transmission pathway. There are rare cases in the WHO data that

include travel history (e.g. “The patient is from Riyadh and flew to the

UK”). We conclude that epidemiological data combined with genetic

data and metadata have strong potential to understand the geographic

progression of an infectious disease. However, reporting standards

need to be improved where travel history does not impinge on privacy.

A transmission graph for MERS-CoV based on viral genomes and place of

isolation metadata. The direction of transmission is represented by the arrow.

The frequency of transmission is indicated by the number. The size of the nodes

indicates betweenness.