Abstract
Based on a panel of 30 provinces and a timeframe from January 2009 to December 2013, we estimate the association between monthly human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) incidence and the relevant Internet search query volumes in Baidu, the most widely used search engine among the Chinese. The pooled mean group (PMG) model show that the Baidu search index (BSI) positively predicts the increase in HIV/AIDS incidence, with a 1% increase in BSI associated with a 2.1% increase in HIV/AIDS incidence on average. This study proposes a promising method to estimate and forecast the incidence of HIV/AIDS, a type of infectious disease that is culturally sensitive and highly unevenly distributed in China; the method can be taken as a complement to a traditional HIV/AIDS surveillance system.
Highlights
Many claim the detection of HIV/AIDS suffers from drastic underreporting in China[8]
In this study, taking advantage of monthly Baidu search index (BSI) data[17], we examine the association of BSI and the monthly trend of HIV/AIDS incidence in 30 Chinese provinces between 2009 and 2013
Our results show that the pooled mean group (PMG) estimator can provide accurate and reliable predictions of monthly HIV/AIDS incidence
Summary
Many claim the detection of HIV/AIDS suffers from drastic underreporting in China[8]. Digital surveillance systems provide an innovative and effective way to facilitate faster detection. HIV/AIDS is by no means easy to detect; accurate forecasting of the incidence of HIV/AIDS has a significant impact on the utilization of resources and the establishment of a preventive foundation for future epidemics. The effective method in the prediction of HIV epidemics is almost nil. Time series analysis has been one of the most commonly practiced methods in epidemiology studies[16]. As with many other infectious diseases, changes in HIV/AIDS incidence are influenced by changing time trends, www.nature.com/scientificreports/. Time series analysis, which consists of many approaches, can effectively deal with such problems (e.g., uncertain geographic coverage and serial correlation). Our results show that the PMG estimator can provide accurate and reliable predictions of monthly HIV/AIDS incidence
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