Emerging application of Google Trends searches on "conjunctivitis" for tracing the course of COVID-19 pandemic

The evolution of the COVID-19 pandemic through the lens of google searches

Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Using data from 221 countries and territories, we demonstrate the capacity of Google search data to anticipate reported COVID-19 cases and understand how containment policies are associated with changes in socioeconomic indicators. First, search interest in COVID-specific symptoms such as "loss of smell" strongly correlated with cases initially, but the association diminished as COVID-19 evolved; general terms such as "COVID symptoms" remained strongly associated with cases. Moreover, trends in search interest preceded trends in reported cases, particularly in the first year of the pandemic. Second, countries with more restrictive containment policies experienced greater search interest in unemployment and mental health terms after policies were implemented, indicating socio-economic externalities. Higher-income countries experienced a larger increase in searches related to unemployment and a larger reduction in relationship and family planning keywords relative to lower-income countries. The results demonstrate that real-time search interest can be a valuable tool to inform policies across multiple stages of the pandemic.

Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review

The probability of future Coronavirus Disease (COVID)-19 waves remains high, thus COVID-19 surveillance and forecasting remains important. Online search engines harvest vast amounts of data from the general population in real time and make these data publicly accessible via such tools as Google Trends (GT). Therefore, the aim of this study was to review the literature about possible use of GT for COVID-19 surveillance and prediction of its outbreaks. We collected and reviewed articles about the possible use of GT for COVID-19 surveillance published in the first 2 years of the pandemic. We resulted in 54 publications that were used in this review. The majority of the studies (83.3%) included in this review showed positive results of the possible use of GT for forecasting COVID-19 outbreaks. Most of the studies were performed in English-speaking countries (61.1%). The most frequently used keyword was "coronavirus" (53.7%), followed by "COVID-19" (31.5%) and "COVID" (20.4%). Many authors have made analyses in multiple countries (46.3%) and obtained the same results for the majority of them, thus showing the robustness of the chosen methods. Various methods including long short-term memory (3.7%), random forest regression (3.7%), Adaboost algorithm (1.9%), autoregressive integrated moving average, neural network autoregression (1.9%), and vector error correction modeling (1.9%) were used for the analysis. It was seen that most of the publications with positive results (72.2%) were using data from the first wave of the COVID-19 pandemic. Later, the search volumes reduced even though the incidence peaked. In most countries, the use of GT data showed to be beneficial for forecasting and surveillance of COVID-19 spread.

Effect of the First Year of COVID-19 Pandemic on Ophthalmological Practice: A Multi-Centre Italian Study with a Focus on Medico-Legal Aspects

During the COVID-19 era, several restrictions on surgery have been imposed to reduce the infectious risk among patients and staff and further preserve the availability of critical care resources. The aim of the study was to assess their impact on the ophthalmological practice and its medico-legal implications. A retrospective review of electronic medical records of the ophthalmological departments of the University of Cagliari (SGD) and University Magna Græcia of Catanzaro (UMG), from 16 March 2020 to 14 March 2021 (52 weeks), were compared with data from the corresponding period of the previous year. Weekly data on the number and type of diagnoses and procedures performed were collected and analysed in relation to the weekly average of the total number of COVID-19 patients in intensive care units (ICUs) and inpatients in Sardinia and Calabria. Results showed a significant decrease in cataract surgery operations by 47% and 31%, respectively, in the SGD and UMG (p < 0.05) during the second semester of the year; this drop occurred at the same time as the increase in COVID-19 patients in ICU and those hospitalised in both regions. Additionally, anterior segment surgery decreased at the UMG by 30% (p < 0.05). Vitreoretinal surgery decreased by 27% at the SGD, differently increased amount 31.5% at UMG (p < 0.05). The pandemic had a dramatic impact on elective surgery in ophthalmology, quantifying the backlog is the first step in order to understanding the measures to be taken in near future.