No abstract

Certain mass media channels offer efficient opportunities to target smoking cessation messages so they reach relatively large audiences of smokers at relatively low cost. The approach used in this study can be applied to other types of health risk factors to improve health communication planning and increase efficiency of program media expenditures.

BackgroundIn late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a novel virus and initiated a series of events that culminated in the global coronavirus disease 2019 (COVID-19) pandemic. Throughout 2020 and the first half of 2021, massive investigational efforts towards identifying, treating, preventing, and slowing the spread of COVID-19 were carried out. Several predictors for clinical outcomes relating to metabolic health were identified.

Many measures have been taken since late 2019 to combat the coronavirus disease (COVID-19) pandemic. National, state, and local governments employed precautions, including mask mandates, stay-at-home orders, and social distancing policies, to alleviate the burden on healthcare workers and slow the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus until an efficacious vaccine was made widely available. By early spring of 2021, three effective and well-tolerated SARS-CoV-2 vaccines emerged and underwent broad distribution. Throughout the course of the COVID-19 vaccination campaign, several key logistical and psychological issues surfaced. Of these, access to vaccines and vaccination hesitancy are cited as two substantial hindrances towards vaccination. Noting the demand for the SARS-CoV-2 vaccine and its highly sensitive storage requirements, accurate dose allocation is critical for vaccinating the population quickly and successfully. Here, we propose the use of social data as a tool to predict vaccination participation by correlating Google searches with state-level daily vaccination. We identified a temporal and regionally-ubiquitous Google search syntax that broadly captures daily vaccination trends. By correlating trends in the search syntax with daily vaccination rates, we were able to quantify the correlation and identify optimal lag periods between Google searches and daily vaccination. This work highlights the importance of analyzing social data as a metric to effectively arrange vaccination roll-outs, identify voluntary vaccination participation, and identify inflection points in vaccination participation. In addition, social data assessments can help direct dose allocation, identify geographic areas that may seek, but lack, access to the vaccines, and actively prepare for fluctuations in vaccination demands.