An agent-based model with antibody dynamics information in COVID-19 epidemic simulation

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control. In order to accomplish this objective, we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual, thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points. Our model also permits the evaluation of diverse prevention and control measures. Based on our findings, the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain; however, they may prove inadequate against highly transmissible and less virulent variants. Additionally, our model excels in its ability to trace back to the initial infected case (patient zero) through early epidemic patterns. Ultimately, our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.

An in silico hybrid continuum-/agent-based procedure to modelling cancer development: Interrogating the interplay amongst glioma invasion, vascularity and necrosis

This paper develops a three-dimensional in silico hybrid model of cancer, which describes the multi-variate phenotypic behaviour of tumour and host cells. The model encompasses the role of cell migration and adhesion, the influence of the extracellular matrix, the effects of oxygen and nutrient availability, and the signalling triggered by chemical cues and growth factors. The proposed in silico hybrid modelling framework combines successfully the advantages of continuum-based and discrete methods, namely the finite element and agent-based method respectively. The framework is thus used to realistically model cancer mechano-biology in a multiscale fashion while maintaining the resolution power of each method in a computationally cost-effective manner. The model is tailored to simulate glioma progression, and is subsequently used to interrogate the balance between the host cells and small sized gliomas, while the go-or-grow phenotype characteristic in glioblastomas is also investigated. Also, cell-cell and cell-matrix interactions are examined with respect to their effect in (macroscopic) tumour growth, brain tissue perfusion and tumour necrosis. Finally, we use the in silico framework to assess differences between low-grade and high-grade glioma growth, demonstrating significant differences in the distribution of cancer as well as host cells, in accordance with reported experimental findings.