Predict The Spread of COVID-19 in Iran with A SEIR Model

Abstract

The current coronavirus disease 2019 (COVID-19) outbreak has recently been declared a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. This paper designed a predictive model based on the mathematical model Susceptible-Exposed-Infective-Recovered (SEIR). SEIR is represented by a set of differential-algebraic equations incorporated with machine learning techniques to fit the data reported to estimate the spread of the COVID-19 epidemic in long-term in the Islamic Republic of Iran up to the end of July 0f 2020. This paper reduced R0 after a certain amount of days to account for containment measures and used delays to allow for lagging official data. Two evaluation criteria, R2 and RMSE, had used in this research which estimates the model on officially reported confirmed cases from different regions in Iran. The results proved the model's effectiveness in simulating and predicting the trend of the COVID-19 outbreak. Results showed the integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak.

Analysis of lung scan imaging using deep multi‐task learning structure for Covid‐19 disease

“…Each convolutional layer(C m ) consists of F m feature maps, where m is the layer number. For the first layer, C (1) , each feature map is obtained by convolving the volume of interest with a weight matrix w (1) i to which a bias term b…”

“…i , is gained by convolving the input x with a kernel. The F (1) weight matrices (one matrix per feature map) are learned by looking at different positions of the input, leading to the extraction of feature explanation. Thus, the weight parameters are shared for all lesions or infection input sites so that the layer can have an equivalence property, which is constant for the input lesion transformations such as translation and rotation.…”

“…It causes severe pneumonia leading to death. Due to high contagion, it should be diagnosed quickly to quarantine the infected person and limit the spread of the disease [1]. Today, the polymerase chain reaction (PCR) is the rich standard for recognizing coronavirus, which is considered as a test to distinguish genetic material from a specific organism such as a virus.…”