Dataset Information

Tracking COVID-19 using online search.

ABSTRACT: Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest-as opposed to infections-using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2-23.2) and 22.1 (17.4-26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

SUBMITTER: Lampos V

PROVIDER: S-EPMC7870878 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Tracking COVID-19 using online search.

Lampos Vasileios V Majumder Maimuna S MS Yom-Tov Elad E Edelstein Michael M Moura Simon S Hamada Yohhei Y Rangaka Molebogeng X MX McKendry Rachel A RA Cox Ingemar J IJ

NPJ digital medicine 20210208 1

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these si ...[more]

PMID: 33558607

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Project description:BACKGROUND:The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts. OBJECTIVE:The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics. METHODS:An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patterns were assessed in relation to disease patterns, significant events, and media reports. RESULTS:Worldwide search terms for shortness of breath, anosmia, dysgeusia and ageusia, headache, chest pain, and sneezing had strong correlations (r>0.60, P<.001) to both new daily confirmed cases and deaths from COVID-19. GT COVID-19 (search term) and GT coronavirus (virus) searches predated real-world confirmed cases by 12 days (r=0.85, SD 0.10 and r=0.76, SD 0.09, respectively, P<.001). Searches for symptoms of diarrhea, fever, shortness of breath, cough, nasal obstruction, and rhinorrhea all had a negative lag greater than 1 week compared to new daily cases, while searches for anosmia and dysgeusia peaked worldwide and in China with positive lags of 5 days and 6 weeks, respectively, corresponding with widespread media coverage of these symptoms in COVID-19. CONCLUSIONS:This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like COVID-19. Although certain online search trends for this disease were influenced by media coverage, many search terms reflected clinical manifestations of the disease and showed strong correlations with real-world cases and deaths.

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.

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Tracking COVID-19 using online search.

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Tracking COVID-19 using online search.

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