key: cord-0430432-s3jfddgk authors: Mutum, Z. M.; Ahmadini, A. A.; Hussein, A. M.; Singh, Y. R. title: Modeling on Wastewater Treatment Process in Saudi Arabia: a perspective of Covid-19 date: 2021-11-24 journal: nan DOI: 10.1101/2021.11.22.21266599 sha: 9a6d317c4a9364f4b42056812d487bc7df784ebe doc_id: 430432 cord_uid: s3jfddgk The novel coronavirus diseases (COVID-19) has resulted in an ongoing pandemic affecting the health system and devastating impact on global economy. The virus has been found in human feces, in sewage and in wastewater treatment plants. We highlight the transmission behavior, occurrence, and persistence of coronavirus in sewage and wastewater treatment plants. Our approach is to follow in the process of identifying a coronavirus hotspot through existing wastewater plants in major cities of Saudi Arabia. The mathematical distributions including log-normal distribution, Gaussian model and susceptible- exposed-infection-recovered- (SEIR) model are adopted to predict the coronavirus load in wastewater plants. This paper highlights not only the potential virus removal techniques from wastewater treatment plants but also to facilitate tracing of SARS-CoV-2 virus in human through wastewater treatment plants. The contamination of wastewater with viruses causing disease is an emerging threat that the global climate change seems to worsen [35] . Water contamination with virus is becoming an important concern with COVID-19 pandemic and the pandemics that most probability are coming in the future. The virus can be transmitted from wastewater treatment plants through the workers that are exposed to the coronavirus. However, persistence of SARS-COV-2 virus in sewage system in a hot climate may destroy the virus, and thus the spreading of the virus may not be significant [39] . Researchers around the world have started analyzing wastewater for the new coronavirus as a way to estimate the total number of infections in a community, given that most people will not be tested. It has been shown that coronavirus can be detected in feces within three days of infection, which is much earlier for people to show symptoms sever enough for them to seek medical care [33] . So, wastewater surveillance has been shown to be helpful in accessing the coronavirus spreading [36] . The infection of COVID-19 disease has correlated with viral RNA, which can be detected by using the real time PCR(RT-qPCR). The coronavirus has been observed to correlate strongly positive (correlation coefficient > 0.9) with local hospital admissions [34] . On March 2, 2020, the first case of COVID-19 infection in Saudi Arabia was identified and announced by the Ministry of Health. From the first week of March, the number of confirmed COVID-19 cases has gradually increased, reaching 2932 confirmed cases on April 9, 2020. A period of increasing infection cases was noticed in June and July 2020. Recently, new cases of infection are gradually decreasing. From January 3, 2020 to November 12, 2021, 548,973 confirmed cases of COVID-19 infection with 8,805 deaths and a total of 46,288,357 vaccinated were recorded in Saudi Arabia [51] . Wastewater approach for predicting coronavirus infection has scarcely been studied in Saudi Arabia. A few research papers and reports [37] [38] [39] have been studied on wastewater surveillance to track COVID-19 in a Saudi Arabia. Therefore, our aim is to identify some research needs that will help our understanding of the occurrence and persistence of coronavirus in wastewater and sewage. A possible procedure will be suggested to identify coronavirus hotspots through wastewater by using some mathematical distribution, including normal distribution, lognormal distribution, and SEIR epidemiology model. In this paper, section 2, highlights the wastewater treatment plants in Saudi Arabia and models. Section 3, focuses on literature containing techniques used for viral removal from wastewater treatment plants. In section 4, we provide material and method used for potentially tracing the coronavirus in sewage and untreated wastewater plants, Section 5, provides the discussion and results of our work. And finally, the section 6 states the conclusion of our study. Saudi Arabia has witnessed one of the fastest socio-economic development over the last decades, and urbanization increases gradually. As a result, water demand for all-purpose has tremendously increased, in a country with hot climate with limited natural water resources. According to Qatrah [4], Saudi Arabia is the world's third largest per capita consumer of water after the United States. The gap of the demand of water supply was filled through ground water resources, water desalination, and minimal use of treated wastewater. So, the existence of wastewater treatment plant is important to bridge the gap of water demand and supply. The treated wastewater is considered as an important part of water resource in Saudi Arabia used it as a major supply for non-potable water demands such as agricultural, industrial and commercial uses [5] . The Ministry of Water and Electricity aims to provide entire sewage collection and treated wastewater services to every city with a population above five thousand by 2025 [6] . There are thirty-three wastewater treatment plants with a capacity of 748 million cubic meters per year, and 15 more are under construction. Water reuse in Saudi Arabia is growing and demanding both at urban and rural areas. The treated wastewater is mainly used for landscaping, irrigation and in industries such as refining. In Riyadh, the capital city of Saudi Arabia, 50 million cubic meter per year is pumped over 40 km and 60 km elevation to irrigate 15,000 hectares of land [7] . The major wastewater treatment plants in Saudi Arabia serving various cities are shown in Table 1 . And, it shows the insufficiency of wastewater management system in Saudi Arabia. Jizan 203 301 381 1 20 0 -22 112 381 Madinah 275 390 496 4 351 5 34 0 -496 Makkah 1100 1532 1911 15 888 6 902 1 113 1911 Najran 78 107 133 0 0 1 60 5 170 133 Northern Borders 47 71 88 2 24 1 24 1 25 88 Qaseem 185 258 328 5 131.5 3 125 0 -328 Riyadh 1066 1513 1890 10 993.5 7 443.5 2 530 1890 Tabouk 120 169 214 1 60 1 15 0 -214 Total 4234 5972 7486 3135 2188.3 1500.4 7486 The most popularly used secondary treatment mechanism for wastewater in Saudi Arabia is conventional activated sludge systems, as shown in Table 2 . Some common treatment techniques are filtrations and chlorination and a few of the waste water treatment plants used reverse osmosis. The major challenges of wastewater treatment plants in Saudi Arabia is the lack of proper connectivity between the sewage system and existing plants. Over the years, the knowledge and understating of biological and chemical wastewater treatment have advanced approaches over the conventional techniques. These new approaches include the application of mathematics, statistics, physics, chemistry and bioprocess engineering. An overview of the sequence of wastewater treatment process followed by most of the existing treatment plants is shown in Figure 1 . Wastewater treatment process consists of mainly a sump pump, a septic tank, anaerobic filter and constructed wetland. The purpose of sump pump (SP) is to remove the coarse solids in the wastewater, and pumps the water to a two-chamber septic tank (ST). The wastewater flows by gravity through an anaerobic filter (UAF) and also flow through constructed wetland (HFCW). Finally, the level tank (LT) controls the water level in the constructed wetland. Wetlands are able to improve wastewater quality by filtering before release to open water. The widely used mathematical model for full scale wastewater treatment plant is the activated sludge model No.1 (ASM1) [1]. ASM1 model describes the biological removal of organic and nitrogen, and validates its optimal performance by statistical models. The more advance mechanical description of biological processes is provided in the updated version of the ASM model. The activated sludge model is one of the most important treatment processes for various wastewater, and about 90% of the municipal wastewater treatment plant used it in their treatment process [2, 3] . Mathematical modeling of wastewater treatment process plays an important role in the process of managing an effective treatment technique for existing plants. In this section, we briefly illustrate some of the mathematical models to study the quality of treated wastewater. The first order kinetic mathematical models are commonly used to compare the contaminants removal efficiency and mass reduction in wastewater treatment plants. The first order kinetic model is a nonlinear model that has been used to predict pollutant removal in wastewater treatment system [8] . The model equation is given as follows; ./ .0 = − , where 780 = 9: <=(?@A) . (1) The 780 is the concentration of the pollutant at the system outlet (mg/liter), 9: is the concentration of the pollutant at the system inlet (mg/liter), and HRT is the duration of hydraulic retention of the system expressed in days. The monitoring data are adjusted to generate the above equation (1) and the similar process for this treatment is mentioned in [9] . The first order kinetic model equation provides the parameter representing the rate of removal of pollutant or contaminants from water. The second-order kinetic mathematical model is to predict substrate removal from the wastewater treatment system is given as, where / HI =O is considered as the model constant. The data adjustment can be done for the wastewater treatment system and for each step by nonlinear least squares. The statistical significance of each parameter model is measured by value and K can be estimated to measure the goodness of fit to validate the model. Multiple linear regression is also commonly used model to study the relationship between the dependent variable, and independent variables also known as predictors [10] . The different water quality parameters measured are used as independent variable, and a water quality parameter outlet are considered as dependent variable. The equation for this model is given as where Y is the independent variable, and X is the predictor variable. Analyzing the multiple regression equations provides useful information for different water quality outflows. The occurrences of coronavirus in wastewater have highlighted the risk of persistent and deadly viral epidemic. Wastewater-based epidemiology (WBE) is a promising toll to assess COVID-19 [15] . At present, many applications on WBE exist, which includes detection of illicit drugs, surveillance of pathogenic enteric viruses, and assessment of potential industrial chemical exposures [16] . Some viruses released from infected human feces can survive for days to months in aqueous environments [17] . For instance, the coronavirus (SARS-CoV-2) could survive for more than 17 days at 4 °C and for three days at 20 °C in hospital sewage, municipal sewage, and chlorine-free tap water [18] . So, the occurrence coronavirus in wastewater is of great concern. It is reported that sewage contributes the transmission of SARS-CoV-2 in urban regions. Later, reinforced the potential for secondary transmission of the disease through wastewater, especially in regions that have unsatisfactory health and sanitation infrastructure [40] . Wastewater is one of the tools to help with surveillance of COVID-19 and its testing could be used as an early-warning sign if the virus returns [30] . Several articles [31] [32] [33] have published confirming the detection of SARS-CoV-2 in sewage. It is necessary to implement proper disinfection of sewage to void infection with virus. Figure 2 illustrates the possible aspects of coronavirus contamination in treated wastewater. One of the most commonly used wastewater disinfection process includes use of ultraviolet (UV) and chlorine. The disinfection process achieved 99.99% inactivation of fecal coliform; fecal coliform concentration in the disinfected effluent was below detection (detection limit is 2 CFU/L) [19] . They observed that no COVID-19 is detected in effluent after disinfection. The disinfection efficiency with chlorine is recommended by applying a dose higher than 6.5 mg/L and a contact time of at least 1.5 hours. It is more efficient to use UV radiation for disinfection of wastewater in hospitals that care for patients with COVID-19 due to the low amount by-products [20] . According to the WHO, the presence of residual chlorine of 0.5mg/L is standard and should be guaranteed in all water systems. Ozone is largely used for disinfection of many viruses in wastewater. According to International Ozone Association (IOA), research and testing has not been conducted on coronavirus. So, a concrete conclusion cannot be made related to the disinfection of SARS-CoV-2 coronavirus by using Ozone [21] . Wastewater treatment using ozone could sufficiently disinfect viruses and provide a solid barrier to prevent them from entering the environment with the effluent and ozone seems to be more effective than chlorine. A little information about the SARS-CoV-2 removal is available by ozone disinfection processes although the coronavirus is expected to be more sensitive to disinfection than other viruses. However, in [13] the authors reported that ozone can be an effective disinfectant for inactivating SARS-CoV-2. According to the researchers from Tel Aviv University have demonstrated that ozone effectively sanitizes against the coronavirus after when exposed to low concentrations of the gas [22] . To assess the disinfection efficiency of the virus in wastewater, the concentration of disinfectant (ozone) and the contact time is applied. In [23] , this value is calculated for specified log inactivation levels of a . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted November 24, 2021. ; number of viruses and surrogates in wastewater at pH 7.6 and 16 °C, with log reduction values for all virus ranging within specific value. Ozone treatment was found to be efficient disinfection process as compared conventional sewage treatment with able to reduce viral concentration by one to two log10 [11] . Sand filtration is one of the most widely used wastewater treatment methods in wastewater treatment plants. One of the advance wastewater treatment plants in Saudi Arabia is located at the north of Dhahran and uses twenty-four DynaSand filters for irrigation purposes [24] . It is frequent used for removal of dissolved particles and usually have less than one-unit log 10 virus removal. However, in [56] the authors reported on the molecular mechanism for unprecedented high virus removal from a practical sand filter. They have developed functionalized sand filters using a water extract of Moringa Oleifera seeds. Then they tested the efficiency of the obtained sand using MS2 bacteriophage virus, achieved a 7 log10 virus removal. The viral removal activity of functionalized sand is due to the protein binding. Some reported that use of zero-valent iron sand filtration can remove virus contamination from wastewater. The zero-valent iron sand filters are efficiently able to remove some specific virus such as MS2, and AiV(<1-2 log), but the removal efficiency varies among different viral species [54] . Water filtration membranes are categorized into four main types based on their pore size, they are: Reverse Osmosis, Nanofiltration, Ultrafiltration and Microfiltration. Figure 3 shows the pore size and capabilities of these membranes in rejecting particles, bacteria and pathogens, and dissolved ions including salts. Since the diameter of the SARS-CoV-2 is between 50 nm to 140 nm [47] , membranes such as Reverse Osmosis, Nanofiltration and Ultrafiltration should be suitable for its removal from wastewater. In [57] , the authors highlighted the use of polymeric and ceramic membranes for virus removal from water. It was reported the viral removal efficiencies were highly variable since the range reported was 0.2-7. The reverse osmosis and high-pressure nanofiltration membranes are popularly used for water treatment and desalination. The treatment process through these high-pressure membrane systems is based on the solute transport by diffusion through the nonporous active membrane layer [14] . In this section we highlight some recent literatures showing the potential use of these membranes for SARS-CoV-2 removal from wastewater treatment plants. Recently, in [41] the authors highlighted the treatment of waterborne pathogens by reverse osmosis. The reverse Osmosis (RO) treatment method is considered as the last grade purification/treatment level. This is because the membranes used in RO are not able to remove bacteria and viruses. Any impurities with molecular weight greater than 200 are not able to pass through RO system. Similarly, the larger the ionic charge of the contaminants, the more likely to be rejected by RO membrane [60] . However, RO can be used in combination with other complementary processes such as pretreatment and posttreatment of particular matters to produce high quality recycled water [42] . The authors reported that the Membrane Bioreactor (MBR)/Reverse Osmosis (OR) systems were providing better virus removal efficiencies in the range of 2.3-2.9 log10. They obtained a nominal 5 log10 removal without the MBR. Reverse osmosis, or combined system (MBR/RO) are more advanced wastewater treatment system having higher virus removal efficient greater than 5.0 log10. This system works in filtering enteric virus or MS2 coliphages which is much smaller than SARS-CoV-2. Szczuka et al. [43] reported that using forward-reverse osmosis for wastewater treatment had achieved higher than 98% rejection of organic contaminants, achieved at least 6.7 log10 removal of bacteriophage MS2 spiked into wastewater and achieved at least 5.4 log10 removal of native E.Coil in graywater and sewage. In [44] , the authors used sand-anthracite filters and a membrane bioreactor/reverse osmosis system for the removal of noroviruses of size 38-40 nm in diameter [45] , from wastewater. The efficiency of the osmosis system makes the concentration of the virus below detection level, which means the virus log removal value should be higher than 6. As ultrafiltration is often considered as one of the best pre-treatment steps prior to reverse osmosis for removal of human pathogens including bacteria, protozoa and viruses from wastewater. Recently, in [46] the authors reviewed the use of ultrafiltration membranes for the removal of bacteria and viruses from wastewater. They reported that use of ultrafiltration produces better water quality compared with conventional treatment due to the very fine pore structure. Using ultrafiltration for viral removal doesn't require preconditioning of water samples, so it is widely used for water quality [49] . Ultrafiltration is a suitable membrane technique for removing coronaviruses with mean particle diameter of 120 nm and envelope diameter of 80 nm [48] . The adsorption property to solids in wastewater may facilitate in increasing the removal of coronavirus. It is also used to study viral presence in wastewater. Three ultrafiltration devices: the Centricon Plus -70, Amicon Ultra-15 and automatic Concentrating Pipette have been successfully used to detect SARS-CoV-2 from wastewater [50] . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted November 24, 2021. ; https://doi.org/10.1101/2021.11.22.21266599 doi: medRxiv preprint Nanotechnology applications for water and wastewater treatment have taken as a promising area and fast developing. It has involved several useful nanoscale materials for wastewater treatment, including nanometals, nano adsorbents, photocatalysts, and nanomembranes. The nanofibers/particles and composite membranes have been successfully for removal of viruses, bacteria, protozoans and other contaminants from wastewater. The ultrafiltration membranes containing silver nanoparticles (AgNPs) have shown efficiency against MS2 bacteriophage virus and also considered as the best for water remediation due to their cost-effectiveness and high antimicrobial activity [52] . It is also reported that silver nanoparticles used in a nanocomposite filter system can be reached up to 100 percent removal of several different viruses. The electrospun nanofiber membranes (ENMs) is popularly used to treat the pathogen contamination in wastewater. It is high flux and high rejection rates compared to conventional membranes, and offers a cost-effective, lightweight, and lower energy efficiency. ENMs is highly porosity with approximately 80% while conventional membranes have 5-35% porosity [53] . Nanofibers containing tetraethoxysilane and ammonium tetrathiomolybdate blended with polyacrylonitrile have rough and branched fiber surface that enhances more than 90% of viral removal efficiency. Hence, the use of nanoparticle alone or as part of composite membranes could be effective against SARS-CoV-2. [52] . Microfiltration membrane has average pore sizes and the viral removal from the wastewater treatment plant is lower as compared to other membranes. In [58] , the authors reported that the virus size (63 nm) is smaller than microfiltration membrane size (0.2 µm), so it can pass through the membrane when permeate flux is high or membranes are clean. Also, in [59] , it is reported that the pre size of some microfiltration is larger than 100 nm, which is more suitable for removal of protozoa and bacteria rather than virus. Mathematical modeling and simulations are used as an important tool to predict the probability and severity of disease outbreaks and provide information to understand the dynamic behaviour through wastewater. We used the Log-normal distribution, Gaussian model and SEIR model to predict the infection of SARS-CoV-2 through wastewater. All the theoretical assumptions and data considered in this study were collected and obtained from the official website of the Ministry of Health Saudi Arabia (https://covid19.moh.gov.sa/). The normal distribution shows some properties and plays an important role in an analytic investigation, such as collection of SARS-CoV-2 traces in untreated wastewater treatment plants. We assume that the data collection from the untreated wastewater plants would follow a normal distribution. The rate of change of viral load as a function of time is given as follows; .O H (0) .0 = 9 9 ( ) − 9 9 ( ), where 9 is the contribution of daily average new viral load, 9 is the measure of the dispersion of dataset relative to the daily average viral load of the untreated wastewater treatment plant ( ). And 9 ( ) is the viral load/concentration of Coronavirus trance in the untreated wastewater treatment plant = 1,2,3, . . . . . . , during the time interval _ ≤ ≤ . Where _ is the initial time and represents the final time. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (5) The exact solution of the above equation (5) The lognormal distribution is a continuous probabilistic function of a given random variable for which the logarithm is normal. It is an essential statistical function to depict many natural phenomena, so we assume to have this distribution in wastewater treatment plants. Various small percentage changes lead to numerous regular growth process; hence they are represented in log scales. If the effect of the variation is not significant, then the distribution will be closer to normal. Suppose, the random change is lognormal distribution, then the variable = ln( ) has a regular spreading of COVID-19 virus. For instance, if = exp( ) follows the regular spreading of the virus, then = exp( ) will follow the lognormal distribution. We assume that the daily collected data would follow the log-normal spreading of the corona virus, then the mathematical notation is given as follows: Comparing between the collected data and the equation (7), we can determine the parameter 9 as follows: where { 9 : = 1, 2, 3, 4,5} represents the number of days within the time interval _ ≤ ≤ . We consider the collection of viral loads from the untreated wastewater treatment plants for five consecutive days. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Gaussian model has been used to predict the time evolution of the COVID-19 pandemic in Germany [25] , Italy [26] and China [27] . The best justification for the Gaussian distribution to detect the dynamic behavior of SARS-CoV-2 is given by central limit theorem. For instance, when several independent random variables are added, their properly normalized sum satisfies the Gaussian distribution even if the original variables are not normally distributed. Numerical simulations of earlier epidemic indicate that the time evolution of epidemic waves is usually shown by an exponential rise until a maximum is reached then decreased rapidly. We adopt a simple Gaussian distribution to estimate dynamic nature of the coronavirus infection through wastewater. Let 9 ( ) be the number of viral loads in untreated wastewater plant per day and its time evolution is given the Gaussian function shown in Figure 5 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted November 24, 2021. Where ( ) denotes the Gaussian distribution for the viral load of coronavirus traces in untreated wastewater treatment plants. The monitored data are often reported in terms of doubling time ( )and effective reproduction factors. The corresponding exponential function at any time is given as; Using the Equation (8) in Equation (7) we get the relative changes in the daily rate, Equating the two results in Equation (7) and (9) we obtain the time-dependent Gaussian doubling time The daily number of new infected cases can also be defined with the cumulative case rate. Using the Equation (10) and the peak time − , the corresponding cumulative number of cases at time in terms of error function can be written as where 070 = √ ¤¥¦ denotes the total number of infected cases. Similar values for cumulative death and infection cases relevant for the first wave of the COVID-19 pandemic were obtained in [28] . Using the Equation (14), we can write the respective relative change as follows; Using the Equation (9) where = √ ln{√2• ≈ 0.614. In Figure 5 (a), we show the Gaussian instantaneous time and cumulative doubling time as a function of time. (a) (b) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted November 24, 2021. ; The simulation of the well-known SEIR model is performed to represent the dynamic behaviors of spreading process of COID-19 epidemic through wastewater. In epidemiology, SEIR model is one of the compartmental epidemic models widely used for characterizing the outbreak of COVID-19. In this model the spread of infection depends on the number of susceptible population and the number of infected populations. The incubation period is considered which individuals have been infected but without showing symptoms. Since the coronavirus disease has a long incubation period, it is reasonable to model the epidemic with another compartment which is Exposed humans which they are infected but not virus spreaders [29] . In SEIR model, we assume that the time is long-term, no vital dynamics and the population size is constant. In this model, individuals are classified into four groups or compartments according to their infectious status. We classify the infection types with and without symptoms. The compartments of the model are as follows: o Susceptible ( ): number of humans that are not infected by the virus but may catch the disease. . .0 The above system of equations (17) (18) (19) (20) can be solved using any numerical methods and simulation can be presented to predict the behavior of SARS-CoV-2 virus. The above equation can be written as follows: . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint The copyright holder for this this version posted November 24, 2021. ; Where ∆ represents a very small step size in the time domain. The parameters , , and defined in the above equations represent the probability of infection, incubation rate and average rate of people recovered at time (in days) respectively. We assume average incubation period to be five days. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The number of possible viral loads in the wastewater plants can be predicted as shown in Figure 6 (a)-(e). From the model diagram, we can see that the population of infectious individuals increases at the beginning of the outbreak and progressively decrease over a period of time. The main feature of this model is to incorporate the importance of coronavirus contamination in untreated wastewater by infectious people. The possible COVID-19 hotspot in different wastewater treatment plants of five major cities in Saudi Arabia can be predicted by using mathematical models log-normal, and Gaussian model. The results are shown in Figure 4 (a), (b), and Figure 5(a), (b) respectively. Also, further prediction of next hotspot was considered by using the susceptible-exposed-infected-recovery (SEIR) mathematical model. The results for predicting possible virus load in wastewater treatment plants are given in Figure 6 (a)-(e). We can assume the viral load threshold is when viral load in wastewater treatment plant is equal to 1. From the Figure 4(a) , it is possible to conclude the region that are risk of maximum COVID-19 infection is from untreated wastewater plants with sharply peaked curves. It can be seen that with the wastewater treatment plant having the black color curve indicates a high number of COVID-19 infection case on the first day, while the last region with red color curve has continuous number of moderate infected persons. However, in Figure 4 (b), the data collected from the wastewater plants were assumed to be following log-normal distribution. It is believed that data collection are done from the wastewater treatment plants from five major cities in Saudi Arabia with daily averages and standard deviation in the case of Gaussian model. It can be observed that wastewater treatment plants with black and green curve are considered as COVID-19 hotspot regions. And, the remaining curves are below the viral load threshold. In particular, we can conclude that these two regions; Makkah (average = 0.2, standard deviation = 0.1) and Riyadh (average = 0.3, standard deviation = 0.4) have higher number of infected persons with SARS-CoV-2 virus. The prediction of hotspot using log-normal model improve agreement with the real data of COVID-19 infection cases observed in major provinces of Saudi Arabia. During the month of March 2020, the province of Makkah had the largest number of COVID-19 infection cases (30.7%), followed by Riyadh (23%) [55] . In general, we can compare all the collected data from the wastewater treatment plants weekly or monthly and each region can be defined a threshold. The COVID-19 hotspot region can be identified from the wastewater treatment plant with a viral load above the threshold value. The Gaussian model can also be used to predict the possible peak time of infection with the data collected from the wastewater treatment plants. From the Figure 5(b) , the maximum probability of infection cases occurs during the time = 4.4 (months), which is approximately four and half months since the first day of the COVID-19 infection case in Saudi Arabia. The cumulative Gaussian model predicts the possible viral load in wastewater treatment plants over time. According to the model, the cumulative cases of the infected people are represented by the red color curve. It signifies that with the moderate rate of viral load in wastewater treatment plant will have possibility of occurrence of COVID-19 virus over long period of time. Using the epidemiological mathematic model SEIR and existing data, simulations can predict the next COVID-19 hotspot region. The model also shows a decrease in the susceptible population as people become exposed and infected, and then recover. The coronavirus load per day in untreated wastewater is predicted for five different regions Figure 6 (a)-(e) shows the numerical simulations of the model with respective infection, incubation and recovery rates for different regions in Saudi Arabia. From the simulation process, the SEIR model with infection type has higher prediction of the hotspot. The Figure 6 (d) represents the hotspot since we assume the threshold value for the SARS-CoV-2 virus load in untreated wastewater treatment plant is 1. We can see the viral load in untreated wastewater is higher then and declined progressively over the period of time. All the mathematical models approach mentioned above, seems to be a promising methodology to predict the possible COVID-19 hotpots. However, our result indicates that traveling restriction, social distancing, vaccination, medical intervention and security respectively are the important measures to flatten the curve. The aim of this research work is to identify COVID-19 hotspot using wastewater collected from wastewater treatment plants of major cities in Saudi Arabia. SARS-CoV-2 virus is shed not only through air but also into untreated wastewater and reaches wastewater treatment plants or the aquatic environment. So, there is need for serious attention in wastewater treatment plants, especially in Saudi Arabia where only a part of wastewater is being treated. We highlighted recently developed wastewater treatment plants based on some virus models that can be used to differentiate virus removal efficiencies. This includes some advanced techniques such as treatment using Ozone, UV, ultrafiltration and nanofiltration membrane-based system. It is the combination of different techniques in primary, secondary and tertiary treatments that would facilitate viral removal treatment process in WWT plants. We use mathematical modeling Gaussian model, Log-normal model and SEIR model to understand the possible coronavirus load in untreated wastewater from WWT plants. The COVID-19 hotspot is predicted using these models where the collected data are plotted as a function of time. The hotspots are identified when the viral load of SARS-COv-2 trace in wastewater is higher than the threshold value. Using the lognormal model for predicting the region with maximum infection of COVID-19 through wastewater provides a promising mathematical model. This model approach predicts two provinces (Riyadh and Makkah) in Saudi Arabia as COVID-19 hotspot which is close to the real data of infection. The Gaussian model helps to estimate the time of possible maximum outbreak of the pandemic. It was predicted the peak outbreak would be after 4.4 months since the first day of the outbreak in Saudi Arabia, which is very appropriate with the real data during June and July 2020 (recorded maximum number of infection). To describe the possible viral load in wastewater treatment plant using SEIR model has more advantages in long-term predicting the nature of the COVID-19 virus. In conclusion, our models follow the process of predicting the COVID-19 hotspot through wastewater. Wastewater treatment plants could be one of solutions to limit the viral spread into the environment and a possible future detection access the Saudi Health Ministry control measures and decisions on infection. 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What do we know? Treatment of waterborne pathogens by reverse osmosis Potable Water Reuse through Advanced Membrane Technology Removal of Pathogens and Chemicals of Emerging Concern by PilotScale FO-RO Hybrid Units Treating RO Concentrate, Graywater, and Sewage for Centralized and Decentralized Potable Reuse Noroviruses in raw sewage, secondary effluents and reclaimed water produced by sand-anthracite filters and membrane bioreactor/reverse osmosis system Ultrafiltration membranes for wastewater and water process engineering: A comprehensive statistical review over the past decade Chapter One-Supramolecular Architecture of the Coronavirus Particle Primary concentration-The critical step in implementing the wastewater based epidemiology for the COVID-19 pandamic: A mini-review Evaluation of two rapid ultrafiltration-based method for SARS-CoV-2 concentration from wastewater COVID-19 and Nanoscience in the Developing World: Rapid Detection and Remediation in Wastewater Nanotechnology for water purification: electrospun nanofibrous membrane in water and wastewater treatment Zero-valent iron sand filtration reduces concentrations of virus-like particles and modifies virome community composition in reclaimed water used for agricultural irrigation Infection Spread, Recovery, and Fatality from Coronavirus in Different Provinces of Saudi Arabia 7 Log Virus Removal in a Simple Functionalized Sand Filter Credibility of polymeric and ceramic membrane filtration in the removal of bacteria and virus form water: A review Capacity of existing wastewater treatment plants to treat SARS-CoV-2. A review Chapter 5-Treatment of waterborne pathogens by microfiltration Waterborne Pathogens: Detection and Treatment The authors would like to thank Scientific Research Ethics Committee, Jazan University, Ministry of Higher Education, Saudi Arabia, for financial support of this research with grant number W4-059.