Behavioral Dynamics of COVID-19
Behavioral Dynamics of COVID-19
Over a year since the World Health Organization declared COVID-19 a pandemic, its true global magnitude remains unknown. Official counts of cases and deaths are known to underestimate the true magnitude of the pandemic, but how much remains uncertain. Differences in testing and treatment capacity, unknowns like asymptomatic transmission, and most importantly, variable responses to the risk of the virus create wide variation in incidence, prevalence, and mortality across countries and over time. This isn’t just about getting the stats right. If official counts of cases and deaths are too low, people may not take adequate precautions and governments may (and have) re-opened their economies too soon, leading to more deaths. Effective responses to the pandemic require an understanding of how these factors interact to shape its trajectory.
To address this need, we developed a model that accounts for behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue, as well as asymptomatic transmission, impacts of weather, disease acuity, and testing and hospital capacity. Estimating this model across all nations that publish sufficient data—a total of 92, spanning about 5 billion people —we found the actual magnitude of the epidemic to be much larger than reported. Specifically, as of the end of 2020, we find cumulative cases were more than seven times larger than official reports, and COVID-19 deaths were about 44% higher than reported. We also found huge variation in death rates, with some countries having over 100 times the per capita deaths of others.
The greatest driver of this wide variation is not demographics, weather, or health care capacity, but rather how responsive are the people and governments of each nation to the threat. Do countries act proactively and aggressively to quash any nascent outbreaks, or do they wait until the situation is severe and hospitals overwhelmed before responding?
Some countries, like Australia, China, New Zealand, and Singapore, have been proactive, responding aggressively to even small spikes in cases, and have largely succeeded in bringing their epidemics under control at death rates below 0.1 per million people per day. Even after caseloads and deaths fell, they remained vigilant, kept masks on and gatherings restricted, and continued testing and tracing until the virus was almost completely stamped out. Despite occasional disruptions from new outbreaks, life in these nations has largely stabilized at a new normal, with few cases and deaths.
Others, including the U.S., many European and South American countries, and India, have been more reactive. Seeking to minimize economic disruption, they delayed action until climbing deaths forced their hands. As each wave subsided, under pressure to reopen their economies, they eased restrictions, only to see cases climbing again. Eventually, faced with rapidly growing outbreaks, they have been forced to lock down again anyway.
Ultimately, countries actually have little choice in how much they must reduce contact levels to control the epidemic. Few communities are willing to tolerate unchecked outbreaks and the horrendous number of deaths that result. By choice or the force of intolerable death rates, all countries have to cut back high-risk interactions – keeping people at home, restricting gatherings, avoiding restaurants and travel, and so on – to control the virus. How much these interactions have to be reduced to keep an outbreak from growing depends largely on the contagiousness of the virus itself, and thus remains similar across nations. What varies across countries is how many cases and deaths it takes to induce strong enough actions to reduce contacts to the required threshold, https://buckfirelaw.com/xanax-alprazolam/ bend the curve, and stop the growing outbreak. In short, until vaccination is widespread all communities pay a similar price to sufficiently bring down their contacts, yet the more responsive ones save many more lives.
To help policymakers and the public better understand these dynamics, we created a free online simulator that allows you to experiment with your own scenarios for vaccination and other policies, and to explore how to change the course of the epidemic over the coming months.
The world is working hard to roll out mass vaccination, but it will still be many months before most people are vaccinated worldwide. Understanding the central role of responsiveness in shaping the dynamics of outbreaks remains critical – it is still not too late for a swift response to both minimize economic disruptions and save lives.
Rahmandad, Lim, and Sterman are coauthors of “Behavioral dynamics of COVID-19: estimating under-reporting, multiple waves, and adherence fatigue across 19 nations,” which is forthcoming in System Dynamic Review. Rahmandad and Lim are coauthors of “Risk-Driven Responses to COVID-19 Eliminate the Tradeoff between Lives and Livelihoods.”
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Using System Dynamics to Improve Patient Care
Using System Dynamics to Improve Patient Care
By Şanser Güz, Orkun İrsoy, and Naz Beril Akan
Why do many people practice and advocate systems thinking in biomedical sciences? The human body is a system with strong regulatory mechanisms that maintain the steady state of internal physiological conditions, homeostasis. The regulatory mechanisms of various subsystems of the body emerge from the interaction of feedback signals and provide the body with an internal balancing structure against natural disturbances. But what if the disturbances are far from natural, what if they are chronic or repetitive or interfering with the system itself? Such disruptions in those well-regulated homeostatic systems are the possible leverage points where systemic analysis can add a great deal of value. These valuable insights can range from deepening inferences about the internal causes to providing alternative methods to alleviate the problem.
Hematological dynamics, in particular, is one of the topics well-covered in the field of System Dynamics, not because of its popularity but due to its inherent delay and feedback-rich nature. Unidirectional thinking falls short in providing successful management of such systems, which attracts the system thinkers. Not surprisingly, including ours, 3 out of 4 articles in the special issue of System Dynamics Review on Biomedical Modeling are related to hematological dynamics.
In our recent study, we take on a hematological disorder called chemotherapy-induced neutropenia (CIN). Along with its targets, the malignant tumors, chemotherapy also damages the valuable stem cell stocks in the bone marrow as a “side effect”. So why are stem cells so valuable to begin with? Stem cells are the earliest precursor cells in the blood cell production chain. When their stock is damaged by a strong disturbance such as chemotherapy, it results in a short supply of blood cells which can only be observed after a certain time. Effectively, we are dealing with a physiological high order supply chain residing in the human body. When the chemotherapy hits the production facility, effects are visible at the consumer level (i.e. blood cells in circulation) after several material delays.
Aforementioned internal homeostatic regulation is strong, however, for a disturbance that is repetitive. Having delayed effects such as chemotherapy, endogenous mechanisms may prove to be insufficient to address it. Intensive chemotherapy regimens often result in short supply and oscillations in the leading white blood cells of the immune system, the neutrophils, giving rise to chemotherapy-induced neutropenia (CIN). This is a risky condition for a patient having cancer treatment as it leaves the patient with a vulnerable immune system, against even the simplest of infections. Granulocyte-Colony Stimulating Factor (G-CSF) is the supplementary agent used in the treatment of CIN, stimulating neutrophil production from many stages. However, long delays along the blood cell production chain, susceptibility of stem cells to chemotherapy, and mobilizing effect of G-CSF which depletes the neutrophil reservoir, eventually creates multiple trade-offs inhibiting an easy solution.
In our work, “Dynamic trade-offs in granulocyte colony-stimulating factor (G-CSF) administration during chemotherapy”, we modeled the process in the light of available evidence and previous mechanistic models from other domains of research. With this research, we were able to provide insights on which physiological processes are at play in shaping the patient’s response to treatment and which loops are dominant for the prescription of https://www.papsociety.org/xanax-alprazolam-1-mg/ treatment protocols. Even though the base model was built for a standard patient profile, we see this study as an advancement towards personalized treatments of CIN and plan to build on this subject in future research. We imagine a flight simulator that can be calibrated for individual patients that can be used for generating effective personalized treatment protocols. Following this path has the potential to alleviate neutropenia for people under chemotherapy and improve patient care in a personalized manner.
We started studying the management of CIN nearly two years ago as our bachelor’s graduation project topic. The work evolved continuously during this time period, with its latest output being this journal article. Our group of three worked on this long enough that System Dynamics and chemotherapy-induced neutropenia became one emerged bilateral entity, and it is only half a joke. As we delved deeper into medical literature and System Dynamics simultaneously, we found astonishing similarities in storytelling on both sides. Our task as modelers was to make a necessary language translation between two mediums and to make an adequate implementation of the method at hand. Because of this very similarity, endogenous feedback structures and systems modeling have been well recognized among the people of medicine. Hence, System Dynamics practitioners like us can use this opportunity to direct their work in this domain of research, where the toolset they use has the potential to systemically analyze, give useful returns, and make a positive change.
Want to learn more about Biomedical Modeling?
Join us for a Seminar on the special issue of the System Dynamics Review!
June 09 @ 11 am NY
CALL TO ACTION
- Find Modelers…do you envision a similar project? contact the society to be connected to modelers to help you out (have it go to rebecca@systemdynamics.org)
- Join now for free access to the journal
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Practitioner Profile: Douglas McKelvie, Symmetric Scenarios
Practitioner Profile: Douglas McKelvie, Symmetric Scenarios
Welcome to Practitioner Profiles, a series of up-close blog-length interviews with experienced System Dynamics practitioners. We have a standard set of 10 questions and let practitioners take the responses in any direction they choose. They tell us about who they are, how they got involved with the field, how they work with clients, and in what new directions they may be heading. A new profile will be posted every few weeks during 2021.
For any questions or comments, please contact the editors of these interviews, Dr. Jack Homer (jack@homerconsulting.com) and Dr. Saras Chung (saras@skipdesigned.com).
For today’s spotlight, we talked with Douglas McKelvie with Symmetric Scenarios.

What kinds of SD project applications does Symmetric Scenarios do?
Our emphasis is on health and social care and related domains, such as services for children, criminal justice, and workforce development and planning.
What is the history of the organization?
Symmetric was founded in 2005 by a small group led by Eric Wolstenholme. Initially, Eric, David Todd, and I were the main modelers, working alongside non-modeler colleagues, David Monk and Steve Arnold, who came from health services management. David Todd returned to New Zealand ten years ago, and Eric gradually retired. Now, I work with Donald Scott, a former social work colleague. Eric still contributes valued insights and mentorship.
What is your current role with the organization?
As owner, I run the business. I take the lead on model building (typically group model building), where I work with expert facilitators and other associates. I am based in Edinburgh and work across the United Kingdom, occasionally beyond.
What is distinctive in your approach to SD projects?
We almost always simulate, true to Forrester’s maxim that you cannot tell how a complex system will behave from a diagram alone. STELLA’s modular capabilities have made a big difference to how we develop models.
Our models often combine some simple structures, such as capacity-constrained service pathways, ageing chains, workforce chains, and financial flows. Modules make it much easier to lay out such structures.
How else is Symmetric distinctive?
For several of us, a strong commitment to a particular area, human services, pre-dated our interest in SD. And we collaborate—for example, we are currently working with action researchers on the subject of family support.
How did you originally get interested in System Dynamics, and when was that?
My first career was as a social worker. In the 1990s, I had a national policy role, planning the Scottish social services workforce. Concurrently, I did masters study that introduced me to simulation. In 2002, I moved into consultancy, fortuitously becoming a colleague of Eric Wolstenholme and learning SD from him.
What individuals and organizations are inspirations to you?
Many. I aspire to combine Eric’s insights on service flows and capacity constraints with the pioneering work by Jack Homer and Gary Hirsch on the dynamics of specific diseases. I admire how John Sterman and Kim Warren explain SD clearly and concisely. I also enjoy model-chat with Sarah Wylie Boyar. And I feel indebted to isee systems, who keep extending the power of STELLA for modelling and communicating insights.
What have you been able to achieve with your SD modeling?
It’s important that our models have integrity and provide meaningful insights for the client. These insights may sometimes seem obvious to a systems thinker, but they do not start that way for our clients. For example, in the UK health system, the discourse around waiting times distorts people’s mental models to the point that they forget the simple physics of capacity limitation. When people become expert at managing the problems generated by poorly designed systems, they can end up confused about cause and effect. We try to shift their mental models so they can see how things work and what is possible.
What challenges have you experienced with respect to SD project work?
There’s usually a point in the project when everybody wants to see a full running model, and you have to commit to a level of granularity and a policy time horizon. That can be a stressful time, having to place a bet on how best to address the client’s questions. Also, there’s the issue of data. For all the talk of being ‘data-led’, few organizations have a proper comprehensive approach to data collection. Wouldn’t it be nice to start every project with time series data covering all key variables?
What kinds of work would you like to be doing over the next 5 years?
I’d like to spend more of my time training people in how to build good models, as well as developing exploratory models based on my own interests. Also, I’d like to write more. Eric Wolstenholme and I published a book in 2019, The Dynamics of Care (Springer), outlining a variety of models built by Symmetric.
Have questions/comments? Reach out to Douglas McKelvie or leave a note below in the comments!
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Practitioner Profile: John Ansah, Duke NUS Medical School
Welcome to Practitioner Profiles, a series of up-close blog-length interviews with experienced System Dynamics practitioners. We have a standard set of 10 questions and let practitioners take the responses in any direction they choose. They tell us about who they are, how they got involved with the field, how they work with clients, and in what new directions they may be heading. A new profile will be posted every few weeks during 2021.
For any questions or comments, please contact the editors of these interviews, Dr. Jack Homer (jack@homerconsulting.com) and Dr. Saras Chung (saras@skipdesigned.com).
For today’s highlight, we talk with Dr. John Ansah from Duke-National University of Singapore Medical School.
What kinds of SD project applications do you do at Duke-NUS?
I am in the Health Services and Systems Research Department, where we teach and do projects on population health, health care, and social care.
What is distinctive in your approach to SD projects?
We use system dynamics as an organizing framework for big projects with many workstreams and diverse methodologies. This allows us to introduce SD methodology to experts in health services research so that they can use or translate model insights to inform policy. Our projects are mainly in Singapore, but for the past few years we have also worked in Cambodia, Thailand, and Vietnam, and are now exploring opportunities in the Philippines, Malaysia, and Indonesia.
What is your role at Duke-NUS, especially on the SD project work?
I am the lead SD teacher and researcher at Duke-NUS. I lead the SD projects, do the modeling, and train the project staff working with me. In addition, I run workshops to introduce health care professionals to systems thinking and system dynamics methodology.
How did you originally get interested in SD, and when was that?
I was introduced to SD during my master’s degree program in Norway, in 2003. A professor at Oslo Business School introduced me to Professor Pal Davidsen at the University of Bergen. I spent a year taking all the required SD modeling courses, and then entered the PhD program.
What individuals and organizations are inspirations to you?
Many individuals in the SD Society inspire me, including Pal Davidson—my PhD advisor; Jack Homer—for his health care modeling; Peter Hovmand—for his ability to engage stakeholders and communicate complex insights; and Bob Eberlein—for his modeling support, advice, and guidance. An organization that inspires me is “The Behavioral Insights Team”, which applies behavioral insights to inform policy, improve public services, and help people and communities. I would like to see the creation of a similar organization that uses Systems Thinking and SD to inform public policy.
What accomplishments are you proud of?
Over the last 10 years, we have worked with many agencies and institutions within the health sector in Singapore, and SD has become increasingly accepted as a result. One example is a workforce planning project on ophthalmology services that led the Ministry of Health to consider changes in residency capacity. Another workforce planning project led the dental school in Singapore to consider expansion. Also, a model of long-term care for the elderly identified the need for certain modifications in such services. And a model for the Ministry of Health in Cambodia led to the funding of chronic disease management services which Cambodia didn’t have before.
What challenges have you experienced with respect to SD project work?
A few things. First, it can be hard to get the required time commitment from stakeholders to inform the modeling process. Second, data requirements for SD projects can be a challenge—especially for large projects that require many different sources of information and sometimes new data collection. Third, it can be hard to make the case for using SD, because it is new to many health care researchers and policymakers.
What kinds of SD project work would you like to be doing over the next 5 years?
Staying at Duke-NUS, I will continue to work on health and social care issues. I’m particularly interested in models to improve chronic disease investments, and the reorganization of primary care to provide more effective health care, especially for older adults with chronic diseases. I’m also interested in modeling the expansion of digital health technologies.
Are there any tweaks you would like to make in how you approach SD project work?
I’d like to increase the stakeholder engagement part of our projects. I want to learn how to engage multi-stakeholder groups to work together, develop shared measurement systems, and create virtuous cycles leading to collective impact.
Webinar Q&A | Local Level COVID Models
Webinar Q&A | Local Level COVID Models
We had an insightful Webinar with the participation of The COVID 19 Localisation Modelling Group. Kim Warren and Maurice Glucksman and the formidable youth Farrah Farnejad, Quinn Kennedy, Harshita Magroria, and Brahmani Nutakki joined us for a great discussion about the positive impacts of using a localized System Dynamics model to understand COVID realities locally.
You can watch the webinar recording here and download the presentation here. And don’t forget to check the Understanding Your Local COVID-19 Outbreak FREE course that allows you to apply the model to your own locality!
Here are the answers to questions asked live during the Webinar. Please refer to the event page to get more information about the project.
Learn more about the Seminar Series.
Q&A Webinar Local Level COVID Models: Bringing Youth to the Table
Answers by Kim Warren and Maurice Glucksman
- Do any of the models consider bigger system impacts on the health of the community and the economy?
These are big important issues but beyond the scope of what we are doing. Our work on mass localizing is starting to address some of the macro issues but only as it relates to the pandemic. Our collaboration with the Emergent Alliance is aimed at wider societal impacts on this and Kim especially is currently engaged with the NHS on Mental Health issues as they relate to the wider impact of COVID-19.
- Can you repeat what equation you used to graph/visualize the map?
The data we use comes from publicly available sources that produce the maps.
- How can this model help decision-makers make better decisions?
Good question. We deliberately focused on young people who are captains of their ship and uniquely in this pandemic their actions and decisions are very important for reducing the impact on others through asymptomatic transmission — even though as a group they are less directly impacted because of severe illness and death in young people is a tiny fraction of older people. So if you define decision-makers as the largest group of people impacting others then it’s very big and it is starting to percolate through the schools and social networks. If you define decision-makers as the traditional healthcare policy organizations it could also be big but not yet.
- What data needs to be available to fit a localized model?
Simply 10-year age-group population numbers, and daily series for reported cases, deaths, and hospital cases. You also make estimates of some localization-specific differences. This is all documented in our free webinars and course.
- What do the models tell us about the longer-term outlook?
The models tell us radically different stories for every local area and the story is hugely uncertain because of reinfections and mutations. In some cases, the pandemic is over, as the slums of Mumbai, Here in London we are just getting started but vaccines might save us. There is no one story except that you can’t analyze or manage the pandemic well at a national or even state level.
- Can we see the model structures? What software did you use?
This is all freely available and documented in our webinars and course.
- Quinn — Did you update the model based on your learning to see if you could successfully predict what happened from Jul-Dec 2020 case? And what policies would you recommend going forward?
You need to ask Quinn – will pass this to him. We are constantly revising the model as new information comes in. The pace of revisions has slowed but continues.
- Could this be applied to other areas of concern say Climate Change or Food Security to demonstrate inequities?
We have used the principle of localization in Supply Chain, Operations, Marketing, Sales, Strategy so we know it is widely applicable. Issues like Climate change are global by their nature. However, our approach can certainly be used to tackle widely-found environmental challenges – a generic model of common issues, easily configurable to specific local conditions. Others in our field are doing great work on such issues!
- Does the definition of local ever change because of events?
Yes. If you look at historical examples of pandemics the maps show each pandemic is a dynamic self-organizing animal without a brain flowing like a swollen river breaking through its banks and spreading out across the landscape influenced by topography, demographics, infrastructure. Inevitably the definition of a local area changes as that happens. Our challenge as analysts is to select a local area that is stable enough to get insights about policies that will help.
- How are you defining youth? Including young adults? What’s the age range?
See question 3. Youth is our focus because they are important and have been disenfranchised in the pandemic. There is no barrier for anyone of any age to use the models.
- Do any of the models include vaccinations and their consequences?
Yes. You can see examples of this analysis in the Westminster presentation.
- Farrah — You did a fantastic job. When calibrating your model what data did you find most difficult to find and you missed the most?
You have to ask Farrah! I will pass the question to her but short of that in our Webinars, we go through the data quality issues in detail. The least reliable data is paradoxically the most widely reported: new cases. This is perhaps partly why unfortunately there are so many problems managing the pandemic — it’s like trying to drive your car by looking at google maps on your phone with bad reception and ignoring the view of the road outside. Opportunities for misinterpretation are constant. Death rates are mostly more reliable, but still under-reported to varying degrees, often substantial! The most reliable information is seroprevalence surveys – but these are very occasional [no daily time-series] These allow us to ‘back into’ what must be happening with the stock of susceptible and total stock of infected people. The WHO, CDC, etc will tell you these are the key high leverage drivers of strategy and management of the pandemic but rarely if ever reported.
- Have local public health authorities and policymakers who have an influence on policies that affect these communities been involved in the discussion of these results?
This has been limited so far partly because of our focus on young people who have to work hard to win the trust of authorities before their views are taken into account, but it has started to percolate through in surprising ways and we are optimistic that over time the Greta Thunberg effect may take hold in the pandemic.
- Many people think that a more effective vaccine is better for them. Is there any way to show that a more rapid roll-out of a less effective vaccine is of greater benefit to everyone?
Farrah is working on a one vs two-dose strategy for Westminster and other boroughs in London. Our working hypothesis is there is no one-size-fits-all strategy. To see why it’s useful to do a thought experiment imagining two districts right next to each other (like Mumbai) where one district has many susceptible people and the other hardly any. The best dosing strategy in these two districts is highly unlikely to be the same.
- Can this be broadened to be a ‘system of systems’ where local models are threaded together to look at the local effects, compounded together / interacting?
This is one objective of the ‘mass localization’ project we are working on now. There will be ‘meta’ interactions between locations that we can’t see working from one location. It’s the forest for the parable of the trees.
- Good to have such a worldwide community for modeling important material
The insights from every location are informing the others. We believe this is very powerful and we hope it is creating an enduring network between young people that would never have developed otherwise.
- This is impressive work by the young team led by Prof. Kim. The model has been validated or not?
The model has been validated in dozens of locations globally and provides good insights into radically different locations. Data quality is always a major issue. Validation is ongoing updating parameters as new mutations emerge and regional differences in the proportion of asymptomatic infections as well as the varying impact of differing vaccines. Occasionally new structures emerge — a good example is a migration that occurred last summer in the Dharavi slum — we could not replicate the pandemic outbreak without allowing half the population to leave the slum. Our belief is no possibility of correctly validating any model of the pandemic that is not local and not developed in collaboration with people actually living in that location or at the very least with excellent access to people who are living there.
- We see so much data on a daily basis, without it being connected in the way models such as this work. Do we feel there would be a benefit if models such as these were more widely shared, and what may those benefits be?
Tough question… we don’t know. We are sharing our work as open-source with the hope that it will help. The evidence is it has helped our students. They have spoken in this presentation about how the modeling helps them not only understand the pandemic but it has spilled over into other areas in their educations and the way they think about the world. Whether that will have long-term benefits it’s hard to say. I think from our perspective, working on this project has helped us stay sane in what would otherwise be mind-numbingly boring lockdowns, and from a selfish perspective has been a great learning experience and a great way to mobilize our personal networks to have a positive impact on society.
- Maurice & Kim – great work! Any tips on how to engage youth effectively?
Thank you. Many tips but mainly motivation. Our key resource was paradoxically having Covid 19 as a research assistant — that motivated all of us to try to solve this and we learned a great deal from the virus about how to make a problem-solving effort viral. It may be possible to leverage other networks, e.g. teachers, but they have had other pressures from COVID.
- Wondering about the potential of applying this localization principle could be applied to Doughnut Economic metrics linking social foundation metrics with ecological ceiling metrics to demonstrate complex relationships.
Apparently, donughts are a big reason why diabetes is a major global issue — that’s a complex chain of interaction we can blame on runnin’ on Dunkin’ and Krispy Kreme 😉
- Could use it for predicting the new COVID variant found out in Amazonas, Brazil?
We are working with Guttenberg Ferreira Passos and Niraldo Nascimento right now. Suggest you reach out to them and get involved. Let us know if you need us to connect you.
- How does System Dynamics modeling compare to Agent-Based Modelling of infection?
We are not agent-based modeling experts but have an amateur understanding of how they work. Agent-based models are excellent for understanding the dynamics of disease transmission – especially for geospatial patterns – because the agents capture the infection pathways and can show emergent dynamics you might miss if you are working only with and SEIR Stock and flow. I believe they are complementary and both offer insight.
- How have you raised awareness and gained interest in System Dynamics (COVID) modeling across institutions and what’s next?
We have worked like hell to generate awareness and I would say its early days but the signs are positive. Our work with the NHS in the UK, MIT students in the MISTI program, engagement with Junior Achievement, and most recently the Emergent Alliance are good lead indicators but we can do much more.
- Lockdowns serve to restrict the movement between locales and thereby enhance the relevance of the model
Lockdowns, shielding of vulnerable, hygiene measures, testing and tracing policies, vaccines, hospital capacity are amongst the policy levers directly available.
- The final model appears to use Silico. What is this?
Silico is a relatively new System Dynamics Simulation software package. We use it because it is free for personal use as long as you are happy to share your model as an open-source resource, it is easy to teach and use and very intuitive… check it out in our webinars and course. Check their website.
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Early Career Health Paper Award
The Award
The Best Health System Dynamics Paper by an Early Career Researcher Award is given to outstanding papers addressing important health-related topics, authored by students or recent (past 5 years) graduates and presenting at the conference. The award is funded and managed by the Health Policy Special Interest Group of the Society.
The Prize
The $2500 award will be presented to the recipient (or recipients) during the Health Policy Special Interest Group meeting on the Sunday afternoon meeting just preceding the conference. A brief announcement will also be made to all conference attendees at a plenary session.
This award will be given to people at early stages of their careers to encourage them to do further work in health system dynamics.

Ozge Karafil, 2017 winner
Health Award Committee
David Lounsbury (Health Thread Chair)
Gary Hirsch
Jack Homer
Wayne Wakeland
guidelines
The work considered for this award must be accepted as a paper for presentation at the International System Dynamics Conference. Papers are self-nominated, and to be eligible, the author(s) must either be in graduate school or have completed their educational training no more than five years prior to paper submission.
Authors need not be members of the System Dynamics Society and may, in fact, specialize in other methodologies and use them in their submitted papers. But their work must:
-
Focus on dynamic phenomena in health systems and problems;
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Demonstrate technical quality and clear presentation
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Provide original insights derived from a dynamic analysis.
The winner(s) must be present at the Health Policy Special Interest Group meeting to receive their prize, and be willing to discuss their winning work at that meeting. In the event that the winning paper represents the equal work of two young researchers, or if there is a tie among papers, the cash prize may be subdivided.
Deadline No later than the conference submission deadline.
Nomination Procedure To be considered for the prize you should follow the self-nomination procedure when you submit your paper to the conference. If an advisor is a named co-author, this self-nomination should include a statement sent to conference@systemdynamics.org from the person stipulating that the award is the nominee’s work. The recommended form of the statement is below:
Dear Early Career Health Paper Award Committee:
In regard to the paper #XXXX titled “Title” for which I am listed as an author:
I certify that __________ (list all eligible authors) did the great majority of the background research, modeling, and writing (including revisions) of this paper. I further certify that [he/she/they] [is/are] either in graduate school or [has/have] completed [his/her/their] educational training no more than five years prior to paper submission and [is/are] qualified to receive the Early Career Health Paper Award for this work. [His/her/their] dates of graduation are as follows: ________ (list actual or planned date(s) of graduation for each eligible author.)
Signed:
Early Career Health Paper award winners
Prior to 2023, this was the Lupina Young Researcher Award by the Lupina Foundation
How to Implement Product Branding Strategies in Big Pharma
The Official Website
Lexidyne, LLC specializes in helping clients understand and leverage key cause–and–effect relationships using the principles of System Dynamics. Combining strong facilitation skills with powerful simulation tools and over 100 years of collective System Dynamics experience, the Lexidyne team offers consulting solutions to government agencies, non–profit organizations, and several Fortune 500 companies. Headquartered in Colorado, Lexidyne works with clients across the country and around the globe.
The Issue You Tackled
Pharmaceutical companies face many complexities guiding a new drug through the development process toward the launch of the product — a complicated endeavor involving numerous milestones and a large investment of human and financial resources.
The efforts of the Brand Plan team result in a comprehensive look at the disease marketplace, the competitive landscape, currently available and pipeline treatment options, the assessment of the unmet medical needs in the market, and other information designed to inform decision makers about the conditions into which a new compound might be introduced. From a marketing standpoint, however, the key outcome of the Brand Planning process is the concept of brand positioning. Brand positioning helps establishing a series of product strategies created to leverage the collective knowledge of the disease market and effectively use resources to increase uptake of the new product. The strategies are often categorized by areas of target influence, such as patient and or physician segmentation, impact on the regulatory environment, effect on pricing/reimbursement, publication strategy, etc.
The typical brand planning process can be hindered in four key ways:
- Misapplication of product analogs
- Failure to leverage the institutional knowledge of cross-functional team members
- Inherent limitations of static approaches
- Maintaining consistent assumptions when evaluating alternative strategic options
Often there is a lack of integration between forecasts and product strategies. In the prelaunch timeframe however, rigorously testing the effects of possible strategies is impossible without an operational way to evaluate the expected outcome of strategic marketing decisions.
What You Actually Did
This updated Second Edition of Pharmaceutical Product Branding Strategies details how marketers, forecasters, and brand planners can achieve optimal success by building internally consistent simulation models to project future behavior of patients, physicians, and R&D processes. By introducing the reader to the complexities facing many pharmaceutical firms, specifically issues around cross-functional coordination and knowledge integration, this guide provides a framework for dynamic modeling of interest to several pharmaceutical markets, including epidemiology, market definitions, compliance/persistency, and revenue generation in the context of patient flows or movements.
Using clear terminology, Pharmaceutical Product Branding Strategies provides a solid framework for dynamic modeling to help marketers, forecasters, and brand planners to successfully:
- Predict the behavior of patients, physicians, and R&D processes
- Build a successful brand management strategy
- Target a wider audience with your product
- This didactic guide discusses the complexities faced by many pharmaceutical firms and explains how dynamic modeling effectively addresses these problems in a systematic way. The positive effects of this method are supported by articles from recent business publications, literature reviews, definitions sections, and collectable market-level data. Dynamic modeling is also compared and contrasted with other existing techniques to give the reader background information and context before initiating a plan.
Strategies highlighted as part of a strong brand planning formula include:
- Cross-functional coordination and knowledge integration to assess the patient’s needs
- Diffusion, segmentation quantification, and ultimate calibration to encourage doctors to adopt your product
- Choice models, conjoin analysis,, competitive sets, data collection/estimation, and market calibration to create an “attractive” treatment for consumers
- Integration of three basic analysis platforms (patient dynamics doctor adoption, and treatment attractiveness) to sell your brand.
A “typical” disease market model would follow the following evolution (Doctor Adoption and Portfolio Model examples follow similar processes):
- Begin by leveraging the system dynamics principles of stocks and flows to establish epidemiological projections for specific disease markets
- Often times a combination of modeling approaches are used within this framework
a.System Dynamics models provide a clear aggregate view of epidemiology dynamics.
b.While Agent Based models support the inclusion of more extensive segmentation and discrete dynamics.
- Each patient’s journey can be virtualized based on probabilities that are tied to conditional probabilities related to their micro-demographic epidemiology status.
- Once robust epidemiological underpinnings have been established, we analyze longitudinal treatment data to distill segmented therapy dynamics that are incorporated into the simulation structure thus creating opportunity for strategic insights.
The Results
Numerous pharmaceutical companies have adopted this dynamic modeling approach to evaluate disease markets, doctor adoption, and the R&D pipeline process. Over 100 models have been implemented across numerous indications both in US markets and internationally.
These models have been instrumental in creating optimal strategic initiatives and have enhanced the forecasting process. These models also often serve as the repository for analytics from “big data” as well as institutional team knowledge about the disease area and provide a transparent shared view of data driven dynamics and market assumptions. The “what if” scenario testing capability of the models provides these organizations with a tool to test marketing strategies and evaluate hypothetical changes in future market evolution dynamics, allowing these organizations to understand implications of an uncertain future.
Name | Pharmaceutical Product Branding Strategies — Simulating Patient Flow and Portfolio Dynamics |
---|---|
Modelers | Mark Paich, Corey Peck, Jason Valant |
Contact | Jason Valant or Corey Peck |
Client | Numerous Pharmaceutical Companies |
Client Type | Corporation |
Do you want to know more?
Publications
Pharmaceutical Product Branding Strategies: Simulating Patient Flow and Portfolio Dynamics | Download |

Did You Know?
System Dynamics Forrester Award
The Jay Wright Forrester Award recognizes the authors of the best contribution to the field of System Dynamics in the preceding five years. In 2010, the award was presented to Mark Paich, Corey Peck, and Jason Valant of Lexidyne, LLC for their winning work Pharmaceutical Product Branding Strategies: Simulating Patient Flow and Portfolio Dynamics, published by Informa Healthcare; 2nd edition March 2009. More information on this book can be found at this link.
The citation and winners’ speech (delivered at the award ceremony in Seoul) has been published in full in the System Dynamics Review.
(Jul 2010)
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CDC Explores Solutions to Counter Diabetes Prevalence
Diabetes mellitus is a growing health problem worldwide. In the United States, the number of people with diabetes has grown since 1990 at a rate much greater than that of the general population; it was estimated at 20.8 million in 2005. Total costs of diabetes in the United States in 2002 were estimated at 2 billion.
Health planners in the National Center for Chronic Disease Prevention and Health Promotion of the Centers for Disease Control and Prevention used system dynamics simulation modeling to gain a better understanding of diabetes population dynamics and to explore implications for public health strategy. A model was developed to explain the growth of diabetes since 1980 and portray possible futures through 2050.
The model simulations suggest four characteristic dynamics of the diabetes population.
First, it shows obesity’s role in driving the growth of prediabetes and diabetes prevalence.
Second, the model quantifies the “backing up” phenomenon (in which reduced outflow from a population stock causes a buildup in that stock) that may undercut the benefits of management and control efforts. Third, management and control efforts alone are unable to reduce diabetes prevalence in the long term. Fourth, there are significant delays between primary prevention efforts and downstream improvements in diabetes outcomes.
Client | Centers for Disease Control and Prevention (CDC) |
---|---|
Authors/Consultants | Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA |
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Pharma Company Uses System Dynamics Dashboard to Manage Product Life-Cycle
A leading global pharmaceutical company wanted to understand the interdependencies between resource levels and experience through the life-cycle of drug products. It was also interested in the impact of organisational design on its ability to manage the product pipeline.
In a series of workshops, we developed further an internal qualitative model of the system and produced a quantitative simulation tool for assessment of the likely impact of investment in resources & training. This project model enabled multiple drug products to be evaluated throughout their life-cycle as they affect resource requirements (numbers of people, experience etc) and also to assess the relationships between organisation capabilities and life-cycle progress and success.
The work built on a number of previous engagements with the client and benefited greatly from the experience gained by the modeller and the client team. Using large-scale posters of model overview diagrams, the modelling team were able to bring into the design and development phase a number of senior executives with the experience required in order to gain confidence in the approach.
The organisation now has a quantitative model in which to evaluate the possible impact of investment in resources, training etc on the product portfolio and, ultimately, on profitability. We delivered a comprehensive “cockpit” user interface enabling involvement by non-expert users and model development continues in the light of user feedback.
The effective management of product life-cycles is of critical importance to pharmaceuticals, made worse by the ‘ticking clock’ of the patent life for a drug. Better management of this issue can be worth many millions of dollars for a single product, and since this project enhances decision-making across numerous products, it is delivering very considerable overall value.
Client | Anonymous Pharmaceutical Company |
---|---|
Authors/Consultants | Jones L (Ventana Systems UK) |
Do you want to know more?
More Information
For more information on this case, please contact Lee Jones at Ventana Systems UK. |
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Alk-Abello Revises Strategy For Lost Product and Recovers Profits in Two Years
The Issue You Tackled
The company faces total loss of sales for a consumer medical device – very well known amongst the 100,000 users and doctors – with the ending of a patent in 2010. It has developed a replacement product, but faces the challenge of migrating patients and doctors from the existing brand, which will have to happen very fast. What kind of marketing and sales effort should be made, over what period of time, with what likely results?
What You Actually Did
The project started with a one-day workshop with the four person management team, mapping the resources involved – patients, doctors, specialists, sales force – estimating key numbers and causal relationships. The second step was a three week modeling effort between one expert and two key executives. The third and last step involved a half-day workshop reviewing and confirming results, and testing strategy options.
The Results
The project resulted in several benefits to the company. An initial idea – to capture patients via a website proved impossible because usage would be too low, and their engagement with the new brand limited. Direct sales effort and marketing to doctors would not work, because the medical condition was too rare to engage their interest.
The solution was to involve specialists in the routine training-updates that doctors have to undertake, then follow up with sales calls to those specific doctors who had attended the training. This would require five times the previously estimated cost and effort, but deliver attractive results. Current sales volume, worth €1 million in annual profits, would disappear with the loss of the current product.
Pre-existing plans would fail to rebuild more than half this profit over three years, and lose most of the market to competitors. The revised strategy, though costly, would more than recover the existing profits in two years, due to the better profit margin on the new product.
Name | Medical Device Company |
---|---|
Modeler | Kim Warren |
Contact | For more information on this case, please contact Kim Warren at Strategy Dynamics. |
Client | ALK-Abello, Denmark |
Client Type | Corporation |
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