A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK
Abstract A reasonable prediction of infectious diseases’ transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. W...
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/86b1e35bfe8a4649bc8dde3b9e1dcff7 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:86b1e35bfe8a4649bc8dde3b9e1dcff7 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:86b1e35bfe8a4649bc8dde3b9e1dcff72021-12-02T18:03:27ZA prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK10.1038/s41598-021-87882-92045-2322https://doaj.org/article/86b1e35bfe8a4649bc8dde3b9e1dcff72021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87882-9https://doaj.org/toc/2045-2322Abstract A reasonable prediction of infectious diseases’ transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, a single intervention is effective in disease control but at huge expense, while combined interventions would be more efficient, among which, enhancing detection number is crucial in the control strategy for COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection numbers in a real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata; it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimic the effect of multiple factors in infectious disease control.Yuxuan ZhangChen GongDawei LiZhi-Wei WangShengda D. PuAlex W. RobertsonHong YuJohn ParringtonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Yuxuan Zhang Chen Gong Dawei Li Zhi-Wei Wang Shengda D. Pu Alex W. Robertson Hong Yu John Parrington A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| description |
Abstract A reasonable prediction of infectious diseases’ transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, a single intervention is effective in disease control but at huge expense, while combined interventions would be more efficient, among which, enhancing detection number is crucial in the control strategy for COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection numbers in a real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata; it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimic the effect of multiple factors in infectious disease control. |
| format |
article |
| author |
Yuxuan Zhang Chen Gong Dawei Li Zhi-Wei Wang Shengda D. Pu Alex W. Robertson Hong Yu John Parrington |
| author_facet |
Yuxuan Zhang Chen Gong Dawei Li Zhi-Wei Wang Shengda D. Pu Alex W. Robertson Hong Yu John Parrington |
| author_sort |
Yuxuan Zhang |
| title |
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| title_short |
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| title_full |
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| title_fullStr |
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| title_full_unstemmed |
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK |
| title_sort |
prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of covid-19 in the uk |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/86b1e35bfe8a4649bc8dde3b9e1dcff7 |
| work_keys_str_mv |
AT yuxuanzhang aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT chengong aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT daweili aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT zhiweiwang aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT shengdadpu aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT alexwrobertson aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT hongyu aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT johnparrington aprognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT yuxuanzhang prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT chengong prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT daweili prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT zhiweiwang prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT shengdadpu prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT alexwrobertson prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT hongyu prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk AT johnparrington prognosticdynamicmodelapplicabletoinfectiousdiseasesprovidingeasilyvisualizedguidesacasestudyofcovid19intheuk |
| _version_ |
1718378696188887040 |