A logistic model and predictions for the spread of the COVID-19 pandemic

Optimal control for COVID-19 pandemic with quarantine and antiviral therapy

In the absence of a proper cure for the disease, the recent pandemic caused by COVID-19 has been focused on isolation strategies and government measures to control the disease, such as lockdown, media coverage, and improve public hygiene. Mathematical models can help when these intervention mechanisms find some optimal strategies for controlling the spread of such diseases. We propose a set of nonlinear dynamic systems with optimal strategy including practical measures to limit the spread of the virus and to diagnose and isolate infected people while maintaining consciousness for citizens. We have used Pontryagin's maximum principle and solved our system by the finite difference method. In the end, several numerical simulations have been executed to verify the proposed model using Matlab. Also, we pursued the resilience of the parameters of control of the nonlinear dynamic systems, so that we can easily handle the pandemic situation.

Advanced deep learning algorithms for infectious disease modeling using clinical data- A Case Study on CoVID-19

BACKGROUND

Infectious disease happens when an individual is defiled by a micro-organism/virus from another person or an animal. It is troublesome that causes hurt at both individual and huge scope scales.

CASE PRESENTATION

The ongoing episode of COVID-19 ailment brought about by the new coronavirus first distinguished in Wuhan China, and its quick spread far and wide, revived the consideration of the world towards the impacts of such plagues on individual's regular daily existence. We attempt to exploit this effectiveness of Advanced deep learning algorithms to predict the Growth of Infectious disease based on time series data and classification based on (symptoms) text data and X-ray image data.

CONCLUSION

Goal is identifying the nature of the phenomenon represented by the sequence of observations and forecasting.

The impact of non-pharmaceutical interventions on the prevention and control of COVID-19 in New York City

The emergence of coronavirus disease 2019 (COVID-19) has infected more than 62 million people worldwide. Control responses varied across countries with different outcomes in terms of epidemic size and social disruption. This study presents an age-specific susceptible-exposed-infected-recovery-death model that considers the unique characteristics of COVID-19 to examine the effectiveness of various non-pharmaceutical interventions (NPIs) in New York City (NYC). Numerical experiments from our model show that the control policies implemented in NYC reduced the number of infections by 72% [interquartile range (IQR) 53-95] and the number of deceased cases by 76% (IQR 58-96) by the end of 2020. Among all the NPIs, social distancing for the entire population and protection for the elderly in public facilities is the most effective control measure in reducing severe infections and deceased cases. School closure policy may not work as effectively as one might expect in terms of reducing the number of deceased cases. Our simulation results provide novel insights into the city-specific implementation of NPIs with minimal social disruption considering the locations and population characteristics.