Prediction of COVID-19 spreading profiles in South Korea, Italy and Iran by data-driven coding

Abstract

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

Rapid review of COVID-19 epidemic estimation studies for Iran

“…General characteristics of reviewed studies: We found 13 articles and 10 reports and included eight published articles / preprint manuscripts [16][17][18][19][20][21][22][23] and three reports [13,24,25]. From the 13 found articles, three articles were kept for a next version of this review [8][9][10], one article was excluded because it was not an epidemic estimation study, and one article was excluded because of absent minimum necessary details on methods.…”

“…Appendix Figure 1 shows the PRISMA studies ow diagram. Four of the eight reviewed articles were published (Moradi et al, Tuite et al, Zareie et al, Zhuang et al) [18,20,21,23] and the other four were in preprint phase at the time that we reviewed them (Ahmadi et al, Ghaffarzadegan et al, Muniz-Rodriguez et al, Zhan et al) [16,17,19,22]. All articles were written in English, and all reports were written in Farsi.…”

“…Five studies used the presumed o cial start date of 2020-02-19 (Ahmadi [16], Muniz-Rodriguez [19], Saberi [13], Zareie [21], Zhan [22]). One study (Moradi [18]) reported their estimates starting from 2020-02-20 without mention of the rationale.…”

“…Most of the studies used sort of compartmental model, however some of them, (Ghaffarzadegan, Zhan, Haghdoost and Mashayekhi) and were more detailed [17,22,24,25]. Three studies used SIR models (Ahmadi [16], Saberi [13], Zareie [21]); three studies used SEIR+ models (Ghaffarzadegan [17], Haghdoost [24], Zhan [22]), and (Mashayekhi [25]) used SLIR+ models, where S denotes Susceptible, E is for Exposed, I is for Infected, R for Recovered, L for Latent, and in any model with a + sign, there are other components for augmentation of model. Four studies did not use explicit disease models (Moradi [18], Muniz-Rodriguez [19], Tuite [20], Zhuang [23]).…”

The epidemiological trends of coronavirus disease (COVID-19) in Iran: February 19 to March 22, 2020

“…Although the authorities have started to confront this epidemic by shutting down the schools and universities, refusing non-local travelers in some provinces, reducing working hours for some days, implementing forms of remote work by many companies for the employees, requesting the individuals to stay at home in self-quarantine, etc., still there is a quick increase in the number of infected cases [5]. According to the literature review, there are limited published papers on Iran's status in the outbreak period of the COVID-19 [5,[14][15][16][17][18]. Therefore, given the rapid outbreak of the COVID-19 in Iran, this study was conducted in order to identify the spreading trend of this disease in Iran and all its provinces from February 19 to March 22, 2020, using the recorded data from the MHME and also to find similar provinces according to the disease spread using K-means cluster analysis (KMCA).…”