After the start of this whole Covid19 pandemic, worries of other viruses have been making rounds. These worries have ranged from variations of the showing up in China, to newly reoccurring worries of 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 . While it is less likely these viruses are worth a concern, their data at least to some regard is worth exploring. Hantavirus H1N1 Covid19 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 , and was part of an on . 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. here open access research article PLOS ONE The above model was related to deferentially expressed proteins in A549 cells related to H7N9 and H1N1pdm09 viral strains. The was also made available through , and the study was due to a grant by the Innovation Project. data set PLOS ONE Shenzhen Science and Technology The third for the above model was also data on . The data was published by the PLOS ONE staff and related to . The above visual is related to infections, and the data shown is raw. data set PLOS ONE "Characteristics, treatment and outcome of Influenza A(H1N1)pdm09-infected CF patients" This above model is on , and is based on a that is also on in the . The author summary of the study can be seen . Hantavirus host assemblages in the Atlantic Forest data set PLOS Journal of Neglected Tropical Diseases here The above model is on a in regards to the pathology of Hantavirus in bats and insectivores in China by species and location. That data was published on , the author summary of the study can be seen . This data model have been visualized from its raw data format. data set PLOS PATHOGENS here The above model is on a published on . 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 , and the authors are listed at the top . 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. data set Dyrad public domain here This above data model is based on a UK for antigenic reactivity in the 2009 H1N1 pandemic. The is part of a research on . The authors summary can be seen , and they are part of the . The data have been visualized in its raw data format. study data set article PLOS ONE here "Centre for Infections, Health Protection Agency, London, United Kingdom" 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 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. decentralized-internet SDK 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.

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