International congress on prevention strategies for healthcare , 2020-11-10

Title : ( Statistical and Mathematical models for predicting Covid-19 outbreaks )

Authors: TOKTAM VAZIFEHDOUST AHMADI , Parastoo Tajzadeh , Mehdi Jabbari Nooghabi , Sohrab Effati , Somayeh Ghiyasi Hafezi , Atefeh Firoozbakhsh ,

Citation: BibTeX | EndNote


Introduction: Covid-19 is a community-acquired infection which can spread in the human population of any origin and endanger the health of many groups in society. The respiratory droplet is one of the most common ways of transmitting the disease in the community. The Covid-19 virus epidemic is currently the most important global health challenge. The aim was the use of statistical and mathematical models to predict the prevalence of Covid-19 disease. Methods: Using search engines Scopus, Google Scholar, Pubmed, ISC, SID, Magiran, Irandoc, Doaj between 2019 to 2020 and using the keywords Statistical models, Mathematical model, Covid-19 was used for external search engines and the keywords of non-pharmacological intervention measures (mask and social distance), Covid-19 were used for internal search engines. Result: Out of 33 articles reviewed, 18 articles were selected based on inclusion criteria. Mathematical models were reviewed in 10 articles (4 quarantines, 4 social distances, 2 masks) and statistical models were reviewed in 8 articles (2 Monte Carlo, 4 logistic regression, 2 Agent-based models). These studies mainly were about social distance, quarantine and use of face masks and face glasses. Discussion & Conclusion: It seems that studies on non-pharmacological interventions such as quarantine, social distance and mask (the rate of virus transmission in social distance control measures is less than the use of mask) can be associated with a large reduction in infection transmission. However, none of these interventions provide complete protection against infection, but it can significantly prevent the spread of Covid-19 virus. The logistic regression statistical models have used more because of big capacity for Covid-19 variables data processing. Monte Carlo and R software also use more importantly because of lack of possibility to construct and execute real data and replicate it in the case of the Covid-19 as the cause and there fore simulation was used . SEIR and SIR as a type of dynamic models of in the simulation environment. The agent -based model has been one of the most studied models in the field of Covid-19 because it examines the social interactions of Covid-19 on people in the community and their relationship with various variables. In general, statistical and mathematical models are still trusted and used in the study of this epidemic, one of the best forecasting tools in the world medical sciences.


, Statistical and Mathematical Models, Prediction, Covid-19.
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author = {VAZIFEHDOUST AHMADI, TOKTAM and پرستو تاج زاده and Jabbari Nooghabi, Mehdi and Effati, Sohrab and سمیه غیاثی حافظی and عاطفه فیروزبخش},
title = {Statistical and Mathematical models for predicting Covid-19 outbreaks},
booktitle = {International congress on prevention strategies for healthcare},
year = {2020},
location = {مشهد, IRAN},
keywords = {Statistical and Mathematical Models; Prediction; Covid-19.},


%0 Conference Proceedings
%T Statistical and Mathematical models for predicting Covid-19 outbreaks
%A پرستو تاج زاده
%A Jabbari Nooghabi, Mehdi
%A Effati, Sohrab
%A سمیه غیاثی حافظی
%A عاطفه فیروزبخش
%J International congress on prevention strategies for healthcare
%D 2020