COVID-19 a nivel local: SEIR+ un modelo para proyectar escenarios epidemiológicos y demandas hacia el sistema sanitario
DOI:
https://doi.org/10.46553/ensayos.3.3.2021.p1-24Palabras clave:
COVID, pronóstico SEIR, demanda sanitariaResumen
Este trabajo presenta un modelo capaz de describir y proyectar la evolución del COVID-19 a
nivel local. Para ello, diseñamos, programamos, y calibramos un modelo epidemiológico “SEIR
plus” que además de los cuatro estados principales del modelo SEIR clásico, describe la
trayectoria de enfermos severos y críticos, estados esenciales para la planificación del sistema
de salud. Además, sumamos particularidades locales, como ser la curva etaria de la población
y medidas de mitigación que impacten en el factor reproductivo. Asimismo, nuestro modelo
es estocástico debido a la incorporación de incertidumbre en variables claves asociadas al
virus y de difícil proyección para el hacedor de políticas. El modelo muestra un buen ajuste
adentro y afuera de la muestra en su aplicación a Ciudad de Buenos Aires y Tucumán,
Argentina. Finalmente, mostramos su aplicación para Tucumán, proyectando un escenario
epidemiológico factible, y las demandas del sistema sanitario.
Descargas
Citas
Adam, D. (2020). Special report: The simulations driving the world's response to COVID-19. Nature, 580(7802), 316-319.
Alimohamadi, Y., Taghdir, M., & Sepandi, M. (2020). The estimate of the basic reproduction number for novel coronavirus disease (COVID-19): a systematic review and meta-analysis. Journal of Preventive Medicine and Public Health, 53(3), 151-157.
Atkeson, A. (2020a). How deadly is COVID-19? Understanding the difficulties with estimation of its fatality rate. Working Paper No. w26965, National Bureau of Economic Research, Cambridge, Massachusetts.
Atkeson, A. (2020b). What will be the economic impact of COVID-19 in the US? Rough estimates of disease scenarios. Working Paper No. w26867, National Bureau of Economic Research, Cambridge, Massachusetts.
Baldwin, R., & di Mauro, B. W. (Eds.) (2020). Economics in the Time of COVID-19. CEPR Press, Centre for Economic Policy Research, London.
Berger, D. W., Herkenhoff, K. F., & Mongey, S. (2020). An SEIR infectious disease model with testing and conditional quarantine. Working Paper No. w2690, National Bureau of Economic Research, Cambridge, Massachusetts.
Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z.... & Gao, W. (2020). Epidemiology and Transmission of COVID-19 in Shenzhen China: Analysis of 391 cases and 1,286 of their close contacts. MedRxiv.
Brauer, F., Castillo-Chavez, C., & Castillo-Chavez, C. (2012). Mathematical Models in Population Biology and Epidemiology (Second edition). Springer.
Chen, T. M., Rui, J., Wang, Q. P., Zhao, Z. Y., Cui, J. A., & Yin, L. (2020). A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infectious diseases of poverty, 9(1), 1-8.
D'Arienzo, M., & Coniglio, A. (2020). Assessment of the SARS-CoV-2 basic reproduction number, R0, based on the early phase of COVID-19 outbreak in Italy. Biosafety and Health, 2(2), 57-59.
Eksin, C., Paarporn, K., & Weitz, J. S. (2019). Systematic biases in disease forecasting– The role of behavior change. Epidemics, 27, 96-105.
Ferguson, N., Laydon, D., Nedjati Gilani, G., Imai, N., Ainslie, K., Baguelin, M. … & Dighe, A. (2020). Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College London.
Flaxman, S., Mishra, S., Gandy, A., Unwin, J.T… & Bhatt, S. (2020a). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261.
Flaxman, S., Mishra, S., Gandy, A., Unwin, H., Coupland, H., Mellan, T. ... & Schmit, N. (2020b). Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London.
Ganyani, T., Kremer, C., Chen, D., Torneri, A., Faes, C., Wallinga, J., & Hens, N. (2020). Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020. Eurosurveillance, 25(17), 2000257.
Giannitsarou, C., Kissler, S., & Toxvaerd, F. (2020). Waning immunity and the second wave: Some projections for SARS-CoV-2. American Economic Review: Insights (forthcoming).
Gomes, C. (2020). Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Brazilian Journal of Implantology and Health Sciences, 2(3).
Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Du, B. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine, 382(18), 1708-1720.
He, J., Guo, Y., Mao, R., & Jiming, Z. (2020). Proportion of asymptomatic coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Journal of Medical Virology, 93(2), 820-830.
Heneghan, C., Brassey, J., & Jefferson, T. (2020). COVID-19: What proportion are asymptomatic? The Centre for Evidence-Based Medicine.
Hu, Z., Ge, Q., Jin, L., & Xiong, M. (2020). Artificial intelligence forecasting of COVID-19 in china. arXiv:2002.07112.
Jiang, X., Rayner, S., & Luo, M. H. (2020). Does SARS‐CoV‐2 has a longer incubation period than SARS and MERS? Journal of Medical Virology, 92(5), 476-478.
Ministerio de Salud Argentina (2020). Sala de Situación COVID-2019 (19/03), 23.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772), 700-721.
Khosravi, A., Chaman, R., Rohani-Rasaf, M., Zare, F., Mehravaran, S., & Emamian, M. H. (2020). The basic reproduction number and prediction of the epidemic size of the novel coronavirus (COVID-19) in Shahroud, Iran. Epidemiology & Infection, 148, 1-7.
Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., ... & Davies, N. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 20(5), 553-558.
Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., ... & Lessler, J. (2020). The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of Internal Medicine, 172(9), 577-582.
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Xing, X. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. The New England Journal of Medicine, 382(13), 1199-1207.
Liu, Y., Gayle, A. A., Wilder-Smith, A., & Rocklöv, J. (2020). The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 27(2).
Neher, R., Aksamentov, I., Noll, N., Albert, J., & Dyrdak, R. (2020). COVID-19 scenarios.
Perera, R. A., Mok, C. K., Tsang, O. T., Lv, H., Ko, R. L., Wu, N. C., ... & Peiris, M. (2020). Serological assays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), March 2020. Eurosurveillance, 25(16), 2000421.
Read, J. M., Bridgen, J. R., Cummings, D. A., Ho, A., & Jewell, C. P. (2020). Novel coronavirus 2019-nCoV: early estimation of
epidemiological parameters and epidemic predictions. MedRxiv.
Shen, C., Taleb, N., & Bar-Yam, Y. (2020). Review of Ferguson et al “Impact of nonpharmaceutical interventions...” New England Complex Systems Institute, School of Engineering, New York University.
Shlomai, A., Leshno, A., Sklan, E. H., & Leshno, M. (2020). Global versus focused isolation during the SARS-CoV-2 pandemic-A cost-effectiveness analysis. MedRxiv.
Sjödin, H., Wilder-Smith, A., Osman, S., Farooq, Z., & Rocklöv, J. (2020). Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020. Eurosurveillance, 25(13), 2000280.
Song, Q. Q., Zhao, H., Fang, L. Q., Liu, W., Zheng, C., & Zhang, Y. (2020). Study on assessing early epidemiological parameters of coronavirus disease epidemic in China. Zhonghua liu xing bing xue za zhi=Zhonghua liuxingbingxue zazhi, 41(4), 461-465.
Task force COVID-19 (2020). Epidemia COVID-19, Aggiornamento nazionale: 23 marzo 2020. Dipartimento Malattie Infettive e Servizio di Informatica, Istituto Superiore di Sanità, Italia.
Toxvaerd, F. (2020). Equilibrium Social Distancing. Cambridge Working Papers in Economics CWPE2021, Faculty of Economics, University of Cambridge https://doi.org/10.17863/CAM.52489
Wilder-Smith, A., Chiew, C. J., & Lee, V. J. (2020). Can we contain the COVID-19 outbreak with the same measures as for SARS? The Lancet Infectious Diseases, 20(5), e102-e107.
Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, 395(10225), 689-697.
Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., ... & Wang, M. H. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases, 92, 214-217.
Zhuang, Z., Zhao, S., Lin, Q., Cao, P., Lou, Y., Yang, L., ... & Xiao, L. (2020). Preliminary estimating the reproduction number of the coronavirus disease (COVID-19) outbreak in Republic of Korea and Italy by 5 March 2020. International Journal of Infectious Diseases, 95, 308-310.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
