Tracking H1N1pdm09, the Hantavirus, and G4 EA H1N1 w/ Data Mining

After the start of this whole Covid19 pandemic, worries of other viruses have been making rounds. These worries have ranged from variations of the Hantavirus showing up in China, to newly reoccurring worries of H1N1 strains. While people aren't too worried about widespread animal to human and human to human transmission, the same was thought about in regards to Covid19. While it is less likely these viruses are worth a concern, their data at least to some regard is worth exploring.

My certainty revolves around the fact that I believe within the next ten years, we will likely see a virus of similar magnitude or cause of concern as Covid19. This is just a guess given how Covid followed up H1N1. How people react should be better prepared as opposed to this time. I hope I am wrong on this "next ten years" prediction and that people go out of there way to annihilate these concerns.

This did however inspire me to look at the data of these viruses from some sources. I didn't go on that much of a data spree and wasn't as detailed as my previous coding challenges given I wanted to make this post rather simple and compact. Also, I have a limited time schedule in terms of models I want to build.

The above model is on antibody responses in mice due to H7N9 and H1N1pdm09 vaccines. The data source can be seen here, and was part of an open access research article on PLOS ONE. The researchers involved were part of Baxter BioScience in Austria. I'm just visualizing said data in a meaningful manner, and the same applies to all other source data related visuals in this article.

The above model was related to deferentially expressed proteins in A549 cells related to H7N9 and H1N1pdm09 viral strains. The data set was also made available through PLOS ONE, and the study was due to a grant by the Shenzhen Science and Technology Innovation Project.

The third data set for the above model was also data on PLOS ONE. The data was published by the PLOS ONE staff and related to "Characteristics, treatment and outcome of Influenza A(H1N1)pdm09-infected CF patients". The above visual is related to infections, and the data shown is raw.

This above model is on Hantavirus host assemblages in the Atlantic Forest, and is based on a data set that is also on PLOS in the Journal of Neglected Tropical Diseases. The author summary of the study can be seen here.

The above model is on a data set in regards to the pathology of Hantavirus in bats and insectivores in China by species and location. That data was published on PLOS PATHOGENS, the author summary of the study can be seen here. This data model have been visualized from its raw data format.

The above model is on a data set published on Dyrad. It is in regards to swine in Mexico, and origins related to the 2009 H1N1 influenza in regards to that swine. The work is in the public domain, and the authors are listed at the top here. The researchers come from various backgrounds and institutions, including the: Icahn School of Medicine at Mount Sinai, National Institutes of Health, Laboratorio Avi-Mex,  KU Leuven, and the University of Edinburgh.

This above data model is based on a UK study for antigenic reactivity in the 2009 H1N1 pandemic. The data set is part of a research article on PLOS ONE. The authors summary can be seen here, and they are part of the "Centre for Infections, Health Protection Agency, London, United Kingdom". The data have been visualized in its raw data format.

Visualizing a bunch of data seems like such a basic project, and that is because it is. This is in no way as complex as the things I have done before or algorithmic pipelines I have built. The question is then, "why do this?". The answer is simple. Sometimes it is best to do things for the purpose of simplicity, looking at what is out there and drawing conclusions. Not everything needs to be spectacularly complex, and this is even true with data.

The question is, what is next? What does data sets like these, and the visualizations inspire me to do? I have a variety of options. I have considered utilizing my decentralized-internet SDK for building grid computing virology pipelines and networks. I also have considered trying to garnish my own data or working with companies who have sequenced data that has been corrupted. The world in terms of this complexity problem, is my oyster.

The question isn't what one can do, but also what one can prevent? More and more extensive data sets from a variety of researchers will allow for predictability models, as well as possible references people can use in regards to lowering economic disasters if such spreads happen again. Whether H1N1, Covid19, or some upcoming pandemic, people are still doing similar mistakes as before. The issue should be based off of formalities, data, and common sense. It doesn't need to be overly politicized or financially milked the way it usually has.