Mohammad S. Jalali

A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans

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 “system of systems.” 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.

Estimation of unknown parameters in system dynamics models using the method of simulated moments

In principle the Method of Simulated Moments (MSM) combines simulation-based methods (e.g. Monte Carlo methods) with non-parametric statistical estimations techniques such as General Method of Moments (GMM). The MSM is useful when there are empirical data related to the behavior of different entities. Different statistical moments (e.g. mean, variance, correlation, etc.) of empirical data can be matched against the moments of model-generated data in order to estimate some structural parameters of the model. In this paper, we introduce the MSM as a non-parametric method of estimating the parameters of dynamic models. The major value of the MSM for estimating dynamic models is in its flexibility to be used with any type of data, including cross-sectional data and time series data.

How Individuals Weigh Their Previous Estimates to Make a New Estimate in the Presence or Absence of Social Influence

Individuals make decisions every day. How they come up with estimates to guide their decisions could be a result of a combination of different information sources such as individual beliefs and previous knowledge, random guesses, and social cues. This study aims to sort out individual estimate assessments over multiple times with the main focus on how individuals weigh their own beliefs vs. those of others in forming their future estimates. Using dynamics modeling, we build on data from an experiment conducted by Lorenz et al. [1] where 144 subjects made five estimates for six factual questions in an isolated manner (no interaction allowed between subjects). We model the dynamic mechanisms of changing estimates for two different scenarios: 1) when individuals are not exposed to any information and 2) when they are under social influence.

Cybersecurity in Hospitals: A Systematic, Organizational Perspective

Background Cybersecurity incidents are a growing threat to the health care industry in general and hospitals in particular. The health care industry has lagged behind other industries in protecting its main stakeholder (ie, patients), and now hospitals must invest considerable capital and effort in protecting their systems. However, this is easier said than done because hospitals are extraordinarily technology-saturated, complex organizations with high end point complexity, internal politics, and regulatory pressures. Objective The purpose of this study was to develop a systematic and organizational perspective for studying (1) the dynamics of cybersecurity capability development at hospitals and (2) how these internal organizational dynamics interact to form a system of hospital cybersecurity in the United States. Methods We conducted interviews with hospital chief information officers, chief information security officers, and health care cybersecurity experts; analyzed the interview data; and developed a system dynamics model that unravels the mechanisms by which hospitals build cybersecurity capabilities. We then use simulation analysis to examine how changes to variables within the model affect the likelihood of cyberattacks across both individual hospitals and a system of hospitals. Results We discuss several key mechanisms that hospitals use to reduce the likelihood of cybercriminal activity. The variable that most influences the risk of cyberattack in a hospital is end point complexity, followed by internal stakeholder alignment. Although resource availability is important in fueling efforts to close cybersecurity capability gaps, low levels of resources could be compensated for by setting a high target level of cybersecurity. Conclusions To enhance cybersecurity capabilities at hospitals, the main focus of chief information officers and chief information security officers should be on reducing end point complexity and improving internal stakeholder alignment. These strategies can solve cybersecurity problems more effectively than blindly pursuing more resources. On a macro level, the cyber vulnerability of a country’s hospital infrastructure is affected by the vulnerabilities of all individual hospitals. In this large system, reducing variation in resource availability makes the whole system less vulnerable—a few hospitals with low resources for cybersecurity threaten the entire infrastructure of health care. In other words, hospitals need to move forward together to make the industry less attractive to cybercriminals. Moreover, although compliance is essential, it does not equal security. Hospitals should set their target level of cybersecurity beyond the requirements of current regulations and policies. As of today, policies mostly address data privacy, not data security. Thus, policy makers need to introduce policies that not only raise the target level of cybersecurity capabilities but also reduce the variability in resource availability across the entire health care system.

Preventive behaviors and perceptions of influenza vaccination among a university student population

Background Every year during influenza season, preventable illnesses occur due to lack of vaccination and failure to adopt the preventive behaviors known as non-pharmaceutical interventions (NPIs). In an effort to study the impact of preventive strategies and policies on behavioral changes during the spread of the H1N1 pandemic in 2009, we examined a sample of undergraduate, graduate and business students at the Massachusetts Institute of Technology (MIT). Methods An online survey was completed by 653 students to assess NPI use, perceptions of influenza vaccinations and effectiveness of preventive health policy strategies during the 2009 H1N1 outbreak. Strategies included e-mails and text messages, posters in corridors and restrooms, and videos. These strategies were implemented during both the first and second waves of the 2009 H1N1 pandemic. Results Despite the widespread campaign, fewer than half of the respondents reported modifying their behaviors. We discovered that .70% of the respondents did not practice any NPIs, and more than half showed lack of knowledge of flu vaccinations. Conclusions Our study results indicate a need for more effective strategies to encourage NPI practices in student populations during outbreaks

The Internet of Things (IoT) Promises New Benefits — And Risks: A Systematic Analysis of Adoption Dynamics of IoT Products

• The rush to adopt products on the Internet of Things (IoT) before securing them will make them attractive to cyber criminals and vulnerable to cyber-incidents. • Organizations can weaken the effects of cyber-incidents by creating cybersecurity capabilities at the beginning of their market growth, particularly detailed response plans with clear action items and owners. • Start-ups, in particular, are vulnerable to the tradeoffs between immediate revenue through accelerated market adoption and risked revenue from security vulnerabilities. • Potential adopters decide to adopt new IoT technologies based on their perception of risk-reward ratio. Internet of Things producers can improve such risk-reward ratio by early and continuous cybersecurity capability development, minimizing customer churn during a cyber crisis.

A flexible method for aggregation of prior statistical findings

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.

Dynamics of Implementation and Maintenance of Organizational Health Interventions

In this study, we present case studies to explore the dynamics of implementation and maintenance of health interventions. We analyze how specific interventions are built and eroded, how the building and erosion mechanisms are interconnected, and why we can see significantly different erosion rates across otherwise similar organizations. We use multiple comparative obesity prevention case studies to provide empirical information on the mechanisms of interest, and use qualitative systems modeling to integrate our evolving understanding into an internally consistent and transparent theory of the phenomenon. Our preliminary results identify reinforcing feedback mechanisms, including design of organizational processes, motivation of stakeholders, and communication among stakeholders, which influence implementation and maintenance of intervention components. Over time, these feedback mechanisms may drive a wedge between otherwise similar organizations, leading to distinct configurations of implementation and maintenance processes.

Measuring Stakeholders’ Perceptions of Cybersecurity for Renewable Energy Systems

Renewable energy systems need to be able to make frequent and rapid adjustments to address shifting solar and wind production. This requires increasingly sophisticated industrial control systems (ICS). But, that also increases the potential risks from cyber-attacks. Despite increasing attention to technical aspects (i.e., software and hardware) of cybersecurity, many professionals and scholars pay little or no attention to its organizational aspects, particularly to stakeholders’ perceptions of the status of cybersecurity within organizations. Given that cybersecurity decisions and policies are mainly made based on stakeholders’ perceived needs and security views, it is critical to measure such perceptions. In this paper, we introduce a methodology for analyzing differences in perceptions of cybersecurity among organizational stakeholders. To measure these perceptions, we first designed House of Security (HoS) as a framework that includes eight constructs of security: confidentiality, integrity, availability, technology resources, financial resources, business strategy, policy and procedures, and culture. We then developed a survey instrument to analyze stakeholders’ perceptions based on these eight constructs. In a pilot study, we used the survey with people in various functional areas and levels of management in two energy and ICS organizations, and conducted a gap analysis to uncover differences in cybersecurity perceptions. This paper introduces the HoS and describes the survey instrument, as well as some of the preliminary findings.

Modeling and estimating the feedback mechanisms among depression, rumination, and stressors in adolescents

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 ‘keeping stressors alive’; 2) Rumination has a direct impact on depressive symptoms; and 3) Both ‘stressors kept alive’ 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 ‘alive’—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.