A prognostic dynamic model applicable to infectious diseases providing easily visualized guides: a case study of COVID-19 in the UK.
Sci Rep
; 11(1): 8412, 2021 04 16.
Article
em En
| MEDLINE
| ID: mdl-33863958
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
/
4_TD
Problema de saúde:
1_doencas_nao_transmissiveis
/
1_doencas_transmissiveis
/
2_enfermedades_transmissibles
/
2_muertes_prematuras_enfermedades_notrasmisibles
/
4_pneumonia
Assunto principal:
COVID-19
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Reino Unido