Category: COVID-19

What Went Wrong With Covid?

On December 26, 2021

It’s a simple question, and probably one which many of us have asked each other and ourselves the past 20 months. How can this world, with its advanced researchers, extensive public health protocols, and past pandemic experience, have been so unprepared for the COVID-19 pandemic? People have blamed poor communication, public noncompliance, even the scientists themselves, but none can completely cover the worsening of the pandemic alone.

For one of my graduate classes this semester, Quantitative Evidence for Infectious Disease Research, I was tasked with answering this question. Instead of limiting my response to any one of the reasons we might jump to for worsening the pandemic, I decided to focus on the scientific community, and the communication of research to the public.

If you’re familiar with scientific research and the scientific method, you’ll know that the process begins with a hypothesis. Many times, there are more than one. The null hypothesis is the hypothesis being tested. The null hypothesis can be any theory, but in epidemiology it will essentially state that there is no causal relationship between a cause and an outcome. For example, a null hypothesis about smoking and lung cancer could posit that there is no causal association between smoking and lung cancer. The data then will either support the null hypothesis or give scientists a cause to reject it. This is where an alternative hypothesis is needed. An alternative hypothesis would state that there is some causal association between cause an outcome, and could say that smoking is a cause for lung cancer.

Epidemiologists and biostatisticians will usually analyze the impact of cause and effect through something called “significance testing“. With this method, the scientists will select a certain significance level, which is the value for which a p-value less than or equal to it is considered statistically significant. The p-value is a measure of probability that the observed difference could have occurred only by chance. Usually, the significance level is set to 0.05, meaning any experiment with a probability of having occurred only by chance equal to 5% or less, would be statistically significant. This is why in some papers you might see researchers state they are 95% confident in their results. Sometimes, these p-values are accompanied by a confidence interval, which is the range of possible values of the estimate being calculated at 95% confidence. In the truest interpretation, according to frequentist statistics, the 95% confidence interval will include the true effect in at least 95% of replications of the process of obtaining the data.

These results have caveats, however. Experiments need to have a large enough sample size to accurately develop results, and the model chosen to analyze the data needs to be correct for the experiment. Unfortunately, this is not always the case in published studies, and many epidemiologists have previously published reports cautioning authors and readers against the common misinterpretations in significance testing (read more here, here, and here). Though published, the COVID-19 pandemic only highlighted this issue further, and many controversial topics over the past 20 months, including the impact of mask-wearing, the contagiousness, or R-naught of the pandemic, and the effectiveness of medications like hydroxychloroquine or ivermectin have fallen victim to these misinterpretations, exacerbating misunderstanding and mistrust of public health authorities.

In the paper attached below, I explore how key publications studying these controversial features of the COVID-19 pandemic succeed or fail in conveying the accuracy and strength of their results to other scientists and, by extension, the public at large. To quote my conclusion:

In a time when scientific results can and should be shared as quickly as possible, researchers and publishers have an additional responsibility to ensure their methods are clearly stated and all results clearly indicate confidence interval ranges and limitations in addition to the basic “significance”

While I am by no means an expert in epidemiology, I feel that this analysis can elucidate how a healthy dose of skepticism is necessary even in scientific quantitative research. Often, we may take for granted the black and white answers of pure science. We can see research as logical and absolute, but there is plenty of room for uncertainty, as the COVID-19 pandemic has shown. I hope this article helps to explain some of the confusion to any readers, and might equip you with the knowledge to approach future papers with questions and determine for yourself how reliable the results are.

Thank you.

What-Went-Wrong-WIth-COVID Download

COVID-19 Variants and What They Mean for the New Vaccines

By Kayla Nikc

On January 7, 2021

In COVID-19

Last month, the FDA approved two vaccines, from Pfizer and Moderna respectively, to begin the process of immunization against COVID-19 across the United States. Despite this major milestone in protective measures, there is a new coronavirus curveball: variants of the already deadly virus.

Most recently, the London variant has made headlines after confirmed cases of the virus in California, Colorado, and Florida, but the WHO has also recorded other variants, such as 501Y.V2 in South Africa. But what do these variants mean for the long-awaited vaccines and their potential protections?

How do Virus Mutations Arise?

In order to understand how these COVID-19 variants might impact the efficacy of the Pfizer and Moderna vaccines, one must first understand how viruses mutate in the first place.

Viruses, though not considered living, contain genetic information in the form of DNA or RNA. DNA, which stands for Deoxyribose nucleic acid, is the genetic compound which forms a signature double-helix and is the primary means of storing genetic information in our cells. RNA, or ribonucleic acid, is another variation of nucleotides. One of the key differences between DNA and RNA is the number of hydroxyl, or -OH groups, on each of their sugar molecules, seen in the image below, with the DNA sugar on the left and the RNA sugar on the right.

https://www.technologynetworks.com/genomics/lists/what-are-the-key-differences-between-dna-and-rna-296719

While the lack of a single hydroxyl group may not seem like it would make a lot of difference, the structure of DNA is far more stable than that of RNA, and this variation in reactivity dictates the roles of DNA and RNA wherever they are used. DNA, with its greater stability, is able to store genetic information for long periods of time and harbor limited mutations, a necessary trait to achieve proper gamete production and cell replication. The greater reactivity of RNA also has its benefits. As a reproducible intermediary, RNA can transport genetic information throughout a cell to be used and then quickly degraded according to a cell’s needs.

Every time genetic information is replicated, there is the possibility that there will be a mutation, either a deletion, insertion, or substitution of one type of nucleic acid for another. DNA polymerases, involved in replication, are able to proofread the insertion of bases to reduce the possibility of these mistakes in the DNA. RNA polymerases lack this ability, meaning any change to the RNA could cause a change in the structure or function of any proteins performing operations throughout the cell.

What Does This Have to do with COVID-19?

As I stated earlier, viruses also make use of DNA and RNA, though usually they will only use one or the other depending on the particular virus’ reproductive strategy. The primary goal of any virus is to produce as many copies of itself as possible and to spread those copies to as many cells as possible. DNA viruses, like herpesvirus, smallpox virus, and papillomavirus, prioritize larger genomes, with greater stability than their RNA virus counterparts, which allows them to encode many proteins to aid in their invasion into a given cell. These viruses, however, owing to their stability and their DNA polymerases, are also less likely to mutate, meaning that vaccines aimed at protecting against these viruses are likely to remain successful for years.

RNA viruses, instead, make use of their greater likelihood for mutation as a way to adapt to the host around them. The flu, part of the Orthomyxoviridae family of viruses, is one example of RNA viruses. The rapid mutations of the flu resulting from its instability as an RNA virus is why there is a new recommended flu shot every fall.

Coronavirus is another example of an RNA virus, which, as we’ve seen, can also rapidly mutate, which, without appropriate distancing and masking to reduce infection rates, could prevent the timely release of an effective vaccine. If COVID-19 is allowed to continue to spread and replicate, new variants have the ability to outpace our vaccine development abilities.

What the Experts Think:

As of now, evolutionary biologists and other experts remain hopeful that the vaccines created by Moderna and Pfizer will continue to be effective against the London and South African variants, however, these results remain inconclusive. According to Nature.com, researchers hope to have more information about the variants and the possible effects on vaccine efficacy by next week. For now, though, adhering to social distancing and masking recommendations from the WHO remains imperative, not only to ensure the health and safety of you and those around you, but also to reduce the opportunity for the generation of future variations.