How many times in the last 18 months have you seen a map that looks like the one above? It looks like a population density map, but in fact, it is a case total for the COVID19 rates from Johns Hopkins Coronavirus Research Center (collected on 24 May 2022). As of mid-May 2022, there have been over 355 million reported cases, and new variants must change to overcome our growing immunity. In order to predict a variant's abundance and effect on the public, Clare Frasier, a graduate student at the University of Hawai'i at Manoa is investigating the links between the rate of new cases, mutations, and selection in the virus.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is the source of COVID19 infection that has changed our everyday lives since early 2020. The genome of this virus has been recently sequenced and revealed to be just about 30,000 base pairs, which is about 100,000x smaller than humans. Genetic mutations lead to the different variants that we have been accustomed to hearing about in the news recently, such as delta and omicron. These mutations can make the virus spread more easily between people and weaken immune responses. Many researchers over the past couple of years have found that the genome of this virus can be broken down into the structural genes (S, E, M, N) and those linked to viral transmissibility and proliferation (such as ORF1, ORF3, ORF7).
In order to investigate the link between case incidence and mutation rate, Frasier with the help of her advisor, Dr. Marguerite Butler, and lab mate, Ethan Hill, focused on two regions of the genome that display high mutation rates (ORF1a and S) and one region of low mutation rate (ORF7a). To focus on the case incidence rates, these scientists gathered data from Global Initiative on Sharing Avian Influenza Data (GISAID) on two SARS-CoV2 variants that spread rapidly, the B1.1.7 (Alpha) variant and the B.1.243 (Hawai'i) variant in an area of high case incidence (Los Angeles County) and low case incidence (the state of Hawai'i). Los Angeles County had nearly double the case incidence rate of Hawai'i, ~20% compared to ~10%, respectively, which may be a result of numerous factors (e.g. population size, mask mandates, quarantine protocols, etc.).
Frasier wanted to determine the rate at which a mutation is accepted and the estimated number of people that one infected individual can infect for two variants to understand potential impacts on human populations. Surprisingly, in Hawai'i, the Alpha variant had higher mutation rates compared to LA County. However, the Hawai'i variant which spread more rapidly in the state also displayed a higher mutation rate indicating that it is not necessarily case rates that lead to these mutations. They saw a higher initial reproductive number for the Alpha variant in LA County than in Hawai'i, but there was a more rapid decrease in this number over time in LA County compared to Hawai'i. This means that the number of people an infected person may infect is higher in LA County but drops off faster than in Hawai'i. The reproductive number for the Hawai'i variant showed similar trends in both Hawai'i and LA County showing again that the incidence rate might not be the driving force.
So is it true, more cases, more mutations, more problems? Well, maybe. It seems there are unique trends in variant mutation rates and selection depending on the number of positive cases. What does this mean for us? We can't yet predict how SARS-CoV2 variants will evolve in the future, but we are starting to see trends in evolution across different case incidence densities.
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