Transmission potential of COVID-19 in Iran

Muniz-Rodriguez, Kamalich; Fung, Isaac Chun-Hai; Ferdosi, Shayesterh R.; Ofori, Sylvia K.; Lee, Yiseul; Tariq, Amna; Chowell, Gerardo

doi:10.1101/2020.03.08.20030643

Cited by 23 publications

(37 citation statements)

References 11 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Resultsmentioning

confidence: 99%

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Rapid review of COVID-19 epidemic estimation studies for Iran

Pourmalek

Hemami

Janani

et al. 2020

Preprint

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Background: To inform the researchers about the methodology and results of epidemic estimation studies performed for COVID-19 epidemic in Iran, we aimed to perform a rapid review.Methods: We searched for and included published articles, preprint manuscripts and reports that estimated numbers of cumulative or daily deaths or cases of COVID-19 in Iran. We found 23 articles or reports and included eight articles (four published, four preprints) and three reports.Results: The included studies provided outputs for a total of 45 study-scenarios. Seven studies used 3-4 compartmental disease models. At the end of month two of the epidemic (2020-04-19), the lowest (and highest) values of predictions were 3762 (97445) for cumulative deaths, 60720 (1489201) for cumulative cases, and at the end of month four (2020-06-20), were 86931 (3002721) for cumulative deaths, and 1602592 (2917927) for cumulative cases.Conclusions: Meticulousness and degree of details reported for disease modeling and statistical methods used in the included studies varied widely. Greater heterogeneity was observed regarding the results of predicted outcomes. Consideration of minimum and preferred reporting items in epidemic estimation studies might better inform future revisions of the available models and new models to be developed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Rapid review of COVID-19 epidemic estimation studies for Iran

Pourmalek

Hemami

Janani

et al. 2020

Preprint

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“…In Europe, COVID-19 cases are most dramatically started to increase from the first week of March 2020. Of these, Italy is grappling with the worst outbreak, with over 35,713 confirmed cases and around 3000 deaths by March 18, 2020 [1][2][3][4][5]. This exponential increase in COVID-19 positive cases in Italy raised turmoil, and the government decree to a lockdown of the entire country [1].…”

Section: Introductionmentioning

confidence: 99%

“…Article on modeling epidemic transition of COVID-19 have been published [1][2][3][4][5][6][7][8] and based on the previous publication from China, South Korea, Iran, and Japan presented the estimation of epidemic trends and transmission rates [1][2][3][4][5][6][7][8]. Through this research, we evaluated the consistency of the containment rules and identified possible SARS-CoV-2 local mutation using the S.E.I.R mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Modelling the epidemiological trend and behavior of COVID-19 in Italy

Rovetta¹,

Bhagavathula

2020

Preprint

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As of March 16, 2020, over 185,000 across the world, Italy became the red hotspot for the COVID-19 pandemic after China. With over 35,000 cases and 2900 deaths reported in the month of March in Italy, it is necessary to stimulate epidemic trend to understand the behavior of COVID-19 in Italy. By S.E.I.R. simulation, we estimated the most representative epidemic parameters occurred from March 1 to 14, 2020, thus being able to evaluate the consistency of the containment rules and identify possible Sars-Cov-2 local mutations. Our estimations are based on some assumptions and limitations exited.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Madadizadeh¹,

Sefidkar²

2020

Preprint

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Background The Coronavirus has crossed the geographical borders of various countries without any restrictions. This study was performed to identify the epidemiological trends of coronavirus disease (COVID-19) in Iran during February 19 to March 22, 2020.Methods This cross sectional study was carried out in 31 provinces by using the daily number of newly infected cases which was announced by the Iranian health authorities from February 19 to March 22, 2020, we explore the trend of outbreak of coronavirus disease in all provinces of Iran and determine some influential factors such as population size, area, population density, distance from original epicenter, altitude, and human development index (HDI) for each province on its spread by Spearman correlation coefficient. K-means cluster analysis (KMCA) also categorized the provinces into 10 separate groups based on CF and ACF of the infected cases at the end of the study period. (ACF).Results There were 21,638 infected, 7,913 recovered and 2,299 death cases with COVID-19 in Iran during the study period. By March 22, CF shows that, most cases are attributed to Tehran province, while ACF shows that most are attributed to Semnan province. There was a positive significant correlation between CF of the infected cases and province population sizes (r=0.45), HDI (r=0.58); province population density (r=0.47); a negative significant correlation between the CF of the infected cases and distance from original epicenter (r=-0.47). KMCA results based on CF showed very high level of outbreak was in Tehran while KMCA based on ACF showed very high level of outbreak were in Semnan and Qom provinces.Conclusions Special attention is needed for the provinces nearer to the original epicenter which are high risk spots in terms of the severe prevalence of COVID-19. In the absence of definite treatment for this infectious disease, the effective solutions to reduce the transmission of the disease are early isolation of the cases, quarantine for close contacts both in families and community and observing personal hygiene.

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Transmission potential of COVID-19 in Iran

Cited by 23 publications

References 11 publications

Rapid review of COVID-19 epidemic estimation studies for Iran

Rapid review of COVID-19 epidemic estimation studies for Iran

Modelling the epidemiological trend and behavior of COVID-19 in Italy

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

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