scholarly journals 691. Real-Time Local Influenza Forecasting Using Smartphone-Connected Thermometer Readings

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S249-S249
Author(s):  
Aaron Miller ◽  
Inder Singh ◽  
Sarah Pilewski ◽  
Vladimir Petrovic ◽  
Philip M Polgreen
Keyword(s):  
Time Series ◽  
Real Time ◽  
Forecast Error ◽  
National Level ◽  
Model Performance ◽  
State Level ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Out Of Sample ◽  
Influenza Activity

Abstract Background Information regarding influenza activity can inform clinical and public health activities. However, current surveillance approaches induce a delay in influenza activity reports (typically 1–2 weeks). Recently, we used data from smartphone connected thermometers to accurately forecast real-time influenza activity at a national level. Because thermometer readings can be geo-located, we used state-level thermometer data to determine whether these data can improve state-level surveillance estimates. Methods We used temperature readings collected by the Kinsa smart-thermometer and mobile device app to develop state-level forecasting models to predict real-time influenza activity (1–2 weeks in advance of surveillance reports). We used state-reported influenza-like illness (ILI) to represent state influenza activity for 48 US states with sufficient surveillance data. Counts of temperature readings, fever episodes and reported symptoms were computed by week. We developed autoregressive time-series models and evaluated model performance in an adaptive out-of-sample manner. We compared baseline time-series models containing lagged state-reported ILI activity to models incorporating exogenous thermometer readings. Results A total of 10,262,212 temperature readings were recorded from October 30, 2015 to March 29, 2018. In nearly all of the 48 states considered, weekly forecasts of ILI activity improved considerably when thermometer readings were incorporated. On average, state-level forecasting accuracy improved by 23.9% compared with baseline time-series models. In many states, such as PA, New Mexico, MA, Virginia, New York and SC, out-of-sample forecast error was reduced by more than 50% when thermometer data were incorporated. In general, forecasts were most accurate in states with the greatest number of device readings. During the 2017–2018 influenza season, the average improvement in forecast accuracy was 24.4%, and thermometer readings improved forecasting accuracy in 41, out of 48, states. Conclusion Data from smart thermometers accurately track real-time influenza activity at a state level. Local surveillance efforts may be improved by incorporating such information. Such data may also be useful for longer-term local forecasts. Disclosures I. Singh, Kinsa Inc.: Board Member, Employee and Shareholder, equity received and Salary. S. Pilewski, Kinsa Inc.: Employee and Shareholder, equity received and Salary. V. Petrovic, Kinsa Inc.: Employee and Shareholder, equity received and Salary.

scholarly journals Improving State-Level Influenza Surveillance by Incorporating Real-Time Smartphone-Connected Thermometer Readings Across Different Geographic Domains

2019 ◽  
Author(s):  
Aaron C Miller ◽  
Ryan A Peterson ◽  
Inder Singh ◽  
Sarah Pilewski ◽  
Philip M Polgreen
Keyword(s):  
Real Time ◽  
Public Health Practice ◽  
Model Performance ◽  
Forecast Accuracy ◽  
State Level ◽  
Spatial Aggregation ◽  
Influenza Surveillance ◽  
Mobile Phone Application ◽  
Forecasting Models ◽  
Influenza Activity

Abstract Background Timely estimates of influenza activity are important for clinical and public health practice. However, traditional surveillance sources may be associated with reporting delays. Smartphone-connected thermometers can capture real-time illness symptoms, and these geo-located readings may help improve state-level forecast accuracy. Methods Temperature recordings were collected from smart thermometers and an associated mobile phone application. Using temperature recordings, we developed forecasting models of real-time state-reported influenza-like illness (ILI) 2 weeks before the availability of published reports. We compared time-series models that incorporated thermometer readings at various levels of spatial aggregation and evaluated out-of-sample model performance in an adaptive manner comparing each model to baseline models without thermometer information. Results More than 12 million temperature readings were recorded from over 500,000 devices from August 30, 2015 to April 15, 2018. Readings were voluntarily reported from anonymous device users, with potentially multiple users for a single device. We developed forecasting models of real-time outpatient ILI for 46 states with sufficient state-reported ILI data. Forecast accuracy improved considerably when information from thermometers was incorporated. On average, thermometer readings reduced the squared error of state-level forecasting by 43% during influenza season and more than 50% in many states. In general, best-performing models tended to result from incorporating thermometer information at multiple levels of spatial aggregation. Conclusion Local forecasts of current influenza activity, measured by outpatient ILI, can be improved by incorporating real-time information from mobile-devices. Information aggregated across neighboring states, regions, and the nation can lead to more reliable forecasts, benefiting local surveillance efforts.

scholarly journals Forecasting Yearling Prices: A Comparison Of Alternative Time Series Models

2011 ◽  
Vol 10 (3) ◽  
pp. 17
Author(s):  
James E. Payne ◽  
Robert R. Sharp ◽  
Susan A. Simmons
Keyword(s):  
Time Series ◽  
North America ◽  
Structural Model ◽  
Arima Model ◽  
The Other ◽  
Time Series Models ◽  
Forecasting Accuracy ◽  
Out Of Sample ◽  
Hard Times ◽  
One Step

The thoroughbred breeding industry in North America has fallen on hard times. The health of this industry is often gauged by prices obtained for yearlings at North American auctions, particularly the average prices of summer sales at Keeneland and Saratoga. We examine various exponential smoothing algorithms along with a market-based structural model, as well as an ARIMA model in generating one-step ahead forecasts. The market-based structural model outperforms the other approaches with respect to both in- and out-of-sample forecasting accuracy.

scholarly journals Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking

2015 ◽  
Vol 31 (4) ◽  
pp. 627-647 ◽  
Author(s):  
Ángel Cuevas ◽  
Enrique M. Quilis ◽  
Antoni Espasa
Keyword(s):  
Time Series ◽  
Business Cycles ◽  
Real Time ◽  
Time Series Models ◽  
National Accounts ◽  
Spanish Economy ◽  
Short Term ◽  
Quarterly Data ◽  
New Instrument ◽  
Term Monitoring

Abstract In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.

scholarly journals Forecasting admissions in psychiatric hospitals before and during Covid-19

2021 ◽  
Author(s):  
Jan Wolff ◽  
Ansgar Klimke ◽  
Michael Marschollek ◽  
Tim Kacprowski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Hospital Admissions ◽  
Model Performance ◽  
Psychiatric Hospitals ◽  
Time Series Models ◽  
Learning Models ◽  
One Step ◽  
Machine Learning Models ◽  
Better Than

Introduction The COVID-19 pandemic has strong effects on most health care systems and individual services providers. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. Methods We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. Our models were trained and validated with data from the first two years and tested in prospectively sliding time-windows in the last two years. Results A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naive forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44), which adjusted more quickly to the shock effects of the COVID-19 pandemic. Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Conclusion Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naive forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, different forecast horizons could be used simultaneously to allow both early planning and quick adjustments to external effects.

scholarly journals Influence of the range of data on the performance of ANN- and SVM- based time series models for reproducing groundwater level observations

2019 ◽  
Author(s):  
Heesung Yoon ◽  
Yongcheol Kim ◽  
Soo-Hyoung Lee ◽  
Kyoochul Ha
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Model Building ◽  
Model Performance ◽  
Support Vector ◽  
Time Series Models ◽  
Ann Model ◽  
Data Set ◽  
Groundwater Level Fluctuations ◽  
Numerical Tests

In the present study, we designed time series models for predicting groundwater level fluctuations using an artificial neural network (ANN) and a support vector machine (SVM). To estimate the model sensitivity to the range of data set for the model building, numerical tests were conducted using hourly measured groundwater level data at a coastal aquifer of Jeju Island in South Korea. The model performance of the two models is similar and acceptable when the range of input variable lies within the data set for the model building. However, when the range of input variables is beyond it, both the models showed abnormal prediction results: an oscillation for the ANN model and a constant value for SVM. The result of the numerical tests indicates that it is necessary to obtain various types of input and output variables and assign them to the model building process for the success of design time series models of groundwater level prediction.

scholarly journals COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1110
Author(s):  
Siroos Shahriari ◽  
Taha Hossein Rashidi ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Time Series Models ◽  
Data Streaming ◽  
Time Data ◽  
Time Prediction ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques

A substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: http://vafaeelab.com/COVID19TS.html.

scholarly journals Dengue outbreaks: unpredictable incidence time series

2019 ◽  
Vol 147 ◽  
Author(s):  
A.F.B. Gabriel ◽  
A.P. Alencar ◽  
S.G.E.K. Miraglia
Keyword(s):  
Time Series ◽  
Sao Paulo ◽  
Time Series Models ◽  
São Paulo ◽  
Temporal Behaviour ◽  
Sarima Model ◽  
Incidence Data ◽  
Out Of Sample ◽  
Dengue Outbreaks ◽  
Sarima Models

AbstractDengue fever is a disease with increasing incidence, now occurring in some regions which were not previously affected. Ribeirão Preto and São Paulo, municipalities in São Paulo state, Brazil, have been highlighted due to the high dengue incidences especially after 2009 and 2013. Therefore, the current study aims to analyse the temporal behaviour of dengue cases in the both municipalities and forecast the number of disease cases in the out-of-sample period, using time series models, especially SARIMA model. We fitted SARIMA models, which satisfactorily meet the dengue incidence data collected in the municipalities of Ribeirão Preto and São Paulo. However, the out-of-sample forecast confidence intervals are very wide and this fact is usually omitted in several papers. Despite the high variability, health services can use these models in order to anticipate disease scenarios, however, one should interpret with prudence since the magnitude of the epidemic may be underestimated.

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