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(1) Models that consider disease effects on the host population tend to focus on pathogen proliferation within the host.
Hence, we consider disease transmission in a finite population in this paper, and let n be the maximum degree.
This model has been studied by many authors [1 11]; however, many literature works did not consider disease resistance in humans.
Further studies should include other variables to compare individuals from different groups as women with higher educational levels and consider disease characteristics (stage, type of treatment, etc).. Future research is necessary to replicate this study in other samples, particularly among women under different treatment conditions and stage of disease.
To consider disease dynamics beyond the initial pandemic period, we have developed a mathematical approach based on percolation methods.
We consider disease parameters estimated for the novel 2009 pH1N1 pandemic as well as more highly transmissible strains of pandemic influenza.
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This model is not yet capable of patient-specific prediction; however, we have used this model to consider disease-level questions regarding patterns of malignant progression (Swanson et al. 2011) and response to anti-angiogenic therapy as seen on imaging (Hawkins-Daarud et al. 2013).
Note that, in this work, we just consider diseases contracted after birth.
This argument alone demonstrates the relevance of considering disease dynamics when assessing the economic value of a vaccination program.
This was considered disease related.
Serum S100A12 levels were also studied while considering disease activity.
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