key: cord-351065-nyfnwrtm
authors: Zhang, Tenghao
title: Integrating GIS technique with Google Trends data to analyse COVID-19 severity and public interest
date: 2020-09-16
journal: Public Health
DOI: 10.1016/j.puhe.2020.09.005
sha: 
doc_id: 351065
cord_uid: nyfnwrtm

nan

At the time of writing, the tally of confirmed novel coronavirus cases worldwide has exceeded 26.6 million. 1 The United States has become the global epicentre since April 2020, and now it is accounted for nearly one-quarter of the world's total cases. Some studies suggest that health related issues can cause anxiety which may lead to increased public attention, typically manifested by online information search. 2,3 Along the same lines, given the substantial regional disparities of COVID-19 case severities across states in the United States, the relationship between regional case severities and the public interest emerges as an imperative for COVID-19-based public health studies.

To investigate the relationship between the above two indicators, geographic information systems (GIS) techniques can play a crucial role. Adams et al.'s (2020) GIS-based study points out the shortcomings of using unnormalized COVID-19 demographic data in choropleth mapping, and their use of the normalized data (confirmed cases per 100,000 people) presents a more accurate visualisation of pandemic severity. 4 While I entirely agree with their point of view and methods, I would like to propose an alternative GIS technique which has the potential to facilitate a better understanding of the research, namely, the cartogram technique. 5,6 A cartogram is a map in which the geometry of areas is distorted to convey the value of an alternative thematic mapping variable. 6 Hence, if the normalized COVID-19related data is used in a cartogram, it can provide some novel perspectives on data interpretation.

To perform the analysis, the data were obtained from two sources. The COVID-19 case data were retrieved from the US health authority (https://cdc.gov/covid-datatracker). I retrieved the total confirmed cases per 100,000 population by state, and then I divided the new confirmed cases (during the past week of data collection) by the total previous cases and obtained a growth of new cases indicator. Public interest was captured by people's Google search data in each state. 7 The data were acquired from the Google Trends service, which uses a normalized relative search volume 

Available from www. worldometer.info (accessed 5th

The role of health anxiety in online health information search

Health anxiety in the digital age: An exploration of psychological determinants of online health information seeking

The disguised pandemic: The importance of data normalization in COVID-19 web mapping

Diffusion-based method for producing density-equalizing maps

Area Cartograms: Their Use and Creation

Monitoring public interest toward pertussis outbreaks: an extensive Google Trends -based analysis

Mapping the changing Internet attention to the spread of coronavirus disease 2019 in China


