Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches.

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut's Good Samaritan Laws and identify effective policy options.

BACKGROUND: Good Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut's Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT). METHODS: We conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD). RESULTS: Our resulting qualitative SD model captures our GMB participants' collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone. CONCLUSIONS: Our qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

Predictive simulation of the water-energy-food nexus for the City of Cape Town.

The City of Cape Town (CoCT), South Africa faced a critical situation between 2015 and 2018 in which the municipal water supply was almost completely exhausted. This situation, commonly referred to as Day Zero in South Africa emanated from a decline in rainfall, resulting in one of the most severe droughts in history. The crisis was also aggravated by rapid population growth and urbanization. CoCT was on the verge of becoming the first city in the past decade to experience a complete cessation of water supply for urban and agricultural purposes. In addition to the effects of low rainfall and population surge, urban energy consumption and increased food demand impacted directly the available water resources. To evaluate the interlinkages between water utilization, water production, energy supply and demand, and food production and demand, this study employed a system dynamics modeling (SDM) approach. The model was developed as a stock and flow diagram utilizing Stella Architect and encompassed five interconnected nodes: water, energy, food, land, and population. The findings revealed that by the end of the 20-year modeling period, the volume of accessible and stored water in all the major dams will be approximately 459 million cubic meters, with residential use accounting for about 85 % of urban water use and agriculture accounting for approximately30.37 % of total water demand. The model illustrates the impacts of precipitation rate, runoff, and evaporation on variables such as land-use change and population dynamics. It is anticipated that the outcomes of this study will serve as valuable inputs for decision-making processes, not only within the CoCT as it aims to mitigate or prevent the recurrence of Day Zero, but also for other cities facing similar challenges.

Can participatory processes lead to changes in the configuration of local mental health networks? A social network analysis.

Systems modeling offers a valuable tool to support strategic decision-making for complex problems because it considers the causal inter-relationships that drive population health outcomes. This tool can be used to simulate policies and initiatives to determine which combinations are likely to deliver the greatest impacts and returns on investment. Systems modeling benefits from participatory approaches where a multidisciplinary stakeholder group actively engages in mapping and contextualizing causal mechanisms driving complex system behaviors. Such approaches can have significant advantages, including that they may improve connection and coordination of the network of stakeholders operating across the system; however, these are often observed in practice as colloquial anecdotes and seldom formally assessed. We used a basic social network analysis to explore the impact on the configuration of the network of mental health providers, decision-makers, and other stakeholders in Bogota, Colombia active in a series of three workshops throughout 2021 and 2022. Overall, our analysis suggests that the participatory process of the systems dynamics exercise impacts the social network's structure, relationships, and dynamics.

From parts to whole: Embracing systems science in community psychology.

With our field's strong focus on context for understanding and acting on social problems, community psychologists have frequently elevated the importance of employing systems thinking and methods that help us to understand systems more effectively. As a result, community psychologists have adopted some methods from the interdisciplinary field of systems science. In this virtual special issue, we will compare how several of these approaches have been used in publications in the AJCP in the last 50 years. We identify differences in their popularity, implementation with communities, and how they create generative discussion in the field. We conclude by looking to the future to explore ways community psychology can deepen engagement with methods from systems science.

Exploring causal mechanisms and essential factors in the prevention and control of chlamydia in Guangdong, Southern China: a study protocol.

BACKGROUND: Chlamydia Trachomatis (CT) is among the most prevalent sexually transmitted diseases (STDs) globally. According to the World Health Organization, more than 131 million people get infected with CT annually. CT is usually transmitted via sexual contact or perinatal exposure and can result in severe long-term complications. In developing nations, particularly, the prevention and control of CT is challenging. Hence, this study will explore the feedback mechanisms of chlamydia prevention and control, as well as identify the essential factors affecting the control and prevention of this infection in China. METHODS: Our study will employ a mixed-methods research design that encompasses both qualitative and quantitative methods. Firstly, we will develop a causal loop diagram (CLD) based on the literature review and optimize it via in-depth interviews with stakeholders. Additionally, we will utilize a quantitative method called MICMAC(Impact Matrix Cross-Reference Multiplication Applied to a Classification tool) to obtain consensus among different stakeholders and pinpoint the key information. Next, the CLD will be transformed into a system dynamics model (SDM) to evaluate the feedback mechanisms within the CLD. The causality in the CLD will be modeled using mathematical equations, which facilitate the transformation into an SDM. As such, we will be able to analyze the dynamic behavior of the system and its response to different decisions. DISCUSSION: Our study offers a systematic perspective on the control and prevention of chlamydia infection through system dynamics modeling, examining the dynamic properties and background factors of the system. The creation of the CLD affords stakeholders the chance to comprehend the functionality of their relationships and improve cooperation. Consequently, by evaluating the outcomes of these simulations, it will be possible to analyze and determine potential interventions and their effects on chlamydia infections. This modeling approach can help us gain insight into the dynamic characteristics of the system, evaluate the potential outcomes of different decisions, and design control strategies to either stabilize the system or adjust its behavior.

A Systematic Review of Systems Science Approaches to Understand and Address Domestic and Gender-Based Violence.

Purpose: We aimed to synthesize insights from systems science approaches applied to domestic and gender-based violence. Methods: We conducted a systematic review of systems science studies (systems thinking, group model-building, agent-based modeling [ABM], system dynamics [SD] modeling, social network analysis [SNA], and network analysis [NA]) applied to domestic or gender-based violence, including victimization, perpetration, prevention, and community responses. We used blinded review to identify papers meeting our inclusion criteria (i.e., peer-reviewed journal article or published book chapter that described a systems science approach to domestic or gender-based violence, broadly defined) and assessed the quality and transparency of each study. Results: Our search yielded 1,841 studies, and 74 studies met our inclusion criteria (45 SNA, 12 NA, 8 ABM, and 3 SD). Although research aims varied across study types, the included studies highlighted social network influences on risks for domestic violence, clustering of risk factors and violence experiences, and potential targets for intervention. We assessed the quality of the included studies as moderate, though only a minority adhered to best practices in model development and dissemination, including stakeholder engagement and sharing of model code. Conclusions: Systems science approaches for the study of domestic and gender-based violence have shed light on the complex processes that characterize domestic violence and its broader context. Future research in this area should include greater dialogue between different types of systems science approaches, consideration of peer and family influences in the same models, and expanded use of best practices, including continued engagement of community stakeholders. Supplementary Information: The online version contains supplementary material available at 10.1007/s10896-023-00578-8.

Complex systems and participatory approaches to address maternal health disparities: Findings from a system dynamics group model building project in North Texas.

Focusing on non-Hispanic Black women (NHBW) in North Texas, this study employed participatory system dynamics modeling to explore three hypotheses: (1) stakeholders will conceptualize structural racism is a pervasive macrostructural force that exerts downstream impacts to shape and perpetuate maternal health disparities among NHBW; (2) stakeholders will identify key causal forces and leverage points that exist across levels of influence; and (3) stakeholders will identify complex interactions, in the form of circular causality, that are present among the key causal forces and leverage points that shape NHBW maternal health disparities. Nine participants engaged in a virtual system dynamics group model-building session that focused on eliciting key variables, behavior-over-time graphs (BOTGs), causal loop diagram (CLD), and targets for action. Participants identified 83 key variables. BOTGs included an average of 6.56 notations and time horizons that, on average, started in 1956. The CLD featured 11 reinforcing and seven balancing feedback loops. Eleven targets for action were identified. Structural racism was revealed as a pervasive macrostructural force that shaped maternal health outcomes among NHBW. Key causal forces and leverage points were identified across levels of influence. Finally, feedback loops within the CLD exhibited circular causality.

Dual-Attitude Decision-Making Processes of Construction Worker Safety Behaviors: A Simulation-Based Approach.

Workplace accidents are of great concern in the construction industry. Most of those accidents are caused by unsafe behavior in the workplace. Many previous studies have analyzed the causes of workers' unsafe behaviors, but few have investigated workers' feelings of insecurity from the perspective of systematic psychological theory. This study developed an attitude-behavior-intervention feedback loop mechanism of construction workers and used the dual-attitude theory to explain the occurrence mechanisms of unsafe behavior. Using this mechanism, an active-intervention system-dynamics model and a passive-intervention system-dynamics model were designed and simulated. The coefficient of the system dynamics equation in the simulation model involved meta-analysis to combine the correlation coefficients of existing studies, which increased the sample size and improved the statistical test efficiency. The results show that an implicit safety attitude has a more significant impact on safety behavior, and the effect of an active intervention is stronger than that of a passive intervention. Based on these results, this paper presents some feasible suggestions to reduce the probability of unsafe worker behaviors occurring.

A Study on CKD Progression and Health Disparities Using System Dynamics Modeling.

Chronic kidney disease (CKD) is one of the most prevalent national health problems in the United States. According to the Center for Disease Control and Prevention (CDC), as of 2019, 37 million of the US's adult population have been estimated to have CKD. In this respect, health disparities are major national concerns regarding the treatments for patients with CKD nationwide. The disparities observed in the healthcare interventions for patients with this disease usually indicate some significant healthcare gaps in the national public health system. However, there is a need for immediate intervention to improve the present healthcare conditions of minorities experiencing CKD nationwide. In this research, the application of system dynamics modeling is proposed to model the CKD progression and health disparities. This process is based on the health interventions administered to minorities experiencing CKD. The graphical results from the model show that there are relationships among the dynamic factors influencing the incidence and prevalence of CKD. Hence, healthcare disparities are inherent challenges in the treatment and management of this disease.

Policy choices for Shanghai responding to challenges of Omicron.

Background: A new wave of Coronavirus disease 2019 (COVID-19) infection driven by Omicron BA.2 subvariant hit Shanghai end of February 2020. With higher transmissibility and milder symptoms, the daily new confirmed cases have soared to more than 20 K within one and a half months. The greatest challenge of Omicron spreading is that the rapidly surging number of infected populations overwhelming the healthcare system. What policy is effective for huge cities to fight against fast-spreading COVID-19 new variant remains a question. Methods: A system dynamics model of the Shanghai Omicron epidemic was developed as an extension of the traditional susceptible-exposed-infected-susceptible recovered (SEIR) model to incorporate the policies, such as contact tracing and quarantine, COVID-19 testing, isolation of areas concerned, and vaccination. Epidemic data from Shanghai Municipal Health Commission were collected for model validation. Results: Three policies were tested with the model: COVID-19 testing, isolation of areas concerned, and vaccination. Maintaining a high level of COVID-19 testing and transfer rate of the infected population can prevent the number of daily new confirmed cases from recurring growth. In the scenario that 50% of the infected population could be transferred for quarantine on daily bases, the daily confirmed asymptomatic cases and symptomatic cases remained at a low level under 100. For isolation of areas concerned, in the scenario with most isolation scope, the peak of daily confirmed asymptomatic and symptomatic cases dropped 18 and 16%, respectively, compared with that in the scenario with least isolation. Regarding vaccination, increasing the vaccination rate from 75 to 95% only slightly reduced the peak of the confirmed cases, but it can reduce the severe cases and death by 170%. Conclusions: The effective policies for Omicron include high level of testing capacity with a combination of RAT and PCR testing to identify and quarantine the infected cases, especially the asymptomatic cases. Immediate home-isolation and fast transfer to centralized quarantine location could help control the spread of the virus. Moreover, to promote the vaccination in vulnerable population could significantly reduce the severe cases and death. These policies could be applicable to all metropolises with huge population facing high transmissible low severity epidemic.

Using participatory system dynamics learning to support Ryan White Planning Council priority setting and resource allocations.

The Ryan White CARE Act provides federal dollars supporting low income people living with HIV/AIDS (PLWH). Regional Ryan White Planning Councils (RWPC) are responsible for setting priorities and deciding CARE Act fund allocations, using local data to identify greatest need. However, RWPC are challenged with interpreting complex epidemiological, service utilization, and community needs data to inform priority setting and resource allocations. We piloted system dynamics (SD) learning, using a validated HIV care continuum SD simulation model calibrated to one northeastern U.S. Ryan White funding area. The pilot applied systems thinking to understand the complex HIV care continuum and to simulate and compare outcomes of various resource allocation decisions. Three scripted workshops provided opportunities to learn the SD modeling process and simulation tool, simulate various resource allocations, and compare population health outcomes. Mixed methods evaluation documented the SD modeling process, member responses to the modeling sessions, and attitudes regarding benefits and limitations of SD modeling for RWPC decision-making. Despite high member turnover and complexity of the SD model, members could understand the simulation model and propose strategies to seek greatest improvements in HIV care retention, viral suppression, and reduced infections. Findings suggests the value of SD modeling to assist RWPC decisions.

A Data-Driven Optimized Mechanism for Improving Online Collaborative Learning: Taking Cognitive Load into Account.

Research on online collaborative learning has explored various methods of collaborative improvement. Recently, learning analytics have been increasingly adopted for ascertaining learners' states and promoting collaborative performance. However, little effort has been made to investigate the transformation of collaborative states or to consider cognitive load as an essential factor for collaborative intervention. By bridging collaborative cognitive load theory and system dynamics modeling methods, this paper revealed the transformation of online learners' collaborative states through data analysis, and then proposed an optimized mechanism to ameliorate online collaboration. A quasi-experiment was conducted with 91 college students to examine the potential of the optimized mechanism in collaborative state transformation, awareness of collaboration, learning achievement, and cognitive load. The promising results demonstrated that students learning with the optimized mechanism performed significantly differently in collaboration and knowledge acquisition, and no additional burden in cognitive load was noted.

The Analysis of Patterns of Two COVID-19 Outbreak Clusters in China.

Since the emergence of COVID-19, there have been many local outbreaks with foci at shopping malls in China. We compared and analyzed the epidemiological and spatiotemporal characteristics of local COVID-19 outbreaks in two commercial locations, a department store building (DSB) in Baodi District, Tianjin, and the Xinfadi wholesale market (XFD) in Fengtai District, Beijing. The spread of the infection at different times was analyzed by the standard deviation elliptical method. The spatial transfer mode demonstrated that outbreaks started at the center of each commercial location and spread to the periphery. The number of cases and the distance from the central outbreak showed an inverse proportional logarithmic function shape. Most cases were distributed within a 10 km radius; infected individuals who lived far from the outbreak center were mainly infected by close-contact transmission at home or in the workplace. There was no efficient and rapid detection method at the time of the DSB outbreak; the main preventative measure was the timing of COVID-19 precautions. Emergency interventions (closing shopping malls and home isolation) were initiated five days before confirmation of the first case from the shopping center. In contrast, XFD closed after the first confirmed cases appeared, but those infected during this outbreak benefitted from efficient nucleic acid testing. Quick results and isolation of infected individuals were the main methods of epidemic control in this area. The difference in the COVID-19 epidemic patterns between the two shopping malls reflects the progress of Chinese technology in the prevention and control of COVID-19.

Food system dynamics structuring nutrition equity in racialized urban neighborhoods.

BACKGROUND: The food system is a social determinant of health and a leverage point for reducing diet-related racial inequities. Yet, food system interventions have not resulted in sustained improvement in dietary outcomes for underrepresented minorities living in neighborhoods with a history of disinvestment. Research is needed to illuminate the dynamics structuring food systems in racialized neighborhoods to inform intervention development. OBJECTIVES: To conduct participatory research examining the complexity and inequity of food systems in historically redlined neighborhoods to identify feedback mechanisms to leverage in efforts to transform system outcomes for racial equity. METHODS: We conducted a mixed-methods study in Cleveland, Ohio, USA from 2018 to 2021 using participatory system dynamic modeling with 30 academic and community partners, in-depth qualitative interviews with 22 key stakeholders, and public convenings with 250 local food policy council affiliates. Data were synthesized into causal loop diagrams depicting feedback mechanisms reinforcing or balancing neighborhood-level food system dynamics. RESULTS: We identified 10 feedback mechanisms structuring nutrition equity, which was identified as a meta-goal for food systems in racialized neighborhoods. Feedback mechanisms were organized in 3 domains: 1) meeting basic food needs with dignity (i.e., side hustle, government benefits, emergency food assistance, stigma, and stereotypes); 2) local food supply and demand dynamics (i.e., healthy food retail, job security, food culture, and norms); and 3) community empowerment and food sovereignty (i.e., community power, urban agriculture, risk of gentrification). Five exogenous factors moderate feedback dynamics: neighborhood crisis, neighborhood investments, household costs, government benefit funding, and voter participation. CONCLUSIONS: We identified nutrition equity as an overarching goal for local food systems, which reflects a state of having freedom, agency, and dignity in food traditions resulting in people and communities healthy in body, mind, and spirit. It is a transformative goal designed to spur system-level interventions that further racial equity through improved local food system dynamics.

Fixes that Fail: A system archetype for examining racialized structures within the food system.

Access to fresh and healthy food within a neighborhood has been identified as a social mechanism contributing to community health. Grounded in the understanding that challenges related to equity within a food system are both structural and systemic, our research demonstrates how systems thinking can further understandings of food system complexity. Within systems thinking, we provide an illustration of how system archetypes offer an analytic tool for examining complex community issues. We map semi-structured interview data from community stakeholders (N = 22) to the "Fixes that Fail" system archetype to illuminate systemic challenges, such as incarceration and poverty, that structure food system inequity in urban communities. Within our research, the "Fixes that Fail" archetype provided a narrative interpretive tool for unveiling complexity within the food system and interdependencies with racialized systems such as criminal justice and labor markets. This system archetype provided an accessible approach for generating narratives about systemic complexity, the production of inequity through racialized forces, and opportunities for transformation.

Systems dynamics and the uncertainties of diagnostics, testing and contact tracing for COVID-19.

The current COVID-19 pandemic contains an unprecedented amount of uncertainty and variability and thus, there is a critical need for understanding of the variation documented in the biological, policy, sociological, and infrastructure responses during an epidemic to support decisions at all levels. With the significant asymptomatic spread of the virus and without an immediate vaccine and pharmaceuticals available, the best feasible strategies for testing and diagnostics, contact tracing, and quarantine need to be optimized. With potentially high false negative test results, infected people would not be enrolled in contact-trace programs and thus, may not be quarantined. Similarly, without broad testing, asymptomatic people are not identified and quarantined. Interconnected system dynamics models can be used to optimize strategies for mitigations for decision support during a pandemic. We use a systems dynamics epidemiology model along with other interconnected system models within public health including hospitals, intensive care units, masks, contact tracing, social distancing, and a newly developed testing and diagnostics model to investigate the uncertainties with testing and to optimize strategies for detecting and diagnosing infected people. Using an orthogonal array Latin Hypercube experimental design, we ran 54 simulations each for two scenarios of 10% and 30% asymptomatic people, varying important inputs for testing and social distancing. Systems dynamics modeling, coupled with computer experimental design and statistical analysis can provide rapid and quantitative results for decision support. Our results show that widespread testing, contacting tracing and quarantine can curtail the pandemic through identifying asymptomatic people in the population.

Projecting supply and demand for pharmacists in pharmacies based on the number of prescriptions and system dynamics modeling.

BACKGROUND: Pharmacists play an important role in promoting people's health in Japan, which has an aging population. Hence, it is necessary that the distribution of pharmacists meets the population's needs in each region. This study projects the future supply and demand for pharmacists in pharmacies to consider an optimal distribution of pharmacists. METHODS: The future supply of pharmacists working in pharmacies in Hokkaido is projected using system dynamics modeling, according to their career path. The demand is projected based on the number of prescriptions, sourced from publicly available sources. The analysis period is 2015-2040. The estimated demand is converted into the number of pharmacists and the sufficiency is evaluated using sufficiency ratio (supply/demand ratio). Sensitivity analyses of the sufficiency ratio were conducted to estimate the effects of changes in parameters such as national exam pass rate, enrollments, attrition rates, the number of prescriptions per pharmacist, and diffusion of newly licensed pharmacists. RESULTS: The projected supply, in 2025 and 2040, is 1.24 and 1.56 times, respectively, as that in 2015 and the demand is 1.11 and 0.98 times, respectively. In 2015, although the sufficiency ratio in Hokkaido overall is 1.19, the ratios are higher in urban medical areas and lower than 1 in rural medical areas, such as Minamihiyama, Emmon, and Nemuro. By 2040, the sufficiency ratios are greater than 1 for all areas except for Emmon and higher than 2 in some areas. The sensitivity analyses found that the sufficiency ratio was most sensitive to diffusion of newly licensed pharmacists and the number of prescriptions per pharmacist. CONCLUSION: Optimal distribution should be considered, as the results reveal a possible shortage in the number of pharmacists in rural medical areas in 2015-2025. Conversely, as the demand is projected to decrease after 2025 with a population decrease, future supply should be determined in order not to cause an oversupply after 2025. Refinements of the projection model should be conducted since the related factors such as the roles of pharmacists will change over time.

A system dynamics model for disease management in poultry production.

The objective of this article was to provide the nonmodeler reader of Poultry Science, an overview of the system dynamics modeling method (SDM) through development of a broiler house disease management simulator (BHDMS). System dynamics modeling uses feedback theory and computer-aided simulation to help elucidate relationships between factors in complex systems, which may be circular or interrupted with long delays. Materials used to build the simulator include data from literature and industry indices. The methods used were the steps in SDM, namely: 1) Identify the problem and boundaries; 2) develop a dynamic hypothesis explaining cause of the problem; 3) build the causal loop diagram (CLD); 4) develop the stock and flow model; 5) conduct model simulations; and 6) model validation. Results presented here are the CLD and stock and flow model of the simulator, results of scenario simulations, and model validity tests. The simulator consists of the main model, the disease submodel, and the antimicrobial use submodel. The main model represents a cycle of production in the broiler house of a specified length of time, which repeats after a specified down time. The disease submodel shows population dynamics in the broiler house in terms of changes over time in number of susceptible, infected, recovered, and dead birds. Production parameters that could be modified in the model include delivery size, grow-out period, down time, and efficacy of antimicrobials. Disease mortality levels, above the set threshold, trigger antimicrobial use in the model. The model showed the effect of antimicrobial use intervention on the population dynamics, namely, on the proportion of the susceptible, infected, recovered, and dead birds in the population. Thus, the BHDMS was able to simulate the effect of the intervention on population dynamics and would facilitate evaluating management interventions such as antimicrobial use.

Can Health Disparity Be Eliminated? The Role of Family Doctor Played in Shanghai, China.

Background: Globally, the elimination of health disparity is a significant policy target. Primary health care has been implemented as a strategy to achieve this target in China for almost 10 years. This study examined whether family doctor (FD) policy in Shanghai contributed to eliminating health disparity as expected. METHODS: System dynamics modeling was performed to construct and simulate a system of health disparity formation (business-as-usual (BAU) scenario, without any interventions), a system with FD intervention (FD scenario), and three other systems with supporting policies (Policy 1/Policy 2/Policy hybrid scenario) from 2013 to 2050. Health disparities were simulated in different scenarios, making it possible to compare the BAU results with those of FD intervention and with other policy interventions. FINDINGS: System dynamics models showed that the FD policy would play a positive role in reducing health disparities in the initial stage, and medical price control-rather than health management-was the dominant mechanism. However, in this model, the health gap was projected to expand again around 2039. The model examined the introduction of two intervention policies, with findings showing that the policy focused on socioeconomic status improvement would be more effective in reducing health disparities, suggesting that socioeconomic status is the fundamental cause of these disparities. CONCLUSIONS: The results indicate that health disparities could be optimized, but not eliminated, as long as differences in socioeconomic status persists.