Scale-Free Networks and the

Human Ebola Virus

By: Hebroon Obaid and Maggie Schramm

History and Introduction

•initially recognized in 1976 in the Democratic Republic of Congo (formerly Zaire)

•member of the RNA virus family called Filoviridae

• 2 known members: Ebola Virus and Marburg Virus

•Cause severe hemorrhagic fevers in humans and other primates

•Four known types of Ebola:

Ebola-Zaire

Ebola-Sudan

Ebola-Ivory Coast

Ebola-Reston

•First three are extremely deadly in humans

•Ebola-Reston causes disease in nonhuman primates

Symptoms

•Symptoms usually occur 2-21 days after infection- standard incubation period.

•Death rate of 50-90%

•Symptoms within a few days:

flu-like (fever, headache, muscle ache, diarrhea, fatigue)

also sore throat, hiccups, itchy eyes, rash, vomiting blood

•Symptoms within a week:

bleeding into internal organs

and from body openings,

chest pain, shock, death

Transmission and Prevention•Direct contact with blood, secretions, organs or semen of an infected person

•Burial ceremonies that include direct contact with the body of an infected person

•Encounters with infected animals- chimpanzees, gorillas, antelope, etc

•Health care workers are at increased risk

•PREVENTION

-containment

-strict barrier nursing techniques

-properly disinfected tools

-EDUCATION

Scale-Free Networks and Disease•Networks characterized by an unequal distribution of links

•Few heavily popular nodes HUBS

•Epidemics are often scale-free networks

•Identifying hubs can lead to more effective treatment

Random Scale-Free

http://complexpc.unizar.es/~yamir/b02650.pdf#search=\'scale%20free%20network%20disease

Disease Spreading in Structured Scale-Free Networks

The Ebola Virus Scale-Free Network

•Possible hubs: hospitals (amplification), clinics, burial ceremonies

•Other sources of elevated transmission rates: substandard facilities, inadequate protection techniques, ineffective sterilization of equipment

Epidemic Model

• Known habitat for hypothetical reservoirs (i.e. bats and small rodents)

• Consider the areas in which outbreaks occur (some are more likely to foster disease spread)

• Differential function that takes into account the death rate due to the disease, probability of contraction, etc.

Data for Modeling• Use data from resources such as the WHO( World Health

Organization) and the CDC

• Ebola Hemorrhagic FeverTable Showing Known Cases and Outbreaks, in Chronological Order: http://www.cdc.gov/ncidod/dvrd/spb/mnpages/dispages/ebotabl.htm

• http://www.stanford.edu/group/virus/filo/eboz.html

• Chronicles of ourtbreaks such as The Hot Zone and Virus Ground Zero

1977 Ebola Zaire Micrograph Taken By Dr. W. Slenczka

Ebola Zaire, October 31, 1976 by Frederick A. Murphy, D.V.M., Ph.D

Prevention Using Modeling

• Using graphs, one sees that prevention starts with health care workers

Manual For Authorities

Standard Precaution with All Patients…

Isolate the Patients… Use Safe Burial

Practices… Conduct Community

Education…Make Advanced Preparations

•Use the data available to understand what we

can do to make communities safer to

Modeling Benefits

• Allows for viewing of expected effects of outbreaks, without a human toll

• Allows for scientists to experiment with “treatment” procedures (i.e. how to quarantine, etc.)

• Faster and more accurate (if programmed correctly) than humans• Can be places on a easy to access medium, such as the internet,

to prevention

Significance Of Data

• Be cautious when traveling, particularly in poverty-stricken areas

• Airline industry facilitates virus spread

• An epidemic originating in a crowded area could be disastrous

•Catastrophic for small, African villages

•Could be the next smallpox

•Understanding and elimination could help suffering regions and people

•Preparation in case of outbreak is key, aswell as prevention

Networks & Ebola HF

The End