COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model

EasyChair Preprint 4593

Abstract

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIR-PAD model provide insight into better management of COVID-19 pandemic in GCC countries.

Keyphrases: COVID-19, Gulf Cooperation Council, SEIR-PADC model, coronavirus disease, dynamics, epidemiological model, outbreak model, prediction

@booklet{EasyChair:4593,
  author    = {Ahmad Sedaghat and Shahab S. Band and Amir Mosavi and László Nádai},
  title     = {COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model},
  howpublished = {EasyChair Preprint 4593},
  year      = {EasyChair, 2020}}