SARS CoV-2 (COVID-19) Coronavirus cases are confirmed throughout the world and millions of people are being put into quarantine. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. The effect of climatic factors on spreading of COVID-19 can play an important role in the new Coronavirus outbreak. In this study, the main parameters, including the number of infected people with COVID-19, population density, intra-provincial movement, and infection days to end of the study period, average temperature, average precipitation, humidity, wind speed, and average solar radiation investigated to understand how can these parameters effects on COVID-19 spreading in Iran?The Partial correlation coefficient (PCC) and Sobol'-Jansen methods are used for analyzing the effect and correlation of variables with the COVID-19 spreading rate. The result of sensitivity analysis shows that the population density, intra-provincial movement have a direct relationship with the infection outbreak. Conversely, areas with low values of wind speed, humidity, and solar radiation exposure to a high rate of infection that support the virus's survival. The provinces such as Tehran, Mazandaran, Alborz, Gilan, and Qom are more susceptible to infection because of high population density, intra-provincial movements and high humidity rate in comparison with Southern provinces. Journal Pre-proof J o u r n a l P r e -p r o o f speed, and humidity. Consequently, based on the geographical maps, the average rate of disease spread in humid provinces is higher than in other areas of Iran, however in arid areas humidity has a reverse relationship with the disease infection rate; the central provinces of Iran are approximately higher than in non-central and southern regions. Journal Pre-proof J o u r n a l P r e -p r o o f 9The effective parameters in the COVID-19 outbreak show that Tehran, Mazandaran, Alborz, Gilan, and Qom people are more exposed to virus spreading because of the high population. Moreover, in provinces as a destination of intra-provincial movements, Tehran, Isfahan, Khorasan Razavi, and Fars population are more susceptible to the COVID-19 virus. The Gilan and Mazandaran provinces have wet weather; therefore, the high infection rate; besides, the wind speed is low in these cities plus Tehran and Gorgan. The southern region of Iran includes Sistan and Baluchestan, Kerman, Hormozgan, and Boushehr have lower infection rate because of high solar radiation. Based on literature results the coronavirus is created because of dramatic solar activity when in a period of years (~10) appearance of two peaks in sunspots creates coronavirus. Therefore, we should expect these types of pandemics once every 10 years. Future studies should pay more attention to provide results based on experimental and observational studies and considering how the factors can affect COVID-19 spreading.Also, long term studies of world climates can anticipate other possible pandemics.

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.

This paper presents a comprehensive framework to manage the main risk events of highway construction pro­jects within three stages: (1) identification of potential risks; (2) assessment and prioritisation of identified risks based on fuzzy FMEA; (3) identification of appropriate response. The main criteria analysed for prioritising potential risk events are cost, time and quality which are quantified and combined using fuzzy AHP. A new expert system is suggested for identifying an appropriate risk response strategy for a risk event based on risk factor, control number and risk alloca­tion. The best response action for a risk event is then identified with respect to the same criteria using “scope expected deviation” (SED) index. The proposed methodology is demonstrated for management of risk events in a construction project of Bijar-Zanjan highway in Iran. For the risk event of “increase in tar price”, deviation from the target values of the criteria is analysed for business-as-usual state plus two risk response actions using SED index. The results show that the response action of “changing paving construction technology from asphalt pavement to RCC pavement” can success­fully cope with the risk event of “increase in tar price” and have the minimum deviation.

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