جستجوی مقالات مرتبط با کلیدواژه « agent based model » در نشریات گروه « پزشکی »

Background The first outbreak of coronavirus disease 2019 (COVID-19) was successfully restrained in many countries around the world by means of a severe lockdown. Now, we are entering the second phase of the pandemics in which the spread of the virus needs to be contained within the limits that national health systems can cope with. This second phase of the epidemics is expected to last until a vaccination is available or herd immunity is reached. Long-term management strategies thus need to be developed.   Methods In this paper we present a new agent-based simulation model “COVID-19 ABM” with which we simulate 4 alternative scenarios for the second “new normality” phase that can help decision-makers to take adequate control and intervention measures.   Results The scenarios resulted in distinctly different outcomes. A continued lockdown could regionally eradicate the virus within a few months, whereas a relaxation back to 80% of former activity-levels was followed by a second outbreak. Contact-tracing as well as adaptive response strategies could keep COVID-19 within limits.   Conclusion The main insights are that low-level voluntary use of tracing apps shows no relevant effects on containing the virus, whereas medium or high-level tracing allows maintaining a considerably higher level of social activity. Adaptive control strategies help in finding the level of least restrictions. A regional approach to adaptive management can further help in fine-tuning the response to regional dynamics and thus minimise negative economic effects.

To predict the behavior of biological systems, mathematical models of biological systems have been shown to be useful. In particular, mathematical models of tumor-immune system interactions have demonstrated promising results in prediction of different behaviors of tumor against the immune system. This study aimed at the introduction of a new model of tumor-immune system interaction, which includes tumor and immune cells as well as myeloid-derived suppressor cells (MDSCs). MDSCs are immune suppressor cells that help the tumor cells to escape the immune system. The structure of this model is agent-based which makes possible to investigate each component as a separate agent. Moreover, in this model, the effect of low dose 5-fluorouracil (5-FU) on MDSCs depletion was considered. Based on the findings of this study, MDSCs had suppressive effect on increment of immune cell number which consequently result in tumor cells escape the immune cells. It has also been demonstrated that low-dose 5-FU could help immune system eliminate the tumor cells through MDSCs depletion. Using this new agent-based model, multiple injection of low-dose 5-FU could eliminate MDSCs and therefore might have the potential to be considered in treatment of cancers.

The goal of this study is introducing a quantified feature for investigating the quality manner and interaction between the immune system and tumor cell. For this purpose, we introduced an agent based model which uses two agents, consists tumor cell and CD8 cells and the environment which consists IL-2 and TGF-β cytokines. This model works using a variety of ratios. The most important ratio of this model is the tumor’s proliferation ratio. We investigated this ratio in three states of tumor-immune system interaction consist elimination, equilibrium and escape using a raw model, then this ratio investigated using models which optimized by experimental data. The results showed that, if model be leaning to the elimination state, this ratio falls faster and if be leaning to the escape state, this ratio will reduce slowly. This result proved by models which used experimental data for optimizing. Therefore, using this ratio we can compare the different manner of tumor-immune system interactions.