Data from: A machine learning method to monitor China’s AIDS epidemics with data from Baidu Trends

Background: AIDS victims’ unwillingness to report their disease, due to
social discrimination against them, makes it hard for disease control
departments to accurately monitor the disease’s dynamics through
traditional surveillance tools, such as over-the-counter drug sales and
hospital or self-reported data. With the diffusion and adoption of the
Internet, the ‘big data’ aggregated from Internet search engines, which
contain users’ information on the concern or reality of their health
status, provide a new opportunity for AIDS surveillance. This paper uses
search engine data to monitor and forecast AIDS in China. Methods: A
machine learning method, artificial neural networks (ANNs), is used to
forecast AIDS occurrences and deaths. Search trend data related to AIDS
from the largest Chinese search engine, Baidu.com, are collected and
selected as the input variables of ANNs, and officially reported actual
AIDS occurrences and deaths are used for the output variable. Three
criteria, the mean absolute percentage error, the root mean squared
percentage error, and the index of agreement, are used to test the
forecasting performance of the ANN method. Results: Based on the monthly
time-series data from January 2011 to June 2017, this article finds that,
under three criteria, the ANN method can lead to satisfactory forecasting
of AIDS occurrences and deaths, regardless of the change of the number of
search queries. Conclusions: Internet-based data should be adopted as a
real-time, cost-effective complement to a traditional AIDS surveillance
system.