Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months. IntroductionIn recent years, there has been an interest in exploiting search query data available through sources such as Google Trends (www.google.com/trends) to model temporal processes. Choi andVarian (2012, 2009) used search query data to model tourism demand, auto sales, home sales, and initial unemployment claims. Ginsberg et al. (2009) relied on Google search queries to model influenza activity in the U.S. Studies have also suggested search query based tools to model consumer behavior (Goel et al. 2010), dengue (Gluskin et al. 2014) and more. Similarly, Yang et al. (2014 used web traffic data to model hotel demand. It is not exactly known how the search query volume algorithm by Google generates its results. Moreover, the time series of search query volume generated by the algorithm changes every week.Puerto Rico has been going through an economic recession since 2006. Leaders on the island have been attempting to find ways to boost the economy. Although hotel registrations from July to November

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Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.

Aim To assess the relationship between oral health and asthma. Methods Data from 1,315 overweight or obese individuals, aged 40-65 years was used. Asthma was self-reported, while periodontitis, bleeding on probing (BOP), and plaque index were determined by clinical examinations. Results Using logistic regression adjusting for gender, smoking status, age, body mass index, family history of asthma, and income level, revealed that the odds ratio (OR) of asthma for a participant with severe periodontitis was 0.44 (95% confidence interval: 0.27, 0.70) that of a participant with none/mild periodontitis . On the other hand, proportion of BOP sites, and plaque index were not statistically significant. For a participant with severe periodontitis, the OR of taking asthma medication was 0.20 (95% confidence interval: 0.09, 0.43) that of a participant with none/mild periodontitis. Moreover, proportion of BOP sites was statistically associated to use of asthma medication while plaque index still remained non-significant. Conclusion Participants with severe periodontitis were less likely to have asthma. Stronger evidence of an inverse association was found when using asthma medication as outcome.