Nowcasting and Forecasting the Spread of COVID-19 in Iran

Abstract

Introduction:As of early December 2019, COVID-19, a disease induced by SARS-COV-2, has started spreading, originated in Wuhan, China, and now on, have infected more than 2 million individuals throughout the world.Purpose: This study aimed to nowcast the COVID-19 outbreak throughout Iran and to forecast the trends of the disease spreading in the upcoming month. Methods:The cumulative incidence and fatality data were extracted from official reports of the National Ministry of Health and Medical Educations of Iran. To formulate the outbreak dynamics, six phenomenological models, as well as a modified mechanistic Susciptible-Exposed-Infectious-Recovered (SEIR) model, were implemented. The models were calibrated with the integrated data, and trends of the epidemic in Iran was then forecasted for the next month. Results:The final outbreak size calculated by the best fitted phenomenological models was estimated to be in the range of 68,486 to 118,923 cases; however, the calibrated SEIR model estimated that the outbreak would rage again, starting from April 26. Moreover, projected by the mechanistic model, approximately half of the infections have undergone undetected. Conclusion:Although the advanced phenomenological models perfectly fitted the data, they are incapable of applying behavioral aspects of the outbreak and hence, are not reliable enough for authorities' decision adoptions. In contrast, the mechanistic SEIR model alarms that the COVID-19 outbreak in Iran may peak for the second time, consequent to lifting the control measures. This implies that the government may implement a more granular decision making to control the outbreak. *

Transmission dynamics and control of COVID-19 in Chile, March-June, 2020

“…1.6, 1.9) which is in accordance with the estimates of the reproduction numbers retrieved from studies conducted in China, Brazil, Korea, Peru, South Africa and Iran that lie in the range of 1.5-7.1 [31][32][33][34][35][36][37][38][39][40]. In contrast, even lower estimates of ( <1) that have been reported in Singapore and Australia can be correlated with the implementation of early strict social distancing interventions in these countries [41,42].…”

“…The estimates of R from our analysis agree with the estimates of R retrieved from studies conducted in the surrounding Latin American countries including Peru and Brazil [71, 72]. Other countries including Korea, South Africa and Iran also exhibit similar estimates of R that lie in the range of 1.5-7.1 [73-80]. In contrast, some other countries including Singapore and Australia have reported much lower estimates of R ( R <1) that can be correlated with the implementation of early strict social distancing interventions in these countries [81, 82].…”

Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

“…The estimates of R from our analysis agree with the estimates of R retrieved from studies conducted in the surrounding Latin American countries including Peru and Brazil [ 71 , 72 ]. Other countries including Korea, China, South Africa and Iran also exhibit similar estimates of R that lie in the range of 1.5–7.1 [ 73 – 80 ]. In contrast, some other countries including Singapore and Australia have reported much lower estimates of R ( R <1) that can be correlated with the implementation of early strict social distancing interventions in these countries [ 81 , 82 ].…”

Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic

“…For example, researchers have assessed the potential to nowcast COVID-19 cases and deaths using Google Trends data available in near-real time [ 11 ], and have applied a range of modeling approaches that leverage reporting delays to estimate the number of not-yet-reported cases and deaths [ 12 , 13 ]. Using mathematical models to exploit COVID-19 transmission dynamics, nowcasting also has been extended to COVID-19 forecasting systems [ 14 , 15 ]. In a majority of these approaches, the nowcasting mechanism relies on accurately estimating the distribution of reporting delays; however, infectious disease transmission contains an important temporal component, in that incidence is correlated from one time point to the next, which has also been shown to improve nowcasting performance, including in COVID-19 applications [ 10 , 16 ].…”