Nowcasting influenza‐like illness (ILI) via a deep learning approach using google search data: An empirical study on Taiwan ILI

2021 ◽  
Author(s):  
Yuxin He ◽  
Yang Zhao ◽  
Yupeng Chen ◽  
Hsiang‐Yu Yuan ◽  
Kwok‐Leung Tsui
Keyword(s):  
Deep Learning ◽  
Empirical Study ◽  
Learning Approach ◽  
Influenza Like Illness ◽  
Search Data ◽  
Google Search

scholarly journals Forecasting influenza-like illness trends in Cameroon using Google Search Data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elaine O. Nsoesie ◽  
Olubusola Oladeji ◽  
Aristide S. Abah Abah ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Mean Squared Error ◽  
Sub Saharan Africa ◽  
Digital Data ◽  
Support Vector ◽  
Surveillance Systems ◽  
African Countries ◽  
Influenza Like Illness ◽  
Search Data ◽  
Sub Saharan ◽  
Google Search

AbstractAlthough acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

scholarly journals Nowcasting Influenza-like Illness Trends in Cameroon

2020 ◽  
Author(s):  
Elaine O. Nsoesie ◽  
Olubusola Oladeji ◽  
Aristide S. Abah Abah ◽  
Martial L. Ndeffo-Mbah
Keyword(s):  
Disease Surveillance ◽  
Respiratory Infections ◽  
Sub Saharan Africa ◽  
Digital Data ◽  
Surveillance Systems ◽  
African Countries ◽  
Influenza Like Illness ◽  
Search Data ◽  
Sub Saharan ◽  
Google Search

ABSTRACTAlthough acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied statistical and machine learning models to forecast 2012 to 2018 trends in ILI cases reported by the Cameroon Ministry of Health (MOH), using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The variance explained by the models based on Google search data were 87.7%, 79.1% and 52.0% for the whole country, the Littoral and Centre regions respectively. Our study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and demonstrates the potential usefulness of search data for monitoring ILI in sub-Saharan African countries.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

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