RESUMEN
The novel Coronavirus (COVID-19) is spreading and has caused a large-scale infection in China since December 2019. This has led to a significant impact on the lives and economy in China and other countries. Here we develop a discrete-time stochastic epidemic model with binomial distributions to study the transmission of the disease. Model parameters are estimated on the basis of fitting to newly reported data from January 11 to February 13, 2020 in China. The estimates of the contact rate and the effective reproductive number support the efficiency of the control measures that have been implemented so far. Simulations show the newly confirmed cases will continue to decline and the total confirmed cases will reach the peak around the end of February of 2020 under the current control measures. The impact of the timing of returning to work is also evaluated on the disease transmission given different strength of protection and control measures.
Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Modelos Biológicos , Pandemias , Neumonía Viral/epidemiología , Número Básico de Reproducción/estadística & datos numéricos , COVID-19 , China/epidemiología , Simulación por Computador , Infecciones por Coronavirus/prevención & control , Infecciones por Coronavirus/transmisión , Humanos , Conceptos Matemáticos , Pandemias/prevención & control , Pandemias/estadística & datos numéricos , Neumonía Viral/prevención & control , Neumonía Viral/transmisión , SARS-CoV-2 , Procesos EstocásticosRESUMEN
BACKGROUND: COVID-19 has spread all around the world. Italy is one of the worst affected countries in Europe. Although there is a trend of relief, the epidemic situation hasn't stabilized yet. This study aims to investigate the dynamics of the disease spread in Italy and provide some suggestions on containing the epidemic. METHODS: We compared Italy's status at the outbreak stage and control measures with Guangdong Province in China by data observation and analysis. A modified autonomous SEIR model was used to study the COVID-19 epidemic and transmission potential during the early stage of the outbreak in Italy. We also utilized a time-dependent dynamic model to study the future disease dynamics in Italy. The impact of various non-pharmaceutical control measures on epidemic was investigated through uncertainty and sensitivity analyses. RESULTS: The comparison of specific measures implemented in the two places and the time when the measures were initiated shows that the initial prevention and control actions in Italy were not sufficiently timely and effective. We estimated parameter values based on available cumulative data and calculated the basic reproduction number to be 4.32 before the national lockdown in Italy. Based on the estimated parameter values, we performed numerical simulations to predict the epidemic trend and evaluate the impact of contact limitation, detection and diagnosis, and individual behavior change due to media coverage on the epidemic. CONCLUSIONS: Italy was in a severe epidemic status and the control measures were not sufficiently timely and effective in the beginning. Non-pharmaceutical interventions, including contact restrictions and improvement of case recognition, play an important role in containing the COVID-19 epidemic. The effect of individual behavior changes due to media update of the outbreak cannot be ignored. For policy-makers, early and strict blockade measures, fast detection and improving media publicity are key to containing the epidemic.
Asunto(s)
Infecciones por Coronavirus/epidemiología , Infecciones por Coronavirus/prevención & control , Pandemias/prevención & control , Neumonía Viral/epidemiología , Neumonía Viral/prevención & control , Algoritmos , Betacoronavirus/aislamiento & purificación , COVID-19 , China/epidemiología , Infecciones por Coronavirus/transmisión , Infecciones por Coronavirus/virología , Brotes de Enfermedades , Humanos , Italia/epidemiología , Modelos Estadísticos , Neumonía Viral/transmisión , Neumonía Viral/virología , Prevalencia , SARS-CoV-2RESUMEN
The 2019 novel coronavirus disease (COVID-19) is running rampantly in China and is swiftly spreading to other countries in the world, which causes a great concern on the global public health. The absence of specific therapeutic treatment or effective vaccine against COVID-19 call for other avenues of the prevention and control measures. Media reporting is thought to be effective to curb the spreading of an emergency disease in the early stage. Cross-correlation analysis based on our collected data demonstrated a strong correlation between media data and the infection case data. Thus we proposed a deterministic dynamical model to examine the interaction of the disease progression and the media reports and to investigate the effectiveness of media reporting on mitigating the spread of COVID-19. The basic reproduction number was estimated as 5.3167 through parameterization of the model with the number of cumulative confirmed cases, the number of cumulative deaths and the daily number of media items. Sensitivity analysis suggested that, during the early phase of the COVID-19 outbreak, enhancing the response rate of the media reporting to the severity of COVID-19, and enhancing the response rate of the public awareness to the media reports, both can bring forward the peak time and reduce the peak size of the infection significantly. These findings suggested that besides improving the medical levels, media coverage can be considered as an effective way to mitigate the disease spreading during the initial stage of an outbreak.
Asunto(s)
Betacoronavirus , Comunicación , Infecciones por Coronavirus/prevención & control , Medios de Comunicación de Masas , Pandemias/prevención & control , Neumonía Viral/prevención & control , Número Básico de Reproducción , COVID-19 , China/epidemiología , Control de Enfermedades Transmisibles/métodos , Simulación por Computador , Infecciones por Coronavirus/epidemiología , Humanos , Modelos Teóricos , Neumonía Viral/epidemiología , Salud Pública , SARS-CoV-2RESUMEN
Modelling integrated pest management (IPM) with a threshold control strategy can be achieved with a non-smooth Filippov dynamical system coupled by an untreated subsystem and a treated subsystem which includes chemical and biological tactics. The releasing constant of natural enemies related to biological control generates the complex dynamics. Comprehensive qualitative analyses reveal that the treated subsystem exists with transcritical, saddle-node, Hopf and Bogdanov-Takens bifurcations, for which the threshold conditions and bifurcation curves are provided. Further, by applying techniques of non-smooth dynamical systems including the Filippov convex method and sliding bifurcation techniques, we first obtain the sliding dynamic equation, and then we analyze the existence and stability of regular/virtual equilibria, pseudo-equilibria, boundary equilibria, sliding segments and sliding bifurcations. In particular, if we choose the economic threshold (ET) as the bifurcation parameter, then interesting dynamical behaviors, including boundary equilibrium â pseudo-homoclinic â touching â buckling â crossing bifurcations, occur in succession. It is interesting to note that although the number of pests in the untreated subsystem could increase and exceed the economic injury level (EIL), the final size could be less than ET and stabilizes at a relative low level due to side effects of the pesticide on natural enemies. However, the side effects can be effectively avoided by increasing the releasing constant, which can maintain the number of pests below the EIL always and thus achieve the control purpose.