Use Piecewise Crow-AMSAA Method to Predict Infection and Death of Corona virus in Iran

Document Type : Original Research Article


1 Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran

2 Aerospace Research Institute (Ministry of Science, Research and Technology), Tehran, Iran


Reliability growth is the positive improvement in a product’s criteria (or parameter) over a period of time due to changes in the design or product process. By analyzing the growth of reliability in a system, it can be seen that at a certain stage of the epidemic, the growth of the transmission and the rate of infection change over time. During the spread of disease, problem areas are identified and knowledge of the disease increased and then initial treatment and tools may be redesigned or reprocessed to take appropriate corrective action. In other words, each stage of the spread of the disease has a different level of growth transmission depending on appropriate corrective action. Therefore, according to this case, there are conditions under which phenomena can be described by Non-Homogeneous Poisson Process (NHPP). However, traditional epidemiological models based on exponential distribution are not able to predict disease growth during different stages of the outbreak. Therefore, in this paper, the Piecewise Crow-AMSAA (NHPP) model, which is based on the Non-Homogeneous Poisson process, is used to predict the growth of infected cases and deaths of Coronavirus outbreak. Initially, the Iran cumulative confirmed case and death data are divided into several sections based on the manual separation to find out each different infection phase at each different time period. Then Crow-AMSAA (NHPP) model is applied to the segmented data. At each stage of the outbreak, the model parameters are estimated independently using the maximum likelihood estimation (MLE) technique. Finally, the growth parameters in each stage are compared with each other and external and environmental factors are identified and examined.


Main Subjects

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