Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Influenza viruses, in 1918, 1957, 1968, and 2009 causing pandemics of contagious respiratory illnesses, have taken away millions of lives worldwide [1][2]. Still, many influenza virus strains are circulating worldwide, infecting people in the USA and other countries [3]. Furthermore, influenza viruses are constantly changing, called "antigenic drift"[4]. These circulating influenza virus strains in humans present a potential threat to another pandemic, like the SARS-CoV-2 pandemic. For this reason, We must revise our risk assessment and preparedness plans to mitigate the risk of another pandemic and should continue to put surveillance on influenza virus strains on different hosts [5]. For surveillance, Estimating the viral strengths or fitness of current circulating virus strains over one another is essential for future disease management and improving the preparedness plan to avoid the next pandemic. Different viral fitness measurement techniques can be adopted to understand the virus epidemiology [6]. In this comparative analysis method, the "Pairwise Differential Population Growth Rate" is calculated with influenza virus strain data of a specific period to estimate the relative pairwise viral fitness of the influenza virus strains. The study considered the different time frames to understand and compare the influenza virus's viral fitness. This study will help in the predictive analysis of influenza data, improve risk assessment and preparedness plans, and compare the current virus trend with the trend during the pandemic. To measure the fitness of one viral strain over another, we have used a log-transformed growth ratio along with other fitness-measuring techniques to capture additional fitness insights. To perform this study, we have collected the virus sequence data country-wise and continent-wise in various time frames. GISAID provides the dataset for this study, a platform to provide genome sequence data of humans and animals [7]. We have used influenza genome sequence data from humans to conduct this study. This study considers only the sequence's subtype, location, and collection date. After collecting the data, we separated the data according to location. The data are grouped to obtain weekly frequency data. Then pairwise viral fitness is obtained by calculating differential population growth rate using a log-transformed ratio among different subtypes. Our analysis reveals significant variations in pairwise viral fitness across different subtypes and regions. We observed that the relative pairwise fitness of influenza virus strains can change rapidly, indicating the need for ongoing surveillance and preparedness and protect public health.

Document Type

posters

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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A Comparative Study on Viral Fitness Among Influenza Virus Strains Across Different Periods and Locations

Influenza viruses, in 1918, 1957, 1968, and 2009 causing pandemics of contagious respiratory illnesses, have taken away millions of lives worldwide [1][2]. Still, many influenza virus strains are circulating worldwide, infecting people in the USA and other countries [3]. Furthermore, influenza viruses are constantly changing, called "antigenic drift"[4]. These circulating influenza virus strains in humans present a potential threat to another pandemic, like the SARS-CoV-2 pandemic. For this reason, We must revise our risk assessment and preparedness plans to mitigate the risk of another pandemic and should continue to put surveillance on influenza virus strains on different hosts [5]. For surveillance, Estimating the viral strengths or fitness of current circulating virus strains over one another is essential for future disease management and improving the preparedness plan to avoid the next pandemic. Different viral fitness measurement techniques can be adopted to understand the virus epidemiology [6]. In this comparative analysis method, the "Pairwise Differential Population Growth Rate" is calculated with influenza virus strain data of a specific period to estimate the relative pairwise viral fitness of the influenza virus strains. The study considered the different time frames to understand and compare the influenza virus's viral fitness. This study will help in the predictive analysis of influenza data, improve risk assessment and preparedness plans, and compare the current virus trend with the trend during the pandemic. To measure the fitness of one viral strain over another, we have used a log-transformed growth ratio along with other fitness-measuring techniques to capture additional fitness insights. To perform this study, we have collected the virus sequence data country-wise and continent-wise in various time frames. GISAID provides the dataset for this study, a platform to provide genome sequence data of humans and animals [7]. We have used influenza genome sequence data from humans to conduct this study. This study considers only the sequence's subtype, location, and collection date. After collecting the data, we separated the data according to location. The data are grouped to obtain weekly frequency data. Then pairwise viral fitness is obtained by calculating differential population growth rate using a log-transformed ratio among different subtypes. Our analysis reveals significant variations in pairwise viral fitness across different subtypes and regions. We observed that the relative pairwise fitness of influenza virus strains can change rapidly, indicating the need for ongoing surveillance and preparedness and protect public health.