A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections
Background: A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-03-01
|
| Series: | Infectious Disease Modelling |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042722001099 |
| _version_ | 1797866928194191360 |
|---|---|
| author | Chaitra Gopalappa Hari Balasubramanian Peter J. Haas |
| author_facet | Chaitra Gopalappa Hari Balasubramanian Peter J. Haas |
| author_sort | Chaitra Gopalappa |
| collection | DOAJ |
| description | Background: A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden, with a large fraction of the burden attributed to HIV. Data also show interactions between HIV and other sexually transmitted infections (STIs), such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without. However, given the wide range in prevalence and incidence burdens of STIs, current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling. Further, causal factors for higher risk of coinfection could be both behavioral (i.e., compounding effects of individual behaviors, network structures, and care behaviors) and biological (i.e., presence of one disease can biologically increase the risk of another). However, the data on the fraction attributed to each are limited. Methods: We present a new mixed agent-based compartmental (MAC) framework for jointly modeling STIs. It uses a combination of a new agent-based evolving network modeling (ABENM) technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases. As a demonstration, we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2, using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs. We simulated sexual transmissions among heterosexual males, heterosexual females, and men who have sex with men (men only and men and women). Setting the biological risk of co-infection to zero, we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics. Results: The contribution of behavioral factors to risk of coinfection was sensitive to disease burden, care access, and population heterogeneity and mixing. The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here, suggesting potential role of biological factors, that should be investigated further specific to an STI. Conclusions: The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features. The numerical analysis serves as proof-of-concept for the application to STIs. Interactions between diseases are influenced by behavioral factors, are sensitive to care access and population features, and are likely exacerbated by biological factors. Social and economic conditions are among key drivers of behaviors that increase STI transmission, and thus, structural interventions are a key part of behavioral interventions. Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention. The new simulation framework is especially suited to simulate behavior as a function of social determinants, and further, to identify optimal combinations of common structural and disease-specific interventions. |
| first_indexed | 2024-04-09T23:33:14Z |
| format | Article |
| id | doaj.art-a452e2ad36004bb9b9dd56e3b60835bc |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-09T23:33:14Z |
| publishDate | 2023-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-a452e2ad36004bb9b9dd56e3b60835bc2023-03-21T04:16:57ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272023-03-018184100A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infectionsChaitra Gopalappa0Hari Balasubramanian1Peter J. Haas2Corresponding author.; University of Massachusetts Amherst, 160 Governors Drive, Amherst, MA, 01003, USAUniversity of Massachusetts Amherst, 160 Governors Drive, Amherst, MA, 01003, USAUniversity of Massachusetts Amherst, 160 Governors Drive, Amherst, MA, 01003, USABackground: A model that jointly simulates infectious diseases with common modes of transmission can serve as a decision-analytic tool to identify optimal intervention combinations for overall disease prevention. In the United States, sexually transmitted infections (STIs) are a huge economic burden, with a large fraction of the burden attributed to HIV. Data also show interactions between HIV and other sexually transmitted infections (STIs), such as higher risk of acquisition and progression of co-infections among persons with HIV compared to persons without. However, given the wide range in prevalence and incidence burdens of STIs, current compartmental or agent-based network simulation methods alone are insufficient or computationally burdensome for joint disease modeling. Further, causal factors for higher risk of coinfection could be both behavioral (i.e., compounding effects of individual behaviors, network structures, and care behaviors) and biological (i.e., presence of one disease can biologically increase the risk of another). However, the data on the fraction attributed to each are limited. Methods: We present a new mixed agent-based compartmental (MAC) framework for jointly modeling STIs. It uses a combination of a new agent-based evolving network modeling (ABENM) technique for lower-prevalence diseases and compartmental modeling for higher-prevalence diseases. As a demonstration, we applied MAC to simulate lower-prevalence HIV in the United States and a higher-prevalence hypothetical Disease 2, using a range of transmission and progression rates to generate burdens replicative of the wide range of STIs. We simulated sexual transmissions among heterosexual males, heterosexual females, and men who have sex with men (men only and men and women). Setting the biological risk of co-infection to zero, we conducted numerical analyses to evaluate the influence of behavioral factors alone on disease dynamics. Results: The contribution of behavioral factors to risk of coinfection was sensitive to disease burden, care access, and population heterogeneity and mixing. The contribution of behavioral factors was generally lower than observed risk of coinfections for the range of hypothetical prevalence studied here, suggesting potential role of biological factors, that should be investigated further specific to an STI. Conclusions: The purpose of this study is to present a new simulation technique for jointly modeling infectious diseases that have common modes of transmission but varying epidemiological features. The numerical analysis serves as proof-of-concept for the application to STIs. Interactions between diseases are influenced by behavioral factors, are sensitive to care access and population features, and are likely exacerbated by biological factors. Social and economic conditions are among key drivers of behaviors that increase STI transmission, and thus, structural interventions are a key part of behavioral interventions. Joint modeling of diseases helps comprehensively simulate behavioral and biological factors of disease interactions to evaluate the true impact of common structural interventions on overall disease prevention. The new simulation framework is especially suited to simulate behavior as a function of social determinants, and further, to identify optimal combinations of common structural and disease-specific interventions.http://www.sciencedirect.com/science/article/pii/S2468042722001099Joint-modeling diseasesMulti-disease modelingSimulation modelingHIV and STIsDiseases and social determinantsSDOH |
| spellingShingle | Chaitra Gopalappa Hari Balasubramanian Peter J. Haas A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections Infectious Disease Modelling Joint-modeling diseases Multi-disease modeling Simulation modeling HIV and STIs Diseases and social determinants SDOH |
| title | A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections |
| title_full | A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections |
| title_fullStr | A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections |
| title_full_unstemmed | A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections |
| title_short | A new mixed agent-based network and compartmental simulation framework for joint modeling of related infectious diseases- application to sexually transmitted infections |
| title_sort | new mixed agent based network and compartmental simulation framework for joint modeling of related infectious diseases application to sexually transmitted infections |
| topic | Joint-modeling diseases Multi-disease modeling Simulation modeling HIV and STIs Diseases and social determinants SDOH |
| url | http://www.sciencedirect.com/science/article/pii/S2468042722001099 |
| work_keys_str_mv | AT chaitragopalappa anewmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections AT haribalasubramanian anewmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections AT peterjhaas anewmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections AT chaitragopalappa newmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections AT haribalasubramanian newmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections AT peterjhaas newmixedagentbasednetworkandcompartmentalsimulationframeworkforjointmodelingofrelatedinfectiousdiseasesapplicationtosexuallytransmittedinfections |