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1.
Sci Rep ; 11(1): 9233, 2021 04 29.
Artigo em Inglês | MEDLINE | ID: mdl-33927224

RESUMO

The COVID-19 pandemic forced authorities worldwide to implement moderate to severe restrictions in order to slow down or suppress the spread of the disease. It has been observed in several countries that a significant number of people fled a city or a region just before strict lockdown measures were implemented. This behavior carries the risk of seeding a large number of infections all at once in regions with otherwise small number of cases. In this work, we investigate the effect of fleeing on the size of an epidemic outbreak in the region under lockdown, and also in the region of destination. We propose a mathematical model that is suitable to describe the spread of an infectious disease over multiple geographic regions. Our approach is flexible to characterize the transmission of different viruses. As an example, we consider the COVID-19 outbreak in Italy. Projection of different scenarios shows that (i) timely and stricter intervention could have significantly lowered the number of cumulative cases in Italy, and (ii) fleeing at the time of lockdown possibly played a minor role in the spread of the disease in the country.


Assuntos
COVID-19/epidemiologia , Controle de Doenças Transmissíveis , Modelos Teóricos , Quarentena , SARS-CoV-2/fisiologia , COVID-19/transmissão , Surtos de Doenças , Previsões , Migração Humana , Humanos , Itália , Pandemias
2.
PLoS One ; 15(9): e0238559, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32886696

RESUMO

The novel coronavirus (SARS-CoV-2), identified in China at the end of December 2019 and causing the disease COVID-19, has meanwhile led to outbreaks all over the globe with about 2.2 million confirmed cases and more than 150,000 deaths as of April 17, 2020. In this work, mathematical models are used to reproduce data of the early evolution of the COVID-19 outbreak in Germany, taking into account the effect of actual and hypothetical non-pharmaceutical interventions. Systems of differential equations of SEIR type are extended to account for undetected infections, stages of infection, and age groups. The models are calibrated on data until April 5. Data from April 6 to 14 are used for model validation. We simulate different possible strategies for the mitigation of the current outbreak, slowing down the spread of the virus and thus reducing the peak in daily diagnosed cases, the demand for hospitalization or intensive care units admissions, and eventually the number of fatalities. Our results suggest that a partial (and gradual) lifting of introduced control measures could soon be possible if accompanied by further increased testing activity, strict isolation of detected cases, and reduced contact to risk groups.


Assuntos
Infecções por Coronavirus/epidemiologia , Modelos Teóricos , Pneumonia Viral/epidemiologia , Adolescente , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , COVID-19 , Criança , Pré-Escolar , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/estatística & dados numéricos , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/transmissão , Transmissão de Doença Infecciosa/estatística & dados numéricos , Alemanha/epidemiologia , Hospitalização/estatística & dados numéricos , Humanos , Lactente , Pessoa de Meia-Idade , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Pneumonia Viral/transmissão
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