Analyzing Changes in US Mobility Trends During 2020

Analysis of COVID-19 Genome Data in the US Radhika Laddha, Nicole Liang, Minh Tri Le, Taylor Anderson, Amira Roess, Hamdi Kavak, Andreas Zufle Phylogenetic analysis of the COVID-19 virus is vital to identify the various strains, where and when the first case of a strain originated, and the spread dynamics of each strain. We extracted COVID-19 phylogenetic data from the GISAID website for two states in the US, Louisiana and Oregon, and parsed the data into a format that was usable. The data includes the geographic flows of many COVID-19 strains and lineages with origin and destination locations, strain names, recorded dates, and the variants from December 2019 to June 2021. Using various network analysis and visualization tools, we create spatial visualizations of the phylogenetic trees from the data. Future work will enrich these visualizations to examine the connectivity, disease, and sociodemographic characteristics of the regions where new strains emerge. By analyzing thousands of cases of various strains provided by GISAID that include the location and timestamp of each node, we hypothesize that similarities in socioeconomic and geographic factors can be drawn between the mutation originating nodes. Our aim is that our visualizations will aid in communicating the flows of the different strains of the COVID-19 pandemic geographically, identify the relationship between strains and the various characteristics of the regions in which they emerge. Thus, we hope that as a result of our research, we can identify the regions that are at risk of the emergence of new strains. From our results, we can deepen our understanding of COVID-19 in hopes of being better prepared for the next pandemic.

Hey everybody this is Radhika, Nicole, and Tri, and we’re gonna be talking about analyzing the mobility of COVID-19 genome data in the US. COVID-19 has affected the everyday life of billions of people across the world and with new strains emerging and originating in various countries, this virus and its spread has become more uncontrollable and deadly, leading to change in the geographic flows globally. Understanding these changes, identifying patterns can allow for improved health guidelines by informing and communicating with the public, being better prepared for the next pandemic, and knowing how to take more effective action by learning from mistakes. Previous studies have only looked at a particular strain, lineage, or variant of a virus locally or just studied cases in one country. So, for the methods of this research project, we acquired the data set from GISAID and Nextstrain databases for the mobility of COVID-19 virus from 2019 to 2021. We extracted the data from a json format file to a more usable format such as graph and dataframe and analyze it in python. And using various network analysis and visualization tools, we can create spacial visualizations that show the mobility of the virus strain with originating and destination locations. Then we can better understand the flow of the disease throughout about a 2 years period. And then, we used some packages in python and we created some visualizations for the map and for the mobility of the virus. So this map is created using data from the GISAID database, but only from January 202 to April of 2020. And these data points are also only either to the US or from the US. Ok so, a little bit more details about the mobility data. So we have acquired the more detailed data from Louisiana state and Oregon state in the US. This focuses more on two states and small counties within the state we can better understand the various specific regions. These are, as you can see on the map, Louisiana data, and it represents nodes and edges on the graph data. And this is the Oregon data. So by taking these cases from our GISAID data and looking at the different factors, which include location, timestamp, other maybe personal factors about the human such as their sex and age, we can look at the similarities in socioeconomic and geographic data and draw conclusions between the nodes in which new strains are originating and spreading from. By using human mobility data, which we were able to access from a paper by Song Gao, we can also examine the connectivity and disease characteristics and identify the regions are possibly at risk for developing new strains. So, from this, the phylogenetic analysis of the COVID-19 virus, it’s vital to identify the various strains, lineages, and variants, where and when the first case of the strain, lineage, or variant originated, and the spread dynamics of each strain. Our visualizations aid in communicating the flows of the different strains, lineages, and variants of the COVID-19 pandemic geographically. We hope that as a result of our research, better controls, policies, and protocols can be implemented by the World Health Organization and Centers for Disease Control. Preparedness is always key.