A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management

Autor: Clara García Samartino, Lía Mayorga, Maria Victoria Sanchez, Luis S. Mayorga, Cristián G. Sánchez, Gabriel Flores, Sofía Masuelli
Rok vydání: 2020
Předmět:
medicine.medical_specialty
CIENCIAS MÉDICAS Y DE LA SALUD
Coronavirus disease 2019 (COVID-19)
Isolation (health care)
Pneumonia
Viral
Healthcare burden
Argentina
Ciencias de la Salud
SEIR mathematical modelling
SARS-COV-2
Disease
Asymptomatic
Patient Isolation
purl.org/becyt/ford/3.3 [https]
03 medical and health sciences
0302 clinical medicine
Pandemic
Epidemiology
medicine
Humans
Epidemiología
030212 general & internal medicine
Epidemics
Intensive care medicine
Asymptomatic Infections
Pandemics
SEIR MATHEMATICAL MODELLING
SARS-CoV-2
business.industry
lcsh:Public aspects of medicine
Public Health
Environmental and Occupational Health
COVID-19
lcsh:RA1-1270
030208 emergency & critical care medicine
Models
Theoretical
HEALTHCARE BURDEN
purl.org/becyt/ford/3 [https]
ASYMPTOMATIC
medicine.symptom
Biostatistics
Coronavirus Infections
business
Research Article
Healthcare system
Zdroj: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
BMC Public Health, Vol 20, Iss 1, Pp 1-11 (2020)
BMC Public Health
ISSN: 1471-2458
DOI: 10.1186/s12889-020-09843-7
Popis: Background: Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Methods: We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere. Results: Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse. Conclusions: Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic. Fil: Mayorga, Lía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina Fil: García Samartino, Clara. Universidad Nacional de Cuyo. Facultad de Odontologia; Argentina Fil: Flores, Gabriel. No especifíca; Fil: Masuelli, Sofía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; Argentina Fil: Sanchez Sanchez, Maria Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina Fil: Mayorga, Luis Segundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos. Universidad Nacional de Cuyo. Facultad de Ciencias Médicas. Instituto de Histología y Embriología de Mendoza Dr. Mario H. Burgos; Argentina Fil: Sánchez, Cristián G.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Interdisciplinario de Ciencias Básicas. - Universidad Nacional de Cuyo. Instituto Interdisciplinario de Ciencias Básicas; Argentina
Databáze: OpenAIRE
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