Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Abstract

BackgroundDengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data.Methodology and principal findingsA Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29).ConclusionsOur study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index

“…In the early stage of the epidemic, there was no unified name for COVID-19. Taking into account the BDI algorithm, a common expression of the Chinese public, and scholars’ research on the main topics discussed by netizens during the epidemic [ 22 ], we selected “Novel coronavirus (新型冠状病毒),” “Pneumonia (肺炎),” “New pneumonia (新型肺炎),” “Novel Coronavirus Pneumonia (新型冠状病毒肺炎),” “Epidemic (疫情),” “Wuhan (武汉),” and “Wuhan Pneumonia (武汉肺炎),” seven Chinese words with large data values as the BDI-related keywords. These keywords include pneumonia, Wuhan, virus, and other words that can represent epidemic events.…”

“…At present, scholars have used Google and Baidu search to obtain internet data. Their applicability in monitoring public attention of public health emergencies such as influenza [ 15 - 18 ], H7N9 [ 19 , 20 ], and Dengue [ 21 , 22 ] has been widely confirmed.…”

Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China

“…However, an analysis 31 pairing information about serotype etiology and symptoms in a subset of cases did not support this hypothesis. A third possibility is that media attention during the 2014 epidemic 32,33 could have heightened awareness of dengue and led to an increase in the number of people detected by the surveillance system 34 . In summary, all of these hypotheses predict that some of the increase in reported cases in 2014 could have been attributable to an increase in the proportion of infections that resulted in detection by public health surveillance, albeit for different reasons.…”

“…Information about DENV serotype was known for some cases, but not at sufficient resolution to be taken into account in our analysis. The proportion of locally acquired cases for which DENV serotype was identified ranged from 11.7% (188 / 1,603 reported cases across Guangdong province) during the relatively low-transmission period from 2005-2011 28 to 0.8% (345 / 45,225 reported cases across Guangdong province) during the large epidemic in 2014 29,32 . As summarized elsewhere 28,29 , DENV-1 appeared to be the dominant serotype in 2005-2011, with reports of the other three serotypes during 2012-2015.…”

Applicability of internet search index for asthma admission forecast using machine learning

“…Ginsberg et al tracked influenza‐like illnesses among the population of the United States and proposed a method for analyzing a large number of Google search queries to accurately estimate the influenza activity in each area of the United States. Li et al used data from the Dengue Baidu Search Index to help develop a predictive model of dengue fever in China. Wang et al combined the local Internet search data from Google Trends with original data collected from hospitals in Taiwan, to forecast the incidence of dementia and dementia‐related outpatient visits.…”