1
|
Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
Collapse
Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| |
Collapse
|
2
|
Rasella D, Morais GADS, Anderle RV, da Silva AF, Lua I, Coelho R, Rubio FA, Magno L, Machado D, Pescarini J, Souza LE, Macinko J, Dourado I. Evaluating the impact of social determinants, conditional cash transfers and primary health care on HIV/AIDS: Study protocol of a retrospective and forecasting approach based on the data integration with a cohort of 100 million Brazilians. PLoS One 2022; 17:e0265253. [PMID: 35316304 PMCID: PMC8939793 DOI: 10.1371/journal.pone.0265253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 11/19/2022] Open
Abstract
Background Despite the great progress made over the last decades, stronger structural interventions are needed to end the HIV/AIDS pandemic in Low and Middle-Income Countries (LMIC). Brazil is one of the largest and data-richest LMIC, with rapidly changing socioeconomic characteristics and an important HIV/AIDS burden. Over the last two decades Brazil has also implemented the world’s largest Conditional Cash Transfer programs, the Bolsa Familia Program (BFP), and one of the most consolidated Primary Health Care (PHC) interventions, the Family Health Strategy (FHS). Objective We will evaluate the effects of socioeconomic determinants, BFP exposure and FHS coverage on HIV/AIDS incidence, treatment adherence, hospitalizations, case fatality, and mortality using unprecedently large aggregate and individual-level longitudinal data. Moreover, we will integrate the retrospective datasets and estimated parameters with comprehensive forecasting models to project HIV/AIDS incidence, prevalence and mortality scenarios up to 2030 according to future socioeconomic conditions and alternative policy implementations. Methods and analysis We will combine individual-level data from all national HIV/AIDS registries with large-scale databases, including the “100 Million Brazilian Cohort”, over a 19-year period (2000–2018). Several approaches will be used for the retrospective quasi-experimental impact evaluations, such as Regression Discontinuity Design (RDD), Random Administrative Delays (RAD) and Propensity Score Matching (PSM), combined with multivariable Poisson regressions for cohort analyses. Moreover, we will explore in depth lagged and long-term effects of changes in living conditions and in exposures to BFP and FHS. We will also investigate the effects of the interventions in a wide range of subpopulations. Finally, we will integrate such retrospective analyses with microsimulation, compartmental and agent-based models to forecast future HIV/AIDS scenarios. Conclusion The unprecedented datasets, analyzed through state-of-the-art quasi-experimental methods and innovative mathematical models will provide essential evidences to the understanding and control of HIV/AIDS epidemic in LMICs such as Brazil.
Collapse
Affiliation(s)
- Davide Rasella
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (FIOCRUZ), Salvador, Brazil
- * E-mail:
| | | | | | | | - Iracema Lua
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
| | - Ronaldo Coelho
- Department of Chronic Conditions and Sexually Transmitted Infections/Department of Health Surveillance/Ministry of Health (DCCI/SVS/MS), Brasília, Brazil
| | - Felipe Alves Rubio
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
| | - Laio Magno
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
- Life Science Department, University of the State of Bahia, Salvador, Brazil
| | - Daiane Machado
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (FIOCRUZ), Salvador, Brazil
| | - Julia Pescarini
- Center for Data and Knowledge Integration for Health (CIDACS), Gonçalo Moniz Institute, Oswaldo Cruz Foundation (FIOCRUZ), Salvador, Brazil
| | - Luis Eugênio Souza
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
| | - James Macinko
- UCLA Fielding School of Public Health, University of California at Los Angeles (UCLA), Los Angeles, California, United States of America
| | - Inês Dourado
- Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
| |
Collapse
|