Fungal contamination of indoor public swimming pools and their dominant physical and chemical properties

Introduction: Considering to the existence of both parasitic and fungal pathogens in the indoor public swimming pools and non-utilization of suitable filtration and disinfection systems in these places, this research aimed to determine the relationship between the indoor public swimming pools and possible pollution with parasitic and fungal agents, as well as physical and chemical characteristics of these pools and compare the results with national standards. Methods: In this study, 11 active indoor swimming pools of Zahedan city were sampled, using plastic pumps techniques, at the middle of winter to the late summer season. A total of 88 water samples (eight water samples from each pool) were examined to determine the residual chlorine, contamination with parasitic and fungal agents, using culture media and slide culture techniques. Results were analyzed with SPSS software (V16) and, Microsoft Excel (V2010). Results: The findings revealed parasitic fungal contamination with Cladosporium, Penicillium, Aspergillus flavus and Aspergillus fumigatus, etc. and the physicochemical factors comply with the minimum standards had which indicates the need for continuous monitoring and control of water filtration and disinfection of water is swimming. Conclusion: The results show reasonable derangement of physicochemical and microbial factors of the evaluated pools. Efforts shall be made by the concerned authorities to provide health education to users, quality water at the pools and to maintain the safety and quality of the water through proper and adequate chlorination

Estimation of Burden of Cystic Echinococcosis in Iran Using Disability Adjusted Life Years (DALYs) in 2018

Background: Human hydatidosis as a public concern has increased in a number of countries that have reduced control programs for the disease due to lack of resources or policies. We aimed to estimate Disability-Adjusted Life Years (DALYs) for human hydatidosis in Iran in 2018. Methods: Data were collected from the Center of Communicable Diseases Control, Ministry of Health &Medical Education, Tehran, Iran in 2018. To calculate DALYs, years of life lost due to premature death (YLL) with years of life with disability (YLD) were calculated according to the formula as DALY = YLL + YLD. The standard life expectancy lost method (SEYLL) was used to calculate the years lost due to premature death. Results: DALYs for human hydatidosis was calculated as 1210.12 years (YLD equals to 177.12 and YLL equals to 1033) in Iran for the year 2018. It was estimated to be 700.2 years for men and 509.8 years for women. DALYs in men were significantly different from women (P= 0.001) so DALYs were more in men than women were. YLD was calculated at 78.228 years in men and 98.892 years in women and in both men and women at 177.12 years. YLD was significantly different in women compared to men (P=0.001), so YLD in women was more than in men. Conclusion: We reached considerable indices for hydatidosis in our study. Therefore, disease prevention and control programs in Iran seem necessary by the policy makers. Keywords: Hydatidosis; Burden; Disability-adjusted life years; Human; Ira

Risk mapping of malaria in Iran from 2009 to 2018: A GIS-Based survey

Background: Since the issues of public health and the spread of diseases are directly related to the region's geography. We aim to determine malaria incidence, spatial distribution, and hot spots in Iran using the GIS for a decade from 2009 to 2018. Methods: GIS was used to analyze the information acquired from the Ministry of Health and Medical Education in Tehran, Iran, and other associated centers between 2009 and 2018. Subsequently, maps of the disease's spatial distribution were constructed and using ArcGIS 10.5 software, the disease's hotspots in Iran were determined. The disease's variables, such as temperature, relative humidity, normalized difference vegetation index (NDVI), and malaria incidence, were correlated using geographically weighted regression (GWR) analysis in ArcGIS 10.5.  Using descriptive statistics and the chi-square test, data were analyzed using Linear Regression Analysis and SPSS 21 software using descriptive statistics. Sistan and Baluchistan, and the Bushehr provinces were hot spots for Malaria. The geographically weighted regression analysis results showed that in Sistan and Baluchistan and Bushehr, Hormozgan, Fars, Qom, Yazd, Kohgiluyeh and Boyer-Ahmad provinces, the highest correlation between temperature, humidity, vegetation density, and the incidence of Malaria was observed (p-value = 0.019).&nbsp

Spatial modeling of visceral leishmaniasis in Iran from 2010 to 2018

Kala-Azar is the most lethal type of leishmaniasis, sporadic in most parts of Iran and prevalent in some provinces. Using the Geographical Information System (GIS) and satellite data analysis, we intended to assess the disease's incidence in Iran. Methods: Using GIS, data received from the Ministry of Health and Medical Education in Tehran, Iran, and other associated institutions between 2010 and 2018 were evaluated. The disease's geographical distribution maps were then constructed, and the disease's hotspots in Iran were identified using spatial analysis using ArcGIS10.5 software. Geographically weighted regression (GWR) analysis in ArcGIS10.5 was used to link disease-influencing variables such as temperature, relative humidity, normalized difference vegetation index (NDVI), and incidence of visceral leishmaniasis. Linear regression analysis, SPSS 21 software descriptive statistics, and chi-square test were used to analyze the data. Results: This study revealed that the provinces of Ardabil, East Azarbaijan, North Khorasan, and Fars were the hot spots of VL. The provinces of Ardabil, East Azarbaijan, North Khorasan, Fars, Bushehr, Semnan, Sistan, Baluchistan, Esfahan, Chaharmahal Bakhtiari, Qom, Golestan, and Kerman had the highest correlation between temperature, vegetation density, and the incidence of Kala Azar, as determined by geographical weighted regression analysis.&nbsp

Hybrid Deep Learning Techniques for Predicting Complex Phenomena: A Review on COVID-19

Complex phenomena have some common characteristics, such as nonlinearity, complexity, and uncertainty. In these phenomena, components typically interact with each other and a part of the system may affect other parts or vice versa. Accordingly, the human brain, the Earth’s global climate, the spreading of viruses, the economic organizations, and some engineering systems such as the transportation systems and power grids can be categorized into these phenomena. Since both analytical approaches and AI methods have some specific characteristics in solving complex problems, a combination of these techniques can lead to new hybrid methods with considerable performance. This is why several types of research have recently been conducted to benefit from these combinations to predict the spreading of COVID-19 and its dynamic behavior. In this review, 80 peer-reviewed articles, book chapters, conference proceedings, and preprints with a focus on employing hybrid methods for forecasting the spreading of COVID-19 published in 2020 have been aggregated and reviewed. These documents have been extracted from Google Scholar and many of them have been indexed on the Web of Science. Since there were many publications on this topic, the most relevant and effective techniques, including statistical models and deep learning (DL) or machine learning (ML) approach, have been surveyed in this research. The main aim of this research is to describe, summarize, and categorize these effective techniques considering their restrictions to be used as trustable references for scientists, researchers, and readers to make an intelligent choice to use the best possible method for their academic needs. Nevertheless, considering the fact that many of these techniques have been used for the first time and need more evaluations, we recommend none of them as an ideal way to be used in their project. Our study has shown that these methods can hold the robustness and reliability of statistical methods and the power of computation of DL ones

A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors