Transcript Scale-Free Networks and the Human Ebola Virus By: Hebroon Obaid and Maggie Schramm.

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
Disease Spreading in Structured Scale-Free Networks
http://complexpc.unizar.es/~yamir/b02650.pdf#search=\'scale%20free%20network%20disease
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 Fever
Table Showing Known Cases and Outbreaks, in Chronological
Order:
http://www.cdc.gov/ncidod/dvrd/spb/mnpages/dispages/ebota
bl.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, as
well as prevention
Networks & Ebola HF
The End