@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{Rahmandad2025,

title = {Incorporating Deep Learning Into System Dynamics: Amortized Bayesian Inference for Scalable Likelihood‐Free Parameter Estimation},

author = {Hazhir Rahmandad and Ali Akhavan and Mohammad S Jalali

},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2025/01/Rahmandad_Amortized-Bayesian_2025.pdf},

year  = {2025},

date = {2025-01-24},

journal = {System Dynamics Review},

volume = {41},

issue = {1},

abstract = {Estimating parameters and their credible intervals for complex system dynamics models is challenging but critical to continuous model improvement and reliable communication with an increasing fraction of audiences. The purpose of this study is to integrate Amortized Bayesian Inference (ABI) methods with system dynamics. Utilizing Neural Posterior Estimation (NPE), we train neural networks using synthetic data (pairs of ground truth parameters and outcome time series) to estimate parameters of system dynamics models. We apply this method to two example models: a simple Random Walk model and a moderately complex SEIRb model. We show that the trained neural networks can output the posterior for parameters instantly given new unseen time series data. Our analysis highlights the potential of ABI to facilitate a principled, scalable, and likelihood-free inference workflow that enhance the integration of models of complex systems with data. Accompanying code streamlines application to diverse system dynamics models.},

keywords = {Artificial intelligence, Methods},

pubstate = {published},

tppubtype = {article}

}

@article{Lim2024,

title = {Improving estimates of the prevalence of opioid use disorder in the United States: Revising Keyes et al.},

author = {Tse Yang Lim and Katherine M. Keyes and Jonathan P. Caulkinsd and Erin J. Stringfellow and Magdalena Cerdá and Mohammad S. Jalali},

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/09/prevalence_of_oud.pdf},

year  = {2024},

date = {2024-09-02},

urldate = {2024-09-02},

journal = {Journal of Addiction Medicine},

abstract = {Objectives 

The United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)–involved and non–opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions.



Methods 

We examine 4 adjustments to Keyes and colleagues’ estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement.



Results 

Revising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues’ original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014–2015 and declining to 3.8 million in 2019.



Conclusions 

The revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.},

keywords = {Methods, Substance use},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Planning, implementing and governing systems-based co-creation: the DISCOVER framework},

author = {Niamh Smith and Michail Georgiou and Mohammad S. Jalali and Sebastien Chastin },

url = {https://mj-lab.mgh.harvard.edu/wp-content/uploads/2024/02/Smith-2024.pdf},

year  = {2024},

date = {2024-01-08},

urldate = {2024-01-08},

journal = {Health Research Policy and Systems},

volume = {22},

issue = {6},

abstract = {Increasingly, public health faces challenges requiring complex, multifaceted and multi-sectoral responses. This calls for systems-based approaches that facilitate the kind of collective and collaborative thinking and working required to address complexity. While the literature on systems thinking, system dynamics and the associated methodologies is extensive, there remains little clear guidance on how to plan, govern and implement participatory systems approaches within a co-creation process.},

keywords = {Methods},

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 = {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{631222,

title = {A Flexible Method for Aggregation of Prior Statistical Findings},

author = {Hazhir Rahmandad and Mohammad S. Jalali and Kamran Paynabar},

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

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {PLOS ONE},

volume = {12},

pages = {e0175111},

abstract = {Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.},

note = {More info about GMA},

keywords = {Methods},

pubstate = {published},

tppubtype = {article}

}