Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model
In the dire need to develop a robust epidemiological surveillance model, this study aimed to determine if the dynamics change parameters of past infectious disease outbreaks would enable early accurate detection of future outbreaks. An optimized regular LSTM could not accurately learn the non-linear...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227624001030 |
| _version_ | 1797267201384775680 |
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| author | Adegboyega Adebayo Olumide O. Obe Akintoba E. Akinwonmi Francis Osang Adeyinka O. Abiodun Stephen Alaba Mogaji |
| author_facet | Adegboyega Adebayo Olumide O. Obe Akintoba E. Akinwonmi Francis Osang Adeyinka O. Abiodun Stephen Alaba Mogaji |
| author_sort | Adegboyega Adebayo |
| collection | DOAJ |
| description | In the dire need to develop a robust epidemiological surveillance model, this study aimed to determine if the dynamics change parameters of past infectious disease outbreaks would enable early accurate detection of future outbreaks. An optimized regular LSTM could not accurately learn the non-linear relationship of the epidemiological data in the peak stage of the pre-vaccination era measles outbreak, which hence influenced the decrease predictability at increasing time series. The result of this study shows that Pitchfork bifurcation of LSTM at 1.5 bifurcation point matches the dynamic change inherent in California's pre-vaccination era measles outbreak at the improved prediction performances of root mean squared error (RMSE) = 0.1684 for the 1962 outbreak, and root mean squared error (RMSE)= 0.2776 for 1964 outbreak. In conclusion, it was observed that the dynamics change parameters are dependent on the stochastic nature of the optimization algorithm engaged, and the chaotic nature of epidemiological data. |
| first_indexed | 2024-04-25T01:12:49Z |
| format | Article |
| id | doaj.art-abbdaa121fde4a458cc4a69416113170 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-25T01:12:49Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-abbdaa121fde4a458cc4a694161131702024-03-10T05:12:32ZengElsevierScientific African2468-22762024-06-0124e02158Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory modelAdegboyega Adebayo0Olumide O. Obe1Akintoba E. Akinwonmi2Francis Osang3Adeyinka O. Abiodun4Stephen Alaba Mogaji5Department of Computer Science, National Open University, University Village Plot 91, Cadastral Zone Nnamdi Azikiwe Expressway Jabi, Abuja, Nigeria; Corresponding author.Department of Computer Science, Federal University of Technology, Akure 340110, NigeriaDepartment of Computer Science, Federal University of Technology, Akure 340110, NigeriaDepartment of Computer Science, National Open University, University Village Plot 91, Cadastral Zone Nnamdi Azikiwe Expressway Jabi, Abuja, NigeriaDepartment of Computer Science, National Open University, University Village Plot 91, Cadastral Zone Nnamdi Azikiwe Expressway Jabi, Abuja, NigeriaDepartment of Computer Science, Federal University Oye-Ekiti, Oye-Are Road, Oye-Ekiti, Ekiti State, NigeriaIn the dire need to develop a robust epidemiological surveillance model, this study aimed to determine if the dynamics change parameters of past infectious disease outbreaks would enable early accurate detection of future outbreaks. An optimized regular LSTM could not accurately learn the non-linear relationship of the epidemiological data in the peak stage of the pre-vaccination era measles outbreak, which hence influenced the decrease predictability at increasing time series. The result of this study shows that Pitchfork bifurcation of LSTM at 1.5 bifurcation point matches the dynamic change inherent in California's pre-vaccination era measles outbreak at the improved prediction performances of root mean squared error (RMSE) = 0.1684 for the 1962 outbreak, and root mean squared error (RMSE)= 0.2776 for 1964 outbreak. In conclusion, it was observed that the dynamics change parameters are dependent on the stochastic nature of the optimization algorithm engaged, and the chaotic nature of epidemiological data.http://www.sciencedirect.com/science/article/pii/S2468227624001030Time-series modelingBifurcation theoryEpidemiologyDeep learningBayesian optimizationLong short term memory algorithm |
| spellingShingle | Adegboyega Adebayo Olumide O. Obe Akintoba E. Akinwonmi Francis Osang Adeyinka O. Abiodun Stephen Alaba Mogaji Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model Scientific African Time-series modeling Bifurcation theory Epidemiology Deep learning Bayesian optimization Long short term memory algorithm |
| title | Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model |
| title_full | Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model |
| title_fullStr | Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model |
| title_full_unstemmed | Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model |
| title_short | Tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time-series long short-term memory model |
| title_sort | tracking dynamics change parameters of chaotic infectious disease outbreak with bifurcated time series long short term memory model |
| topic | Time-series modeling Bifurcation theory Epidemiology Deep learning Bayesian optimization Long short term memory algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2468227624001030 |
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