Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.

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.

This research aims to produce an antioxidant and biodegradable polysaccharide film by using macroalgae agar and sulfated polysaccharide. Agar and sulfated polysaccharide (fucoidan) were extracted fromGracilaria corticataandSargassum angustifoliummacroalgae. Five treatments were conducted: (A) agar film (1%, W:V), (C) chitosan film (1%, W:V + 1% acetic acid), (AC) agar:chitosan composite (50:50, V:V), (ACF) AC film with fucoidan (0.5%, W:V), and (ACFA) ACF film with citric acid (30% of the dry weight of film) as a cross‐linking agent. Then, 0.75% (V:V) of glycerol was added to all films. The physical, mechanical, antioxidant, color variations, microstructure (SEM), and Fourier transform infrared (FT‐IR) spectroscopy were investigated. Based on the results, modifying the agar film with chitosan improved the mechanical strength, humidity, and solubility in the AC composite film (p < .05). Further, adding sulfated polysaccharide and citric acid cross‐linking agent to the agar–chitosan composite led to a significant decrease in solubility, humidity, and permeability to water vapor in ACFA films (p < .05), indicating strong cross‐linking and reduction in film pores based on the SEM pictures and FTIR results. However, the physical and mechanical properties of the agar‐based film obtained from Gracilaria algae can be improved by adding chitosan and citric acid cross‐linking agent, and the addition of fucoidan obtained from Sargassum algae has improved its antioxidant properties. This biodegradable film can be a good candidate for preserving perishable products.

The aim of this study was to develop a new active coating of layer‐by‐layer (LbL) structure composed of alginate (as polyanions) and chitosan (as a polycation) containing sulfated polysaccharide (fucoidan) from Sargassum angustifolium, to protect rainbow trout fillets during refrigerated storage. Chitosan and alginate do not combine with each other as a homogeneous solution, so they are suitable for multilayer coatings. The results demonstrated that coating samples with chitosan and fucoidan significantly improved the quality of fish fillets and extended their shelf life from 6 to 16 days. The chemical values (TBARS and TVB‐N) and bacterial growth (total viable count (TVC), total psychrophilic count (PTC), and lactic acid bacteria (LAB)) indicated lower levels in the LbL coating samples containing fucoidan compared to the alginate and control samples. Among the different coating samples, the LbL coating with fucoidan (AChF1) exhibited lower weight loss, improved chromaticity (L*, a*, and b*), and minimal changes in mechanical and sensory evaluations. Based on the findings, AChF1 was the most effective treatment for increasing the shelf life of rainbow trout fillets during refrigerated storage. Therefore, it has potential applications in the food packaging industry.