 Hi, my name is Vicente Ramirez, I am a PhD candidate at the University of California, Merced, the Department of Public Health, and I'm presenting a talk titled Sniffing Through the Evidence, Leveraging Shiny to Conduct Med Analysis on COVID-19 and Smell-Os. A quick recap is that the SARS coronavirus 2 appeared in late 2019 and began to spread globally in early 2020. With the start of the global pandemic, countries worldwide declared shutdowns of mandatory self-isolations. Reports that around this time began to emerge of strange symptoms occurring alongside this virus. These symptoms often occurred in other respiratory viruses, but there was a magnitude to which they occurred in COVID-19 patients that was causing for alarms to be running. A lot of lights were shining on smell loss during this time. Smell loss occurred in other respiratory viruses, but it was not as prevalent as it was in COVID-19 patients, and it was occurring without nasal congestion, which was a lot. We can talk about the chemical senses, but let's not get too deep into them. I think the most important thing to realize here is the way that we describe our senses. It's a little bit different than what the biological definition is. For instance, we know that flavor is composed of olfaction, gestation, and chemistesis. That is, our senses smell, our ability to taste, and the general chemical sense that makes up things like spiciness, or menthol, cooling, or numbing. We'll say something like, that tastes really spicy, or we'll have a dessert that has a little too much orange zest in there. We'll say, oh, that's too citrusy for me. It tastes too citrusy. When I say it tastes too citrusy or it tastes too spicy, I'm using both of those terms incorrectly. But it is common in the English language to describe both of them as taste, because what we describe as flavor is taste. And so this does not match with the biological definition and with the medical definition. It's important to consider this when we describe patients self-reporting their symptoms and saying, I've lost my sense of taste. It could be that they've lost their sense of smell, a major component of flavor. And they are mistaken as taste loss. What we do know is that COVID-19 affects all three of these senses. When we start our analysis, we aim to examine two main points. What is the burden of COVID-19 on the senses? Specifically on smell. And is there a difference in how researchers are measuring sensory loss? We collected papers from research databases using a search strategy consisting of keywords like anosmia, smell loss, SARS-CoV-2, and COVID-19. We set out specific criteria used to eliminate papers into our analysis. This was, was there data available? Was a PCR test used to diagnose COVID-19? And how did they recruit participants? Two of our co-authors then qualitatively rated each study based on criteria which may measure the level of bias for each state. This risk of bias assessment was done to make sure that studies were representative of the populations we wished to measure and that there wasn't any inherent bias in the methodology. We found that 6 out of 34 studies used direct measures or objective measures to measure smell loss. These objective measures can include things like smelling a series of chemicals and identifying them. They're commonplace in diagnosing a factory disorder and are used in the clinical setting and the research setting. 28 out of 34 studies use more subjective measures. This includes self-report, questionnaires, phone interviews, and surveys. A random effects meta-analysis gave a polled estimate of 77% that is 3 in 4 patients with COVID-19 will lose their sense of smell. This is for the direct measure. For the more subjective measure, we have 44%. This is a huge discrepancy between the two. But what is clear is that COVID-19 is causing smell loss and magnitudes that are several times higher than seen with other respiratory viruses. We can take the direct measure at face value. It's used to diagnose a factory disorder in other settings. If we assume that 77% of people are losing their sense of smell when infected with COVID-19, then we can also assume that only two-thirds of them are suffering from COVID-19 recognize their symptoms. That is where this 44% comes into play. Smell loss is rather common in COVID-19. This can be seen in our meta-analysis and throughout the literature. It's less common in other respiratory viruses. It exists in influenza, but nowhere near at this level. In fact, there's a paper here that has the title. Recent smell loss is the best projector of COVID-19 among individuals with recent respiratory symptoms. This eludes to the fact that development of smell screenings that can be fast, easy, cheap, and accurate and can quickly be deployed in airports at work concerts. It might be able to catch more COVID-19 than the current preventative measures, like looking at temperature. It's also worth noting that the level of symptoms has changed between variants. Here we see a report made in December 2021 comparing the Delta and the Omicron variant. We see that there's a significant difference between reporting the loss of taste and loss of smell with the Omicron variant reporting much lower levels. When we were conducting our meta-analysis, we realized that papers were being released almost daily. The literature was moving fast, and so we needed to move with it. We decided to make a web dashboard which can constantly be updated and the findings can be disseminated to our research committee. We needed to be cost efficient and not require too many technical skills, as my technical skills were not as great as they are now. So this knocked down using AWS services or using a personal server. At the time, these skills that were needed to do this were going to be a hurdle. In fact, this was my first dive into shiny applications. We anticipated hundreds of visitors per day in the beginning, and this didn't in fact happen. So it had to be able to scale up. We quickly found a solution. Flex Dashboard provided an easy-to-use solution for making web dashboards an art, and they can be implemented into shiny so that they are interactive. This is done without a learning curve. In fact, it's like writing any other R Markdown file, which I was already familiar with. Google Sheets and the API provided by Google provide an easy-to-manage data solution, which we can call on to constantly update our data. This is a nice solution because we can easily edit our data through a spreadsheet. We were editing our data almost daily. Shiny Apps.io also provided an easy-to-use solution to host the application. It was both cost efficient and required almost no technical skills to get it running. We were also super grateful to R Studio as they provided us with over a year of hosting for free because we were hosting a coronavirus-based web application. This was really helpful considering that I'm a graduate student. Before we jump into the Google Sheet and the COVID-19 dashboard, I want to give acknowledgement to our co-authors. Danielle Reed is the PI of the study and the Associate Director of the Monochemical Census Center. Mackenzie Hannam is a postdoc in Danielle Reed's lab and is co-first author on this paper. Sarah Lipson, or Archie Scala, Riley Kerriman, Sarah Marks, and on the next page, Riley Koch helped to review papers, maintain our database, and really carry a lot of the groundwork that was done. The study would not have been done without the constant and daily work of these members. Polly Joseph is a researcher and clinician who has helped us understand measures of the clinical applications here, and Kailu Lin is a co-author who helped review and validate the analysis prior to publication. We collected relevant information on the number of cases, the number of subjects, the subgroup, whether or not the study was using objective measures or subjective measures, and then we took down notes on each study. When a new study was added, it was added as a new row. If a study was to be included, it needed to be checked by two of our co-authors. Here we see checked final denotes this. If a study was excluded, it needed to be checked by two of our co-authors as well. That is, here we see a study was excluded because the methods recruited patients with previous olfactory disorders. Here, a study was excluded because there was no data available. We can take a look at our dashboard. The home page gives a quick summary on what could be found on our dashboard. Each of these icons here is described and corresponds to the tabs up here. We use the meta package in order to visualize and run our meta analysis. We can see that there's a difference between our 34 study analysis and this current analysis. That is that the random effects estimate for objective measures has decreased down to 67% and for subjective, it's decreased down to 40%. We can also visualize where studies are coming from. We've used ggplot and ggplotly, which uses the plotly library in order to create an interactive map. We can zoom in. We can hover over each of these. We see that an abundance of studies comes from Italy, US, Turkey, India, France, Spain, Germany, China. We also used HTML widgets in order to include interactive tables. Here, we have a table of each of our included studies, which we can sort by the number of subjects included or whether or not the study was objective or subjective. Here, we have a list of our objective studies. We include a DOI so that users of our dashboard can quickly access these studies. Excluded studies are also important. Because they were excluded, it's just because they didn't have data or because there was not a COVID test. They are still important for different topics at hand. So we opted to include them. We allow readers to understand why each study was excluded and the DOI so the viewers can access them. We have a source code for the original analysis on GitHub and we include our contact information as well as a little bit of a background on each of us. Lastly, we give the user of this dashboard the ability to share to social media platforms. Here, we have Twitter, Facebook, Google+, LinkedIn, and Pinterest.