ArticlePloS one2020
Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Article
- Pandemic vulnerability index of US cities: A hybrid knowledge-based and data-driven approach.Sustainable cities and society · 2023Article
- Influence of the Demographic, Social, and Environmental Factors on the COVID-19 Pandemic-Analysis of the Local Variations Using Geographically Weighted Regression.International journal of environmental research and public health · 2022Article
- Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.International journal of environmental research and public health · 2022Article
- Estimating Risk of Introduction of Ebola Virus Disease from the Democratic Republic of Congo to Tanzania: A Qualitative Assessment.Epidemiologia (Basel, Switzerland) · 2022Article
- Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- An evaluative model for assessing pandemic resilience at the neighborhood level: The case of Tehran.Sustainable cities and society · 2021Article
- Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.Journal of medical Internet research · 2021Article
- Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey.Sustainable cities and society · 2021Article
- Rapid review of COVID-19 epidemic estimation studies for Iran.BMC public health · 2021Review
- NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms.Nonlinear dynamics · 2021Article
- Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models.Beni-Suef University journal of basic and applied sciences · 2021Article
- What Can COVID-19 Teach Us about Using AI in Pandemics?Healthcare (Basel, Switzerland) · 2020Article
- Advances in Microbial Diagnostics: Machine Learning and Nanotechnology for Zoonotic Disease Control.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
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7 authors.
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Abstract
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
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