ArticleInternational journal of biometeorology2020
Using big data to predict pertussis infections in Jinan city, China: a time series analysis.
Article in International journal of biometeorology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 28 citations in OpenAlex.
- Combined short-term exposure to meteorological, pollution factors and pertussis in different groups from Jining, China.Journal of global health · 2024Article
- Impact of outdoor air pollution on the incidence of pertussis in China: a time-series study.BMC public health · 2023Article
- Climate and human health: a review of publication trends in the International Journal of Biometeorology.International journal of biometeorology · 2023Review
- Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao.International journal of environmental research and public health · 2023Article
- Developing spatio-temporal approach to predict economic dynamics based on online news.Scientific reports · 2022Article
- A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China.Frontiers in public health · 2022Article
- Article
- Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China.Environmental science and pollution research international · 2021Article
- The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018.Scientific reports · 2020Article
- Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019.Scientific reports · 2020Article
- The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China.The Science of the total environment · 2020Article
- An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China.Infection and drug resistance · 2020Article
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Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (r
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