@article{Hasgul2025,

title = {Stimulant-involved overdose deaths: Constructing dynamic hypotheses},

author = {Zeynep Hasgul and Arielle R Deutsch and Mohammad S Jalali and Erin J Stringfellow},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2025/01/Hasgul_stimulant_overdose_2025.pdf

https://mj-lab.mgh.harvard.edu/wp-content/uploads/2025/03/Stimulant-Involved-ODs.pdf

},

year  = {2025},

date = {2025-01-27},

urldate = {2025-01-27},

journal = {International Journal of Drug Policy},

volume = {136},

pages = {104702},

abstract = {The overdose epidemic in the United States is evolving, with a rise in stimulant (cocaine and/or methamphetamine)-only and opioid and stimulant-involved overdose deaths for reasons that remain unclear. We conducted interviews and group model building workshops in Massachusetts and South Dakota. Building on these data and extant research, we identified six dynamic hypotheses, explaining changes in stimulant-involved overdose trends, visualized using causal loop diagrams. For stimulant- and opioid-involved overdose deaths, three dynamic hypotheses emerged: (1) accidental exposure to fentanyl from stimulants; (2) primary stimulant users increasingly using opioids, often with resignation; (3) primary opioid (especially fentanyl) users increasingly using stimulants to balance the sedating effect of fentanyl. For stimulant-only overdose deaths, three additional dynamic hypotheses emerged: (1) disbelief that death could occur from stimulants alone, and doubt in testing capabilities to detect fentanyl; (2) the stimulant supply has changed, leading to higher unpredictability and thus higher overdose risk; and (3) long-term stimulant use contributing to deteriorating health and increasing overdose risk. These hypotheses likely each explain a portion of the recent trends in stimulant-involved overdoses. However, confusion and uncertainty around the drug supply emerged as a central theme, underscoring the chaotic and unpredictable nature of the stimulant market. Our findings indicate the need for research to develop targeted public health interventions, including analyzing the extent of the effect of contamination on overdoses, reducing confusion about the stimulant supply, and examining historical stimulant use trends.},

keywords = {Methods, Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Koiso2024,

title = {Modeling approaches to inform travel-related policies for COVID-19 containment: a scoping review and future directions},

author = {Satoshi Koiso and Eren Gulbas and Lotanna Dike and Nora M. Mulroy and Andrea L. Ciaranello and Kenneth A. Freedberg and Mohammad S. Jalali and Allison T. Walker and Edward T. Ryan and Regina C. LaRocque and Emily P. Hyle},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/12/Koiso_travel_2024.pdf},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

journal = {Travel Medicine and Infectious Disease},

abstract = {Background

Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies.



Methods

We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor.



Results

The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches.



Conclusion

Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.},

keywords = {Infectious diseases, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Cost-Effectiveness of Increasing Access to Colorectal Cancer Diagnosis: Analysis From Thailand},

author = {Peeradon Wongseree and Zeynep Hasgul and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/06/Wongseree_VIH_RI.pdf},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

journal = {Value in Health Regional Issues},

volume = {43},

abstract = {Objectives

The purpose of this study is to evaluate the cost-effectiveness of increasing access to colorectal cancer (CRC) diagnosis, considering resource limitations in Thailand.



Methods

We analyzed the cost-effectiveness of increasing access to fecal immunochemical test screening (strategy I), symptom evaluation (strategy II), and their combination through healthcare and societal perspectives using Colo-Sim, a simulation model of CRC care. We extended our analysis by adding a risk-stratification score (RS) to the strategies. We analyzed all strategies under the currently limited annual colonoscopy capacity and sufficient capacity. We estimated quality-adjusted life-years (QALYs) and costs over 2023 to 2047 and performed sensitivity analyses.



Results

Annual costs for CRC care will increase over 25 years in Thailand, resulting in a cumulative cost of 323B Thai baht (THB). Each strategy results in higher QALYs gained and additional costs. With the current colonoscopy capacity and willingness-to-pay threshold of 160 000 THB, strategy I with and without RS is not cost-effective. Strategy II + RS is the most cost-effective, resulting in 0.68 million QALYs gained with additional costs of 66B THB. Under sufficient colonoscopy capacity, all strategies are deemed cost-effective, with the combined approach (strategy I + II + RS) being the most favorable, achieving the highest QALYs (1.55 million) at an additional cost of 131 billion THB. This strategy also maintains the highest probability of being cost-effective at any willingness-to-pay threshold above 96 000 THB.



Conclusions

In Thailand, fecal immunochemical test screening, symptom evaluation, and RS use can achieve the highest QALYs; however, boosting colonoscopy capacity is essential for cost-effectiveness.},

keywords = {Cancer, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Generative AI and Simulation Modeling: How Should You (Not) Use Large Language Models Like ChatGPT	},

author = {Ali Akhavan and Mohammad S. Jalali },

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/07/Generative_AI_sim_mod.pdf},

year  = {2024},

date = {2024-01-18},

urldate = {2024-01-18},

journal = {System Dynamics Review},

abstract = {Generative Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) and chatbots like ChatGPT, hold promise for advancing simulation modeling in various domains. Despite their growing prominence and associated debates, there remains a gap in comprehending the potential of generative AI in this field and a lack of guidelines for its effective deployment. This paper endeavors to bridge these gaps. We discuss the applications of ChatGPT through an example of modeling COVID-19’s impact on economic growth in the United States. Although we utilize ChatGPT, our guidelines are generic and can be applied to a broader range of generative AI tools and platforms. Our work presents a systematic approach for integrating generative AI across the simulation research continuum, from problem articulation to insight derivation and documentation, independent of the specific simulation modeling method chosen. We emphasize that while these tools offer enhancements in refining ideas and expediting processes, they should complement rather than replace critical thinking inherent to research.},

keywords = {Artificial intelligence, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {In response to: “Never the strongest: reconciling the four schools of thought in system dynamics in the debate on quality” — beyond pragmatism},

author = {Mohammad S. Jalali and Hesam Mahmoudi},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/12/Jalali_2023_In-response-to-Never-the-strongest.pdf},

year  = {2023},

date = {2023-12-19},

urldate = {2023-12-19},

journal = {System Dynamics Review},

keywords = {Methods, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Why Similar Policies Resulted In Different COVID-19 Outcomes: How Responsiveness And Culture Influenced Mortality Rates},

author = {Tse Yang Lim and Ran Xu and Nick Ruktanonchai and Omar Saucedo and Lauren M. Childs and Mohammad S. Jalali and Hazhir Rahmandad and Navid Ghaffarzadegan},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/12/lim-et-al-2023-HealthAffairs.pdf},

year  = {2023},

date = {2023-12-06},

urldate = {2023-12-06},

journal = {Health Affairs},

volume = {42},

issue = {12},

abstract = {In the first two years of the COVID-19 pandemic, per capita mortality varied by more than a hundredfold across countries, despite most implementing similar nonpharmaceutical interventions. Factors such as policy stringency, gross domestic product, and age distribution explain only a small fraction of mortality variation. To address this puzzle, we built on a previously validated pandemic model in which perceived risk altered societal responses affecting SARS-CoV-2 transmission. Using data from more than 100 countries, we found that a key factor explaining heterogeneous death rates was not the policy responses themselves but rather variation in responsiveness. Responsiveness measures how sensitive communities are to evolving mortality risks and how readily they adopt nonpharmaceutical interventions in response, to curb transmission. We further found that responsiveness correlated with two cultural constructs across countries: uncertainty avoidance and power distance. Our findings show that more responsive adoption of similar policies saves many lives, with important implications for the design and implementation of responses to future outbreaks.},

keywords = {Infectious diseases, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Strengthening a weak link: transparency of causal loop diagrams — current state and recommendations},

author = {Mohammad S. Jalali and Elizabeth Beaulieu},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/11/Transparency-of-CLDs.pdf},

year  = {2023},

date = {2023-11-12},

urldate = {2023-11-12},

journal = {System Dynamics Review},

abstract = {Transparency is a critical aspect of systems science. While transparency of quantitative models has been assessed, transparency of their qualitative structures has been less scrutinized. We assess the transparency of causal loop diagrams (CLDs), a key qualitative visualization tool in system dynamics. We evaluate System Dynamics Review (SDR) publications and a sample of most-cited comparable articles in other journals. We assess the inclusion of a plain-language methods statement, overall discernibility of the methods, and identification of causal link sources. Reviewing 72 articles (SDR: 36; other journals: 36), only 44%, 38%, and 25% fully satisfy each criterion, respectively. SDR articles are characterized by higher transparency in the clarity of CLD development method and communication of causal link sources, yet the potential for enhancement is evident. We provide specific recommendations to increase the transparency of CLDs. Transparent reporting benefits original research authors, future expansion of CLDs, and the systems science community. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.},

keywords = {Methods, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Understanding the dynamics emerging from the interplay among poor mental wellbeing, energy balance-related behaviors, and obesity prevalence in adolescents: A simulation-based study},

author = {Anaely Aguiar and Furkan Önal and Eduard Romanenko and Gaironeesa Hendricks and Anne-Siri Fismen and Emmanuel Nwosu and Natalie Savona and Janetta Harbron and Laurence Blanchard and Sondre Herstad and Cécile Knai and Oddrun Samdal and Harry Rutter and Nanna Lien and Mohammad S. Jalali and Birgit Kopainsky},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/09/Aguiar_et_al_2023.pdf},

year  = {2023},

date = {2023-09-27},

urldate = {2023-09-27},

journal = {Obesity Reviews},

pages = {e13628},

abstract = {Both obesity and poor mental wellbeing have a high prevalence in European youth. Adolescents in six countries identified mental wellbeing factors as main drivers of youth obesity through systems mapping. This study sought to (1) explore the dynamics of the interplay between poor mental wellbeing, energy balance-related behaviors, and adolescent overweight and obesity prevalence and (2) test the effect of intervention point scenarios to reduce adolescent obesity. Drawing on the youth-generated systems maps and a literature synthesis, we built a simulation model that represents the links from major feedback pathways for poor mental wellbeing to changes in dietary, physical activity, and sleep behaviors. The model was calibrated using survey data from Norway, expert input, and literature and shows a good fit between simulated behavior and available statistical data. The simulations indicate that adolescent mental wellbeing is harmed by socio-cultural pressures and stressors, which trigger reinforcing feedback mechanisms related to emotional/binge eating, lack of motivation to engage in physical activity, and sleep difficulty. Targeting a combination of intervention points that support a 25% reduction of pressure on body image and psychosocial stress showed potentially favorable effects on mental wellbeing—doubling on average for boys and girls and decreasing obesity prevalence by over 4%.},

keywords = {Obesity, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Dynamics of colorectal cancer screening in low and middle-income countries: A modeling analysis from Thailand},

author = {Peeradon Wongseree and Zeynep Hasgul and Borwornsom Leerapan and Cherdsak Iramaneerat and Pochamana Phisalprapa and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/09/ColoSIM_2023.pdf

https://mj-lab.mgh.harvard.edu/colo-sim-model},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Preventive Medicine},

issue = {175},

pages = {107694},

abstract = {Background

Low and middle-income countries face constraints for early colorectal cancer (CRC) detection, including restricted access to care and low colonoscopy capacity. Considering these constraints, we studied strategies for increasing access to early CRC detection and reducing CRC progression and mortality rates in Thailand.



Methods

We developed a system dynamics model to simulate CRC death and progression trends. We analyzed the impacts of increased access to screening via fecal immunochemical test and colonoscopy, improving access to CRC diagnosis among symptomatic individuals, and their combination.



Results

Projecting the status quo (2023−2032), deaths per 100K people increase from 87.5 to 115.4, and CRC progressions per 100K people rise from 131.8 to 159.8. In 2032, improved screening access prevents 2.5 CRC deaths and 2.5 progressions per 100K people, with cumulative prevented 7K deaths and 9K progressions, respectively. Improved symptom evaluation access prevents 7.5 CRC deaths per 100K with no effect on progression, totaling 35K saved lives. A combined approach prevents 9.3 deaths and 1.8 progressions per 100K, or 41K and 7K cumulatively. The combined strategy prevents most deaths; however, there is a tradeoff: It prevents fewer CRC progressions than screening access improvement. Increasing the current annual colonoscopy capacity (200K) to sufficient capacity (681K), the combined strategy achieves the best results, preventing 15.0 CRC deaths and 10.3 CRC progressions per 100K people, or 54K and 30K cumulatively.



Conclusion

Until colonoscopy capacity increases, enhanced screening and symptom evaluation are needed simultaneously to curb CRC deaths, albeit not the best strategy for CRC progression prevention.},

keywords = {Cancer, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{Claypool2023,

title = {Cost-effectiveness of increasing buprenorphine treatment initiation, duration, and capacity among individuals who use opioids},

author = {Anneke Claypool and Catherine DiGennaro and W. Alton Russell and Melike Yildirim and Alan Zhang and Zuri Reid and Erin Stringfellow and Benjamin Bearnot and Bruce Schackman and Keith Humphreys and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/07/Claypool_2023.pdf},

year  = {2023},

date = {2023-05-19},

urldate = {2023-05-19},

journal = {JAMA Health Forum},

volume = {4},

issue = {5},

pages = {e231080},

abstract = {Importance: Buprenorphine is an effective and cost-effective medication to treat opioid use disorder (OUD), but is not readily available to many people with OUD in the US. The current cost-effectiveness literature does not consider interventions that concurrently increase buprenorphine initiation, duration, and capacity.

Objective: To conduct a cost-effectiveness analysis and compare interventions associated with increased buprenorphine treatment initiation, duration, and capacity.

Design and Setting: This study modeled the effects of 5 interventions individually and in combination using SOURCE, a recent system dynamics model of prescription opioid and illicit opioid use, treatment, and remission, calibrated to US data from 1999 to 2020. The analysis was run during a 12-year time horizon from 2021 to 2032, with lifetime follow-up. A probabilistic sensitivity analysis on intervention effectiveness and costs was conducted. Analyses were performed from April 2021 through March 2023. Modeled participants included people with opioid misuse and OUD in the US.

Interventions: Interventions included emergency department buprenorphine initiation, contingency management, psychotherapy, telehealth, and expansion of hub-and-spoke treatment programs, individually and in combination.

Main Outcomes and Measures: Total national opioid overdose deaths, quality-adjusted life years (QALYs) gained, and costs from the societal and health care perspective.

Results: Projections showed that contingency management expansion would avert 3530 opioid overdose deaths over 12 years, more than any other single-intervention strategy. Interventions that increased buprenorphine treatment duration initially were associated with an increased number of opioid overdose deaths in the absence of expanded treatment capacity. With an incremental cost- effectiveness ratio of $19 381 per QALY gained (2021 USD), the strategy that expanded contingency management, hub-and-spoke training, emergency department initiation, and telehealth was the preferred strategy for any willingness-to-pay threshold from $20 000 to $200 000/QALY gained, as it was associated with increased treatment duration and capacity simultaneously.

Conclusion and Relevance: This modeling analysis simulated the effects of implementing several intervention strategies across the buprenorphine cascade of care and found that strategies that were concurrently associated with increased buprenorphine treatment initiation, duration, and capacity were cost-effective.},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Yildirim2023,

title = {Increasing the initiation of antiretroviral therapy through optimal placement of diagnostic technologies for pediatric HIV in Zimbabwe: a modeling analysis},

author = {Melike Yildirim and Karen A. Webb and Andrea L. Ciaranello and Alyssa K. Amick and Angela Mushavi and Anesu Chimwaza and Anneke Claypool and Tendayi Murape and Nicole C. McCann and Clare Flanagan and Mohammad S. Jalali

},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/06/Yildirim_IJID_2023.pdf},

year  = {2023},

date = {2023-05-19},

urldate = {2023-05-19},

journal = {The International Journal of Infectious Diseases},

abstract = {Objectives: Point-of-care (POC) devices for infant HIV testing provide timely result-return and increase antiretroviral (ART) initiation. We aimed to optimally locate POC devices to increase 30-day ART initiation in Matabeleland South, Zimbabwe.

Methods: We developed an optimization model to identify the locations for limited POC devices at health facilities, maximizing the number of infants who receive HIV test results and initiate ART within 30 days of testing. We compared location-optimization model results to non-model-based decision heuristics, which are more practical and less data-intensive. Heuristics assign POC devices based on demand, test positivity, laboratory result-return probability, and POC machine functionality.

Results: With the current placement of 11 existing POC machines, 37% of all tested infants with HIV were projected to receive results and 35% were projected to initiate ART within 30 days of testing. With optimal placement of existing machines, 46% were projected to receive results and 44% to initiate ART within 30 days, retaining three machines in current locations, moving eight to new facilities. Relocation based on the highest POC device functionality would be the best-performing heuristic decision (44% receiving results and 42% initiating ART within 30 days); although, it still would not perform as well as the optimization-based approach.

Conclusion: Optimal and ad hoc heuristic relocation of limited POC machines would increase timely result-return and ART initiation, without further, often costly, interventions. Location optimization can enhance decision-making regarding the placement of medical technologies for HIV care.},

keywords = {Infectious diseases, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{Deutsch2023b,

title = {Grounding alcohol simulation models in empirical and theoretical alcohol research: a model for a Northern Plains population in the United States},

author = {Arielle Deutsch and Nikki Motabar and Edward Chang and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/alcohol-misuse-model

https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/06/Deutsch_SDR_2023.pdf},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

journal = {System Dynamics Review},

abstract = {The growing number of systems science simulation models for alcohol use (AU) are often disconnected from AU models within empirical and theoretical alcohol research. As AU prevention/intervention efforts are typically grounded in alcohol research, this disconnect may reduce policy testing results, impact, and implementation. We developed a simulation model guided by AU research (accounting for the multiple AU stages defined by AU behavior and risk for harm and diverse transitions between stages). Simulated projections were compared to historical data to evaluate model accuracy and potential policy leverage points for prevention and intervention at risky drinking (RD) and alcohol use disorder (AUD) stages. Results indicated prevention provided the greatest RD and AUD reduction; however, focusing exclusively on AUD prevention may not be effective for long-term change, given the continued increase in RD. This study makes a case for the strength and importance of aligning subject-based research with systems science simulation models.},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Stringfellow2023,

title = {Enumerating contributions of fentanyls and other factors to the unprecedented 2020 rise in opioid overdose deaths: model-based analysis},

author = {Erin J. Stringfellow and Tse Yang Lim and Catherine DiGennaro and Zeynep Hasgül and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/04/fent_effect_2023.pdf},

year  = {2023},

date = {2023-04-12},

urldate = {2023-04-12},

journal = {PNAS Nexus},

volume = {2},

number = {4},

pages = {pgad064},

abstract = {In 2020, the ongoing US opioid overdose crisis collided with the emerging COVID-19 pandemic. Opioid overdose deaths (OODs) rose an unprecedented 38%, due to a combination of COVID-19 disrupting services essential to people who use drugs, continued increases in fentanyls in the illicit drug supply, and other factors. How much did these factors contribute to increased OODs? We used a validated simulation model of the opioid overdose crisis, SOURCE, to estimate excess OODs in 2020 and the distribution of that excess attributable to various factors. Factors affecting OODs that could have been disrupted by COVID-19, and for which data were available, included opioid prescribing, naloxone distribution, and receipt of medications for opioid use disorder. We also accounted for fentanyls’ presence in the heroin supply. We estimated a total of 18,276 potential excess OODs, including 1,792 lives saved due to increases in buprenorphine receipt and naloxone distribution and decreases in opioid prescribing. Critically, growth in fentanyls drove 43% (7,879) of the excess OODs. A further 8% is attributable to first-ever declines in methadone maintenance treatment and extended-released injectable naltrexone treatment, most likely due to COVID-19-related disruptions. In all, 49% of potential excess OODs remain unexplained, at least some of which are likely due to additional COVID-19-related disruptions. While the confluence of various COVID-19-related factors could have been responsible for more than half of excess OODs, fentanyls continued to play a singular role in excess OODs, highlighting the urgency of mitigating their effects on overdoses.},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Stringfellow2023bb,

title = {Long-term Effects of Increasing Buprenorphine Treatment-Seeking, Duration, and Capacity on Opioid Overdose Fatalities: a Model-based Analysis},

author = {Erin J. Stringfellow and 

Tse Yang Lim and 

Catherine DiGennaro and 

Ziyuan Zhang and 

Pritika Paramasivam and 

Benjamin Bearnot and 

Keith Humphreys and 

Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2023/04/Long_Term_Effects_of_Increasing_Buprenorphine.pdf},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {Journal of Addiction Medicine},

abstract = {Objectives 

Because buprenorphine treatment of opioid use disorder reduces opioid overdose deaths (OODs), expanding access to care is an important policy and clinical care goal. Policymakers must choose within capacity limitations whether to expand the number of people with opioid use disorder who are treated or extend duration for existing patients. This inherent tradeoff could be made less acute with expanded buprenorphine treatment capacity.



Methods 

To inform such decisions, we used a validated simulation model to project the effects of increasing buprenorphine treatment-seeking, average episode duration, and capacity (patients per provider) on OODs in the United States from 2023 to 2033, varying the start time to assess the effects of implementation delays.



Results 

Results show that increasing treatment duration alone could cost lives in the short term by reducing capacity for new admissions yet save more lives in the long term than accomplished by only increasing treatment seeking. Increasing provider capacity had negligible effects. The most effective 2-policy combination was increasing capacity and duration simultaneously, which would reduce OODs up to 18.6% over a decade. By 2033, the greatest reduction in OODs (≥20%) was achieved when capacity was doubled and average duration reached 2 years, but only if the policy changes started in 2023. Delaying even a year diminishes the benefits. Treatment-seeking increases were equally beneficial whether they began in 2023 or 2025 but of only marginal benefit beyond what capacity and duration achieved.



Conclusions 

If policymakers only target 2 policies to reduce OODs, they should be to increase capacity and duration, enacted quickly and aggressively.},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Stringfellow2021,

title = {Reducing Opioid Use Disorder and Overdose in the United States: A Dynamic Modeling Analysis},

author = {Erin J. Stringfellow and Tse Yang Lim and Keith Humphreys and Catherine DiGennaro and Celia Stafford and Elizabeth Beaulieu and Jack Homer and Wayne Wakeland and Benjamin Bearnot and R. Kathryn McHugh and John Kelly and Lukas Glos and Sara Eggers and Reza Kazemi and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2022/12/Stringfellow_2022_SciAdv.pdf},

doi = {10.1126/sciadv.abm8147},

year  = {2022},

date = {2022-06-24},

journal = {Science Advances},

volume = {8},

number = {25},

abstract = {Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers’ capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.

},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{Horvat2022,

title = {The impacts of biosecurity measures on Campylobacter contamination in broiler houses and slaughterhouses in the Netherlands: A simulation modelling approach},

author = {Horvat, Andrijana and Luning, Pieternel A and Catherine DiGennaro and Rommens, Edien and van Daalen, Els and Koene, Miriam and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/campydynamics/},

year  = {2022},

date = {2022-03-01},

urldate = {2022-03-01},

journal = {Food Control},

volume = {114},

abstract = {Intestinal campylobacteriosis, caused by Campylobacter ingestion, is the most reported zoonosis in the EU; it is societally costly and can lead to more severe sequelae. To reduce Campylobacter infections, biosecurity measures at both farms and slaughterhouses are warranted. However, the potential improvements achieved by these interventions have not been quantified. We used a systems science approach to develop a simulation model, synthesizing information from interviews with stakeholders in the Dutch broiler production industry and the current literature. The model includes both farms and slaughterhouses in a “system of systems,” helping to clarify the complexity of interrelated components of these systems and analyse the impact of various interventions. Insects, transportation crates, farm personnel, and catchers were identified as potential Campylobacter sources and modelled as elements of feedback loops. Insect control, farm hygiene, visitor control, thinning, and transportation control interventions were analysed. The model was shown to accurately describe the seasonality of Campylobacter, which supports its validity. Model simulation revealed that insect control interventions had the strongest impacts, followed by combined farm hygiene and visitor control, and combined thinning and transportation control. Insect control interventions alone reduced the peak percentage of contaminated chickens from 51% to 26% and the peak percentage of highly contaminated (>1000 CFU/g) neck samples of chicken carcasses from 13% to 8%. Implementing all interventions concurrently reduced the peak percentages of contaminated chickens to 5% and highly contaminated chicken neck samples to 2%. These results suggest that multiple biosecurity measures must be implemented to reduce Campylobacter contamination.



},

keywords = {Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{643800,

title = {Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy over Half a Century},

author = {Mohammad S. Jalali and Catherine DiGennaro and Abby Guitar and Karen Lew and Hazhir Rahmandad},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/12/Jalali_evolution_reproducibility_2021.pdf},

year  = {2021},

date = {2021-07-29},

urldate = {2021-07-29},

journal = {Epidemiologic Reviews},

abstract = {Background: Simulation models are increasingly used to inform health policy. We provide an overview of applications of simulation models in health policy, analyze the use of best reporting practices, and assess the reproducibility of existing studies. 

Method: Studies that used simulation modeling as the core method to address any health policy questions were included. Health policy domain distribution and changes in quality over time were well-characterized using MeSH terms and model characteristics, respectively. Reproducibility was assessed using predefined, categorical criteria. 

Findings: 1,613 studies were analyzed. We found an exponential growth in the number of studies over the past half century, with the highest growth in dynamic modeling approaches. The largest subset of studies is focused on disease policy models (70%), within which pathological conditions, viral diseases, neoplasms, and cardiovascular diseases account for one-third of the articles. Nearly half of the studies do not report the details of their models. A subset of 100 articles (50 highly cited and 50 random) were selected to analyze in-depth criteria for reporting quality and reproducibility. Significant gaps between best modeling practices could be found in both the random and highly cited samples; only seven of 26 in-depth evaluation criteria were satisfied by more than 80% of samples. We found no evidence that the highly cited samples adhered better to the modeling best practices. 

Interpretation: Our results suggest crucial areas for increased applications of simulation modeling, and opportunities to enhance the rigor and documentation in the conduct and reporting of simulation modeling in health policy.},

keywords = {Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{651959,

title = {Transparency Assessment of COVID-19 Models},

author = {Mohammad S. Jalali and Catherine DiGennaro and Devi Sridhar},

url = {https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19.pdf

https://scholar.harvard.edu/files/jalali/files/transparency_assessment_covid19_appendix.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {The Lancet Global Health},

volume = {8},

number = {12},

pages = {E1459-E1460},

abstract = {As the COVID-19 pandemic has caused major societal unrest, modelers have worked to project future trends of COVID-19 and predict upcoming challenges and impacts of policy action. These models, alone or in aggregate, are influential for decision-makers at every level. Therefore, the method and documentation of COVID-19 models must be highly transparent to ensure that projections and consequential policies put forth have sound epistemological grounds. We evaluated 29 COVID-19 models receiving high attention levels within the scientific community and/or informing government responses. We evaluated these models against 27 transparency criteria. We found high levels of transparency in model documentation aspects such as reporting uncertainty analysis; however, about half of the models do not share code and a quarter do not report equations. These discrepancies underscore the need for transparency and reproducibility to be at the forefront of researcherstextquoteright priorities, especially during a global health crisis when stakes are critically high.},

keywords = {Infectious diseases, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{661337,

title = {The opioid crisis: need for systems science research},

author = {Mohammad S. Jalali and Michael Botticelli and Rachael Hwang and Howard K Koh and Kathryn R McHugh},

url = {https://scholar.harvard.edu/files/jalali/files/opioid_crisis_systems_science.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Health Research Policy and Systems},

volume = {18},

number = {88},

abstract = {The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.},

keywords = {Simulation modeling, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{647807,

title = {Adoption of new medical technologies: The effects of insurance coverage vs continuing medical education},

author = {Amir T Namin and Vahab Vahdat and Catherine DiGennaro and Roham Amid and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/adoption_insurance_vs_education.pdf},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Health Policy and Technology},

volume = {9},

number = {1},

pages = {31-41},

abstract = {Medical technologies innovate rapidly and responsively to patient needs, but the adoption of the latest technologies in practice can be delayed by lack of knowledge and ability to pay. Customized individually made (CIM) knee implants potentially provide an option for individuals to maintain moderate to high activity levels with fewer surgical revisions following a total knee replacement, however they are costlier upfront. Not only is the technology more expensive, but insurance typically covers around 50% (versus 90% for older off-the-shelf knee implants). We used a recent simulation model and analyzed the effects on overall adoption of CIM through 2026 and found that continuing medical education (CME)—a common intervention to increase the adoption of new medical technologies through increasing practitioner knowledge and comfort with the new technologies—can increase the adoption of CIM to 48% in the short term, but increasing insurance coverage to be equal to OTS knee replacement coverage increases the adoption to 87% in the sustained long term. Efforts to implement CME are well-placed and will increase the rate of adoption, however the combination of CME and increased insurance coverage provides the most benefit, with the technology reaching 80% of the population undergoing total knee replacement by 2021.},

keywords = {Adoption dynamics, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631218,

title = {The Internet of Things Promises New Benefits and Risks: A Systematic Analysis of Adoption Dynamics of IoT Products},

author = {Mohammad S. Jalali and Jessica P Kaiser and Michael Siegel and Stuart Madnick},

url = {https://scholar.harvard.edu/files/jalali/files/adoption_dynamics_of_iot_products.pdf},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {IEEE Security and privacy},

volume = {17},

number = {2},

pages = {39-48},

keywords = {Adoption dynamics, Cybersecurity, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631231,

title = {Dynamics of intervention adoption, implementation, and maintenance inside organizations: The case of an obesity prevention initiative},

author = {Mohammad S. Jalali and Hazhir Rahmandad and Sally Lawrence Bullock and Seung Hee Lee-Kwan and Joel Gittelsohn and Alice Ammerman},

url = {https://scholar.harvard.edu/files/jalali/files/dynamics_of_intervention_adoption.pdf, dynamics of intervention adoption.pdf

https://scholar.harvard.edu/files/jalali/files/vensimfiles.zip, Vensim Files},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Social Science & Medicine},

volume = {224},

pages = {67-76},

abstract = {Overall impact of public health prevention interventions relies not only on the average efficacy of an intervention, but also on the successful adoption, implementation, and maintenance (AIM) of that intervention. In this study, we aim to understand the dynamics that regulate AIM of organizational level intervention programs. We focus on two well-documented obesity prevention interventions, implemented in food carry-outs and stores in low-income urban areas of Baltimore, Maryland, which aimed to improve dietary behaviour for adults by providing access to healthier foods and point-of-purchase promotions. Building on data from field observations, in-depth interviews, and data discussed in previous publications, as well as the strategy and organizational behaviour literature, we developed a system dynamics model of the key processes of AIM. With simulation analysis, we show several reinforcing mechanisms that span stakeholder motivation, communications, and implementation quality and costs can turn small changes in the process of AIM into big difference in the overall impact of the intervention. Specifically, small changes in the allocation of resources to communication with stakeholders of intervention could have a nonlinear long-term impact if those additional resources can turn stakeholders into allies of the intervention, reducing the erosion rates and enhancing sustainability. We present how the dynamics surrounding communication, motivation, and erosion can create significant heterogeneity in the overall impact of otherwise similar interventions. Therefore, careful monitoring of how those dynamics unfold, and timely adjustments to keep the intervention on track are critical for successful implementation and maintenance.},

keywords = {Adoption dynamics, Obesity, Participatory modeling, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631232,

title = {The Adoption of New Medical Technologies: The Case of Customized Individually Made Knee Implants},

author = {Amir T Namin and Mohammad S. Jalali and Vahab Vahdat and Hany S Bedair and Mary I O'Connor and Sagar Kamarthi and Jacqueline A Isaacs},

url = {https://scholar.harvard.edu/files/jalali/files/theadoptionofnewmedicaltechnologies.pdf

https://scholar.harvard.edu/files/jalali/files/supporting_information.pdf

https://scholar.harvard.edu/files/jalali/files/modeling_documentation_and_instruction_for_reproducibility_mdir.pdf},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Value in Health},

volume = {22},

number = {4},

pages = {423-430},

abstract = {Objectives: To investigate the impact of insurance coverage on the adoption of customized individually made (CIM) knee implants, and to compare patient outcomes and cost-effectiveness of off-the-shelf (OTS) and CIM implants.Study Design: A system dynamics simulation model is developed to study adoption dynamics of CIM and meet the research objectives.Methods: The model reproduced the historical data on primary and revision knee replacement implants obtained from the literature and the Nationwide Inpatient Sample. Then, the dynamics of adoption of CIM implants were simulated from 2018 to 2026. The rate of 90-day readmission, 3-year revision surgery, recovery period, time savings in operating rooms, and the associated cost within three years of primary knee replacement implants were used as performance metrics.Results: The simulation results indicate that, by 2026, an adoption rate of 90% for CIM implants can reduce the number of readmissions and revision surgeries by 62% and 39%, respectively, and can save hospitals and surgeons 6% on procedure time, and cut down cumulative healthcare costs by approximately $38 billion.Conclusions: CIM implants have the potential to deliver high-quality care while decreasing overall healthcare costs, but their adoption requires the expansion of current insurance coverage. This work presents a first systematic study to understand the dynamics of adoption of CIM knee implants and instrumentation. More broadly, the current modeling approach and systems thinking perspective could be utilized to consider the adoption of any emerging customized therapies for personalized medicine.},

keywords = {Adoption dynamics, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631225,

title = {Modeling and Estimating the Feedback Mechanisms among Depression, Rumination, and Stressors in Adolescents},

author = {Niyousha Hosseinichimeh and Andrea K Wittenborn and Jennifer Rick and Mohammad S. Jalali and Hazhir Rahmandad},

url = {https://scholar.harvard.edu/files/jalali/files/modeling_depression.pdf},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {PLOS ONE},

volume = {13},

number = {9},

pages = {e0204389},

abstract = {The systemic interactions among depressive symptoms, rumination, and stress are important to understanding depression but have not yet been quantified. In this article, we present a system dynamics simulation model of depression that captures the reciprocal relationships among stressors, rumination, and depression. Building on the response styles theory, this model formalizes three interdependent mechanisms: 1) Rumination contributes to textquoteleftkeeping stressors alivetextquoteright; 2) Rumination has a direct impact on depressive symptoms; and 3) Both textquoteleftstressors kept alivetextquoteright and current depressive symptoms contribute to rumination. The strength of these mechanisms is estimated using data from 661 adolescents (353 girls and 308 boys) from two middle schools (grades 6–8). These estimates indicate that rumination contributes to depression by keeping stressors textquoteleftalivetextquoteright—and the individual activated—even after the stressor has ended. This mechanism is stronger among girls than boys, increasing their vulnerability to a rumination reinforcing loop. Different profiles of depression emerge over time depending on initial levels of depressive symptoms, rumination, and stressors as well as the occurrence rate for stressors; levels of rumination and occurrence of stressors are stronger contributors to long-term depression. Our systems model is a steppingstone towards a more comprehensive understanding of depression in which reinforcing feedback mechanisms play a significant role. Future research is needed to expand this simulation model to incorporate other drivers of depression and provide a more holistic tool for studying depression.},

keywords = {Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631229,

title = {Decision-Making and Biases in Cybersecurity Capability Development: Evidence from a Simulation Game Experiment},

author = {Mohammad S. Jalali and Michael Siegel and Stuart Madnick},

url = {https://scholar.harvard.edu/files/jalali/files/decision-making_in_cybersecurity.pdf},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Journal of Strategic Information Systems},

abstract = {We developed a simulation game to study the effectiveness of decision-makers in overcoming two complexities in building cybersecurity capabilities: potential delays in capability development; and uncertainties in predicting cyber incidents. Analyzing 1479 simulation runs, we compared the performances of a group of experienced professionals with those of an inexperienced control group. Experienced subjects did not understand the mechanisms of delays any better than inexperienced subjects; however, experienced subjects were better able to learn the need for proactive decision-making through an iterative process. Both groups exhibited similar errors when dealing with the uncertainty of cyber incidents. Our findings highlight the importance of training for decision-makers with a focus on systems thinking skills, and lay the groundwork for future research on uncovering mental biases about the complexities of cybersecurity.},

keywords = {Cybersecurity, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631223,

title = {Minimization of Drug Shortages in Pharmaceutical Supply Chains: A Simulation-Based Analysis of Drug Recall Patterns and Inventory Policies},

author = {Rana Azghandi and Jacqueline Griffin and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/drug_shortage.pdf},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Complexity},

pages = {1-14},

abstract = {The drug shortage crisis in the last decade not only increased health care costs but also jeopardized patientstextquoteright health across the United States. Ensuring that any drug is available to patients at health care centers is a problem that official health care administrators and other stakeholders of supply chains continue to face. Furthermore, managing pharmaceutical supply chains is very complex, as inevitable disruptions occur in these supply chains (exogenous factors), which are then followed by decisions members make after such disruptions (internal factors). Disruptions may occur due to increased demand, a product recall, or a manufacturer disruption, among which product recalls—which happens frequently in pharmaceutical supply chains—are least studied. We employ a mathematical simulation model to examine the effects of product recalls considering different disruption profiles, e.g., the propagation in time and space, and the interactions of decision makers on drug shortages to ascertain how these shortages can be mitigated by changing inventory policy decisions. We also measure the effects of different policy approaches on supply chain disruptions, using two performance measures: inventory levels and shortages of products at health care centers. We then analyze the results using an approach similar to data envelopment analysis to characterize the efficient frontier (best inventory policies) for varying cost ratios of the two performance measures as they correspond to the different disruption patterns. This analysis provides insights into the consequences of choosing an inappropriate inventory policy when disruptions take place.},

keywords = {Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631215,

title = {Estimating the parameters of system dynamics models using indirect inference},

author = {Niyousha Hosseinichimeh and Hazhir Rahmandad and Mohammad S. Jalali and Andrea K Wittenborn},

url = {https://scholar.harvard.edu/files/jalali/files/parameter_estimation_using_indirect_inference.pdf},

isbn = {0883-7066},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {System Dynamics Review},

volume = {32},

pages = {156-180},

abstract = {There is limited methodological guidance for estimating system dynamics (SD) models using datasets common to social sciences that include few data points over time for many units under analysis. Here, we introduce indirect inference, a simulation-based estimation method that can be applied to common datasets and is applicable to SD models that often include intractable likelihood functions. In this method, the model parameters are found by ensuring that simulated data from the model and available empirical data produce similar auxiliary statistics. The method requires few assumptions about the structure of the model and error-generating processes and thus can be used in a variety of applications. We demonstrate the method in estimating an SD model of depression and rumination using a panel dataset. The overall results suggest that indirect inference can extend the application of SD models to new topics and leverage common panel datasets to provide unique insights.},

keywords = {Simulation modeling},

pubstate = {published},

tppubtype = {article}

}

@article{631213,

title = {A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans},

author = {Navid Ghaffarzadegan and Alireza Ebrahimvandi and Mohammad S. Jalali},

url = {https://scholar.harvard.edu/files/jalali/files/a_dynamic_model_for_ptsd.pdf},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {PLOS ONE},

volume = {11},

pages = {e0161405},

abstract = {Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a textquotedblleftsystem of systems.textquotedblright This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.},

keywords = {Mental health, Simulation modeling},

pubstate = {published},

tppubtype = {article}

}