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BackgroundPrevious studies have suggested associations between trends of web searches and COVID-19 traditional metrics. It remains unclear whether models incorporating trends of digital searches lead to better predictions. MethodsAn open-access web application was developed to evaluate Google Trends and traditional COVID-19 metrics via an interactive framework based on principal components analysis (PCA) and time series modelling. The app facilitates the analysis of symptom search behavior associated with COVID-19 disease in 188 countries. In this study, we selected data of eight countries as case studies to represent all continents. PCA was used to perform data dimensionality reduction, and three different time series models (Error Trend Seasonality, Autoregressive integrated moving average, and feed-forward neural network autoregression) were used to predict COVID-19 metrics in the upcoming 14 days. The models were compared in terms of prediction ability using the root-mean-square error (RMSE) of the first principal component (PC1). Predictive ability of models generated with both Google Trends data and conventional COVID-19 metrics were compared with those fitted with conventional COVID-19 metrics only. FindingsThe degree of correlation and the best time-lag varied as a function of the selected country and topic searched; in general, the optimal time-lag was within 15 days. Overall, predictions of PC1 based on both searched termed and COVID-19 traditional metrics performed better than those not including Google searches (median [IQR]: 1.43 [0.74-2.36] vs. 1.78 [0.95-2.88], respectively), but the improvement in prediction varied as a function of the selected country and timeframe. The best model varied as a function of country, time range, and period of time selected. Models based on a 7-day moving average led to considerably smaller RMSE values as opposed to those calculated with raw data (median [IQR]: 0.74 [0.47-1.22] vs. 2.15 [1.55-3.89], respectively). InterpretationThe inclusion of digital online searches in statistical models may improve the prediction of the COVID-19 epidemic. FundingEOSCsecretariat.eu has received funding from the European Unions Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.
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Purpose: Aging impairs corneal nerve density and sensitivity. Substance P (SP), a neuropeptide secreted by sensory nerves, regulates nerve morphology and nociception. Here, we investigate the relationship between aging, nerve morphology, and SP expression in mouse and human corneas. Methods: SP levels in mouse corneas (wild type and substance P-knockout) and human corneas and tears were quantified with an ELISA assay. Corneal total nerve length (TNL) was measured with whole-mount ß3-tubulin immunofluorescence in mouse and in vivo laser corneal confocal microscopy in humans. SP and ß3-tubulin stained cross-sections were used to assess the colocalization of SP and nerves in human and mouse corneas. Ocular surface nociception was assessed with a wiping test in mice. Results: SP colocalizes with sub-basal neurons in mice and humans. In WT mice, SP levels decrease with age (P = 0.0045, 8 vs. 52 weeks; P = 0.004, 26 vs. 52 weeks) as well as TNL (P = 0.018, 8 vs. 26 weeks; P = 0.0001, 8 vs. 52 weeks). Knockout mice show a greater TNL reduction (8 vs. 26 weeks, P = 0.0016) than WT mice. In the oldest WT and age-matched KO mice, nociception is impaired (P = 0.007 and P < 0.0001, respectively), and KO mice sensitivity is restored by topical SP treatment. In humans, SP levels are reduced in old subject corneas and correlate, in tears, with age (P = 0.0368); TNL also decreases in older patients (P = 0.0002). Conclusions: Age-associated corneal nerve loss is paralleled by reduction of SP expression in mice and humans. SP promotes the maintenance of normal nerve morphology in the long term and modulates nociception in the cornea.
