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A Model for COVID-19 Prediction in Iran Based on China Parameters
Bushra Zareie1,2, Amin Roshani3, Mohammad Ali Mansournia4
1Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Background:
The rapid spread of COVID-19 virus from China to other countries and outbreaks of disease require an epidemiological analysis of the disease in the shortest time and an increased awareness of effective interventions. The purpose of this study was to estimate the COVID-19 epidemic in Iran based on the SIR model. The results of the analysis of the epidemiological data of Iran from January 22 to March 24, 2020 were investigated and prediction was made until April 15, 2020.
Methods:
By estimating the three parameters of time-dependent transmission rate, time-dependent recovery rate, and timedependent death rate from Covid-19 outbreak in China, and using the number of Covid-19 infections in Iran, we predicted the number of patients for the next month in Iran. Each of these parameters was estimated using GAM models. All analyses were conducted in R software using the mgcv package.
Results:
Based on our predictions of Iran about 29000 people will be infected from March 25 to April 15, 2020. On average, 1292 people with COVID-19 are expected to be infected daily in Iran. The epidemic peaks within 3 days (March 25 to March 27, 2020) and reaches its highest point on March 25, 2020 with 1715 infected cases.
Conclusion:
The most important point is to emphasize the timing of the epidemic peak, hospital readiness, government measures and public readiness to reduce social contact.
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01:18Steps in Outbreak Investigation
01:03Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.