Modeling and Forecasting Trend of COVID-19 Epidemic in Iran

Ali Ahmadi; Majid Shirani; Fereydoon Rahmani

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03. 17.20037671 doi: medRxiv preprint University Medical of Sciences and Pasteur Institute of Iran were used (18) . Patient population growth, epidemic curves, and recovered and deceased individuals were used to conceptual framework of epidemic and predict the COVID-19 epidemic trend. we used classical infectious disease (Susceptible→Exposed→Infected→Removed: SEIR) model in this frame work (19). Different scenarios were designed and implemented for modeling and forecasting. First, based on a search for reliable sources of disease trends and epidemic curves across the world, the curve of Iran was also drawn (10, 16, 20) . Focused and scientific group discussion sessions were held with experts on epidemiology, biostatistics, and mathematics, infectious diseases specialists as well as healthcare managers on the topic, the scenarios were discussed and agreement was reached on the application of the final scenarios. to predict the growth of epidemic different models were used. In the first scenario, the most optimistic estimation and control of the epidemic was during an incubation period (van model) and considering the ideal model. In this scenario traced contacts are isolated immediately on symptom onset (and not before) and isolation prevents all transmission were used. In the second scenario, an intermediate and fit-to-data model (Gompertz) was used. In the third scenario, the use of the growth rate is greater than the first and second models, and in fact opposite to the first scenario (LSE). To select the scenarios, fit the data with the models and growth rate of the cases were used. The Gompertz growth, von Bertalanffy growth equation and curve fitting by LSE method with cubic polynomial for Epidemic forecasts were run in MATLAB software. Models are presented as the following differential equations: