Integrating geographic information system technique with Google Trends data to analyse COVID-19 severity and public interest

Visualizing the Geography of HIV Observational Cohorts With Density-Adjusted Cartograms

BACKGROUND

Maps are potent tools for describing the spatial distribution of population and disease characteristics and, thereby, for appropriately targeting public health interventions. People with HIV (PWH) tend to live in densely populated and spatially compact areas that may be difficult to visualize on maps using unadjusted geographic or political borders.

SETTING

To illustrate these challenges, we used geographic data from adult PWH at the Vanderbilt Comprehensive Care Clinic (VCCC) in Nashville, Tennessee, and aggregated data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) from 1998 to 2015.

METHODS

We compared choropleth maps that use differential shading of political/geographic boundaries with density-adjusted cartograms that allow for shading and deformed boundaries according to a variable of interest, such as PWH.

RESULTS

Cartograms enlarged high-burden areas and shrank low-burden areas of PWH, improving visual interpretation of where to focus HIV prevention and mitigation efforts, when compared with choropleth maps. Cartograms may also demonstrate cohort representativeness of underlying populations (eg, Tennessee for VCCC or the United States for NA-ACCORD), which can guide efforts to assess external validity and improve generalizability.

CONCLUSION

Choropleth maps and cartograms offer powerful visual evidence of the geographic distribution of HIV disease and cohort representation and should be used to guide targeted public health interventions.

Global interest in vaccines during the COVID-19 pandemic: Evidence from Google Trends

COVID-19 (coronavirus disease 2019) vaccines have become available; now, everyone has the opportunity to get vaccinated. We used Google Trends (GT) data to assess the global public interest in COVID-19 vaccines during the pandemic. For the analysis, a period of 17 months was chosen (from Jan 19, 2020, to Jul 04, 2021). Interest in user queries was tracked by keywords (corona vaccine, COVID-19 vaccine development, Sputnik v, Pfizer vaccine, AstraZeneca vaccine, etc.). The geographic analysis of queries was also carried out. The interest of users in the vaccine is significantly increasing. It is focused on the side effects of vaccines, and users pay attention to vaccines' developers from different countries. The correlation between the scientific publications devoted to vaccine development and such requests of users on the internet is absent. This study shows that internet search patterns can be used to gauge public attitudes towards coronavirus vaccination. Safety concerns consistently high follow an interest in vaccine side effects. This data can be used to track and predict attitudes towards vaccination of populations from COVID-19 in different countries before global vaccination becomes available to help mitigate the adverse effects of the pandemic.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.