Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions.
For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analy...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0221564 |
| _version_ | 1818362090855858176 |
|---|---|
| author | Florian Miksch Beate Jahn Kurt Junshean Espinosa Jagpreet Chhatwal Uwe Siebert Nikolas Popper |
| author_facet | Florian Miksch Beate Jahn Kurt Junshean Espinosa Jagpreet Chhatwal Uwe Siebert Nikolas Popper |
| author_sort | Florian Miksch |
| collection | DOAJ |
| description | For the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity. |
| first_indexed | 2024-12-13T21:27:03Z |
| format | Article |
| id | doaj.art-94f6579d56144d118d474dffcd01b4d5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T21:27:03Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-94f6579d56144d118d474dffcd01b4d52022-12-21T23:30:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01148e022156410.1371/journal.pone.0221564Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions.Florian MikschBeate JahnKurt Junshean EspinosaJagpreet ChhatwalUwe SiebertNikolas PopperFor the evaluation of infectious-diseases interventions, the transmissible nature of such diseases plays a central role. Agent-based models (ABM) allow for dynamic transmission modeling but publications are limited. We aim to provide an overview of important characteristics of ABM for decision-analytic modeling of infectious diseases. A case study of dengue epidemics illustrates model characteristics, conceptualization, calibration and model analysis. First, major characteristics of ABM are outlined and discussed based on ISPOR and ISPOR-SMDM Good Practice guidelines. Second, in our case study, we modeled a dengue outbreak in Cebu City (Philippines) to assess the impact interventions to control the relative growth of the mosquito population. Model outcomes include prevalence and incidence of infected persons. The modular ABM simulates persons and mosquitoes over an annual time horizon considering daily time steps. The model was calibrated and validated. ABM is a dynamic, individual-level modeling approach that is capable to reproduce direct and indirect effects of interventions for infectious diseases. The ability to replicate emerging behavior and to include human behavior or the behavior of other agents is a distinguishing modeling characteristic (e.g., compared to Markov models). Modeling behavior may, however, require extensive calibration and validation. The analyzed hypothetical effectiveness of dengue interventions showed that a reduced human-mosquito ratio of 1:2.5 during rainy seasons leads already to a substantial decrease of infected persons. ABM can support decision-analyses for infectious diseases including disease dynamics, emerging behavior, and providing a high level of reusability due to modularity.https://doi.org/10.1371/journal.pone.0221564 |
| spellingShingle | Florian Miksch Beate Jahn Kurt Junshean Espinosa Jagpreet Chhatwal Uwe Siebert Nikolas Popper Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. PLoS ONE |
| title | Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. |
| title_full | Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. |
| title_fullStr | Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. |
| title_full_unstemmed | Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. |
| title_short | Why should we apply ABM for decision analysis for infectious diseases?-An example for dengue interventions. |
| title_sort | why should we apply abm for decision analysis for infectious diseases an example for dengue interventions |
| url | https://doi.org/10.1371/journal.pone.0221564 |
| work_keys_str_mv | AT florianmiksch whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions AT beatejahn whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions AT kurtjunsheanespinosa whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions AT jagpreetchhatwal whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions AT uwesiebert whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions AT nikolaspopper whyshouldweapplyabmfordecisionanalysisforinfectiousdiseasesanexamplefordengueinterventions |