A model for COVID-19 prediction in Iran based on China parameters

Abstract

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 Methods: By estimating the three parameters of time-dependent transmission rate, timedependent recovery rate, and time-dependent mortality 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.Findings: On average, 925 people with COVID-19 are expected to be infected daily in Iran. The epidemic peaks within one week (15.03.2020 to 03.21.2020) and reaches its highest point on 03.18.2020 with 1126 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.

Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan

“…Nevertheless, according to actual data, a second peak started in October with more than 700,000 total infected cases 35 . Similar inconsistencies between predictions of SIR-based models and actual data for the COVID-19 epidemic could be observed in other studies 28 , 36 – 38 .…”

“…The simulations for Wuhan and Italy allowed starting the modeling of the epidemic in Isfahan with a rough estimation of R 0 value. Although, each outbreak is described with a unique R 0 in classical SIR models 28 , 29 , we decided to use a set of R 0 values for different time intervals to account for the variations of the community behavior and inconsistency of social distancing regulations. At the time of model construction, the actual epidemiological data were available for the episode from Feb 14th to April 11th.…”

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques

“…Then the predictions are prone to biases. In most of the studies, parameters estimated from data collected in the first affected countries such as China were used to derive estimates of parameters in other countries ( Zareie et al, 2020 ) even though it is unlikely that epidemics follow identical paths in all regions of the world ( Jewell et al, 2020 ). An additional point that might also explain the departure of predictions from values actually observed is linked to the fact that predictions are among others intended to guide public health policies for controlling spread of epidemics.…”

Forecasting Covid-19 epidemic in Isfahan using a dynamic modeling approach

“…Although in classical SIR models, each outbreak is described with a unique ! 25,26 , we decided to use a set of ! values for different time intervals to account for the variations of the community behavior and inconsistency of social distancing regulations.…”