It has been a while since I posted here, mainly because I just got working from one thing into another. That led to two preprints and several blog posts where I tried to explain several mathematical techniques to characterize several aspects of viral diseases. Most techniques are being developed using SARS cov2 as a template. You can acces the different resources in the following links.
SARS-COV2 seasonality and adaptation are driven by solar activity.
https://www.researchsquare.com/article/rs-2797280/v3
Applications of Sliding Sampling to Biological Sequences.
https://www.researchsquare.com/article/rs-1691291/v1
SARS-Cov2 seasonality and adaptation are driven by solar activity.
https://github.com/TavoGLC/SARSCov2Solar
Applications of Sliding Sampling to Biological Sequences.
https://github.com/TavoGLC/SlidingSampling
Substack
Medium
https://tavoglc.medium.com/list/machine-learning-and-covid19-0659a2c0bb92
As the ability of the different techniques to capture a particular behavior of SARS Cov2 grows also grows the computational resources needed to develop such techniques. Also, some other analyses have not been presented in the preprints and are only in the blog posts and vice versa. Conclutions of the preprints hold by themselves but I think having everything in a single place will be the best solution. However, the academic paper format is not suitable for such purpose.
A longer format such as a book will be the best solution as it will allow me to explain the intersection between math, and biological sequences. As well as introductory topics and in-depth explanations of the coding techniques.
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