Current status of intestinal parasitic infections and associated risk factors in rural population of Guilan province, northern Iran: trichostrongyliasis is the most prevalent helminthic infection.

Aim: This study aimed to determine the distribution of enteric parasitic infections and related risk factors among rural communities of Guilan province, Northern Iran, and to compare the results with the situation in the past. Background: Intestinal parasitic infections are still considered as a major public health concern, particularly in human communities with poor economy and sanitation. Methods: This cross-sectional study was performed in rural areas of Masal and Shanderman district from February to December 2020. A total of 917 stool samples were collected and examined for presence of intestinal helminthes and protozoa using direct, formalin-ether and Kato-Katz techniques. Results: A total of 156 (17%) out of 917 examined individuals were infected with intestinal parasites. The overall prevalence of protozoa, helminths and mixed infections were 11.8% (108/917), 4.5% (41/917) and 0.8% (7/917), respectively. Blastocystis was the most prevalent intestinal protozoa (9.6%) followed by Giardia lamblia (1.9%), Endolimax nana (1.1%), E. coli (0.8%) and Entamoeba hartmani (0.1%). The highest prevalence of intestinal helminths belonged to Trichostongylus spp. (3.5%) and Strongyloides stercoralis (1.3%). Statistical analysis showed significant association between giardiasis and sex (P<0.03). On the other hand, prevalence of enteric helminths was influenced by close contact with livestock, keeping herbivorous animals at home, job, education, and consumption of uncooked vegetables (P<0.05). Conclusion: The findings indicate a decreasing trend in the prevalence of intestinal parasitic infections in Guilan province in comparison to the past few decades. Hookworm infections, which was very prevalent in the area, are now rare, while trichostrongylosis showed a high prevalence in rural residents of the study area.

Corrigendum to "Molecular detection of Toxoplasma gondii in chicken hearts from markets and retail stores in Northern Iran" [Food and Waterborne Parasitology 27 (2022) e00166].

[This corrects the article DOI: 10.1016/j.fawpar.2022.e00166.].

Latent toxoplasmosis in COVID-19 patients and link with higher mortality in COVID-19 male patients.

BACKGROUND: Immunocompromised patients may be at risk for reactivating the toxoplasmosis infection; therefore, early diagnosis would be highly desirable in these individuals. This study evaluated the possible association between coronavirus disease 2019 (COVID-19) and latent Toxoplasma gondii infection in Guilan province, Iran. MATERIALS AND METHODS: The study was performed among 210 COVID-19 patients referred to Guilan University of Medical Sciences hospitals in 2022. Peripheral blood samples were taken for serum separation, collected into tubes, and kept at - 20 °C until use. Blood samples were obtained from COVID-19 patients. IgG antibody to Toxoplasma gondii was detected by a commercial ELISA kit. Accordingly, IgG absorbance levels <9 were considered harmful, 9-11 was considered borderline, and >11 was positive. RESULTS: Toxoplasma IgG antibodies were found in 73.9 % of patients with COVID-19 in male patients. The seroprevalence of Toxoplasma in dead and lived COVID-19 male patients was 83.3 % and 66.7 %, respectively, and this difference was significant. A present study found a significant correlation between the rising titer of Toxoplasma IgG and the severity of COVID-19. There was no significant difference between the hospitalization duration factor and the seropositivity rate. CONCLUSION: Regarding the significant association between the rising titer of Toxoplasma IgG and the severity of COVID-19. The findings demonstrated an association between the severity and mortality rate of COVID-19 with higher titer Anti-Toxoplasma IgG antibodies. Toxoplasmosis is currently considered a risk factor for COVID-19.

Seroepidemiological Study of Visceral Leishmaniasis (Kala-Azar) in Children under 12 Years Old in North of Iran: An Observational Study in 2019-2020.

Background: Visceral leishmaniasis (VL) caused by the Leishmania donovani complex that is transmitted by the bites of female sandflies. Mediterranean type of VL caused by L. infantum. While, Roudbar County of Guilan Province has been introduced as a suspected cutaneous leishmaniasis focus; there are no published data on the seroprevalence of VL in Guilan Province. We aimed to investigate the sero-prevalence of this disease in Roudbar County. Methods: This descriptive cross-sectional study was carried out in 2019-2020 among children less than 12 years of age to determine the seroprevalence of VL by direct agglutination test (DAT). Blood samples were randomly collected from 918 children under 12 years of age refers to the public health center in the clusters. Results: Out of 918 children, 14 (1.52%) showed anti-Leishmania antibodies, with 4 (0.43%), 2 (0.21%), 8 (0.87%) anti-L. infantum antibodies at titers 1:800, 1:1600 and ≥1: 3200 respectively. All children with anti-Leishmania antibody titers of ≥1:800 were evaluated by a physician. Clinical manifestation of VL including fever, anemia and hepatosplenomegaly observed in a 6-year-old boy from Defraz village with anti-Leishmania antibody of titers ≥102400.This patient was admitted to the pediatric hospital in Rasht, capital of Guilan province, Iran and was successfully treated. Conclusion: VL is being circulated with low prevalence in children up to 12 years old in Roudbar, northern part of Iran. Accordingly, it is critical to improve the awareness of physicians and public health supervisors about the importance of this fatal disease in Guilan province and especially in Roudbar area.

Determining Effective Environmental Factors in the Distribution of Endangered Endemic Medicinal Plant Species Using the BMLR Model: The Example of Wild Celery (Kelussia odoratissima Mozaff., Apiaceae) in Zagros (Iran).

Kelussia odoratissima Mozaff. is a medicinal species native to Iran. The goal of this research was to determine the environmental factors important for the distribution of K. doratissima in Iran using BMLR modeling. Six random transects were established throughout the species' habitat, and 220 quadrats with an area of 4 m2 were plotted. The canopy cover percentages of K. doratissima were estimated in each quadrat. Topographic factors, including elevation, slope, and aspect maps, were generated by creating DEM images. Land use, land evaluation, evaporation, temperature, and precipitation maps of the area were created accordingly. The data collected from the experiments were analyzed using the Minitab and R statistical packages. To determine the effect of the studied factors in the distribution of K. doratissima, we ran a set of backward multiple linear regressions. The results showed that the effects of evaporation, elevation, and slope were significant in the species' distribution, with elevation having a positive effect and evaporation and slope showing negative effects. Further, elevation had the highest effect on distribution (greatest absolute value of beta at 9.660). The next most significant factors in the plant's distribution were evaporation (beta = 8.282) and slope (beta = 0.807), respectively.

Molecular detection of Toxoplasma gondii in chicken hearts from markets and retail stores in Northern Iran.

Detection of Toxoplasma gondii in chicken products indicates risk of transmission to consumers. The objective of the current study was to investigate the molecular prevalence of T. gondii in free-ranging and industrial chickens in Guilan province, Northern Iran. A total of 150 chicken heart samples including 75 free-range and 75 industrial chickens were collected from farmers' markets and chicken retailers in Guilan, Northern Iran, between October 2017 and August 2018. Genomic DNA were extracted from samples and examined for evidence of T. gondii using polymerase chain reaction (PCR) targeting the B1 gene. The B1-positive samples were further analyzed by nested-PCR for SAG1 gene. Of the 150 samples, T. gondii DNA fragments were detected in 59 (39.3%), including 30 (40%) free-range and 29 (38.7%) industrial chicken. No significant differences of T. gondii DNA detection was observed between the free-range and industrial chicken samples (p = 0.73). Four selected positive samples were used for amplifying and sequencing of the SAG1 gene. The results revealed that all four sequences of SAG1 had 100% similarity with T. gondii sequences previously isolated from an AIDS/HIV patient in Mazandaran province, Northern Iran. Furthermore, the phylogenetic analysis demonstrated that all four sequences were closely related to Type I of T. gondii. However, our Type I identification is preliminary and needs to be confirmed by further multilocus sequence typing (MLST) analysis. The findings of the present study provide new data about the presence of T. gondii DNA in chicken hearts in the study area. These results confirm that chicken can be used as sentinels for environment contamination; however, further studies are needed to determine the viability of T. gondii in chicken hearts from Iran for risk assessment.

Molecular characterization of Trichomonas infections in women of Ilam City, southwestern Iran.

Trichomoniasis is a sexually transmitted infection (STI) caused by the flagellated protozoan Trichomonas vaginalis. Little information is available on the epidemiology and genetic diversity of T. vaginalis in Ilam City, southwestern Iran. A descriptive cross-sectional investigation was carried out between July 2017 and December 2018 on the suspected women patients referred to eight gynecology clinics of Ilam City for probable Trichomonas infection. They were undergone a set of clinical, parasitological, and molecular examinations. During clinical consultation, posterior vaginal fornix secretions and urine samples were gathered from the participants. For the reasons such as physical conditions and cultural and religious constraints, most of participating women, especially young girls due to their virginity, preferred to give urine samples instead of vaginal discharge. The presence of Trichomonas was diagnosed by microscopic examination and molecular detection using conventional PCR targeting ITS1-rDNA. A total of 1765 suspected individuals were examined clinically via vaginal secretions (495 specimens) and urine samples (1270 specimens). Of them, 21 (1.18%) cases, including 13 vaginal secretions and 8 urine samples, were positive for Trichomonas infection by microscopy. Slightly more than half of the patients (11/21, 52.4%) complained of vulvar itching, burning, and frequent urination. Cervical lesions, patchy erythema, and vaginal discharge were recorded in 28.6%, 23.8%, and 19% of the patients respectively. All patients with positive microscopic identification were confirmed by amplification of 450-bp fragment of ITS1-rDNA. Phylogenetic analysis revealed a high rate of genetic homogeneity in which all our isolates together with homologous sequences from China, Philippines, Austria, and USA were clustered within the same clade. A statistically significant relationship was recorded between the patients positive for trichomoniasis and the presence of chronic disease (e.g., diabetes, immune system deficiency).

Modeling the stochastic mechanism of sensor using a hybrid method based on seasonal autoregressive integrated moving average time series and generalized estimating equations.

Sensor, which is one of the main components of control system, plays its vital role in measuring state and output system variables and highlights the importance of having desired statistical information about sensor output signals because it can be monitored, stored, or used as the primary input signal in other devices. However, these signals display noises (i.e. system noise and measurement noise) and even if the effects of system noises are faded away or removed from measured data, there is still some stochastic noise remained in the measurements. Even though SARIMA has been effective in modeling the stochastic noise in the sensor, the present study has found out the necessity of designing a novel approach including a combination of seasonal autoregressive integrated moving average (SARIMA) and polynomial generalized estimating equations (PGEE), to evaluate the stochastic behavior of sensors. Finally, the study tried to employ the proposed approach in real load-cell sensor data to examine its effectiveness.

Serological Evaluation of Toxoplasmosis and Related Risk Factors Among HIV+/AIDS Patients in Northern Iran.

PURPOSE: Toxoplasma gondii is an important opportunistic intracellular protozoan parasite that can cause severe sequelae and even death in immunodeficient patients. This study aimed to evaluate the seroprevalence and risk factors of toxoplasmosis among HIV+/AIDS patients of the study area. METHODS: A cross-sectional study was carried out on 121 registered HIV+/AIDS patients of behavioral diseases consultation center (BDCC) in Guilan province, north of Iran. Anti-Toxoplasma IgG and IgM antibodies were measured by ELISA technique in the serum samples. IgG avidity was measured for IgG- plus IgM-positive cases. Also, the relationship between T. gondii infection and related demographic and clinical characteristics were investigated. RESULTS: Anti-T. gondii IgG and IgM antibodies were detected in 60.3 and 4.9% of patients, respectively. One case of acute toxoplasmosis (0.83%) was detected using an IgG avidity test. A significant correlation was observed between toxoplasmosis with age and marital status in these patients. The mean CD4+count of HIV+/AIDS patients was 549 ± 27 cells/µl. Viral load in 69.7% of the HIV patients was less than1000 copies/ml. CONCLUSION: None of the T. gondii IgM-positive HIV patients received anti-Toxoplasma prophylaxis. This finding highlights the importance of T. gondii infection surveillance in HIV patients. Regarding the high prevalence of toxoplasmosis in the study population, educational efforts are recommended to prevent reactivation or acquiring primary infection and developing severe cases of toxoplasmosis in immunocompromised HIV+/AIDS patients.

Detection of potentially pathogenic free-living amoebae from the Caspian Sea and hospital ward dust of teaching hospitals in Guilan, Iran.

Free-living amoebae (FLA) thrive in diverse environmental conditions. The present study aimed to define the FLA distribution from the Caspian Sea as well as from hospital ward dust from Guilan, Iran. Seawater (20) and hospital ward dust samples (100) were collected from May to June 2018. Seawater samples were vacuum filtered through a 0.45 µm pore-size membrane. Dust was collected using sterile gauze, washed with sterile distilled water, with washings collected thereafter. Washings were similarly filtered as seawater samples. FLA from the filtered material was cultivated in non-nutrient agar. Molecular analysis was performed by PCR and sequencing using specific primers for Acanthamoeba, Naegleria, and Vermamoeba/Hartmanella. Culture and PCR returned 50 and 65% positivity, respectively, for seawater samples where sequencing revealed Acanthamoeba T2, T5 and T6 genotypes and A. palestinensis and A. lenticulata, as well as N. dobsoni and N. clarki. In addition, 30% amoebic growth and 16% PCR detection were observed from hospital ward dust samples where sequencing revealed Acanthamoeba T2, T4 and T11 genotypes and A. castellanii, A. palestinensis and A. stevensoni as well as N. clarki. For both seawater and dust samples, Acanthamoeba was the dominant isolate. The detection of potentially pathogenic FLA from seawater may pose a threat to the public, while the presence of the same in dust spells threats to both hospital staff and patients, in particular, immunocompromised individuals. Public education, awareness, improved sanitation and hygiene, and the crafting of diagnostic strategies for the early detection of FLA in humans are necessary for the mitigation and management of potential human infection cases.

Factor analysis approach to classify COVID-19 datasets in several regions.

The aim of this research is to investigate the relationships between the counts of cases with Covid-19 and the deaths due to it in seven countries that are severely affected from this pandemic disease. First, the Pearson's correlation is used to determine the relationships among these countries. Then, the factor analysis is applied to categorize these countries based on their relationships.

In vitro bioaccessibility, phase partitioning, and health risk of potentially toxic elements in dust of an iron mining and industrial complex.

Dust emitted from mining, ore processing, and tailing dumps have direct effects on miners who work close to these operations. The Gol-E-Gohar (GEG) mining and industrial company is one of the most important iron concentrate producers in the Middle East. The objective of the present study was to estimate the distribution, fractionation, and oral bioaccessibility of potentially toxic elements (PTEs) in dust generated by the GEG mining and industrial company. Total PTE content including Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, V, and Zn was quantified for suspended particulate matter (PM) in PM2.5, PM10, and total suspended particulate matter (TSP). As, Cd, Co, Cu, Fe, Ni, and Pb were quantified in fallout dust samples for oral bioaccessibility using in vitro Unified BARGE (UBM) Method and modified BCR fractionation analysis. Enrichment factors (EF) were calculated for the studied elements in PM; Cu, Fe, and As were found to be extremely enriched. Oral bioaccessibility of selected PTEs in fallout dust samples ranged from 0.35% to 41.55% and 0.06-37.58% in the gastric and intestinal phases, respectively. Regression modeling revealed that the bioaccessibilities of the PTEs could mostly be explained by total concentrations in dust particles. Average daily intake (ADI) calculations revealed that the intake of PTEs did not exceed the tolerable daily intake (TDI) values and as such was not considered a significant risk to workers. Additionally, the hazard quotients (HQ) and carcinogenic risk (CR) values were lower than the acceptable level. This study can provide further risk assessment and management of PTE pollution in occupational environments.

Strongyloides stercoralis and other intestinal parasites in patients receiving immunosuppressive drugs in northern Iran: a closer look at risk factors.

OBJECTIVES: The objective of this study was to evaluate the prevalence of Strongyloides stercoralis and other intestinal parasites in patients receiving immunosuppressive drugs in northern Iran and to investigate related risk factors. METHODS: This cross-sectional study was conducted among 494 patients receiving immunosuppressive drugs, including cancer patients undergoing chemotherapy (n=188) and those treated with prolonged corticosteroid administration (n=306). All fresh fecal samples were examined using the direct wet-mount, formalin ethyl acetate concentration, and agar plate culture techniques. RESULTS: In total, 16.8% of patients were positive for at least 1 intestinal parasite; the helminthic and protozoan infection rates were 5.1% and 12.3%, respectively. The infection rate was significantly higher in corticosteroid-treated individuals (19.6%) than cancer patients (12.2%) (p<0.05). The prevalence rate of S. stercoralis among patients receiving chemotherapy and those treated with corticosteroids were 4.3% and 5.2%, respectively. The prevalence rate of S. stercoralis infection was significantly higher in older patients (p<0.05). CONCLUSIONS: Strongyloidiasis is one of the most common parasites among patients receiving immunosuppressive drugs in northern Iran. Early diagnosis and proper treatment of these patients are necessary to minimize the complications of severe strongyloidiasis.

A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent.

The mathematical model's usage in water quality prediction has received more interest recently. In this research, the potential of random forest regression (RFR), Bayesian multiple linear regression (BMLR), and multiple linear regression (MLR) were examined to predict the amount of 2,4-dichlorophenoxy acetic acid (2,4-D) elimination by rice husk biochar from synthetic wastewater, using five input operating parameters including initial 2,4-D concentration, adsorbent dosage, pH, reaction time, and temperature. The equilibrium and kinetic adsorption data were fitted best to the Freundlich and pseudo-first-order models. The thermodynamic parameters also indicated the exothermic and spontaneous nature of adsorption. The modeling results indicated an R2 of 0.994, 0.992, and 0.945 and RMSE of 1.92, 6.17, and 2.10 for the relationship between the model-estimated and measured values of 2,4-D removal for RFR, BMLR, and MLR, respectively. Overall performances indicated more proficiency of RFR than the BMLR and MLR models due to its capability in capturing the non-linear relationships between input data and their associated removal capacities. The sensitivity analysis demonstrated that the 2,4-D adsorption process is more sensitive to initial 2,4-D concentration and adsorbent dosage. Thus, it is possible to permanently monitor waters more cost-effectively with the suggested model application.

Time series modelling to forecast the confirmed and recovered cases of COVID-19.

Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the "confirmed" and "recovered" COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP-SMN-AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries.

The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.

Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models.

Coronaviruses are a huge family of viruses that affect neurological, gastrointestinal, hepatic and respiratory systems. The numbers of confirmed cases are increased daily in different countries, especially in Unites State America, Spain, Italy, Germany, China, Iran, South Korea and others. The spread of the COVID-19 has many dangers and needs strict special plans and policies. Therefore, to consider the plans and policies, the predicting and forecasting the future confirmed cases are critical. The time series models are useful to model data that are gathered and indexed by time. Symmetry of error's distribution is an essential condition in classical time series. But there exist cases in the real practical world that assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, the distribution of the error has been considered to be two-piece scale mixtures of normal (TP-SMN). The proposed time series models works well than ordinary Gaussian and symmetry models (especially for COVID-19 datasets), and were fitted initially to the historical COVID-19 datasets. Then, the time series that has the best fit to each of the dataset is selected. Finally, the selected models are applied to predict the number of confirmed cases and the death rate of COVID-19 in the world.

Intestinal protozoan and helminthic infections among hemodialysis and cancer patients.

Intestinal parasitic infections (IPIs) can be a severe threat to immunocompromised patients. This is particularly true for those undergoing chemotherapy and hemodialysis. The present research is aimed at identifying intestinal parasites that might be present in immunocompromised patients. In this cross-sectional study 1040 stool samples were collected from March to September 2017. Six hundred and forty-one stool samples from immunocompromised patients (279 samples from hemodialysis patients and 362 samples from chemotherapy patients) and 399 samples from the control group were collected in Guilan province, Iran. The samples were tested by direct, formalin-ether methods for protozoa and ova of intestinal parasites and Ziehl-Neelsen staining methods for coccidian parasites such as Cryptosporidium species. The overall parasitic infection rate was highest (15%) in hemodialysis patients and 11.3% in chemotherapy patients, whereas the lowest rate was observed (7.3%) in the control group. The infectivity rates were statistically significant (P = 0.008) when compared with the control group. The parasites found were Blastocystis hominis (8.9% of the cases), Entamoeba coli (1.6%), Iodamoeba butschlii (0.8%), Endolimax nana (0.6%), Chilomastix mesnili (0.5%), Strongyloides stercoralis (0.5%), and Taenia species (0.15%), whereas Giardia lamblia was detected only in the control group. There was not a correlation between prevalence of parasites with age or education levels of the infected individuals. Results of the present study suggest that periodic stool examinations in special parasitological laboratories should be included as part of routine and general medical care.

Seroepidemiology and Associated Risk Factors of Toxoplasma gondii in Hemodialysis Patients.

PURPOSE: Immunocompromised patients may be at risk for reactivation of the toxoplasmosis infection, because of defection in cell-mediated immunity. Therefore, early diagnosis would be highly desirable in these individuals. This case-control study was designed to increase information about toxoplasmosis in hemodialysis (HD) patients in Guilan province, Iran. METHODS: The study was performed among 150 patients and 150 controls referred to hospitals of Guilan University of Medical Sciences during 2018-2019. Questionnaire forms, including demographic and epidemiological information, were completed. Peripheral blood samples were taken for serum separation and were collected into tubes and then kept at - 20 °C until use. IgG and IgM antibodies to Toxoplasma gondii were detected by a commercial ELISA kit. Accordingly, IgG absorbance levels < 9 were considered negative, 9-11 was considered borderline, and > 11 was positive; IgM absorbance levels < 0.9 were considered negative, 0.9-1.1 was assumed to be borderline, and > 1.1 was positive. RESULTS: Throughout the study, 72.0% of HD patients and 64.7% of the control group were positive for anti-Toxoplasma IgG antibody subsequently. 2% of HD patients and 0.7% of the control group were positive for anti-Toxoplasma IgM antibody and these difference weren't significant between control and ones with HD (P > 0.05). There was no significant difference between dialysis duration factor and the seropositivity rate. Seroprevalence of T. gondii infection did not vary significantly with age, educational level, residence and presence of a cat at home. On the contrary, seroprevalence varied significantly with gender and consumption of raw vegetables. CONCLUSION: Because of the high percentage of positivity for Toxoplasma IgG antibodies in hemodialysis patients, we suggest a periodically screening program to carry out for monitoring and evaluating the possible dissemination of toxoplasmosis during hemodialysis.

Time series modelling to forecast the confirmed and recovered cases of COVID-19.

Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the "confirmed" and "recovered" COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP-SMN-AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.