Vesicular stomatitis forecasting based on Google Trends
نویسندگان
چکیده
BACKGROUND Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. METHODS American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. RESULTS For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. CONCLUSION This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast.
منابع مشابه
University of Portsmouth Portsmouth Hants United Kingdom Po1 2up Searching for the Picture: Forecasting Uk Cinema Admissions Making Use of Google Trends Data
This paper investigates whether Google Trends search information can improve forecasts of cinema admissions, over and above those based on seasonal patterns in the data. Using monthly data for the UK for the period 2004(1) to 2008(12) we examine various forecasting models that incorporate Google Trends search information. We find clear evidence that Google Trends data on searches relevant to ci...
متن کاملForecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique
Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Cov...
متن کاملLow validity of Google Trends for behavioral forecasting of national suicide rates
Recent research suggests that search volumes of the most popular search engine worldwide, Google, provided via Google Trends, could be associated with national suicide rates in the USA, UK, and some Asian countries. However, search volumes have mostly been studied in an ad hoc fashion, without controls for spurious associations. This study evaluated the validity and utility of Google Trends sea...
متن کاملVesicular Stomatitis Virus–Based Vaccines against Lassa and Ebola Viruses
We demonstrated that previous vaccination with a vesicular stomatitis virus (VSV)-based Lassa virus vaccine does not alter protective efficacy of subsequent vaccination with a VSV-based Ebola virus vaccine. These findings demonstrate the utility of VSV-based vaccines against divergent viral pathogens, even when preexisting immunity to the vaccine vector is present.
متن کامل