The Incorporating Human Behavior in Epidemiological Models (IHBEM) Program supports research that incorporates research on social and behavioral processes in mathematical epidemiological models.The program provides support for projects that involve balanced participation from the mathematical sciences and from the social, behavioral, and economic sciences. The purpose of the Incorporating Human Behavior in Epidemiological Models activity is to support interdisciplinary collaborations that integrate
research on behavioral and/or social processes in mathematical epidemiological models. Projects supported under this activity should be
collaborative in nature and depend for their advancement on the coordinated interaction of two or more PIs/co-PIs, with balanced participation
from both the mathematical sciences and the social, behavioral, and economic sciences. Additional participants from other disciplines, especially
the biological sciences, are also welcome.
Each project should focus on a significant and well-delineated research challenge that integrates behavioral and social processes into
mathematical epidemiological models. Examples of research challenges include, but not are limited to:
1) Behavioral realism and sensitivity analysis. A common crucial flaw attributed to epidemiological models of the COVID-19 pandemic has been a
failure to incorporate realistic models of behavior. The challenge, therefore, is to incorporate realism while at the same time maintaining the
tractability of the models. This realism includes differences in behavioral and social distributions along different characteristics of human
populations and their intersections, including income, age, region, religion, race and ethnicity, gender, and education. Accompanying the
incorporation of these behavioral models should be sensitivity analyses that determine how and to what extent these characteristics matter for
predicting the outcomes of different pandemic-related interventions.
2) Incorporation of behavioral change. People's behavior changes over time: it may change as they acquire more information, in strategic response
to others' (including organizations' and governments') behaviors, because of fatigue or increasing stress or increasing deprivation, and so forth.
The implication is that incorporating fixed assumptions about behavior into epidemiological models may be inadequate for understanding how
best to respond to a pandemic. A dynamic approach that embraces how behavior changes over time will be important.
3) Incorporation of multiple environments: climate, seasonal, political, social. Climate affects human epidemiology in many ways, one of those being
that it affects human behavior. Climate change also affects animal habitats, which can affect human-animal interactions such that some pathogens
originally occurring only among animals may become transmissible in a human population. Climate is related to seasonal variation, which again
has strong effects on patterns of human behavior. Political and social environments, while fundamentally different from climatic and seasonal
environments, also exert a strong influence on behavior. Incorporation of these and other environmental considerations into epidemiological
models is essential especially because of how they vary over different localities and how they change over time.
4) Incorporation of population heterogeneity and policy models. The COVID-19 pandemic has highlighted that disease may affect different segments
of the population differently. Considering this heterogeneity, it may be important to model endogenous mechanisms describing decision-makers'
inaction of public health policies (such as social distancing, mask wearing, vaccination) and social policies (for example, eviction moratoria,
childcare provisions, and employment flexibility), and how these rules influence public health and welfare. This involves modeling political factors,
communication, behavioral responses, and interactions between dynamically coupled processes.
5) Data needs for rich mathematical epidemiological models. As rich theoretical models are built with, for example, different possible transmission
methods and behavioral responses, it becomes valuable to analyze what data are required to validate the models, and what data are necessary to
separate and arbitrate between models, while acknowledging the limitations posed by data that are either observational or unrepresentative.
Projects need not focus on human pathogens or human hosts, although such research must still incorporate human behavior and/or social
processes. Examples of research on non-human systems include: how changes in behaviors or farming practices affect the transmission of footand-mouth disease, how economic factors affect the global spread of pathogens through the pet trade, or how hunting affects the spread of
prions in white-tail deer.
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All proposals should describe clearly the research challenges associated with the proposed mathematical epidemiological models and the
approaches of incorporating social, behavioral, or economic processes, as well as the applicability of the models in real populations. In addition,
the training of students and postdoctoral researchers at the intersection of the mathematical sciences and social, behavioral, and economic
sciences is encouraged. Discuss how trainees will be recruited, mentored, and retained, and explain how these efforts will increase participation of
people from all demographics, thereby including those underrepresented in the scientific enterprise. Research teams are required to disseminate
the results of their work in a timely and effective fashion.
NIDA is specifically interested in proposals that support scientific research on drug use and its health and social consequences across the
spectrum, from occasional use to problematic use and substance use disorders (SUDs), that integrates social, behavioral, or economic processes
to respond to the public health crisis such as drug overdose, HIV, and HCV. Some examples of areas of interest include:
Using technology and advanced statistical methods to inform our understanding of both social, behavioral and neurobiological
components of drug use that are strongly influenced by diverse environmental and social factors in the context of responding to public
health crisis such as drug overdose, HIV, and HCV.
The development and validation of technologies, analytics, and models to help individuals gather, manage, and use data and information
related to drug use and their personal health in the context of responding to public health crisis such as drug overdose, HIV, and HCV.
Methods and algorithms for aggregation of data including, but not limited to, electronic health records (EHRs), laboratory generated data,
environmental, and/or behavioral data.
Diagnostic/monitoring tools and technology platforms to optimize drug use interventions and delivery, in the context of responding to
public health crisis such as drug overdose, HIV, and HCV.