Infections rates for the sexually transmitted disease (STD) plummeted to a historic low in 2000. However, the number of infections has since steadily increased from 2.2 cases per 100,000 people in 2000 to 8.7 cases per 100,000 in 2015 and 2016, according to the Centers for Disease Control and Prevention (CDC).
Researchers at the University of California, Los Angeles, recently collaborated with the CDC to develop an artificial intelligence program that can identify where and when a syphilis outbreak could occur by using Google Trends.
The researchers used Google Trends, which tracks the rate of different search terms in Google, to predict the number of weekly syphilis case reports in different states by using 25 risk-related keywords for recent primary and secondary syphilis searches from 2012 and 2014. They compared the number of primary and secondary syphilis cases predicted with Google Trends with cases reported to the CDC during the same time period.
According to the report, the researchers "joined 155 weeks of Google Trends data with a one-week lag to weekly syphilis data for a total of 7,750 data points." In other words, 155 weeks of Google trends data was connected to syphilis data reported a week later. The data was multiplied by 50 states to generate 7,750 data points.
The models developed by the researchers predicted "with high accuracy" the primary and secondary syphilis counts for every US state for 144 out of 155 weeks studied.
According to lead researcher Sean Young, executive director of the University of California Institute for Prediction Technology, the program could help public health officials respond quickly to STD outbreaks and eliminate the present five-year average delay between the onset of an outbreak and its detection.
"Often times, once the CDC finds out about disease outbreaks, there have been so many cases that have already been spread and transmitted that it's just become a disaster," Young told The Washington Times.
"So if we can get ahead of the curve, that would really help," he noted.
"We found an association between freely available internet search query data and weekly reported cases of primary and secondary syphilis. Results suggest the need for further exploration on whether and how internet search data can be integrated into public health monitoring systems for sexually transmitted infections," the researchers wrote in a report published recently in the Epidemiology journal.
According to the CDC, syphilis can present itself in four stages: primary, secondary, latent and tertiary. Primary syphilis involves sores at the original site of infection, which is usually around the genitals, anus, rectum or in and around the mouth. A person with secondary syphilis generally experiences skin rash, swollen lymph nodes and fever. No symptoms are exhibited in the latent stage. Tertiary syphilis can result in serious health problems that affect the heart, brain and other organs of the body if not treated and is routinely diagnosed with multiple tests.