IDENTIFYING A ROLE FOR GENOMIC EPIDEMIOLOGY IN POST-PANDEMIC INFLUENZA SURVEILLANCE IN KANSAS

Abstract

I completed my Applied Practice Experience (APE) at the Bureau of Epidemiology and Public Health Informatics at the Kansas Department of Health and Environment (KDHE) office in Topeka, Kansas. At KDHE, I completed a three-phase project to identify opportunities and challenges for implementing a genomic surveillance program for influenza in Kansas. Genomic epidemiology is an emerging field in public health, in large part due to the vast amount of genomic data made available during the SARS-CoV-2 pandemic. Now that infrastructure exists to generate genomic surveillance data, an open question remains about how best to use these resources to monitor other infectious diseases now that the peak of the pandemic has subsided. Therefore, the main goal of my APE was to review the current literature on influenza and SARS- CoV-2 to understand their genomic epidemiology, analyze the current limited genomic data for influenza in Kansas as a pilot case, and conduct system-level analysis of the U.S. influenza surveillance network to identify a role for genomic epidemiology at the state level. The first phase of the project was a literature review on the genomic epidemiology of influenza viruses, namely regarding how the novel genomic epidemiology tools from the SARS- CoV-2 pandemic can be used to monitor influenza. From this literature review, I wrote two short, plain-language articles describing the genomic epidemiology of both SARS-CoV-2 and influenza (products 1 and 2) for inclusion in training materials for epidemiologists unfamiliar with genomic epidemiology. The article focuses on the nomenclature and designations of the two viruses and includes information on how genomic epidemiologists surveil them. The literature review highlighted transmission between animals and humans and informed the direction of my phylogenetic analysis of influenza A sequences from Kansas to include humans and non-human animals.

Following the literature review, the second phase of the project analyzed influenza A genomes collected in Kansas by the Kansas Health and Environment Laboratories (KHEL) and genomes obtained from the National Center for Biotechnology Information (NCBI) database. From these data, I made phylogenetic trees to visualize the evolutionary relationships between the viruses sampled. These trees were annotated with metadata, including host species, genome subtype, and date of collection for the hemagglutinin (HA) and neuraminidase (NA) genes (products 3 and 4). Notably, some samples from different hosts clustered phylogenetically and temporally on the hemagglutinin tree for the H1 subtype, suggesting possible transmission events between humans and non-human animals. In the final phase of the project, I conducted systems-level analysis of the U.S. influenza surveillance system. First, I completed a process map to identify key players and systems involved in the complicated and often uncoordinated U.S. influenza surveillance system (product 5). Secondly, I created a causal loop diagram to identify the dynamics of the system and to identify challenges brought about by the disjointed system, particularly how they may lead to unrepresentative data collection and, therefore, bias decision making (product 6). Lastly, I utilized system archetypes to identify dynamics that influence public perceptions and how those perceptions feed back to influence the availability of resources for influenza surveillance (product 7). Following the analysis, I compiled my findings into an oral presentation which I delivered to epidemiologists at KDHE at the conclusion of the APE (product 8).