The efficacy of COVID-19 detection in Canis lupus familiaris
Prolific exists to empower great research. That's why we were delighted to hear how Morgan Bigalk of Washington State University used Prolific for her research into COVID-19 detection in Canis lupus familiaris. Morgan went on to be awarded the WSU Distinctive Undergraduate Thesis of 2022 for her work.
In February 2022, we surveyed 450 adults (>18 years of age) distributed across the United States to ascertain the population’s confidence on the efficacy of COVID-19 scent detection dogs compared with other current COVID-19 testing methods, such as Reverse Transcription Polymerase Chain Reaction (RT-PCR) and at-home testing kits.
The population sample was representative of the adult demographic in the US based on gender, age, and ethnicity. We also collected education level, previous positive or negative COVID-19 test results, type(s) of COVID-19 testing method(s) experienced, and personal comfort level with dogs.
The objective of this study was to elucidate the US population’s perspective on COVID-19 scent detection dogs, and to determine the level of support for this form of detection method if implemented nationally and globally.
The results showed variability based on age, ethnicity, gender, and education level. The data were analyzed using three different quantitative methods: Correlation Analysis (CA),Principal Components Analysis (PCA), and Unweighted Pair Group Method with ArithmeticMean (UPGMA) cluster analysis.
The CA table shows variables aligned on the left side and top of the table and corresponding r-values, where the variables intersect. The r-values in light grey boxes show significant correlations at the P < 0.05 a priori level of significance. The red ovals indicate negative and the blue positive correlations. The larger the red or blue oval, the higher the correlation.
The highest significant correlations were observed between COVID-19 testing methods, shown in the middle of the CA table. These results suggested survey participants experienced similar COVID-19 testing methods when tested for the virus. The bottom right of the CA table provided evidence for government approval and funding and acceptance of worldwide implementation of scent detection dogs as a source of COVID-19 testing, indicating survey respondents support scent detection dogs as a reliable source of detection for future pandemics, and rated COVID-19 detection dogs as a more viable testing approach for emerging COVID-19 variants.
For example, a positive moderate and significant correlation was identified for participants supporting government approval and support to implement COVID-19 scent detection dogs worldwide. Additionally, another moderate positive, significant correlation was observed between the validity of COVID-19 scent detection dogs to identify emerging variants and detection dogs’ reliability in future pandemics.
Two different PCA biplots were interpreted from all produced biplots. The first biplot, PCA Components 1 vs. 2 compared the first and second principal components. Two distinct variable clusters were revealed: one on the right and one on the left of the y-axis. These clusters indicated similarities in the responses collected from the survey participants for all variables.
Based on the position of age and education, we can infer these variables likely had an important role in influencing the reliability of the different testing methods. Age and education also played a key role in influencing the respondents’ reported comfort levels with dogs. Because age is isolated on the right side of the y-axis, the results suggested age did not influence any remaining variables tested in the survey, such as those on the left of the y-axis.
At the bottom left quadrant, these variables explored the COVID-19 test types experienced by the participants and the specific testing methods resulting in COVID-19 positive results. These variables were allied, indicating these variables exhibited no influence on any other part of the results, i.e. the survey participants’ experiences with COVID-19 and COVID-19 testing did not influence any of the other variables examined.
The second biplot compared the first and third principal components. The same two distinct clusters of variables were evident, however there was one notable difference, i.e. the location of the COVID-19 test variable. This variable asked the participants if they had ever been tested for COVID-19. For PCA Components 1 vs. 3, the variable is more appropriately clustered with COVID-19 testing methods and reported COVID-19 test results. It is often necessary to examine different components and dimensions when analyzing data for a more accurate understanding of sets of relationships.
UPGMA cluster analysis is a distance analysis method that clusters variables based on similar participant responses to variables. The strength of the relationship between a cluster is designated by the distance each node is away from the axis. The UPGMA cluster analysis results were congruent with CA and PCA. The clusters exhibited similar relationships between and among variables. Notably, the exact two distinct variable groups revealed in the PCA biplots were clustered in the UPGMA cluster analysis. Additional interpretation of how these variables clustered and grouped in the UPGMA and PCA, respectively was supported by the r-values in the CA.
Most the significant correlations were moderate in strength, indicating there was a wide spectrum of participant responses to the variables. These results suggested the US population requires more education regarding disease scent detection dogs for research to continue. The survey and subsequent analytic methods indicated great promise for the future of COVID-19 scent detection dogs.
The first step in public acceptance is clearly outreach and education regarding disease scent detection dogs. The survey population would likely be in favor of implementing COVID-19 scent detection dogs worldwide if the survey sample had increased awareness of scent detection dogs’ potential. Ultimately, the responses gathered from the survey point to a bright future for detection dogs in our current and future pandemics.
Prolific was used to distribute our survey nationwide based on their capacity to target our specific demographic, resulting in the best opportunity for a regional unbiased participant population. Prolific was user-friendly when creating and publishing the survey. The staff was accessible, friendly, and made the process easy. We wanted our study to be a part of a platform that values quality of research, and we felt we could instil our trust in the team at Prolific. We truly value Prolific and their team’s support.
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