Dataset Information

Vesicular stomatitis forecasting based on Google Trends.

ABSTRACT: 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.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.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.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.

SUBMITTER: Wang J

PROVIDER: S-EPMC5792013 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Vesicular stomatitis forecasting based on Google Trends.

Wang JianYing J Zhang Tong T Lu Yi Y Zhou GuangYa G Chen Qin Q Niu Bing B

PloS one 20180131 1

<h4>Background</h4>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.<h4>Methods</h4>American VS outbreaks data were collected from OIE. The data for VS keywords were obtaine ...[more]

PMID: 29385198

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Project description:BackgroundColorectal cancer (CRC) is the third most prevalent and second most lethal form of cancer in the world. Consequently, CRC cancer prevalence projections are essential for assessing the future burden of the disease, planning resource allocation, and developing service delivery strategies, as well as for grasping the shifting environment of cancer risk factors. However, unlike cancer incidence and mortality rates, national and international agencies do not routinely issue projections for cancer prevalence. Moreover, the limited or even nonexistent cancer statistics for large portions of the world, along with the high heterogeneity among world nations, further complicate the task of producing timely and accurate CRC prevalence projections. In this situation, population interest, as shown by Internet searches, can be very important for improving cancer statistics and, in the long run, for helping cancer research.MethodsThis study aims to model, nowcast and forecast the CRC prevalence at the global level using a three-step framework that incorporates three well-established univariate statistical and machine-learning models. First, data mining is performed to evaluate the relevancy of Google Trends (GT) data as a surrogate for the number of CRC survivors. The results demonstrate that population web-search interest in the term "colonoscopy" is the most reliable indicator to nowcast CRC disease prevalence. Then, various statistical and machine-learning models, including ARIMA, ETS, and FNNAR, are trained and tested using relevant GT time series. Finally, the updated monthly query series spanning 2004-2022 and the best forecasting model in terms of out-of-sample forecasting ability (i.e., the neural network autoregression) are utilized to generate point forecasts up to 2025.ResultsResults show that the number of people with colorectal cancer will continue to rise over the next 24 months. This in turn emphasizes the urgency for public policies aimed at reducing the population's exposure to the principal modifiable risk factors, such as lifestyle and nutrition. In addition, given the major drop in population interest in CRC during the first wave of the COVID-19 pandemic, the findings suggest that public health authorities should implement measures to increase cancer screening rates during pandemics. This in turn would deliver positive externalities, including the mitigation of the global burden and the enhancement of the quality of official statistics.

Project description:Vesicular stomatitis Indiana virus (VSIV), formerly known as vesicular stomatitis virus (VSV) Indiana (VSVIND), is a model virus that is exceptionally sensitive to the inhibitory action of interferons (IFNs). Interferons induce an antiviral state by stimulating the expression of hundreds of interferon-stimulated genes (ISGs). These ISGs can constrain viral replication, limit tissue tropism, reduce pathogenicity, and inhibit viral transmission. Since VSIV is used as a backbone for multiple oncolytic and vaccine strategies, understanding how ISGs restrict VSIV not only helps in understanding VSIV-induced pathogenesis but also helps us evaluate and understand the safety and efficacy of VSIV-based therapies. Thus, there is a need to identify and characterize the ISGs that possess anti-VSIV activity. Using arrayed ISG expression screening, we identified TRIM69 as an ISG that potently inhibits VSIV. This inhibition was highly specific as multiple viruses, including influenza A virus, HIV-1, Rift Valley fever virus, and dengue virus, were unaffected by TRIM69. Indeed, just one amino acid substitution in VSIV can govern sensitivity/resistance to TRIM69. Furthermore, TRIM69 is highly divergent in human populations and exhibits signatures of positive selection that are consistent with this gene playing a key role in antiviral immunity. We propose that TRIM69 is an IFN-induced inhibitor of VSIV and speculate that TRIM69 could be important in limiting VSIV pathogenesis and might influence the specificity and/or efficacy of vesiculovirus-based therapies.IMPORTANCE Vesicular stomatitis Indiana virus (VSIV) is a veterinary pathogen that is also used as a backbone for many oncolytic and vaccine strategies. In natural and therapeutic settings, viral infections like VSIV are sensed by the host, and as a result the host cells make proteins that can protect them from viruses. In the case of VSIV, these antiviral proteins constrain viral replication and protect most healthy tissues from virus infection. In order to understand how VSIV causes disease and how healthy tissues are protected from VSIV-based therapies, it is crucial that we identify the proteins that inhibit VSIV. Here, we show that TRIM69 is an antiviral defense that can potently and specifically block VSIV infection.

Dataset Information

Vesicular stomatitis forecasting based on Google Trends.

Publications

Vesicular stomatitis forecasting based on Google Trends.

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