Dataset Information

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query.

ABSTRACT: Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implying potential individual actions, and freely provide open, timelier, and large-scope search query data for helping explore regional phenomena and problems. In this paper, we endeavor to explore the possibility of adopting such data to depict population migration. Based on the search query from Baidu search engine, three migration attention indexes (MAIs) are constructed to capture public migration attention in cyber space. Taking three major urban agglomerations in China as case study, we conduct the correlation analysis among the cyber MAIs and population migration in geographical space. Results have shown that external-MAI and local-MAI can positively reflect the population migration inner regions and across regions from a holistic lens and that intercity-MAI can be a helpful supplement for the delineation of specific population flow. Along with the accumulation of cyber search query data, its potential in exploring population migration can be further reinforced.

SUBMITTER: Li C

PROVIDER: S-EPMC7177813 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query.

Li Chun C He Jianhua J Duan Xingwu X

International journal of environmental research and public health 20200401 7

Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implyin ...[more]

PMID: 32244573

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Project description:BackgroundThe use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.MethodsWe gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004-2011) and Bangkok (monthly incidence, 2004-2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using r² and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.ResultsThe linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an r² = 0.943, and a correlation of 0.869 between fitted and observed. In Singapore, the model has an r² = 0.948, and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.ConclusionsInternet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.

Project description:BackgroundThe coronavirus disease of 2019 (COVID-19) has had catastrophic effects worldwide. Mounting efforts for vaccination against COVID-19 have achieved tremendous progress. Online searching is a voluntary behavior of people might reflect the public attention and awareness.. Screening and analyzing the details of vaccine related searches may help the government to grasp the trend of public opinion and provide a reference for vaccination strategies and future efforts to protect public health.MethodsThree terms related to COVID-19 and COVID-19 vaccine as well as daily relative search volumes (RSV) were retrieved in the Baidu Index (BDI) from 1 January 2020 to 1 July 2021 in China. Besides the national total data, those of the individual provinces/cities/region of Beijing, Shanghai, Guangdong, Heilongjiang, Sichuan, and Tibet were also included. Vaccine-related policies were also gathered during this period. The vaccination rates within China were derived from the National Health Commission of the People's Republic of China, from 23 March 2021 to 1 July 2021. The searching index was calculated by the searching volume and curve graphs were used to demonstrate the variation and the related trend of the RSV and vaccination rates.ResultsA total of 548 days' BDI data were retrieved. The national and provincial curves of the BDI exhibited similar fluctuating upward trends, with 5 obvious rises, especially in COVID-19 vaccine searching volume. The vaccination number was correlated with the searching volume growth of COVID-19 vaccine and vaccine uptake (r=0.382, P<0.001; r=0.256, P=0.010). Relevant vaccination events corresponded to the variation searching trend and were attributed to or were influenced by the searching variation.ConclusionsPublic awareness about vaccination against COVID-19 was related to the implementation of vaccine policies. Positive vaccine-related policy and high public awareness about vaccination could play a vital role in maximizing the vaccination uptake. Advanced internet data grabbing could consolidate public information in an efficient and timely manner. These findings would support efforts to utilize the big data monitoring of the public opinion to forecast and guide the public health policies. Dynamic monitoring as well as prevention and timely adjustment under this supervision could be expected.

Dataset Information

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query.

Publications

The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query.

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