Analyzing the Association of Google Trends and Temperature with Rocky Mountain Spotted Fever in the United States 2004-2015

Analyzing the Association of Google Trends and Temperature with Rocky Mountain Spotted Fever in the United States 2004-2015 INTRODUCTION: Rocky Mountain Spotted Fever (RMSF) is a vector-borne disease spread through infected ticks. Climate influences survival and distribution of ticks which as an effect on exposure with humans. Since 2000, the incidence has increased from 1.7 cases per million person-years to 14.3 cases per million person-years in 2012. Around this time, Google was founded has become the premiere search engine in the United States market with the number of unique monthly visitors surpassing one billion for the first time in May 2011. Google Trends is a public tool provided by Google Inc. that shows how often a search term is entered relative to total-search volume across various regions, tracking data since Google’s public offering release in 2004. AIM: This study examines the association between Google Trends, temperature, and onset cases of Rocky Mountain Spotted Fever in the United States from 2004-2015. METHODS: This is a retrospective cross-sectional study; data was obtained from the National Notifiable Disease Surveillance System (NNDSS), National Oceanic and Atmosphere Administration (NOAA), and Google Trends. Thirty-four states were examined based on Spotted Fever Rickettsiosis (SFR) incidence in 2014 according to the Centers for Disease Control and Prevention (CDC). The average minimum temperature, average temperature, and average maximum temperature for the 34 states was collected from the NOAA website. Google Trends data was based on the search term “Rocky Mountain Spotted Fever”. SAS was used to conduct simple and multiple regression analysis to examine the association between SFR onset cases, temperature, and Google Trend’s data. RESULTS: From 2004-2015, a total of 25,993 onset cases were recorded across 34 states. North Carolina (5777 onset cases) had the most recorded while Connecticut (2 onset cases) had the least recorded. Statistical significance was measured at p ≤ 0.05. When examining the United States, the model (onset case = Interest Over Time) was statistically significant, the predictor Interest Over Time explained 52.62% of the variance (R = 0.5262, F1,143=157.69, p < 0.0001). Interest Over Time was also found to be statistically significant (β = 6.57, t1 = 12.56, p < 0.0001). When examining data at a state level, average temperature, as a predictor for onset cases, was statistically significant across 31 out of 34 states (31/34). Average minimum temperature (31/34) and average maximum temperature (31/34) also had the same statistical significant ratio as average temperature. Google trends was statistically significant for 14 out of 34 states (14/34). Only 5 out of 34 states had both variables as statistically significant when measured as predictors. CONCLUSION: The results from this study shows that Google Trends has at best modest reliability in determining the epidemiology of Rocky Mountain Spotted Fever. Temperature does show an association to onset cases, but we must keep in mind that temperature primarily describes the association for exposure to infection rather than actual onset cases. Overall, it is unclear what kind of influence Google Trends has and require further studies. ANALYZING THE ASSOCIATION OF GOOGLE TRENDS DATA AND TEMPERATURE WITH ROCKY MOUNTAIN SPOTTED FEVER IN THE UNITED STATES 2004-2015 by DAVID SUN M.P.H. , GEORGIA STATE UNIVERSITY B.S., EMORY UNIVERSITY A Thesis Submitted to the Graduate Faculty of Georgia State University in Partial Fulfillment of the Requirements for the Degree MASTER OF PUBLIC HEALTH ATLANTA, GEORGIA 30303