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Qu X, Yu C, He Q, Li Z, Houser SH, Zhang W, Li D. Effect of the COVID-19 Mitigation Measure on Dental Care Needs in 17 Countries: A Regression Discontinuity Analysis. Front Public Health 2022; 10:890469. [PMID: 35712318 PMCID: PMC9194817 DOI: 10.3389/fpubh.2022.890469] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/11/2022] [Indexed: 02/05/2023] Open
Abstract
Objectives The effect of COVID-19 mitigation measures on different oral health care needs is unclear. This study aimed to estimate the effect of COVID-19 mitigation measures on different types of oral health care utilization needs and explore the heterogeneity of such effects in different countries by using real-time Internet search data. Methods Data were obtained from Google Trends and other public databases. The monthly relative search volume (RSV) of the search topics "toothache," "gingivitis," "dentures," "orthodontics," and "mouth ulcer" from January 2004 to June 2021 was collected for analysis. The RSV value of each topics before and after COVID-19 was the primary outcome, which was estimated by regression discontinuity analysis (RD). The effect bandwidth time after the COVID-19 outbreak was estimated by the data-driven optimal mean square error bandwidth method. Effect heterogeneity of COVID-19 on dental care was also evaluated in different dental care categories and in countries with different human development index (HDI) rankings, dentist densities, and population age structures. Results A total of 17,850 monthly RSV from 17 countries were used for analysis. The RD results indicated that advanced dental care was significantly decreased (OR: 0.63, 95% CI: 0.47-0.85) after the COVID-19 outbreak, while emergency dental care toothache was significantly increased (OR: 1.54, 95% CI: 0.99-2.37) 4 months after the COVID-19 outbreak. Compared to the countries with low HDI and low dentist density, the effect was much more evident in countries with high HDI and high dentist density. Conclusions COVID-19 mitigation measures have different effects on people with various dental care needs worldwide. Dental care services should be defined into essential care and advanced care according to specific socioeconomic status in different countries. Targeted health strategies should be conducted to satisfy different dental care needs in countries.
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Affiliation(s)
- Xing Qu
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China
| | - Chenxi Yu
- College of Economics and Management, Sichuan Normal University, Chengdu, China
| | - Qingyue He
- Southwest Medical University, Chengdu, China.,Center of Health Care Management, Chengdu First People's Hospital, Chengdu Integrated TCM & Western Medicine, Chengdu, China
| | - Ziran Li
- School of Public Finance and Taxation, Southwestern University of Finance and Economics, Chengdu, China
| | - Shannon H Houser
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Wei Zhang
- Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.,West China Biomedical Big Data Center, Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ding Li
- Institute of Development Studies, Southwestern University of Finance and Economics, Chengdu, China
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Nann D, Walker M, Frauenfeld L, Ferenci T, Sulyok M. Forecasting the future number of pertussis cases using data from Google Trends. Heliyon 2021; 7:e08386. [PMID: 34825092 PMCID: PMC8605298 DOI: 10.1016/j.heliyon.2021.e08386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/01/2021] [Accepted: 11/10/2021] [Indexed: 11/17/2022] Open
Abstract
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. Pertussis-related Google Trends Data (GTD) showed a weak but significant correlation with the reported weekly number of pertussis cases. We fitted a SARIMA models to estimate reported weekly pertussis case numbers The GTD-expanded models achieved significantly better predictive accuracy than the traditional model over a one-year-period. Corrected Akaike Information Criteria also favored the GTD-Expanded SARIMA model. The use of GTD should be considered as a method to enhance pertussis forecasting.
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Affiliation(s)
- Dominik Nann
- Institute of Pathology and Neuropathology, Department of Pathology, Eberhard Karls University, University Clinics Tübingen, Tübingen, Germany
| | - Mark Walker
- Department of the Natural and Built Environment, Sheffield Hallam University, Sheffield, United Kingdom
| | - Leonie Frauenfeld
- Institute of Pathology and Neuropathology, Department of Pathology, Eberhard Karls University, University Clinics Tübingen, Tübingen, Germany
| | - Tamás Ferenci
- Physiological Controls Research Center, Óbuda University, Budapest, Hungary.,Corvinus University of Budapest, Department of Statistics, Budapest, Hungary
| | - Mihály Sulyok
- Institute of Pathology and Neuropathology, Department of Pathology, Eberhard Karls University, University Clinics Tübingen, Tübingen, Germany.,Institute of Tropical Medicine, Eberhard Karls University, University Clinics Tübingen, Germany
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EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and Internet-Sourced Data. SENSORS 2021; 21:s21144665. [PMID: 34300403 PMCID: PMC8309494 DOI: 10.3390/s21144665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/16/2021] [Accepted: 07/05/2021] [Indexed: 11/23/2022]
Abstract
Due to the prevalence of globalization and the surge in people’s traffic, diseases are spreading more rapidly than ever and the risks of sporadic contamination are becoming higher than before. Disease warnings continue to rely on censored data, but these warning systems have failed to cope with the speed of disease proliferation. Due to the risks associated with the problem, there have been many studies on disease outbreak surveillance systems, but existing systems have limitations in monitoring disease-related topics and internationalization. With the advent of online news, social media and search engines, social and web data contain rich unexplored data that can be leveraged to provide accurate, timely disease activities and risks. In this study, we develop an infectious disease surveillance system for extracting information related to emerging diseases from a variety of Internet-sourced data. We also propose an effective deep learning-based data filtering and ranking algorithm. This system provides nation-specific disease outbreak information, disease-related topic ranking, a number of reports per district and disease through various visualization techniques such as a map, graph, chart, correlation and coefficient, and word cloud. Our system provides an automated web-based service, and it is free for all users and live in operation.
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Cohen SA, Cohen LE, Tijerina JD. The impact of monthly campaigns and other high-profile media coverage on public interest in 13 malignancies: a Google Trends analysis. Ecancermedicalscience 2020; 14:1154. [PMID: 33574899 PMCID: PMC7864687 DOI: 10.3332/ecancer.2020.1154] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Indexed: 01/13/2023] Open
Abstract
It is estimated that more than 600,000 people will die of cancer in the United States in 2020. Annual cancer diagnoses throughout the country are expected to rise in the coming years, which may further strain the American healthcare system. As such, it is vital that public health campaigns intended to reduce cancer morbidity and mortality are successful. Monthly cancer awareness campaigns have been used in the past to raise awareness and funding for various malignancies. One notable example is the 'Pink October' campaign to raise awareness for breast cancer. There has been limited study, however, on the effectiveness of cancer awareness campaigns for other cancers such as colorectal cancer, prostate cancer and cervical cancer. High-profile media coverage of celebrity cancer diagnoses and/or cancer-related deaths is another method by which knowledge of common cancers is dispersed to the public. In this study, we evaluate the impact of monthly cancer awareness campaigns as well as celebrity cancer diagnoses and/or deaths on Internet search traffic regarding various malignancies. We used the Google Trends database to evaluate public interest in 13 different cancers (and their respective cancer screening methods, when applicable) from January 2010 to June 2020. Public interest in 6 of 13 cancers (cervical cancer, colorectal cancer, skin cancer, ovarian cancer, breast cancer and lung cancer) was significantly higher in their respective awareness months when compared to the rest of the year. Furthermore, peak public interest for 9 of 13 cancers was associated with a media-event such as a monthly awareness campaign or celebrity diagnoses and/or death. Our findings illustrate the important role that the media plays in facilitating public interest in common cancers and their screening methods. Cancer awareness months can serve as an effective tool to increase Internet search traffic regarding a given malignancy. In the future, public health agencies can attempt to utilise increased search traffic to better educate the public, raise funds and improve enrolment in cancer screening programmes that reduce cancer morbidity and mortality.
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Affiliation(s)
- Samuel A Cohen
- Stanford University School of Medicine, 291 Campus Drive, Stanford, 94305 CA, USA
| | - Landon E Cohen
- Keck School of Medicine of USC, 1975 Zonal Avenue, Los Angeles, 90089 CA, USA
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Peters DPC, McVey DS, Elias EH, Pelzel‐McCluskey AM, Derner JD, Burruss ND, Schrader TS, Yao J, Pauszek SJ, Lombard J, Rodriguez LL. Big data–model integration and AI for vector‐borne disease prediction. Ecosphere 2020. [DOI: 10.1002/ecs2.3157] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Affiliation(s)
- Debra P. C. Peters
- US Department of Agriculture Agricultural Research Service Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
| | - D. Scott McVey
- US Department of Agriculture Agricultural Research Service Center for Grain and Animal Health Research Arthropod‐Borne Animal Diseases Research Unit Manhattan Kansas 66506 USA
| | - Emile H. Elias
- US Department of Agriculture Agricultural Research Service Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
| | - Angela M. Pelzel‐McCluskey
- US Department of Agriculture, Animal and Plant Health Inspection Service Veterinary Services Fort Collins Colorado 80526 USA
| | - Justin D. Derner
- US Department of Agriculture Agricultural Research Service Rangeland Resources and Systems Research Unit Cheyenne Wyoming 82009 USA
| | - N. Dylan Burruss
- Jornada Experimental Range New Mexico State University Las Cruces New Mexico 88003 USA
| | - T. Scott Schrader
- US Department of Agriculture Agricultural Research Service Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
| | - Jin Yao
- US Department of Agriculture Agricultural Research Service Jornada Experimental Range Unit, and Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
| | - Steven J. Pauszek
- US Department of Agriculture, Agricultural Research Service Plum Island Animal Disease Center Orient Point New York 11957 USA
| | - Jason Lombard
- US Department of Agriculture, Animal and Plant Health Inspection Service Veterinary Services Fort Collins Colorado 80526 USA
| | - Luis L. Rodriguez
- US Department of Agriculture, Agricultural Research Service Plum Island Animal Disease Center Orient Point New York 11957 USA
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Frauenfeld L, Nann D, Sulyok Z, Feng YS, Sulyok M. Forecasting tuberculosis using diabetes-related google trends data. Pathog Glob Health 2020; 114:236-241. [PMID: 32453658 DOI: 10.1080/20477724.2020.1767854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) and diabetes epidemics to forecast TB case numbers. Thus, we extended TB prediction models based on traditional data with diabetes-related Google searches. We obtained data on the weekly case numbers of TB in Germany from June 8th, 2014, to May 5th, 2019. Internet search data were obtained from a Google Trends (GTD) search for 'diabetes' to the corresponding interval. A seasonal autoregressive moving average (SARIMA) model (0,1,1) (1,0,0) [52] was selected to describe the weekly TB case numbers with and without GTD as an external regressor. We cross-validated the SARIMA models to obtain the root mean squared errors (RMSE). We repeated this procedure with autoregressive feed-forward neural network (NNAR) models using 5-fold cross-validation. To simulate a data-poor surveillance setting, we also tested traditional and GTD-extended models against a hold-out dataset using a decreased 52-week-long period with missing values for training. Cross-validation resulted in an RMSE of 20.83 for the traditional model and 18.56 for the GTD-extended model. Cross-validation of the NNAR models showed a mean RMSE of 19.49 for the traditional model and 18.99 for the GTD-extended model. When we tested the models trained on a decreased dataset with missing values, the GTD-extended models achieved significantly better prediction than the traditional models (p < 0.001). The GTD-extended models outperformed the traditional models in all assessed model evaluation parameters. Using online activity-based data regarding diabetes can improve TB forecasting, but further validation is warranted.
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Affiliation(s)
- Leonie Frauenfeld
- Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany
| | - Dominik Nann
- Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany
| | - Zita Sulyok
- Institute of Tropical Medicine, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72074, Germany
| | - You-Shan Feng
- Department of Clinical Epidemiology and Applied Biometry, University Hospital of Tübingen , Tübingen 72076, Germany
| | - Mihály Sulyok
- Institute for Pathology and Neuropathology, Eberhard Karls University, University Hospital of Tübingen , Tübingen 72076, Germany
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Quintanilha LF, Souza LN, Sanches D, Demarco RS, Fukutani KF. The impact of cancer campaigns in Brazil: a Google Trends analysis. Ecancermedicalscience 2019; 13:963. [PMID: 31645890 PMCID: PMC6786828 DOI: 10.3332/ecancer.2019.963] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Indexed: 12/15/2022] Open
Abstract
It is estimated that more than 600,000 new cases of cancer will be reported in Brazil during the 2018-2019 biennium, especially with regard to prostate, breast, lung and colorectal cancers. Due to the high prevalence, incidence and mortality rates of these diseases, cancer campaigns such as 'Pink October' and 'Blue November' were strongly promoted in the past decade throughout the country to raise awareness of breast and prostate cancer, respectively. Nevertheless, whether the implementation of these campaigns has been proven efficient is still unknown. In the present study, we analysed the effectiveness of these campaigns on eliciting population online interest for cancer information. The Google Trends database was evaluated for the relative Internet search popularity for the terms 'breast cancer' and 'prostate cancer' from 2014 to 2019. Aside from some regional differences, we found that there was a high demand for 'breast cancer' and, to a lesser extent, 'prostate cancer' searches in a seasonal fashion (during October and November, respectively). Despite the worldwide high incidence of lung and colorectal cancers, searches including these keywords did not show increases in any specific period of the year, demonstrating the efficiency of the 'Pink October' and 'Blue November' campaigns in engaging the interest of the Brazilian population on the subject. These results allow us to infer that campaigns are effective in mobilising the attention of the Brazilian population with regard to breast and prostate cancers, but the practical aspects in reducing incidence and mortality should still be discussed.
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Affiliation(s)
- Luiz Fernando Quintanilha
- Universidade Salvador, Laureate Universities, Salvador 41770-235, Brazil
- Centro Universitário FTC, Faculdade de Medicina, Salvador 41741-590, Brazil
| | - Laumar Neves Souza
- Universidade Salvador, Laureate Universities, Salvador 41770-235, Brazil
| | - Daniel Sanches
- Division of Arts and Sciences, South Florida State College, Avon Park, FL 33825, USA
| | | | - Kiyoshi Ferreira Fukutani
- Centro Universitário FTC, Faculdade de Medicina, Salvador 41741-590, Brazil
- Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador 40296-710, Brazil
- Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Fundação José Silveira, Salvador 40210-320, Brazil
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Bragazzi NL, Mahroum N. Google Trends Predicts Present and Future Plague Cases During the Plague Outbreak in Madagascar: Infodemiological Study. JMIR Public Health Surveill 2019; 5:e13142. [PMID: 30763255 PMCID: PMC6429048 DOI: 10.2196/13142] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 01/17/2019] [Accepted: 01/18/2019] [Indexed: 01/08/2023] Open
Abstract
Background Plague is a highly infectious zoonotic disease caused by the bacillus Yersinia pestis. Three major forms of the disease are known: bubonic, septicemic, and pneumonic plague. Though highly related to the past, plague still represents a global public health concern. Cases of plague continue to be reported worldwide. In recent months, pneumonic plague cases have been reported in Madagascar. However, despite such a long-standing and rich history, it is rather difficult to get a comprehensive overview of the general situation. Within the framework of electronic health (eHealth), in which people increasingly search the internet looking for health-related material, new information and communication technologies could enable researchers to get a wealth of data, which could complement traditional surveillance of infectious diseases. Objective In this study, we aimed to assess public reaction regarding the recent plague outbreak in Madagascar by quantitatively characterizing the public’s interest. Methods We captured public interest using Google Trends (GT) and correlated it to epidemiological real-world data in terms of incidence rate and spread pattern. Results Statistically significant positive correlations were found between GT search data and confirmed (R2=0.549), suspected (R2=0.265), and probable (R2=0.518) cases. From a geospatial standpoint, plague-related GT queries were concentrated in Toamasina (100%), Toliara (68%), and Antananarivo (65%). Concerning the forecasting models, the 1-day lag model was selected as the best regression model. Conclusions An earlier digital Web search reaction could potentially contribute to better management of outbreaks, for example, by designing ad hoc interventions that could contain the infection both locally and at the international level, reducing its spread.
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Affiliation(s)
- Nicola Luigi Bragazzi
- Department of Health Sciences, Postgraduate School of Public Health, University of Genoa, Genoa, Italy
| | - Naim Mahroum
- Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
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Lippi G, Cervellin G. Is digital epidemiology reliable?-insight from updated cancer statistics. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:15. [PMID: 30788362 DOI: 10.21037/atm.2018.11.55] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
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Kapitány-Fövény M, Ferenci T, Sulyok Z, Kegele J, Richter H, Vályi-Nagy I, Sulyok M. Can Google Trends data improve forecasting of Lyme disease incidence? Zoonoses Public Health 2018; 66:101-107. [PMID: 30447056 DOI: 10.1111/zph.12539] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 09/30/2018] [Accepted: 10/21/2018] [Indexed: 01/14/2023]
Abstract
BACKGROUND Online activity-based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick-borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends. METHODS Data on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching "Borreliose" in Germany for the "last 5 years" as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set. RESULTS Forecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values-expanded model. The difference between the predictive performances was insignificant (Diebold-Mariano Test, p-value = 0.4152). CONCLUSION Google Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.
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Affiliation(s)
- Máté Kapitány-Fövény
- Faculty of Health Sciences, Semmelweis University, Budapest, Hungary.,Nyírő Gyula National Institute of Psychiatry and Addictions, Budapest, Hungary
| | - Tamás Ferenci
- John von Neumann Faculty of Informatics, Physiological Controls Group, Óbuda University, Budapest, Hungary
| | - Zita Sulyok
- Institute of Tropical Medicine, Eberhard Karls University, Tübingen, Germany
| | - Josua Kegele
- Department of Neurology and Epileptology, Neurology Clinics, Eberhard Karls University, Tübingen, Germany
| | - Hardy Richter
- Department of Neurology and Stroke, Neurology Clinics, Eberhard Karls University, Tübingen, Germany
| | - István Vályi-Nagy
- South-Pest Central Hospital, National Institute of Hematology and Infectious Diseases, Budapest, Hungary
| | - Mihály Sulyok
- Department of Neurology and Stroke, Neurology Clinics, Eberhard Karls University, Tübingen, Germany
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