Por favor, use este identificador para citar o enlazar este ítem:
http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2251| A mechanistic population balance model to evaluate the impact of interventions on infectious disease outbreaks: Case for COVID19 | |
| Jorge Rodriguez. Juan M Acuna. Joao M Uratani. Mauricio Paton. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.04.04.20053017 | |
| Infectious diseases, especially when new and highly contagious, could be devastating producing epidemic outbreaks and pandemics. Predicting the outcomes of such events in relation to possible interventions is crucial for societal and healthcare planning and forecasting of resource needs. Deterministic and mechanistic models can capture the main known phenomena of epidemics while also allowing for a meaningful interpretation of results. In this work a deterministic mechanistic population balance model was developed. The model describes individuals in a population by infection stage and age group. The population is treated as in a close well mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The present model provides a steppingstone to build upon and its current low complexity retains accessibility to non experts and policy makers to comprehend the variables and phenomena at play. The impact of specific interventions on the outbreak time course, number of cases and outcome of fatalities were evaluated including that of available critical care. Data available from the COVID19 outbreak as of early April 2020 was used. Key findings in our results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) an increase in the number of critical care beds could save up to eight lives per extra bed in a million population with the current parameters used; (iv) the use of protective equipment (PPE) appears effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) infection recognition through random testing of the population, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities but only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while R0 values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at R0 values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone; (viii) a threshold value, equivalent to that for R0, appears to exist for the daily fatality rate at which to end isolation measures, this is significant as the fatality rate is (unlike R0) very accurately known. Any interpretation of these results for the COVID19 outbreak predictions and interventions should be considered only qualitatively at this stage due to the low confidence (lack of complete and valid data) on the parameter values available at the time of writing. Any quantitative interpretation of the results must be accompanied with a critical discussion in terms of the model limitations and its frame of application. | |
| www.medrxiv.org | |
| 2020 | |
| Artículo | |
| https://www.medrxiv.org/content/10.1101/2020.04.04.20053017v1.full.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
Cargar archivos:
| Fichero | Tamaño | Formato | |
|---|---|---|---|
| 1101349.pdf | 1.74 MB | Adobe PDF | Visualizar/Abrir |