Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach
Determining initial variables and key parameters, such as case fatality ratio (CFR), dynamic case fatality ratio (DCFR), reproduction number (<inline-formula> <tex-math notation="LaTeX">$R_{0}$ </tex-math></inline-formula>), and so on, helps shed more light on the t...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10360128/ |
| _version_ | 1797321026154004480 |
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| author | Yuqing Sun Zhonghua Zhang Gaochang Zhao |
| author_facet | Yuqing Sun Zhonghua Zhang Gaochang Zhao |
| author_sort | Yuqing Sun |
| collection | DOAJ |
| description | Determining initial variables and key parameters, such as case fatality ratio (CFR), dynamic case fatality ratio (DCFR), reproduction number (<inline-formula> <tex-math notation="LaTeX">$R_{0}$ </tex-math></inline-formula>), and so on, helps shed more light on the transmission and control of emerging and re-emerging infectious diseases. Here, we established a SAIUHR model, which describes the dynamic changes of susceptible, asymptomatic infectious, under-reported symptomatic infectious, hospitalized and recovered individuals. And we proposed a novel approach based on our model to calculate the report rate, starting time, basic reproduction number, the initial conditions for the compartments, CFR and DCFR. Finally, we apply our method to epidemiological datasets from China, Italy, Germany, and France. The results show that the goodness of fit for the cumulative confirmed cases is greater than 97.45% in each of the countries, DCFR is more effective than CFR in predicting the future tend of infectious disease, and improving the report rate, raising the control strength and shortening the wait time are the effective strategies against infectious diseases. This study highlights the implications of taking proper restrictions and strong policies to deal with emerging and re-emerging infectious diseases from their spread in the early stage. |
| first_indexed | 2024-03-08T04:52:32Z |
| format | Article |
| id | doaj.art-57d340084cb44e5e913f260cda6e139d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:52:32Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-57d340084cb44e5e913f260cda6e139d2024-02-08T00:02:15ZengIEEEIEEE Access2169-35362024-01-0112180121802010.1109/ACCESS.2023.334292010360128Method and Application of Estimating Epidemiological Parameters Based on Data-Driven ApproachYuqing Sun0https://orcid.org/0009-0005-8748-4064Zhonghua Zhang1Gaochang Zhao2School of Science, Xi’an University of Science and Technology, Xi’an, ChinaSchool of Science, Xi’an University of Science and Technology, Xi’an, ChinaSchool of Science, Xi’an University of Science and Technology, Xi’an, ChinaDetermining initial variables and key parameters, such as case fatality ratio (CFR), dynamic case fatality ratio (DCFR), reproduction number (<inline-formula> <tex-math notation="LaTeX">$R_{0}$ </tex-math></inline-formula>), and so on, helps shed more light on the transmission and control of emerging and re-emerging infectious diseases. Here, we established a SAIUHR model, which describes the dynamic changes of susceptible, asymptomatic infectious, under-reported symptomatic infectious, hospitalized and recovered individuals. And we proposed a novel approach based on our model to calculate the report rate, starting time, basic reproduction number, the initial conditions for the compartments, CFR and DCFR. Finally, we apply our method to epidemiological datasets from China, Italy, Germany, and France. The results show that the goodness of fit for the cumulative confirmed cases is greater than 97.45% in each of the countries, DCFR is more effective than CFR in predicting the future tend of infectious disease, and improving the report rate, raising the control strength and shortening the wait time are the effective strategies against infectious diseases. This study highlights the implications of taking proper restrictions and strong policies to deal with emerging and re-emerging infectious diseases from their spread in the early stage.https://ieeexplore.ieee.org/document/10360128/Basic reproduction numberdata-drivenepidemic modelparameter estimation |
| spellingShingle | Yuqing Sun Zhonghua Zhang Gaochang Zhao Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach IEEE Access Basic reproduction number data-driven epidemic model parameter estimation |
| title | Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach |
| title_full | Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach |
| title_fullStr | Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach |
| title_full_unstemmed | Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach |
| title_short | Method and Application of Estimating Epidemiological Parameters Based on Data-Driven Approach |
| title_sort | method and application of estimating epidemiological parameters based on data driven approach |
| topic | Basic reproduction number data-driven epidemic model parameter estimation |
| url | https://ieeexplore.ieee.org/document/10360128/ |
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