GAO-20-582SP 1 (2020-06-04)

handle is hein.gao/gaobaebeo0001 and id is 1 raw text is: 
                            Science,  Technology Assessment,
G AO0                       and  Analytics



SCIENCE & TECH SPOTLIGHT:

COVID-19 MODELING


What  is it? Infectious disease models are mathematical representations
that help researchers analyze the dynamics of a disease. They have
played a prominent role in the COVID-19 response in the United States
and abroad, for example in projecting new infections, deaths, and the
potential need for health care resources.

How  does  it work? The equations used in a model are based on
biological knowledge, data on the disease, or both. Models can be put into
two broad categories: mechanistic and statistical (fig. 1).


                   Mechanistic Modeling
                     Return to susceptible rate

          Infectious        Incubation         Recovery
             rate              rate               rate
 Susceptible        Exposed   -       I nfectious --   Recovered


                     Statistical  Modeling
                            Fit to available data
                                         _   Projection
                                                       Projection
                                                     >uncertainty
   Raw  input data               Statistical techniques applied
Source: GAO illustration of standard mechanistic model (Mechanistic Modeling, top); GAO
illustration of standard statistical modeling (Statistical Modeling, bottom). I GAO-20-582SP


  gu  1 There are generally two broad categories of infectious disease models:
mechanistic models, which use scientific understanding of disease dynamics and human
behavior, and statistical models, which rely only on patterns in the data.
Mechanistic models use equations to represent the mechanics of how
a disease progresses, based on scientific understanding of disease
dynamics  and human  behavior. Researchers can estimate the effect
of proposed interventions, such as social (i.e., physical) distancing, by
adjusting the model and rerunning it.

One  widely used mechanistic model is the susceptible-exposed-infectious-
recovered (SEIR) model. It describes how a population moves through the
stages of a disease. For example, movement from susceptible to exposed
can be based on the rate of infections. For COVID-19, Imperial College
London  built an SEIR-based model that refines this rate of movement
using population size, age distribution, and social contact patterns. The
model projections based on this rate and other inputs can help estimate
disease impacts.


Statistical models, by contrast, use only data and not disease biology or
behavior. For example, they might use data on reported deaths to forecast
future deaths or hospital needs.

The Institute for Health Metrics and Evaluation (IHME) developed a
statistical model that forecasts COVID-19 infections, deaths, hospital
needs  (e.g., beds or ventilators), and when states may be able to relax
social distancing. To do so, researchers use data on, among other things,
deaths, how the disease spread in other locations, and when states
implemented  social distancing.

Models  may also combine statistical and mechanistic elements. For
example, in May 2020, researchers added  an SEIR component  to the
IHME  model to quantify how people move through the phases of disease.

Each  model has a distinct purpose that must be considered when
interpreting its results. For example, statistical models may be designed to
project the need for hospital beds or ventilators, based on the assumption
that past trends will continue. But if human behavior changes-for
instance, if social distancing is relaxed-then the forecasts are likely to be
less accurate. Similarly, mechanistic models may be designed to provide
insight on hypothetical scenarios, in which case their results are useful for
comparing  scenarios, but not as predictions.

How  mature  is it? The field of infectious disease modeling is well
established, but the maturity of a specific model depends upon the quality,
completeness, and  accuracy of the data. Early in an outbreak, data and
knowledge  of the disease may be scarce (as is the case with COVID-19),
and model  predictions are unlikely to be accurate. However, as data and
knowledge  of the disease become more available, the model matures as
its predictions can become more accurate and precise (fig. 2). Models can
still be useful early in an outbreak because they can provide quantitative
estimates to help decision makers respond to a range of possible
scenarios. This has been the case for the COVID-19 pandemic.

Models  can also be useful even when they do not provide an accurate
estimate of the final results. For example, the Imperial College model
explored several scenarios, including one without intervention, which
projected over 2 million U.S. deaths by October 2020. This estimate is no
longer useful as a prediction because that scenario did not occur, but it did
prove helpful in showing the potential benefit of early intervention.


GAO-20-582SP COVID-19 Modeling

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