Population Modeling Working Group

Working Group Leads:

Madhav Marathe (mmarathe@vbi.vt.edu)

Sergey Nuzhdin ( snuzhdin@usc.edu)

Alison Galvani (alison.galvani@gmail.com)


Goals and Objectives:

One of the fundamental challenges of multiscale modeling is to provide insights into collective processes and phenomena emerging in populations of individuals (or individual components) from their basic characteristics and interactions. Put more simply, it is the problem of scaling behavior from one to many, to identify relevant summary statistics and the order parameters that operate on higher scales. While our focus is mainly on human populations, the mathematical, statistical and computational issues that arise in predicting population-level properties (e.g., population dynamics, growth, extinction, migration etc) also arise in populations of molecules, bacteria, viruses, organelles, cells, and tissues.

  • The primary goal of this WG is to survey the field of population modeling across multiple scales, introduce basic population concepts used in statistics, genetics and survival analysis, and to provide links to resources and available software. The WG invites discussion of examples, case studies and important papers in the field. It is meant to be and open and evolving forum.

Other goals to consider:

  • To seek to map the circuits of the brain, measure the fluctuating patterns of electrical and chemical activity flowing within those circuits, and understand how their interplay creates our unique cognitive and behavioral capabilities; in varying social contexts, and comparing health and disease.

How brain works in the context of social interactions is a frontier theme in Social Science, Biology, Engineering, Economy, and Policy. For example, young men with the 10R allele of the dopamine transporter gene DAT1 are more likely to form social groups with antisocial peers and to engage in delinquent behavior, but only if they previously experienced social stress in their families.

The following scientific goals will have high priorities:

  • Identify and provide experimental access to the different brain cell types to determine their roles in health and disease
  • Generate circuit diagrams that vary in resolution from synapses to the whole brain
  • Produce a dynamic picture of the functioning brain by developing and applying improved methods for large-scale monitoring of neural activity
  • Link brain activity to behavior with precise interventional and pharmacological tools that change neural circuit dynamics
  • Produce conceptual foundations for understanding the biological basis of mental processes through development of new theoretical and data analysis tools
  • Develop innovative technologies to understand the human brain and treat its disorders; create and support integrated brain research networks
  • Integrate new technological and conceptual approaches produced in the other goals to discover how dynamic patterns of neural activity are transformed into cognition, emotion, perception, and action in health and disease

While achieving these goals, we will: pursue human studies and non-human models in parallel, cross boundaries in interdisciplinary collaborations, integrate spatial and temporal scales using mathematical models, establish platforms for preserving and sharing data, validate and disseminate technology, consider ethical implications of neuroscience research.


MSM 2017 (10th Anniversary) meeting Discussion Items







 Image iconeeo.jpg

In this open discussion section our primary purpose is to have a broad  on the future of modeling for infectious disease epidemiology.

We welcome interested individuals to join us for a vibrant discussion!


In societies – both animal and human – many individuals interact with one another. These social interactions can affect group size and composition, and conversely, group size and composition can affect social interactions among individuals. Individuals within societies differ in important ways from one another; for example in their likelihood of associating with, or attacking other individuals; and if they are attacked themselves, they may differ in how they adjust their own behavior based on that experience.

All this suggests that differences among individuals in mean levels of behavior and behavioral plasticity must affect, and be affected by, higher-level properties of groups and societies. However, untangling the effects of individual differences in behavior and behavioral plasticity on the social and spatial patterns of groups and societies is refractory to current modeling and analytic tools. Feedbacks between behavior at the level of individuals and behavior at the level of groups and societies must be understood in order to predict the behaviors and their key health outcomes at each of these levels.

This working group in the past has been very broad and there has been much discussion on the specific topics to include human behavior that can impact public health in myriad ways. As we know, human behavior affects disease transmission, for instance by determining the numbers of contacts an individual has. It also impacts medical decision-making and thus the likelihood of successful implementation of public health policies, such as vaccination recommendations. Traditionally epidemiological modelers have assumed that optimal vaccination coverage will also achieve perfect adherence from the public. However, behavioral modeling has revealed that strategies that are optimal for the population are not necessarily optimal for the individual, depending on the utilities and externalities of specific policies. Consequently, modeling that considers both transmission dynamics and human behavior can facilitate the design of public health strategies that are both effective and cost-effective, as well as likely to achieve a high level of public adherence.

The goals specific to this theme are: (1) to improve the predictability of likelihood that alternative public health policies will be effective and cost-effective; (2) to further our understanding of the factors that motivate individuals in making medical decisions, taking into account the interplay among biological systems, individual decision-making and social and cultural influences; (3) to advance uncertainty quantification for the parameterization of population and multi-scale models from survey and epidemiological data;(4) to achieve and maintain a higher, uniform standard of patient care while reducing cost and patient discomfort.

The working group has a web portal through SimTK:

It holds discussions on the mailing list:

The group captured the variety of population modelers by providing examples published in several papers:

1) Population Modeling Working Group - Editor: Jacob Barhak, Population Modeling by Examples (WIP) - SpringSim 2015 , April 12 - 15, Alexandria, VA, USA . Paper available Here and Here. Presentation available Here

2) Population Modeling Working Group - Editor: Robert Smith?, Population Modeling by Examples II - SummerSim 2016 , July 24 - 27, Montreal, CA. Paper available Here and HerePresentation available Here

3) Population Modeling Working Group - Editor: Jacob Barhak, Population Modeling by Examples III - SummerSim 2017 , July 9 - 12, Bellevue, WA, USA. Paper available Here and Here . Presentation available Here




Naren Ramakrishnan, PhD
Shweta Bansal, PhD
Goals and Objectives: 



Additional Information: 

Funding Opportunities:

R13 to foster/build interdisciplinary research teams: http://grants.nih.gov/grants/guide/pa-files/PA-10-106.html

PAR-11-203 Interagency U01 on Predictive Multiscale Models for Biomedical, Biological, Behavioral, Environmental and Clinical Research: http://grants.nih.gov/grants/guide/pa-files/PAR-11-203.html

PAR-13374 and PAR-13339: Modeling social behavior


  • What are the challenges and goals of population modeling?

On Mon, Sep 23, 2013, Jacob Barhak <jacob.barhak@gmail.com> discussed challenges and goals for the Population Modeling Working Group

Please view his comments at http://www.imagwiki.nibib.nih.gov/mediawiki/index.php?title=Talk:Population_Modeling_Working_Group.


Challenges and Opportunities:

Disease Modeling:

  • Cancer:
    • Scales: genomic, cell-level, tissue-level, population-level
    • Targets:
      • a) temporal patterns: incidence, survival, mortality
      • b) spatial patterns: geographic/ethnic and socioeconomic factors, risk factors, ...
      • c) impact of screening and interventions
    • Data:
      • a) genomic (transcriptome, genome, metabolome, proteome, ...)
      • b) genetic model systems (e.g. murine), chemical carcinogenesis, animal studies, ...
      • c) cohort data, case-control data
      • d) registry data (e.g. SEER)
    • Methods/Models & Approaches:

(fill in)

  • Infectious Diseases
    • Scales:
    • Targets:
    • Data:
    • Methods/Models & Approaches:
  • Other (e.g. economic development, ethnic conflict, war, ...)
Working Group Activities: 



Notes From the Workgroup Meeting Held on 3-Sep-2014




  • Kim Chen
  • Madhav Marthe
  • Sergei Nuzhdin
  • Kung Sik
  • Daniel cook
  • Atesmachew Hailegiorgis 
  • Jacob Barhak
  • Stephen Marcus 
  • Harel Dahari
  • Susan Volman
  • Felim Mac Gabhann
  • Iraj Hosseini
  • R. Joseph Bender
  • Liang-Hui Chu (Lawrance)

We recommend that all participants to post a link to their project and contact information as a comment. Thank you!

Main Discussions

  • Introduction of participants and their work
  • Debate about MultiScale and connecting the scales
  • What should be the focus and how do we define population?

Examples of Populations Models Mentioned

  • Genotype populations
  • Modeling population personalities
  • Chronic disease progression
  • Mental health model
  • Liver cell interactions
  • Lung structure variations
  • Interactions between individuals in infection
  • Predator prey models
  • Infectious disease
  • Population migration
  • Environment change impact on population
  • In-host viral dynamics
  • Personalized Multi Scale models
  • Virtual clinical trials
  • Virtual population of HIV patients
  • Social modeling

Points to Consider

  • Population has meaning in many levels: cells, individuals, groups, inside and outside the skin.
  • Heterogeneity of a population seems a defining feature of above the skin.
  • Collective behavior models should take population heterogeneity into account.
  • Multi-Scale means modeling at least two scales.
  • Can geographical scale be considered for the multi-scale debate?

What is Population Modeling- This Definition was Accepted by the Participants

  • Modeling a collection of entities with different levels of heterogeneity 

Ideas Worth Exploring for the Workgroup:

  • Webinars
  • Collaborations
  • Organizing publication
  • Organizing a session

Action Items:

  • Upload participant relevant work links to the wiki
  • Share this wiki with colleagues

Relevant Funding Announcements Mentioned

  • Par 13374 - modeling social behavior
  • Par 13339  



Notes From the Workgroup Meeting Held on 8-Sep-2015


The meeting involved each participant introducing own work and expressing current interest:


Steven Railsback:

Ecological modeler - keynote speaker.

Less concerned about model fitness and  more interested if a model useful for a particular goal.

Modeling populations an emergent behavior of underlying mechanisms in environment.

Madhav Marathe:

Heading a lab focused on population modeling including urban planning large scale crisis and epidemiology.

Understanding population modeling as it pertains to "One Health".

Paul Marjoram:

Mathematical population genetics - population of tumors - social population of models.

Concern: Model fit is relative and arbitrarily

Drew Pruett:

Monte Carlo Sampling using a physiological model.

Translating models to utility.

Being in interest to the NIH is not the same as being of interest the clinician.

Bruce Lee:

Virtual populations, in virtual locations. Impact of policies and regulations on populations. Agent based,  DES, Markov.

How modeling can help decision makers in offering world levels?

How to market models?

Stephen Marcus:

Population modeling is at the heart of the NIH mission of public health.

Optimistic that other groups are getting interested.

Bishal Paudel:

Drug responses in cancer cells.

Having a platform to combine individual and population level.

Elebeoba May:

Infectious disease capturing the host and pathogen background.

Trying to quantify the data from clinical studies.

Wladimir Alonso:

Using models to extract hidden and public health useful knowledge from big data.

Examples include Public health studies including vaccination.

Jacob Barhak:

Disease modeling software tools. Object oriented population generation.

Interested in modular modeling of data one has no access to.



  • Stephen promised some book recommendations
  • Jacob raised the Bio Medical and Population modeling Track track forming in SummerSim
  • The population modeling mailing list has more than 110 members – looking for increased activities this year

Conclusion from activity this year:

  • The discussion about data sources in the population modeling mailing list before the IMAG meeting demonstrates the usefulness of gathering all population modeler in one list.


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