1
|
Schuerkamp R, Liang L, Rice KL, Giabbanelli PJ. Simulation Models for Suicide Prevention: A Survey of the State-of-the-Art. COMPUTERS (BASEL, SWITZERLAND) 2023; 12:10.3390/computers12070132. [PMID: 37869477 PMCID: PMC10588059 DOI: 10.3390/computers12070132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.
Collapse
Affiliation(s)
- Ryan Schuerkamp
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Luke Liang
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| | - Ketra L. Rice
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Philippe J. Giabbanelli
- Department of Computer Science & Software Engineering, Miami University, Oxford, OH 45056, USA
| |
Collapse
|
2
|
Qian W, Stanley KG, Osgood ND. Impacts of observation frequency on proximity contact data and modeled transmission dynamics. PLoS Comput Biol 2023; 19:e1010917. [PMID: 36848398 PMCID: PMC9997969 DOI: 10.1371/journal.pcbi.1010917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 03/09/2023] [Accepted: 02/03/2023] [Indexed: 03/01/2023] Open
Abstract
Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.
Collapse
Affiliation(s)
- Weicheng Qian
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- * E-mail:
| | - Kevin Gordon Stanley
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Nathaniel David Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
- Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK, Canada
- Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada
| |
Collapse
|
3
|
Peters EM, Dong LY, Thomas T, Khalaj S, Balbuena L, Baetz M, Osgood N, Bowen R. Instability of Suicidal Ideation in Patients Hospitalized for Depression: An Exploratory Study Using Smartphone Ecological Momentary Assessment. Arch Suicide Res 2022; 26:56-69. [PMID: 32654657 DOI: 10.1080/13811118.2020.1783410] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
This study used ecological momentary assessment (EMA) to explore the correlates of suicidal ideation (SI) instability in patients hospitalized for depression and SI. Thirty-nine adult inpatients were given smartphones with visual analogue scales to rate current depressed mood, anger/irritability, feeling socially connected, and SI three times a day throughout hospitalization. Affective Lability Scales (ALS) were also completed at baseline. SI instability was correlated with SI intensity, depressed mood instability, and social connection instability. Social connection instability was not associated with SI instability after controlling for depressed mood instability. ALS scores were not associated with EMA-derived SI instability. Participants with multiple past suicide attempts experienced greater SI instability. More research examining the clinical significance of SI instability is warranted.
Collapse
|
4
|
Safarishahrbijari A, Teyhouee A, Waldner C, Liu J, Osgood ND. Predictive accuracy of particle filtering in dynamic models supporting outbreak projections. BMC Infect Dis 2017; 17:648. [PMID: 28950831 PMCID: PMC5615804 DOI: 10.1186/s12879-017-2726-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 09/12/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. METHODS Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. RESULTS We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. CONCLUSION Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.
Collapse
Affiliation(s)
- Anahita Safarishahrbijari
- Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Building, 110 Science Place, Saskatoon, SK - S7N5C9, Canada.
| | - Aydin Teyhouee
- Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Building, 110 Science Place, Saskatoon, SK - S7N5C9, Canada
| | - Cheryl Waldner
- Western College of Veterinary Medicine, University of Saskatchewan, Campus Drive, Saskatoon, Canada
| | - Juxin Liu
- Department of Mathematics and Statistics, University of Saskatchewan, College Drive, Saskatoon, Canada
| | - Nathaniel D Osgood
- Department of Computer Science, University of Saskatchewan, 176 Thorvaldson Building, 110 Science Place, Saskatoon, SK - S7N5C9, Canada
| |
Collapse
|
5
|
Marshall DA, Burgos-Liz L, Pasupathy KS, Padula WV, IJzerman MJ, Wong PK, Higashi MK, Engbers J, Wiebe S, Crown W, Osgood ND. Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research. PHARMACOECONOMICS 2016; 34:115-26. [PMID: 26497003 DOI: 10.1007/s40273-015-0330-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.
Collapse
Affiliation(s)
- Deborah A Marshall
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C56 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.
| | - Lina Burgos-Liz
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C58 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada
| | - Kalyan S Pasupathy
- Clinical Engineering Learning Lab, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | - William V Padula
- Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Maarten J IJzerman
- Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | - Peter K Wong
- Illinois Divisions and HSHS Medical Group, Hospital Sisters Health System (HSHS), Belleville, IL, USA
| | | | - Jordan Engbers
- Clinical Research Unit, University of Calgary, Calgary, AB, Canada
| | - Samuel Wiebe
- Clinical Research Unit, University of Calgary, Calgary, AB, Canada
| | | | - Nathaniel D Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, Canada
- Department of Community Health & Epidemiology and Bioengineering Division, University of Saskatchewan, Saskatoon, SK, Canada
| |
Collapse
|
6
|
Youssef M, Scoglio C. Mitigation of epidemics in contact networks through optimal contact adaptation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2013; 10:1227-51. [PMID: 23906209 PMCID: PMC3857636 DOI: 10.3934/mbe.2013.10.1227] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
This paper presents an optimal control problem formulation to minimize the total number of infection cases during the spread of susceptible-infected-recovered SIR epidemics in contact networks. In the new approach, contact weighted are reduced among nodes and a global minimum contact level is preserved in the network. In addition, the infection cost and the cost associated with the contact reduction are linearly combined in a single objective function. Hence, the optimal control formulation addresses the tradeoff between minimization of total infection cases and minimization of contact weights reduction. Using Pontryagin theorem, the obtained solution is a unique candidate representing the dynamical weighted contact network. To find the near-optimal solution in a decentralized way, we propose two heuristics based on Bang-Bang control function and on a piecewise nonlinear control function, respectively. We perform extensive simulations to evaluate the two heuristics on different networks. Our results show that the piecewise nonlinear control function outperforms the well-known Bang-Bang control function in minimizing both the total number of infection cases and the reduction of contact weights. Finally, our results show awareness of the infection level at which the mitigation strategies are effectively applied to the contact weights.
Collapse
Affiliation(s)
- Mina Youssef
- K-State Epicenter, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506-5204, United States.
| | | |
Collapse
|