Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE)

doi:10.21203/rs.3.rs-2498780/v1

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Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE)

https://doi.org/10.21203/rs.3.rs-2498780/v1

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published 14 Jan, 2024

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Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants . Wastewater-based epidemiology (WBE) is considered as a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman' r = 0.23 - 0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTPs influent (> 90 %). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hot spot model combined with geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from sewer system. In addition, socio-economic factors namely population density, land-use, and tax-income were successfully identified as the potentials drivers which substantially affect the onset of COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also other epidemic issues in the future.

COVID-19

WBE

wastewater

spatiotemporal

GIS

socio-economic

The ongoing COVID-19 pandemic which has been started since late 2019 has left many problems to various sectors of global communities, especially for public health and economy sectors (Barrios et al. 2021; Tiwari et al. 2022; Wang et al. 2022b). Many countries have been facing enormous challenge in providing adequate healthcare system in response to this pandemic. As of December 2022, there have been over 650,000,000 confirmed cases of COVID-19, including over 6,700,000 deaths globally (WHO 2022). Eventually, this pandemic has also affected Taiwan with its 24 million population number. More than 5 million citizens in Taiwan have been infected with COVID-19 along with nearly 10,000 deaths, of which the northern region of Taiwan has been hardest hit, accounting for more than 50% of the country's total infections (CDC 2022). Along with the rapid rise of the COVID-19 crisis, the Taiwanese government has also implemented many mitigation measures to deal with the outbreak, such as tracing and tracking the transmission, mass testing, social distance, mask wearing, vaccine expansion, etc. (Wang et al. 2020). However, despite the radical measures taken and successfully resolved the “first wave” in May 2021, the introduction of the new variant once again created the “second wave” on the island. Thus, an urgent problem that the Taiwanese government must solve is to trace the hot spot of viral onset to make timely decisions.

In addition to respiratory transmission, the possibility of SARS-CoV-2 infection through human fluids (such as urine, feces, stool, etc.) is of growing issues recently (Amirian 2020; Gu et al. 2020; Wu et al. 2020). Wastewater surveillance has been widely utilized to investigate pathogenic signals from wastewater via analyzing samples collected from communal sewer systems, municipal treatment plants or even independent facilities including hospital, dormitory, and schools (La Rosa et al. 2020; Sherchan et al. 2020; Acosta et al. 2021; Ahmed et al. 2021b; Gibas et al. 2021; Haak et al. 2022; Li et al. 2022a; Lu et al. 2022). This approach is known as wastewater-based epidemiology (WBE) which allows the decision-makers to develop an early warning measures for COVID-19 outbreaks in the community. To date, many studies have reported that before clinically COVID-19 cases were reported, SARS-CoV-2 RNA was found in wastewater samples from wastewater treatment facilities (WWTPs) (Kumar et al. 2020; La Rosa et al. 2020; Medema et al. 2020; Ahmed et al. 2021b; Lastra et al. 2022).

More intriguingly, more than 18% of COVID-19 infected patients do not exhibit any symptoms, nonetheless they are still contagious and could influence the COVID-19 transmission (Lavezzo et al., 2020; Yuan et al., 2021). Thus, WBE approach could provide an accurate view of the silent spread of asymptomatic carriers in the community (Prado et al. 2021; Kaya et al. 2022). Under the current circumstances, disease surveillance through WBE approach is considered a potential strategy for the Taiwanese government in preventing the further pandemic situation from other potential diseases, including virus or bacterial pandemics in the future.

Recently, spatiotemporal analysis models of pandemic transmissibility together with the correlation analysis of the region's socio-geographical features have provided a potential approach in tracing regional pandemic hotspots for timely intervention (Greenwald et al. 2021; Haak et al. 2022; Pérez-Cataluña et al. 2022). From this view, wastewater sampling from upstream sewer system and downstream treatment plant throughout Taoyuan City was carried out during the entire epidemic period of COVID-19 in 2022. Due to its proximity to Taoyuan International Airport, the principal entry quarantine point in Taiwan, and the concentration of hotels in this area, Taoyuan is regarded as a region with an extremely high risk of infection. Thus, the objectives of this study were (1) to examine the presence of SARS-CoV-2 in wastewater samples from 8 WWTPs in Taoyuan City, Taiwan; (2) to evaluate the shedding of SARS-CoV-2 in sewers through a spatial-temporal distribution model to identify hotspot areas; and (3) to establish the relationship between population density, land use, income tax and COVID-19 epidemic situation using geographic information system (GIS). The findings from this study may provide useful information for decision-makers in conjunction with local governments in establishing a long-term monitoring system for SARS-CoV-2 variability.

2.1. Sampling sites and sample collection

To monitor the presence of SARS-CoV-2, influent wastewater samples were collected at 8 WWTPs in Taoyuan city, including Taoyuan North District, Guishan, Daxi, Fuxing, Sanmin, Shimen, Yangmei, and Wenqing, from 10th January to 17th November, 2022. In addition, the study is based on “Taoyuan City Sewerage Cloud Intelligent Management System” to select sampling locations in sewers from upstream (manholes) to downstream (WWTPs). Weekly samples were collected from points in the sewer network from 8th April to 21st July, 2022. Figure 1 shows the detail of manholes and WWTPs sites in this study. The characteristics of WWTPs, including capacity, service population, treatment process, etc., are presented in Table 1. The location of each manhole are summarized in Table S1. All samples were weekly obtained via by collecting 1 L of it in a plastic bottle and then preserving it in cooling box. Samples are then transferred to the laboratory where it will be stored in a 4^oC refrigerator for preservation purposes.

Table 1

**Characteristics of WWTPs in Taoyuan City**
WWTP	Service population	Capacity (CMD)	Reclamation process
Taoyuan	112,853	67,045	TNCU + MBR
Guishan	19,000	28,574	DOC
Yangmei	8933	5,149	AS
Wenqing	7234	3,767	AO + MBR
Daxi	5691	4,950	TNCU
Shimen	3197	4,088	TNCU
Fuxing	441	228	A2O
Sanmin	135	99	SBR + artificial wetland
Notes: TNCU: Taiwan National Central University; MBR: membrane bioreactor; AS: activated sludge; DOC: deep oxidation canal; AO: Anoxic oxic; A2O: Anaerobic-Anoxic-Oxic; SBR: Sequencing batch reactors

2.2. Wastewater SARS-CoV-2 concentrations

The poly ethylene glycol (PEG) precipitation method was deployed for the pretreatment process before performing SARS-CoV-2 RNA analysis in the samples. First, PEG8000 (100 g/L) and NaCl (22.5 g/L) were mixed with 200 mL of sample solution, shaken at 4^oC for 2 h and then incubated overnight at 4^oC (JA-14 Fixed-Angle Rotor, Beckman Coulter®). The next day, collected samples were centrifuged at 12,000 x g for 2 h at 4^oC. Following the centrifugation process, supernatant was removed, and the pellet was then re-suspended in 5 ml of nuclease-free water and subjected to RNA extraction.

2.3. RNA extraction and SARS-CoV-2 quantification

RNA was extracted from the concentrated samples by using Using AllPrep PowerViral DNA/RNA kit (QIAGEN, Inc., Germantown, MD) according to the manufacturer's instructions. Then, SARS-CoV-2 RNA was quantified using the CDC N1 (Nucleocapsid) primer and probe set from Promega's Induced Real-time PCR (GoTaq® Probe 1) detection system utilized by US CDC via one-step RT-qPCR method. Moreover, the primer/probe set for locus obtained from Integrated DNA Technologies, and the RNA transcribed in vitro from the novel coronavirus positive control group (nCoVPC) were acted as positive control group for RT-qPCR.

During the early stage of preparation process, standard concentration was prepared by diluting it with a ratio of 2:18 to set the value from 10⁶ to 1 gene copy per sample. This kind of treatment was done through repeated replication (2 times) for each sample. Sterile nuclease-free water was used for the unlabelled experimental control group in a total volume of 20 µL per RT-qPCR, containing RT-qPCR reagent, primer/probe mixture, and 5 µL RNA template. The RT-qPCR is consisted of reverse transcription at 45^oC for 15 min, denaturation at 95^oC for 2 min, followed by 45 cycles of denaturation for 3 s at 95^oC and finally 30 s annealing/extension and plate read at 55°C. The assay limit of detection was set at a cycle threshold (Ct) cutoff value ≤ 45 cycles and were used to calculate the viral load, which was reported as GC/L, in the original sample.

2.4. Hotspot identification models

Getis-Ord-Gi statistical analysis method was also deployed to identify hot and cold spots spatially. Hotspot is identified when a complication and those adjacent to it have large amounts/ influence to the corresponding values. When the Getis-Ord-Gi GI statistic is 1, 2, 3, the confidence level is 99%, 95%, 90%, respectively. The Getis-Ord-Gi statistic is computed as follows (Getis and Ord 2010):

$${G}_{i}^{*}=\frac{{\sum }_{j=1}^{n}{W}_{ij}{X}_{j}- \stackrel{-}{X} {\sum }_{j=1}^{n}{W}_{ij}}{S\sqrt{\frac{\left[n{\sum }_{j=1}^{n}{W}_{ij}^{2}- {\left({\sum }_{j=1}^{n}{W}_{ij}\right)}^{2}\right]}{n-1}}}$$

Followed with that, this study applied Getis-Ord Gi* spatial method combined with GIS simulation to visualize the local pockets distributions of hot and/or cold spots (Gi* analysis) through SARS-CoV-2 RNA concentration at sampling sites in the sewer system. In addition, the inverse distance weight (IDW) model was applied to estimate the variance treaty from the Gi* value of the hot/cold spots.

2.5. Data analysis

Any non-detect samples were assumed to be equal to zero in order to calculate averages and standard deviations of SARS-CoV-2 (GC/L). Although the calculation of gc/L was sufficient for comparisons between WWTP, the raw data were normalized based on daily flow and population size by converting GC/L to million viral gene copies per capita per day (GC/cap/d) to account for dilution and randomness effect using Eq. 2 (Ahmed et al. 2015):

GC = (gene copies / L wastewater) × (L wastewater influent / day) × (1 / population) (2)

In addition, the genomic data obtained from the sewer system was not normalized due to the difficulty of estimating the population serving and flowrate in each individual sewer system. Furthermore, the sewer system in Taiwan is a closed system, thus the effect of dilution from rainfall are less likely to be happen in this system.

The Spearman-rank correlation was deployed to investigate the correlation between measured SARS-CoV-2 gene concentration (GC/cap/d) from sampling sites and reported positive cases during sample collection days. In addition, spatio-temporal model based on viral concentrations in wastewater was used to determine the extent of transmission in the community. Furthermore, heat map used in this study is based on the length of the drain pipe from sampling manhole location to the primary treatment facility, and the horizontal axis presents the changing trend over time. Sewer pipe length was determined from sewage network maps in ArcGIS software version 10.7.1 ESRI, Redlands, CA, USA. OriginPro 2019b and ArcGIS data analysis software were used to draw the figures in this research.

COVID-19 cases number in Taiwan districts throughout the sample period were retrieved from reports of confirmed cases from Taiwan's Centers for Disease Control (CDC) (www.cdc.gov.tw). The locations of sampling sewers were obtained from Taoyuan Municipal Sewerage Cloud Intelligent Management System (www.sewergis.tycg.gov.tw). Land use data such as land use type, road information, population and elevation are collected from the National Center for Mapping and Land Surveys (www.nlsc.gov.tw). Population density data was extracted from Taoyuan City Household Registration Office (www.tyhr.tycg.gov.tw) and calculated in ArcGIS software version 10.7.1 ESRI. In addition, the average income tax data for each region is collected from the Taiwan Ministry of Finance Fiscal Information Center (www.eip.fia.gov.tw).

3.1. Presence of SARS-CoV-2 in WWTPs

In order to assess the presence of SARS-CoV-2 in WWTPs, water samples from eight WWTPs in Taoyuan were collected and analyzed based on the major outbreak phases of COVID-19 in Taiwan. In this study, the major outbreak phases of SARS-CoV-2 in Taiwan is consisted of four phases which are early-outbreak (1st January to 20th April 2022), outbreak (20th April to 31st May 2022), post-outbreak (1st June to 31st July 2022), and rebound period (1st August to 1st November 2022). Rebound period of COVID-19 cases in Taiwan was identified as a potential result from the easing up of epidemic prevention measures. Figure 2 shows the trend of daily confirmed COVID-19 cases and cumulative number of cases alongside the major epidemic prevention policies during 2022 (Data retrieved on 30th November, 2022). Throughout the entire pandemic period, pathogenic signals could be detected in the influent WWTPs with an increase in the number of clinical COVID-19 cases. The first SARS-CoV-2 RNA detection was recorded on 21st February (Taoyuan and Guishan WWTP) and quickly spread to neighboring areas only 2 weeks later. Consequently, rising detection levels of SARS-CoV-2 RNA has been consistently detected in all WWTPs since the end of April 2022, correlating with an increase in the number of clinical COVID-19 cases in Taoyuan City and the signal is attenuated in the post-outbreak period.

In the early outbreak period, SARS-CoV-2 RNA was found in the influent of all eight WWTPs located in Taoyuan (Taiwan). The observed viral concentrations load of SARS-CoV-2 RNA tend to be greatly varied among eight WWTPs. The average viral load in Taoyuan city WWTP reached 9.07 x 10⁷ ± 1.92 x 10⁷ gene copies per capita per day (GC/cap/d). Meanwhile, higher concentrations of SARS-CoV-2 RNA were detected in other cities, 1.08 x 10⁸ ± 8.80 x 10⁷ GC/cap/d (Wenqing), 1.21 x 10⁸ ± 2.09 x 10⁸ GC/cap/d (Yangmei), 4.35 x 10⁸ ± 3.70 x 10⁸ GC/cap/d (Guishan), and 9.23 x 10⁸ ± 1.49 x 10⁸ GC/cap/d (Shimen) (Fig. 3). The distribution of average viral load concentrations found in the influent of eight WWTPs during this period are highly varied. Despite Taoyuan city has the highest number of confirmed cases (36 confirmed cases) compared to other seven WWTPs (< 25 confirmed cases), the average viral concentration in the WWTP is lowest after normalization with flowrate and population serving number. This indicated that there might be a very significant effect of dilutions which happened in the Taoyuan WWTP during early outbreak sampling period as it also has the highest serving capacity and daily flowrates (Table 1) among others seven WWTPs (Fang et al. 2022).

Regardless of that, positive results of RNA occurrence in the influent of WWTPs were detected more frequently in the Taoyuan WWTP compared to others (Fig. 3). During the early outbreak period, wastewater sampling were conducted approximately 4 times in each WWTP. Samples with PCR results higher than limit of detection (LoD) were regarded as positive identification of SARS-CoV-2 RNA. It can be seen from Fig. 3. that among four repetitions of sampling, all of the samples from Taoyuan WWTP were identified as positive. On the other hand, the samples which originally come from districts with less number of confirmed cases such as Daxi, Wenqing, and Yangmei have lower frequency of positive RNA results in their influent channel. This finding is consistent with previous study where positive results were detected more frequently in WWTPs from regions with higher number of COVID-19 incidences (Serra-Compte et al. 2021).

Following that, the spread of COVID-19 incidence was reported in other districts and the cumulative case was increased rapidly after 20 April and peaked on 18 May with a total of 14,043 confirmed cases (Fig. 2). During this period, Taiwan government imposed nationwide level 3 epidemic prevention measures where all citizens were instructed to stay home most of the time (Everington 2021). Despite so, the number of confirmed case in the Northern part of Taiwan was still continuously increasing and SARS-CoV-2 RNA was detected consistently along with the increase of confirmed medical infections cases. Apparently, the concentration of SARS-CoV-2 increased rapidly in all eight WWTPs compared to the previous period (Fig. 3). The highest concentrations of SARS-CoV-2 RNA were detected in the influent of Daxi and Yangmei WWTPs, 1.78 x 10¹⁰ ± 0.9 x 10¹⁰ GC/cap/d and 2.71 x 10¹⁰ ± 0.4 x 10⁷ GC/cap/d, respectively. Although the number of COVID-19 cases was 4–10 times lower than central processing plant (Taoyuan). This contrary result could be explained by the high degree of population aggregation at this site resulting in a dilution of the SARS-CoV-2 detection signal in the wastewater sample (Ahmed et al. 2022). Furthermore, the concentrations of SARS-CoV-2 RNA in the wastewater samples of this study were higher than those reported previously (Table S2). This may be due to differences characteristics of WWTP, the abundance of SARS-CoV-2 in wastewater affected by pandemic levels in the community and differences in SARS-CoV-2 RNA detection procedures in each study (Sherchan et al. 2020; Agrawal et al. 2021; Amoah et al. 2022).

During post-outbreak period, the concentration of SARS-CoV-2 RNA decreased in some areas (e.g. Taoyuan, Guishan, Yangmei, Wenqing) and showed signs of increasing in Daxi and Shimen. Despite the decrease in the number of confirmed cases, the concentration of SARS-CoV-2 in wastewater in this region increased by 69% and 41% for Daxi and Shimen. This can be attributed to the temporal variation of RNA in wastewater and vaccination process which incited different shedding rates of RNA (Siedner et al. 2022; Hopkins et al. 2023). Li and co-workers estimated the total number of infections from SARS-CoV-2 RNA copies from wastewater at WWTP USA (Li et al. 2022b). The results indicated that more than half of the cases are asymptomatic or unconfirmed through the medical surveillance system. It is attributed to the fecal shedding which may begin before individuals seek medical attention and eventually spreads silently in the community (Wu et al. 2022). In addition, many studies have also revealed that there is not always a correlation between viral concentrations in wastewater and the number of infections reported in the community (Barrios et al., 2021; Kumar et al., 2021). Thus, it is necessary to determine the contribution of RNA from asymptomatic COVID-19 infected individuals to these uncertainties.

In addition to the post-outbreak period, this study also identified second peak trend in both SARS-CoV-2 RNA viral load concentrations and daily confirmed case during the COVID-19 rebound period in Taiwan (Fig. 2 and Fig. 3). The rebound period (1st August to 1st November 2022) of COVID-19 in Taiwan were most likely occurred due to the combination effects of reduction in epidemic prevention measures and the rise of new SARS-CoV-2 virus variant named “Omicron BA.4 & BA.5” (Taiwan CDC 2022). In terms of observed viral load concentrations in the WWTPs influent, clear rebound peaks were identified in Taoyuan and Guishan WWTPs with 1.34 x 10⁹ ± 0.85 x 10⁹ GC/cap/d, and 3.88 x 10⁹ ± 0.98 x 10⁹ respectively during middle October 2022. The viral load concentrations value in this period is definitely higher than post-outbreak period but still lower compared to during the peak outbreak period in middle of July (Fig. 3). This finding further confirmed the ability of waste-water based surveillance to identify the SARS-CoV-2 RNA transmission process in different matrix.

In order to accurately correlate the viral concentrations in wastewater with the epidemiological data, Fig. 3 also highlights the linear relationship between the SARS-CoV-2 RNA viral load concentrations in wastewater and the cases number reported on the sampling date for all locations via the Spearman correlation coefficient. Across the entire study period, the wastewater viral concentrations were variedly correlated with the number of COVID-19 cases in each district (Spearman' r = 0.23–0.76). These correlations suggested that an increase in the case number may occur concurrently with or the increase in SARS-CoV-2 RNA viral load concentrations in wastewater, while a decrease in SARS-CoV-2 RNA in wastewater may lead to a decrease in the case number (Ai et al., 2021; Cutrupi et al., 2022). It is worth noting that the amount of asymptomatic or pre-symptomatic individuals may also contribute to the uncertainty of the reported clinical cases and influenced the correlation assessment.

Another remark that is worth discussing regarding to the presence of SARS-CoV-2 RNA in the WWTPs is the ability of existing treatment technology to destroy/ eliminate the viruses RNA during the treatment process (Kumar et al. 2021b; Serra-Compte et al. 2021). This study investigated eight WWTPs in Taoyuan which uses different types of treatment technology ranging from conventional activated sludge (CAS), membrane bioreactor (MBR), and others (Table 1). It can be seen from Fig. 3 that most of the viral load concentrations in the influent of WWTPs are not retained after process. Hence, it explains the negative results of PCR test from samples collected in the effluent channel of the eight WWTPs. In addition, the elimination performances of the virus RNA in the water lines are varied among the eight WWTPs. Table 2 summarized the elimination performances of various treatment technology for SARS-CoV-2 RNA in wastewater.

Table 2

**Elimination performances of various treatment technology to remove SARS-CoV-2 RNA in WWTPs**
Country	Treatment technology	Observed SARS-CoV-2 RNA concentration (GC/L)		Removal value (GC/L)	References
Country	Treatment technology	Influent (Pre-treatment)	Effluent (Post-treatment)	Removal value (GC/L)	References
Spain	CAS	1.90 x 10³	0.20 x 10³	1.7 x 10³ (89.5%)	(Serra-Compte et al. 2021)
France	AS + Nutrient Removal + Clarification	5.0 x 10³	0.20 x 10³	4.8 x 10³ (96%)
France	MBR	7.90 x 10³	0.12 x 10³	7.78 x 10³ (98.5%)
India	SBR + Chlorination	1.60 x 10³	0.25 x 10³	1.35 x 10³ (84.4%)	(Kumar et al. 2021b)
India	Constructed Wetlands	0.63 x 10³	0.32 x 10³	0.31 x 10³ (49.2%)	(Kumar et al. 2021b)
Japan	CAS	5.01 x 10³	0.06 x 10³	4.95 x 10³ (98.8%)	(Wang et al. 2022a)
	MBR	5.01 x 10³	0.15 x 10³	4.76 x 10³ (95%)
	A2O	1.90 x 10³	0.06 x 10³	1.84 x 10³ (96.8%)
USA	CAS + Chlorination	7.5 x 10³	N.D.	> 7.0 x 10³ (90.3%)	(Sherchan et al. 2020)
Taiwan	TNCU + MBR (Taoyuan)	3.5 x 10⁶	1.1 x 10⁶	2.4 x 10⁶ (68.6%)	This study
	DOC (Guishan)	2.6 x 10⁶	0.26 x 10⁶	2.40 x 10⁶ (92.3%)
	CAS (Yangmei)	7.3 x 10⁶	0.34 x 10⁶	7.00 x 10⁶ (95.9%)
	AO + MBR (Wenqing)	4.2 x 10⁶	0.31 x 10⁶	3.80 x 10⁶ (90.5%)
	TNCU (Daxi)	2.5 x 10⁶	0.45 x 10⁶	2.05 x 10⁶ (82%)
	TNCU (Shimen)	1.8 x 10⁶	0.48 x 10⁶	1.32 x 10⁶ (73.3%)

According to the results, the SARS-CoV-2 RNA elimination performances of the eight WWTPs in Taoyuan ranged from 68.6% − 95.8% (Table 2). The removal performance of each WWTP is highly depended on the types of treatment technology which are utilized in treatment plant. For instance, CAS and DOC from Yangmei and Guishan WWTPs could effectively remove the SARS-CoV-2 RNA for up to 95.9% and 92.3%. Similar results were identified in WWTPs from different countries with the same treatment technologies. In Spain WWTP, CAS technology could remove up to 89.5% of the virus RNA (Serra-Compte et al. 2021) while in Japan WWTP, CAS have successfully eliminated 98.8% viral load concentrations from the influent channel (Wang et al. 2022a). Apart from CAS, membrane bioreactor, A2O, and chlorination technologies have been reported for having effective performances in eliminating the virus RNA from the wastewater influent (Table 2). However, the persistence of SARS-CoV-2 in the effluents and their infectious potential is of concern in wastewater recovery and reuse.

The result of this study confirmed that SARS-CoV-2 RNA has existed in the influent of WWTPs and it can be monitored through wastewater surveillance system. Several types of technologies have been confirmed to be able to destroy/ eliminate the SARS-CoV-2 RNA load in the WWTPs. In view of these research findings, Based on these results, it can be emphasized that wastewater monitoring and removal performance in WWTPs could help prevent waterborne virus transmission. In addition, "upstream" surveillance for SARS-CoV-2 could be a useful tool for more granular detection of SARS-CoV-2 in catchments with lower overall COVID-19 disease burden. Furthermore, SARS-CoV-2 upstream surveillance could be a useful tool to detect trends in infectious signaling for timely decision-making.

3.2. Trends of SARS-CoV-2 concentration upstream sewershed to WWTPs

The contribution of SARS-CoV-2 RNA in the upstream sewershed to downstream wastewater treatment plants (WWTPs) is illustrated on heatmap during the sampling period (Fig. 4). The heatmap plot showed SARS-CoV-2 concentration value detected in the primary manholes which has been sorted based on their respective distance to WWTPs (furthest to nearest) (Data was depicted in Table S1). Similarities in population size of the region are used to compare and provide general trends to monitor viral circulation in the community.

In general, the presence of SARS-CoV-2 RNA was detected in most locations of the sewer system in this study. However, the number of reported clinical cases and viral concentration level of the virus often distinct between different sites and it may affect the detection results of SARS-CoV-2 downstream of the treatment plant. Various variables could influence the fate and stability of SARS-CoV-2 during its migration from the sewage system to WWTPs, which may result in "false signals." For instance, the time for stool of an infected person with or without symptoms to reach WWTP is usually from 6–8 h (Rimoldi et al. 2020), to a few days (Wurtzer et al. 2021), or up to seven weeks (Wu et al. 2020). In addition, environmental factors (i.e. wastewater composition, temperature, pH, etc.) can also affect the stability and transmissibility of SARS-CoV-2 or other viruses along the sewer (Krivoňáková et al. 2021; Foladori et al. 2022).

During early stage of COVID-19 outbreak (April), the upstream sewer at Luzhu, Shimen and Daxi experienced significant effects with highest concentrations of SARS-CoV-2 RNA found at 5.48 x 10⁵, 4.44 x 10⁵ and 3.6 x 10⁵ GC/L. Meanwhile, the large sewershed areas had lower viral load of SARS-CoV-2 or below detectable value. The concentrations of SARS-CoV-2 RNA observed in all sewers in the study area were associated with the trend of severe disease cases in May, 2022. Viral loads ranged from 4.2 x 10⁴ to 1.2 x 10⁸ GC/L and tends to decrease after that. This trend is consistent with the trend prediction models in WBS, Nourbakhsh and co-workers indicating that the prolongation of fecal shedding time (14–73 days) after infection is the main cause of this downward trend (Nourbakhsh et al., 2022).

Spatial distribution trends of SARS-CoV-2 were compared across sampling sites in the sewer network to downstream WWTPs. Ahmed and colleagues reported that differences in sewer size and scale could affect the degradation/accumulation of SARS-CoV-2 RNA (Ahmed et al. 2021a). Indeed, for sewershed areas with high population sizes (Taoyuan and Bade), concentrations of SARS-CoV-2 tended to accumulate at sampling sites closer to WWTPs. The opposite trend was found for areas of small average population where a breakdown in SARS-CoV-2 concentrations was well observed. One probable explanation for this phenomena is that flow dilution affects viral transit, resulting in a "false signal" at the sewer's distal end (Ahmed et al. 2021a; Li et al. 2022a). In addition, the flow of wastewater from some sewers is negative with SARS-CoV-2 which may dilute the wastewater flow generated from the hotspots, thus leading to reduced concentrations of SARS-CoV-2 at the influent of the WWTPs (Wang et al. 2022b). Hence, future studies need to further elucidate the temporal variation and decay/accumulation processes of SARS-CoV-2 RNA concentrations in the sewer system.

3.3. Identification of hot spot clusters

To determine the transmission and origin trends of SARS-CoV-2 in the community, spatial modeling was used to identify clusters, hot spots and cold spots through the Getis-Ord Gi* statistical tests. The clusters and spots analysis were performed on the acquired dataset of different pandemic phases (i.e. early-outbreak, outbreak, and post-outbreak). However, due to resource constraints, not all sewers that existed in the study area were sampled. Hence, IDW imputation method was used to estimate the required covariance from the observation results of the surrounding sampling sites (Chen et al. 2021).

In the early-outbreak period, Luzhu and Longtan suffered significant impacts with significant clusters of hotspots (Fig. 5a). The Gi* characteristics and RNA concentrations at each sewer site are summarized in Table S3. This result is in agreement with the RNA detection in the influent WWTP. The viral RNA numbers observed in the Luzhu and Shimen sewers varied from 1.2 to 4.4 x 10⁵ GC/L, whereas surrounding areas with low viral RNA concentrations, even undetectable, are classified as cold spots by IDW. The origin of this infection chain was come from a cluster infection that started at the Datan Power Plant in Taoyuan City on March 27th, when there was a high number of migrant workers contracted with the virus and the access to medical resources was still limited (Everington 2022). Unfortunately, sampling activity was not conducted during this period, and several COVID-19 hot spots during that time were also not precisely identified. Hence, it explained on why Taoyuan is identified as cold spot cluster during this period (Fig. 5a). Since this early cluster was started around a bar with entertainment services, the infection chain was easily spread into other important Taoyuan city nodes and its surrounding districts including Luzhu and Shimen. Apart from that, Asfaw and co-workers also revealed that 1.44% of commercial land is highly susceptible to the risk of COVID-19 infection due to the limited space in this area, the high mobility, and the high level of human access and object contact could increase the risk of disease transmission (Asfaw et al. 2022).

Despite Taiwan's strict regulations, the rapid transmission of this virus has been linked to a surge in the number of COVID-19 cases in mid-May 2022 which were characterized by the increased case numbers in busy districts such as Yangmei and Taoyuan. The rapid increase in the concentration of SARS-CoV-2 in the sewer system is also explained for the contribution to influent WWTP. Specifically, 8 out of 9 sewers at Yangmei were found to be positive for SARS-CoV-2, the highest with 1.1 x 10⁸ GC/L and 5/22 sewers at Taoyuan were detected with 5.2 x 10⁶ GC/L (Fig. 5b). The results indicated that the pattern of disease-driven spread with locations where nearby hotspots often become hot spots on their own within one to two weeks. Apparently, the remaining areas of Taoyuan and part of Guishan were influenced later (Fig. 5c, d). This result is also consistent with recent studies (Haak et al. 2022; Nelson et al. 2022). It can be recognized that the application of the Gi* model can reveal the potential disease risk as well as explain the underlying origin of the infection with high certainty. In addition, socio-economic factors may also play role in the spread and development of hotspots during epidemics.

On the other hand, the trend of distribution of hotspots in the spatial model is consistent with the socio-economic characteristics of the region. Herein, the distribution of population density, personal income tax and land use purposes are illustrated (Fig. 6). The colors describe the weight of each site in describing the vulnerability of the area from very low to extremely high (Table 3).

Table 3

**Weights of the risk indice**
COVID-19 risk indicators	Classes	Weight	Index
Land-use	Commercial	5	Very high
	Mixed residence	4	High
	Service	3	Moderate
	Industry	2	Low
	Miscellaneous	1	Very low
People Density (People per km²)	< 2000	1	Very low
	2000–5000	2	Low
	5000–10000	3	Moderate
	10000–20000	4	High
	> 20000	5	Very high
Comprehensive Income Taxation (In thousands)	> 1000	1	Very low
	800–1000	2	Low
	650–800	3	Moderate
	500–650	4	High
	< 500	5	Very high

Figure 6a shows the pattern of population density in Taoyuan paired with identified hotspots from previous analysis. It can be inferred from the figure that the hot spots for virus outbreak were mostly identified in very highly populated districts such as Taoyuan, Luzhu, Guishan and Yangmei. The analyzed land-use (Fig. 6b) and income-tax (Fig. 6c) patterns also showed the same trend where hot spot clusters were obviously identified in the corresponding districts which have a very high number of these potential confounding socio-economical factors. These results indicated that the socio-economical factors might become the major drivers in the development of COVID-19 outbreak cases in Taiwan. Individuals who are residing in a very high density and land-use districts may face an intensified COVID-19 infection risk due to the nature of their tight and communal living space (Howard 2022). On the other hand, districts with high income taxes are mostly made up of industrial and business sectors, which have been the primary source of COVID-19 outbreaks in Taiwan (Everington 2022).

Other studies have reported similar implications related to the socioeconomic factor contributions in the development of COVID-19 outbreak. Lancaster and co-workers confirmed the strong association between healthcare availability and median income of citizen to the number incidents of COVID-19 in particular districts (Lancaster et al. 2022). On the other hand, Mollalo and co-workers proved that income inequality was also become an influential factor that explained the COVID-19 incidence in tri-state area of USA (Mollalo et al. 2020). Combined with this study findings, it can be inferred that the results of wastewater-based surveillance could potentially become a powerful tool for assisting decision maker in developing a proactive and well-prepared public health strategy.

Although there is no strong correlation between viral concentrations in wastewater and the number of clinical cases reported daily, this study provides an important basis for identifying individuals that are quietly spreading in the community. The individuals who experienced asymptomatic or mild symptoms from COVID-19 infections are reported to be more reluctant in notifying corresponding health care personnel. Furthermore, they often opted to undergo self-quarantine at home than in a designated place for quarantine. This resulted in clinical results that did not reflect the total number of infected people at that time. The WBE approach is a useful tool that can easily estimate the total number of infections from viral gene concentrations in wastewater and the rate of fecal shedding of infected individuals. From there, an early warning system can become a powerful tool for policy-makers to make decisions to prevent and protect public health.

In addition, the application of the spatial-temporal distribution model by using GIS to detect hotspots and retrieve the origin was successful in this study. This model provides early detection of outbreak hotspots in areas with high population density and high commercial-service activity, while this is delayed for small areas. This implies the surveillance data from the study could inform the disease control center and local government to issue timely warnings to prevent the disease from spreading further in the city. Furthermore, the extension of this WBE approach can be used as a reference source for countries where testing databases are scarce during the pandemic. Apart from analyzing the correlation between COVID-19 gene concentration and incident case, this work has also identified the potentials influence of population density, land-use, and tax income. The GIS approach conducted in this study have provided an easier way to investigate the influence of socio-economic factors to COVID-19 issues through the asssistance of IDW and hot spot models and it is applicable to any city across the world. Therefore, future studies and research could utilize and develop this approach to produce an easily reproducible method to analyze the progressivity of COVID-19 or other pathogen outbreaks in the future.

This study provides an approach to extend the knowledge on wastewater-based surveillance via spatial-temporal distribution model from the upstream sewer to the primary treatment plant. It is confirmed in this study that the SARS-CoV-2 RNA are present in the influent of WWTPs and the viral load concentrations were varied across eight WWTPs. Several treatment technologies including CAS, MBR, and A2O are proven to be able to effectively eliminate the virus RNA for up to > 90% from WWTPs influent. The travel of the virus from the feces of an infected person to the sewer system until it reaches the wastewater treatment plant shows a significant degradation due to the geographical distance. In addition, the upstream sampling implementation of the sewer showed high sensitivity for the detection of SARS-CoV-2 at different sites. The IDW and hot spot model were successfully used in simulating regional hotspots before, during and after the outbreak. In addition socio-economic factors namely population density, land-use, and tax-income were identified as the potentials drivers which substantially affect the onset of COVID-19 outbreak in Taiwan. Results obtained from this study provide an insight for policymakers to establish early warning systems for specific regions. This might reduce the burden on clinical trials and prevents the timely onset of SARS-CoV-2 or other potential virus-related pandemic in the future.

Ethical Approval

This article does not contain any study with humans and animals performed by any authors.

Consent to Participate

Not applicable

Consent to Publish

Not applicable

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Duyen P.H Tran, and Bo-Cheng You. The first draft of the manuscript was written by Duyen P.H Tran. All authors have reviewed and commented on previous versions of the manuscript. Ya-Fen Wang, and Sheng-Jie You were in charge of supervision, validations, and project funding acquisitions. All authors have read and approved the final manuscript.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Fundings

This study was financially supported by the Department of Water Resources, Taiwan R.O.C [Grant Number: 1101124-P1].

Availability of data and materials

The authors declare that all data supporting the findings of this study are available within the article [and its supplementary information files]

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Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE)

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Sampling sites and sample collection

2.2. Wastewater SARS-CoV-2 concentrations

2.3. RNA extraction and SARS-CoV-2 quantification

2.4. Hotspot identification models

2.5. Data analysis

3. Results And Discussion

3.1. Presence of SARS-CoV-2 in WWTPs

3.2. Trends of SARS-CoV-2 concentration upstream sewershed to WWTPs

3.3. Identification of hot spot clusters

4. Implications

5. Conclusions

Declarations

References

Supplementary Files

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