FRED (A Framework for Reconstructing Epidemiological Dynamics) is a freely available open-source agent-based modeling system for
exploring the spatial and temporal patterns of epidemics.
The system is currently undergoing maintenance. Please visit again later.
Welcome to the FRED Measles Epidemic Simulator
FRED (A Framework for Reconstructing Epidemiological Dynamics) is a freely available open-source agent-based modeling system for
exploring the spatial and temporal patterns of epidemics.
This site shows possible outbreaks following the introduction of a single measles case in selected US cities. The model shows the importance of a high
vaccination rate in providing protection for the entire community.
Funding
This work is supported by the National Institute of General Medical Sciences under MIDAS grant
U54GM088491 and by the Bill and Melinda Gates Foundation. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation
of the software.
FRED simulates the spread of
infectious disease through an artificial population
that accurately represents the demographic and
geographic distributions in a given region, including
realistic household, school, and workplace social
contact patterns. Each simulation begins with the
infection of a single school-age child, and as people
interact in households, neighborhoods, schools, and
workplaces, the disease may be transmitted from an
infectious person to a susceptible person. Simulations
are currently available for every state and county in
the United States, and for selected international
locations.
FRED was developed by the
University of Pittsburgh Public Health Dynamics
Laboratory in collaboration with the Pittsburgh
Supercomputing Center and the School of Computer Science
at Carnegie Mellon University.
The Measles Simulations
The simulation begins with a single school-age child contracting measles, and shows the possible spread of the disease
in the nine months after the initial case. Red dots
show the household location of infected
people, and blue dots show the household location of
recovered people. If more than a few cases appear, this
indicates that herd immunity has
been lost and the disease spreads easily. If only a few cases appear, herd immunity is still in place.
Two scenarios are shown. In one
scenario, it is assumed that 80% of children 1 to 15
years old have been vaccinated against measles. In the
other scenario, it is assumed that 95% of children 1 to
15 years old have been vaccinated against measles.
Everyone else is assumed to have 95% vaccine coverage.
In most cases, the difference between the 80% coverage
scenario and the 95% coverage scenario is quite
dramatic. This shows the importance of a high
vaccination rate in providing protection for the entire
community.
The model makes several
assumptions that might affect the results:
1. It is assumed that infants less than 1 year
of age have residual immunity from their mothers, and that 95% people
over 15 years of age have been vaccinated. These assumptions are
conservative: infant immunity probably wanes between 6 months and one
year of age, and the vaccination rate among those over 15 years old may
be less than 95% in practice. The results of the model may therefore
underestimate the number of cases.
2. It is assumed that the measles vaccine is 97%
effective in producing immunity in those vaccinated. In reality, a
single dose of vacine is estimated to have 93% effectiveness, and two
doses are required to achieve 97% effectiveness (CDC:
https://www.cdc.gov/measles/hcp/). The model makes the optimistic
assumption that all vaccinees have received two doses of vaccine. This
assumption may also cause the model to underestimate the number of
cases.
3. The model assumes that vaccination rates do
not vary substantially by location. In fact, it is known that
vaccination coverage varies by locations, with clusters of lower
vaccination rate occurring in some locations, possibly due to
anti-vaccination sentiment. Other studies have shown that clustering of
low immunity tends to increase the risk of measles outbreaks in those
areas. Therefore, the assumtion of uniform coverage is conservative,
predicting fewer measles cases than might occur in practice due to
clusters of lower immunity.
4. How people behave when they are sick has a
big impact on the spread of disease. The model assumes that individuals
in the prodromal period (the few days before the rash appears) will stay
home due to their symptoms about 50% of the time. This assumption is
similar to assumptions made in influenza models, since the symptoms
during the prodromal period are similar to influenza. The model assumes
that once the rash appears, 90% of individuals who are not already
confined to the home will decide each day to stay home for the remainder
of the illness. While these assumptions appear to be reasonable, they
have not been established through systematic studies. Therefore, the
number of cases predicted by the model may be higher or lower than
observed in practice, depending on the rapidity with which measles
patients withdraw to the home.
5. Public health interventions have a large
impact on the size of measles outbreaks. When a measles case occurs, it
is common practice in the United States to conduct an investigation that
tries to identify each possible contact the infectious person may have
had during the infectious period, isolating and vaccinating any
potentially susceptible contacts. This is called "contact tracing". It
is generally impossible to determine all the contact, especially those
made in the general community (Porco 2015). In the current model,
contact tracing is assumed to successfully identify about 50% of the
contacts and convince them to isolate themselves at home. Different
levels of success in contact tracing might increase of decrease the
number of measles cases (Porco 2015).
Funding
This work is supported by the National Institute of General Medical Sciences under MIDAS grant
U54GM088491 and by the Bill and Melinda Gates Foundation. The funders had no role in the study design, data collection and analysis,
decision to publish, or preparation of the software.
Simulation of the spread of a
measles outbreak showing the effects of population vaccination coverage:
Instructions:
1. Select a state
2. Select a city
3. Click to see the results of a measles outbreak
with vaccination coverage of 80% (below herd immunity threshold).
4. Press Done
5. Click to see the results of a measles outbeak
with vaccination coverage of 95% (providing herd immunity).
