Nowcasting

Bańbura, Marta; Giannone, Domenico; Reichlin, Lucrezia

doi:10.2139/ssrn.1717887

Cited by 42 publications

(28 citation statements)

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“…With hindsight, this was an essential step. 6 In this short section, we describe the data collection process with references to external libraries or tools that have been used.…”

Section: Collecting Storing and Processing Twitter Datamentioning

confidence: 99%

“…We use an encyclopedic reference 5 as well as a more informal reference where potential flu patients discuss their personal experiences. 6 After preprocessing (tokenisation, stop-word removal), we extract a set of K = 1560 stemmed candidate markers (1-grams). The latter is denoted by…”

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

“…This formation choice for the set of candidate markers is discussed further and justified in Sections 4.5 and 4. 6.…”

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

“…The results of our method are captured in Table 4. 6. Most of the possible training/validating choices lead to high linear correlations.…”

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

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Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

Lampos¹

2012

Preprint

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And if the dam breaks open many years too soon, and if there is no room upon the hill, and if your head explodes with dark forebodings too, I'll see you on the dark side of the moon." Pink Floyd ∼ Brain Damage Declaration I declare that the work in this thesis was carried out in accordance with the requirements of the University's Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate's own work. Work done in collaboration with, or with the assistance of, others, is indicated as such.Any views expressed in the thesis are those of the author.

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“…With hindsight, this was an essential step. 6 In this short section, we describe the data collection process with references to external libraries or tools that have been used.…”

Section: Collecting Storing and Processing Twitter Datamentioning

confidence: 99%

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

“…This formation choice for the set of candidate markers is discussed further and justified in Sections 4.5 and 4. 6.…”

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

“…The results of our method are captured in Table 4. 6. Most of the possible training/validating choices lead to high linear correlations.…”

Section: Automatic Extraction Of Ili Textual Markersmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

Lampos¹

2012

Preprint

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“…Nowcasting, as defined by Banbura et al [2010] at the European Central Bank, is the process of predicting the present, the very recent past, and very near future using time series data known to be incomplete. An example from economics is using monthly data to nowcast the current state of important indicators for an economy such as GDP or income.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

Hawryluk,

Hoeltgebaum,

Mishra

et al. 2021

Preprint

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Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. a This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and a small sample of expert human predictions. Our approach has substantial practical utility in disease modellingby applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.

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Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

et al. 2022

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IMPORTANCEOpioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional study, signals from 5 overdoserelated, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTSStatistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and −1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCEResults of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

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Nowcasting

Cited by 42 publications

References 0 publications

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

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