A generalized discrete dynamic model for human epidemics
A discrete dynamic model for human epidemics was developed in present study. The model included major parameters as transmission strength and its dynamic changes, mean incubation period, hospitalization time (i.e., the time from illness to hospitalization), non-hospitalization (i.e., outside hospita...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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International Academy of Ecology and Environmental Sciences
2020-09-01
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| Series: | Computational Ecology and Software |
| Subjects: | |
| Online Access: | http://www.iaees.org/publications/journals/ces/articles/2020-10(3)/a-generalized-discrete-dynamic-model-for-human-epidemics.pdf |
| _version_ | 1818033641987506176 |
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| author | WenJun Zhang ZeLiang Chen Yi Lu, et al. |
| author_facet | WenJun Zhang ZeLiang Chen Yi Lu, et al. |
| author_sort | WenJun Zhang |
| collection | DOAJ |
| description | A discrete dynamic model for human epidemics was developed in present study. The model included major parameters as transmission strength and its dynamic changes, mean incubation period, hospitalization time (i.e., the time from illness to hospitalization), non-hospitalization (i.e., outside hospitals) daily mortality, non-hospitalization daily recovery rate, and hospitalization proportion (proportion of cases for hospitalization), etc. Sensitivity analysis of the model indicated the total cumulative cases significantly increased with the increase of initial transmission strength and hospitalization time. The total cumulative cases significantly decreased with the increase of transmission strength's dynamic decline and hospitalization proportion, and decreased with the increase of non-hospitalization daily mortality and non-hospitalization daily recovery rate. The total cumulative cases significantly increased with the decrease of mean incubation period. Sensitivity analysis demonstrated that dynamic change of transmission strength is one of the most important and controllable factors. In addition, reducing the delay for hospitalization (i.e., hospitalization time) is much effective in weakening disease epidemic. Enhancing immunity to recover from the disease is of importance for increasing non-hospitalization recovery rate. |
| first_indexed | 2024-12-10T06:26:30Z |
| format | Article |
| id | doaj.art-307f3bd2e5214d1986a54d185e9b0d78 |
| institution | Directory Open Access Journal |
| issn | 2220-721X 2220-721X |
| language | English |
| last_indexed | 2024-12-10T06:26:30Z |
| publishDate | 2020-09-01 |
| publisher | International Academy of Ecology and Environmental Sciences |
| record_format | Article |
| series | Computational Ecology and Software |
| spelling | doaj.art-307f3bd2e5214d1986a54d185e9b0d782022-12-22T01:59:11ZengInternational Academy of Ecology and Environmental SciencesComputational Ecology and Software2220-721X2220-721X2020-09-0110394104A generalized discrete dynamic model for human epidemicsWenJun Zhang0ZeLiang Chen1Yi Lu, et al.2School of Life Sciences, Sun Yat-sen University, Guangzhou, P. R. ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, P. R. ChinaDepartment of Health Law, Policy and Management, School of Public Health, Boston University, U.S.AA discrete dynamic model for human epidemics was developed in present study. The model included major parameters as transmission strength and its dynamic changes, mean incubation period, hospitalization time (i.e., the time from illness to hospitalization), non-hospitalization (i.e., outside hospitals) daily mortality, non-hospitalization daily recovery rate, and hospitalization proportion (proportion of cases for hospitalization), etc. Sensitivity analysis of the model indicated the total cumulative cases significantly increased with the increase of initial transmission strength and hospitalization time. The total cumulative cases significantly decreased with the increase of transmission strength's dynamic decline and hospitalization proportion, and decreased with the increase of non-hospitalization daily mortality and non-hospitalization daily recovery rate. The total cumulative cases significantly increased with the decrease of mean incubation period. Sensitivity analysis demonstrated that dynamic change of transmission strength is one of the most important and controllable factors. In addition, reducing the delay for hospitalization (i.e., hospitalization time) is much effective in weakening disease epidemic. Enhancing immunity to recover from the disease is of importance for increasing non-hospitalization recovery rate.http://www.iaees.org/publications/journals/ces/articles/2020-10(3)/a-generalized-discrete-dynamic-model-for-human-epidemics.pdfdiscrete dynamic modeldifference and differential equationshuman epidemicshospitalization timehospitalization proportionincubation periodtransmission strengthcovid-19 |
| spellingShingle | WenJun Zhang ZeLiang Chen Yi Lu, et al. A generalized discrete dynamic model for human epidemics Computational Ecology and Software discrete dynamic model difference and differential equations human epidemics hospitalization time hospitalization proportion incubation period transmission strength covid-19 |
| title | A generalized discrete dynamic model for human epidemics |
| title_full | A generalized discrete dynamic model for human epidemics |
| title_fullStr | A generalized discrete dynamic model for human epidemics |
| title_full_unstemmed | A generalized discrete dynamic model for human epidemics |
| title_short | A generalized discrete dynamic model for human epidemics |
| title_sort | generalized discrete dynamic model for human epidemics |
| topic | discrete dynamic model difference and differential equations human epidemics hospitalization time hospitalization proportion incubation period transmission strength covid-19 |
| url | http://www.iaees.org/publications/journals/ces/articles/2020-10(3)/a-generalized-discrete-dynamic-model-for-human-epidemics.pdf |
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