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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: Nann, Dominik, Walker, Mark, Frauenfeld, Leonie, Ferenci, Tamás, Sulyok, Mihály
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605298/
https://www.ncbi.nlm.nih.gov/pubmed/34825092
http://dx.doi.org/10.1016/j.heliyon.2021.e08386
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author Nann, Dominik
Walker, Mark
Frauenfeld, Leonie
Ferenci, Tamás
Sulyok, Mihály
author_facet Nann, Dominik
Walker, Mark
Frauenfeld, Leonie
Ferenci, Tamás
Sulyok, Mihály
author_sort Nann, Dominik
collection PubMed
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.
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spelling pubmed-86052982021-11-24 Forecasting the future number of pertussis cases using data from Google Trends Nann, Dominik Walker, Mark Frauenfeld, Leonie Ferenci, Tamás Sulyok, Mihály Heliyon Research Article 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. Elsevier 2021-11-12 /pmc/articles/PMC8605298/ /pubmed/34825092 http://dx.doi.org/10.1016/j.heliyon.2021.e08386 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Nann, Dominik
Walker, Mark
Frauenfeld, Leonie
Ferenci, Tamás
Sulyok, Mihály
Forecasting the future number of pertussis cases using data from Google Trends
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605298/
https://www.ncbi.nlm.nih.gov/pubmed/34825092
http://dx.doi.org/10.1016/j.heliyon.2021.e08386
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