(subkilometer) satellite-based measures of environmental variables, such as land surface temperature,
is one novel big data stream that can be combined
with other data sources via machine-learning
algorithms to determine, for example, the likely
range and local transmission intensity of vector-borne infections such as ZIKV (19, 20). Similar
approaches have been used to disaggregate census data, yielding high-resolution maps of population density and demographics (33), thus tackling
the perennial problem in epidemiology of determining the population at risk. Another trend is
increasing availability of data streams that characterize mobility, such as air-travel flows (34).
Novel data streams on mobility are also becoming
increasingly important; mobile phone-call records
yield unprecedented temporal and spatial resolution on human mobility and aggregation (35).
As ever, potential biases and limitations must be
considered carefully; for example, spatial locations
can only be mapped when a call is made, and
mobile phone ownership is not necessarily representative of populations of interest.
Both phylodynamic and “big data” techniques
have been enabled by increasing availability of
affordable, high-performance computing resources.
These resources also allow for
implementation of statistical
and modeling techniques that
were once prohibitively computationally expensive and have
improved the rigor of models
of emergent pathogens, particularly the quantification of
uncertainty. Computationally intensive techniques that integrate across multiple predictive
models (36) are leading to clear improvements in
forecasting of established pathogens and may
provide similar benefits for emergent pathogens. Likewise, with new techniques and enough
computational power (though sometimes more
than is currently available), essentially any prob-abilistic model construction can be fit to data.
This allows researchers to combine often complex representations of the transmission process
with techniques of statistical inference to estimate critical transmission properties while taking into account the large-scale uncertainty in
the underlying transmission tree (37).
As models become more flexible and easier
to fit, there is the promise of updating results
in “real time” as the response to an emerging
pathogen develops. Realizing this potential requires improvements in the way that data flow
through health systems, as well as how data are
combined and processed by models. Data must
be updated in a timely manner, and forecasts
and inferences must be sensibly adjusted as new
data arrive and old data are modified. Rapid modeling exercises can be critical in making timely decisions and guiding interventions and field studies
in a rapidly changing environment. For instance,
models played a critical role in the design of vaccine trials during the Ebola outbreak (38). However, such real-time efforts remain sporadic and
The immunological landscape on which a
pathogen emerges can have profound effects on
its spread, with the immunologic imprint of related viruses potentially providing protection (39)
or increasing disease severity in affected subpopulations (40). Immunological signatures also
provide a marker of previous exposure and can
reveal whether a pathogen is truly novel or has
circulated undetected in human populations before. However, this immunological landscape has
historically been part of the “dark matter” of epi-demiologic information; serologic laboratory and
analytic techniques have lagged behind developments in the molecular analysis of genomic
data, and there are few sources of data on the
preemergence immune status of populations.
Establishment of a global serum bank, combined with improved methods for efficiently
testing for a broad range of immunological markers, could provide an invaluable resource for
responding to emergence events (41). However, a
commensurate improvement to how such information is incorporated into disease models is
The ultimate achievement in modeling emergent pathogens would be to develop models of
sufficient biological and ecological sophistication to identify emergent disease threats
before they entered the human
population. Identifying and sequencing previously unknown
viruses is a necessary first step,
but “virus hunting” activities
are of limited utility without
some way to assess which viruses pose a threat. We know
generalities—for example, that viruses are more
likely to jump between closely related species
(42), or that RNA viruses’ rapid rate of mutation may make them more prone to emergence
events than DNA viruses (43). And we can theoretically assess how the relationship between
introduction frequency, R0, and the number of
mutations needed to efficiently transmit among
humans affects emergence rates (43). Yet, we lack
the depth of understanding of the relation between genotype and phenotype to assess which
viruses will spread and cause disease in the human
population and which will not (44). However, our
ability to observe “viral chatter” between human
and animal populations is ever increasing and
may soon lead to the breakthroughs needed to
identify likely emerging threats.
Future emergence events
Global biosecurity depends on our ability to ef-
fectively confront emerging infectious disease
threats. Mechanistic models, which capture our
scientific understanding of disease processes, will
continue to play an important role in assessing
and responding to pathogen emergence. Although
these methods have numerous limitations and
pitfalls, and it may sometimes be difficult to tell
good work from bad, they provide vital information
to the global health response that is unavailable
through other means. Continued methodological
improvements that take advantage of new sources
of data will increase the range and accuracy of in-
ferences that can be made from leveraging in-
fectious disease models. Tighter integration with
public health practice and development of re-
sources at the ready may increase the time-
liness and quality of analyses to inform the public
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of results from
can be difficult.”
EMERGING INFECTIOUS DISEASES