Forecasting the future number of pertussis cases using data from Google Trends

Background: Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed. Methods:...

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Autores principales:	Dominik Nann, Mark Walker, Leonie Frauenfeld, Tamás Ferenci, Mihály Sulyok
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	ARIMA Forecasting Surveillance Pertussis Infodemiology Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/99620cafca7b44a0a8ad585bf77705af
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spelling	oai:doaj.org-article:99620cafca7b44a0a8ad585bf77705af2021-12-02T05:02:58ZForecasting the future number of pertussis cases using data from Google Trends2405-844010.1016/j.heliyon.2021.e08386https://doaj.org/article/99620cafca7b44a0a8ad585bf77705af2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021024890https://doaj.org/toc/2405-8440Background: Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed. Methods: SARIMA models were fitted to describe reported weekly pertussis case numbers over a four-year period in Germany. Pertussis-related Google Trends data (GTD) was added as an external regressor. Predictions were made by the models, both with and without GTD, and compared with values within the validation dataset over a one-year and for a two-weeks period. Results: Predictions of the traditional model using solely reported case numbers resulted in an RMSE (residual mean squared error) of 192.65 and 207.8, a mean absolute percentage error (MAPE) of 58.59 and 72.1, and a mean absolute error (MAE) 169.53 and 190.53 for the one-year and for the two-weeks period, respectively. The GTD expanded model achieved better forecasting accuracy (RMSE: 144.22 and 201.78), a MAPE 43.86, and 68.54 and a MAE of 124.46 and 178.96. Corrected Akaike Information Criteria also favored the GTD expanded model (1750.98 vs. 1746.73). The difference between the predictive performances was significant when using a two-sided Diebold-Mariano test (DM value: 6.86, p < 0.001) for the one-year period. Conclusion: Internet-based surveillance data enhanced the predictive ability of a traditionally based model and should be considered as a method to enhance future disease modeling.Dominik NannMark WalkerLeonie FrauenfeldTamás FerenciMihály SulyokElsevierarticleARIMAForecastingSurveillancePertussisInfodemiologyScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08386- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ARIMA Forecasting Surveillance Pertussis Infodemiology Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	ARIMA Forecasting Surveillance Pertussis Infodemiology Science (General) Q1-390 Social sciences (General) H1-99 Dominik Nann Mark Walker Leonie Frauenfeld Tamás Ferenci Mihály Sulyok Forecasting the future number of pertussis cases using data from Google Trends
description	Background: Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed. Methods: SARIMA models were fitted to describe reported weekly pertussis case numbers over a four-year period in Germany. Pertussis-related Google Trends data (GTD) was added as an external regressor. Predictions were made by the models, both with and without GTD, and compared with values within the validation dataset over a one-year and for a two-weeks period. Results: Predictions of the traditional model using solely reported case numbers resulted in an RMSE (residual mean squared error) of 192.65 and 207.8, a mean absolute percentage error (MAPE) of 58.59 and 72.1, and a mean absolute error (MAE) 169.53 and 190.53 for the one-year and for the two-weeks period, respectively. The GTD expanded model achieved better forecasting accuracy (RMSE: 144.22 and 201.78), a MAPE 43.86, and 68.54 and a MAE of 124.46 and 178.96. Corrected Akaike Information Criteria also favored the GTD expanded model (1750.98 vs. 1746.73). The difference between the predictive performances was significant when using a two-sided Diebold-Mariano test (DM value: 6.86, p < 0.001) for the one-year period. Conclusion: Internet-based surveillance data enhanced the predictive ability of a traditionally based model and should be considered as a method to enhance future disease modeling.
format	article
author	Dominik Nann Mark Walker Leonie Frauenfeld Tamás Ferenci Mihály Sulyok
author_facet	Dominik Nann Mark Walker Leonie Frauenfeld Tamás Ferenci Mihály Sulyok
author_sort	Dominik Nann
title	Forecasting the future number of pertussis cases using data from Google Trends
title_short	Forecasting the future number of pertussis cases using data from Google Trends
title_full	Forecasting the future number of pertussis cases using data from Google Trends
title_fullStr	Forecasting the future number of pertussis cases using data from Google Trends
title_full_unstemmed	Forecasting the future number of pertussis cases using data from Google Trends
title_sort	forecasting the future number of pertussis cases using data from google trends
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/99620cafca7b44a0a8ad585bf77705af
work_keys_str_mv	AT dominiknann forecastingthefuturenumberofpertussiscasesusingdatafromgoogletrends AT markwalker forecastingthefuturenumberofpertussiscasesusingdatafromgoogletrends AT leoniefrauenfeld forecastingthefuturenumberofpertussiscasesusingdatafromgoogletrends AT tamasferenci forecastingthefuturenumberofpertussiscasesusingdatafromgoogletrends AT mihalysulyok forecastingthefuturenumberofpertussiscasesusingdatafromgoogletrends
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Forecasting the future number of pertussis cases using data from Google Trends

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