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. Author manuscript; available in PMC: 2026 Jan 10.
Published in final edited form as: Sci Total Environ. 2024 Dec 27;959:178217. doi: 10.1016/j.scitotenv.2024.178217

Wastewater Based Measures of COVID-19 and Associations with Children’s Absenteeism at Grade Schools

Helena M Solo-Gabriele 1, Gabriela Guevara 2, Naresh Kumar 3, Ayaaz Amirali 1, Kristina M Babler 1,a, Cynthia C Beaver 4,5, Samuel Comerford 4, Maria Ferraris 2, Natasha Schaefer Solle 4,5, Mark E Sharkey 4, Lisa Gwynn 2,*
PMCID: PMC11981082  NIHMSID: NIHMS2044855  PMID: 40212729

Abstract

During the COVID-19 pandemic schools closed due to concerns over disease spread resulting in lost learning opportunities. One approach for documenting disease spread includes wastewater (WW) surveillance of the virus that causes COVID-19 (Severe Acute Respiratory Syndrome Coronavirus 2, SARS-CoV-2) and other infectious pathogens. The objective of this study was to evaluate whether wastewater can be used to track children’s health at grade schools in an underserved community, which was vulnerable due to limited health-based data and difficulties in implementing mitigation measures. The 18-month study was initiated during January 2022 at 9 grade schools (3 high, 2 middle, and 4 elementary schools) characterized as low income. Children’s health was evaluated through absenteeism due to difficulties in attaining representative clinical diagnoses through school-based clinics. Wastewater measurements of SARS-CoV-2 were available weekly through grab sample collection and RNA extraction followed by quantification using qPCR. The average absenteeism rate was 7.1%, ranging from 4.6% to 12.5% per school. Fraction of WW samples positive for SARS-CoV-2 was 38% with SARS-Cov-2 levels ranging from detection limits (100 gc/L) to a maximum of 10.2 million gc/L. When data were aggregated across all schools, a statistically significant association was observed between weekly absenteeism rates and WW SARS-CoV-2 with a one percent increase in the loge WW SARS-CoV-2 associated with a 1.4% increase in student absence (p < 0.05). When evaluating the data by individual school, this association was strongest at schools with enclosed architecture characterized by limited natural ventilation. For schools with limited resources for clinical diagnosis of illnesses, school absenteeism coupled with wastewater-based monitoring should be utilized for assessing overall health of student populations. Strategies to maintain schools open during pandemics should include consideration of school architecture along with appropriate messaging of WW monitoring results to inform administrators and families.

Keywords: COVID-19, SARS-CoV-2, School Absenteeism, Wastewater, Wastewater-Based Surveillance

Graphical Abstract

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1. INTRODUCTION

The COVID-19 pandemic affected people of all ages. Although children generally experienced lower infection severity (Hua et al., 2020; Xu et al., 2020), they were impacted by lockdowns, by difficulties in mitigating disease spread, and by delays in vaccine availability and uptake. With the lockdowns, education ministries from more than 143 countries reported country-wide school closures, affecting about 68% of enrolled students globally (Onyeaka et al., 2021, UNESCO 2020). School closures impacted children’s physical health, psychological health (Meherali et al., 2021), and social, developmental, and behavioral health (Photopoulos et al., 2023; Irwin et al., 2022). These impacts were compounded by disparities which affected disproportionately Black (Kullar et al., 2020; Newton et al., 2023; Sandoiu et al., 2020) and Hispanic/Latino (Macias et al., 2020; Wen et al., 2023) populations in the US. Overall, lockdowns brought about a loss of learning and negative emotional experiences to children, especially children from minority groups who experienced disparities in access to health care.

As lockdowns and other government measures to mitigate the spread of the virus were relaxed, specific mitigation measures were put in place at the schools to facilitate in-person learning. These included social distancing, individual measures of protection, and improved ventilation (NAS, 2020; McLeod et al., 2022). Schools in the US were advised to maintain a 2-meter distance requirement, with an updated reduced 1-meter requirement (va den Berg et al., 2021). However, teachers reported that enforcing social distancing was difficult. (Schwartz, 2021; Uscher-Pines et al., 2020; Wilson et al., 2022). Individual measures of protection, such as masking and hand washing, were also important in mitigation efforts (Aronson & Shope, 2020). Both measures are difficult to control in this population (Assaithiany et al., 2021; Chittleborough et al., 2012). Compounding the difficulties in implementing individual measures of protection were policies outside of the schools that would prohibit requiring vaccination, clinical testing, and mask mandates. The study schools that were included as part of this study were within an area which did not permit mask mandates and thus the use of masks was strictly voluntary among the populations within the schools.

Improving ventilation in schools to prevent COVID-19 was also recommended (U.S. Department of Education, 2023; CDC, 2023), but many schools were not able to address these costly recommendations with inequitable access and support seen nationally (Mark-Carew et al., 2023). Even so, studies show that with optimized Heating, Ventilation and Air Condition (HVAC) systems, aerosol transmission of viruses is still possible through its airflow (Sodiq et al., 2021; Nannu Shankar et al. 2022).

Delays in vaccine uptake for the pediatric population added to the difficulties in controlling disease spread in schools. Almost one year after the beginning of the pandemic the vaccine was available in the U.S. on December 11, 2020, for people aged 16 years and older (HHS, 2023). Availability of the vaccine for adolescents ages 12–15 and ages 5–11 was made on May 10, 2021, and October 29, 2021, respectively (HHS, 2023). It was not until June 17, 2022, (HHS, 2023) that the COVID-19 vaccine was available for children from 6 months of age. In addition to delays in availability of the vaccine for the pediatric populations, parents were reluctant to vaccinate their children due to concerns about vaccine safety (Garbin et al., 2023). The delay in vaccine availability coupled with hesitancy by parents resulted in low rates of vaccination among children. As of May 3, 2023, the CDC reported that among children across the US aged 6 months to 17 years, an estimated 56% (40.5 million) had not received a first dose of the COVID-19 vaccine (AAP, 2023).

Given the difficulties in implementing mitigation measures and vaccine availability and uptake for the pediatric population, other measures were needed during the pandemic to document the prevalence of disease within the school setting, to provide stakeholders (i.e., parents and school administrators) with information that would be useful to keep children safe. During the pandemic, it was learned that measurements of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) RNA in wastewater was correlated with the prevalence of disease (Betancourt et al., 2021; Medema et al., 2020; Peccia et al., 2020). This was observed on a large scale, in data collected from regional wastewater treatment plants, and at small scales such as on university campuses (Zhan et al., 2022, 2023) and even for individual student residence halls (Li et al., 2023, Amirali et al., 2024). Few wastewater studies, however, focused on grade schools.

For the few studies that have evaluated wastewater at grade schools for SARS-CoV-2 (Table 1), all studies evaluated correlations between SARS-CoV-2 in wastewater and clinical cases of COVID-19. One study evaluated mitigation strategies (e.g., clinical testing and quarantine) in lowering levels of SARS-CoV-2 in school wastewater (Lopez Marin et al., 2023). Another developed a communication campaign to inform student families about SARS-CoV-2 detection in the schools (Fielding-Miller et al., 2023). All these studies concluded that the wastewater SARS-CoV-2 measured at the schools was positively correlated with COVID-19 cases. No studies, however, have evaluated the correlations of wastewater SARS-CoV-2 with surrogate measures of illness, such as absenteeism. This is especially important for low-resourced settings where clinical testing may be difficult to access and where families may be reluctant to have children tested due to perceived socio-economic disparities, reduced health literacy and mistrust. The objective of this study was to evaluate the ability of wastewater SARS-CoV-2 levels collected at schools to correlate with the health status of the student population within the school as measured by student absenteeism rate. Schools chosen for this study were characterized by student demographics (minoritized and low-income populations) that placed them at risk for disparities in health care access, making it difficult to collect representative clinical COVID-19 data. This study is the first study we are aware of that uses absenteeism (instead of confirmed clinical cases) as the proxy for clinical data.

Table 1.

Summary of studies that focused on grade school measurements of SARS-CoV-2 in wastewater.

Author, Year City/Country Data Collection Findings
Fielding-Miller et al., 2023 California, USA November 2020-March 2021 Passive environmental surveillance detected the presence of COVID-19 cases in grade school settings with a high degree of accuracy, where 93% of identified cases were associated with an environmental sample (67% with a wastewater sample and 40% with a surface sample).
Lopez Marin et al., 2023 Czech Republic October 2021-December 2021 Different strategies to stop the spread of the COVID-19, such as detection and quarantine, had a significant impact on the amount of SARS-CoV-2 RNA in the wastewater. Schools with staff who took a more active role exhibited lower amounts of RNA in the wastewater. Additionally, wastewater based epidemiological models were found to be reliable, with some schools showing the presence of SARS-CoV-2 RNA in the wastewater correlating with the clinical based data.
Wolken, et al., 2023 Texas, USA December 2020-May 2022 Grade school wastewater levels of SARS-CoV-2 and influenza were strongly associated with COVID-19 cases in the school and community rates.
Kim and Boehm, 2023 California, USA April 2022-June 2022 Grade school levels of SARS-CoV-2 RNA in liquid and solid fraction of wastewater detected COVID-19 with comparable detection frequency, where 100% of liquid samples and 75% of solid samples were positive for the RNA when clinical testing was positive.
Hassard et al., 2023 England October 2020-July 2021 Passive wastewater surveillance identified cases of COVID-19 in addition to detection of other viral and bacterial pathogens with the use of metagenomic sequencing.
Crowe et al., 2021 Nebraska, USA November 2020-December 2020 Weekly screening of asymptomatic students and staff by PCR testing was associated with increased SARS-CoV-2 detection. Additionally, SARS-CoV-2 RNA was detected in all wastewater samples in the schools.
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2. MATERIALS AND METHODS

2.1. School Sites

This study focused on nine grade schools all located in Miami-Dade County, Florida, US. Eight of the nine schools were located in northeast Miami-Dade County, whereas one school was located in central Miami-Dade. The nine schools included three high schools (grades 9–12, referenced as H1, H2, and H3), two middle schools (grades 6 to 8, referenced as M1 and M2) and four elementary schools (grades pre-K5 to 5, referenced as E1, E2, E3, and E4) (Table 2). The student population of the nine schools collectively were characterized by predominantly minoritized populations of low income. The school student populations were 63% Black on average and 26% Hispanic/Latino on average (Table 2). All schools were Title I schools receiving federal funding due to high proportions of low-income students as indicated by the fraction (89 to 94%) of the student population receiving reduced or free lunch.

Table 2.

Characteristics of the 9 Schools Included as Part of this Study.

School ID School Level Enrollmenta Majority Race/Ethnicity Absenteeism (%)b Vaccinated Fractionc Architectural Design and AC System Year Built Sampling Point Fraction WW Positive Mean WWd (gc/L) Median WW (gc/L) Maximum WW (gc/L)
Regular 9 months Regular Summer Extended Summer
H1 High 1,064 0 0 62% Hispanic, 36% Black 12.5 38% Open. Central Air 1986 Sewer hole 29.8% 181,000 BDL 10,200,000
H2 High 1,170 192 192 23% Hispanic, 71% Black 8.9 30% Closed. Central Air 1971 Sewer hole 55.6% 137,000 750 4,630,000
H3 High 1,756 102 103 14% Hispanic, 83% Black 9.3 25% Closed. Central Air 2016 Sewer hole 43.5% 65,700 BDL 2,280,000
M1 Middle 1,107 527 0 23% Hispanic, 70% Black 5.7 28% Open. One building is closed. 1957 Sewer hole 51.7% 46,400 BDL 1,300,000
M2 Middle 750 412 0 14% Hispanic, 84% Black 7.4 16% Closed. Central Air 1954 Combined Sewer hole 26.2% 1,890 BDL 49,900
E1 Elementary 461 0 0 4.6 4% Closed. Central Air 2008
E2 Elementary 485 297 0 25% Hispanic, 70% Black 5.2 7% Open. Window Units 1926 Pump Station 32.8% 12,240 BDL 357,000
E3 Elementary 555 0 0 57% Hispanic, 31% Black 5.6 15% Open. Central Air 1957 Pump Station 33.3% 26,500 BDL 992,000
E4 Elementary 516 0 0 21% Hispanic, 72% Black 5.0 10% Open. Window Units 1956 Pump Station 31.0% 8,870 BDL 222,000
Overall 7,863 1530 295 26% Hispanic, 63% Black 7.1 22.7% 38.2% 59,900 BDLe 10,200,000
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a

Enrollment during period of record (Week of January 10, 2022 through Week of May 29, 2023).

b

Absenteeism provided for 9 month regular school year. Breakdown of absenteeism by Fall versus Spring provided in supplement.

c

Vaccination Fraction. Corresponds to second shot administration as documented the week of March 21, 2022.

d

Assumes Below Detection Limit (BDL) values at 100 gc/L.

e

Median considering all samples was BDL. Median considering only positive samples was 5,540 gc/L.

Throughout the period of this study (January 2022 to June 2023), schools were in full operation after being closed from March through October 2020 due to the COVID-19 pandemic. The regular 9-month school year runs from the third week of August through the first week of June of each year, with a reduced schedule during the summer months (June, July and August). Classes in Miami-Dade County are held 5 days per week except for holidays and summer break (Details about school breaks are given in the supplement). Student enrollment for all schools collectively during the period of study was 7,863 during the regular 9-month school year, 1,530 during the regular summer school period, and 295 during a two-week extended summer school period (Table 2).

The architectural designs of the schools were different, with each school undergoing initial construction between 1926 and 2016. The main differentiating architectural design features included whether the schools had a closed architectural design (hallways indoors, Schools H2, H3, M2, and E1) or an open architectural design (hallways outdoors, remaining schools) (Figure 1), and whether the air conditioning systems at the schools were characterized by window units (Schools E2 and E4) or central air (all remaining schools) (Table 2). School M1 also included a separate building addition which was air-conditioned within a closed architectural design.

Figure 1.

Figure 1.

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Examples of schools with open architecture (left) versus those with closed architecture (right). Schools with open architecture have hallways open to the outdoor environment whereas schools with closed architecture have hallways within the indoor environment. Images taken from Google Earth. Schools with closed architecture had stronger correlations between wastewater SARS-CoV-2 concentrations and absenteeism.

2.2. Clinical Data

The collection of health data was reviewed by the University of Miami Internal Review Board (IRB 20210665, IRB 20210164). Health data included absenteeism data by each of the nine schools, clinical testing data from the school health clinics, and information about vaccination rates.

All schools had health clinics co-located within each school which were staffed with school nurses. Testing for COVID-19 was conducted in these school clinics using a molecular rapid test kit, which yielded nasal swab results in minutes. Students were consented to receive services in the clinics, and results were communicated to parents, and positive results were reported to the Florida Department of Health and school district per county protocol. Quarantine instructions were then provided to the parent. Re-testing was also offered when students were ready to return to school. In addition to test results, student symptoms were also recorded by the clinic nurse.

Vaccination data was obtained from the state immunization registry (Florida SHOTS https://flshotsusers.com/) which is input by vaccine providers at the time of immunization. Children vaccinated for COVID-19 in Florida are recorded within this registry. Each school also has their own vaccination database which is manually entered by school staff from copies of vaccination records submitted by parents upon registering their child to attend the school. This secondary database internal to the school was not used because proof of COVID-19 was not a requirement at the school level and therefore COVID-19 immunization records were not up-to-date within this internal database. It is presumed that the State registry was able to capture the majority of the vaccinations as the student’s residences were in Florida.

2.3. Absenteeism

Absentee data was collected daily and aggregated weekly at the individual school level. The research team worked with each school’s registrar to gather the information either in-person or electronically, depending on the way the data was submitted by teachers. These processes varied by school, so the team was sensitive to these challenges and adapted appropriately. The captured data was recorded into a master file for analysis.

2.4. Wastewater Sample Collection and Analysis

Wastewater sampling sites were chosen to correspond to the cumulative wastewater from the school building(s), to obtain samples that were representative of the entire school. The selection of the locations was based upon discussions with school facilities personnel who shared building construction drawings and who dye tested the wastewater sewer lines to confirm the interconnections with the bathroom facilities. Sampling at schools E2, E3, and E4 were from pump station wet wells, which are large underground reservoirs which accumulate wastewater from the school and pump the wastewater once it reaches a set level. The wet wells provide some mixing and compositing of the wastewater. Sampling at schools H1, H2, H3, M1, and M2/E1 corresponded to grab samples from a sanitary sewer hole which consisted of flowing wastewater. Schools M2/E1 were directly adjacent to one another with interconnected sewer lines. As such only one sample was collected representing both schools. Therefore, for the 9 schools, wastewater samples were collected from 8 sites. Additional details about the sampling sites are provided in the supplemental text.

Samples were collected when schools were open for classes between January 12, 2022, through June 6, 2023. Sampling occurred every Tuesday at the same time (± 1h) throughout the sampling period for consistency in the population-wastewater-use patterns between sampling intervals. Samples were collected by lowering a new 2-liter bottle on a chain. Wastewater from this larger 2 L bottle was poured into a smaller 0.5 L bottle using a coarse screen (about 3 mm opening size) and a funnel. Upon filling, the 0.5 L bottle was closed and placed in a cooler with frozen freezer packs. Upon the collection of the 8 samples, they were delivered to the laboratory for immediate processing. All persons handling wastewater followed the University’s policies of Health and Safety including the use of personal protective equipment and disinfection of equipment and of areas that may had been in contact with wastewater.

Processing followed the methods described earlier (Babler et al., 2022; Sharkey et al., 2021; Zhan et al., 2023) which involved pretreating the sample, concentrating the sample by electronegative filtration, nucleic acid extraction, and detection of molecular targets by quantitative Polymerase Chain Reaction (qPCR). In brief, pretreatment involved the addition of human coronavirus-OC43 (OC43) as a recovery control for the entire process inclusive of filtration, lysis, extraction, and PCR quantification steps. Samples were prepared for filtration by adding magnesium chloride (MgCl2) and reducing the pH to between 3.5 and 4.5. The samples were then filtered through electronegative filters (mixed-cellulose ester, 0.45 μm pore size, EMD Millipore: #HAWP04700) to capture particulates and free-floating viruses by charge. Filters were then folded and placed into 1.5 mL of lysis buffer preservative (1x DNA/RNA Shield). The lysis buffer was found to be optimal for RNA recovery as studies showed that recoveries were higher for RNA viruses without physical lysis such as bead beating (Babler et al. 2023). RNA was extracted from 250 μL lysate using the Zymo Research Quick-RNA Viral Kit to produce 10 μL of purified RNA. Following extraction of the RNA, 30 μL of HIV RNA was added to the sample RNA to assess for inhibition.

The concentration of the molecular targets (units of genomic copies (gc) per liter of wastewater) was determined by Volcano 2nd Generation (V2G) qPCR. Quantification focused on five targets, SARS-CoV-2 (N3 nucleocapsid gene), two targets that are indicators of human waste contributions (Pepper mild mottle virus, PMMoV, and beta-2 microglobulin, B2M), and the two control targets, OC43 and HIV. The average, sample recovery determined by OC43 was 20.4% (standard deviation of 11%). HIV values were consistently within 2 cycles of the nuclease-free water control, and so samples were considered uninhibited. All samples tested for PMMoV (n=193) and B2M (n=224) with the exception of one were positive confirming contributions of human waste. The mean ± standard deviations of the PMMoV and B2M were 92.3×106 ± 131×106, and 4.1×105 ± 3.3×105, respectively. These values were higher on average than values obtained from the local wastewater treatment plant for community level sewage (wastewater treatment plant at 76.7×106 ± 89.1×106 (n=124) and 1.9×105 ± 1.6 ×105 (n=89)). Additional details about the wastewater analysis procedure are provided in the supplemental text including the checklist for Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE, Bustin et al. 2009).

2.5. Statistical Analysis

In this study two data sets were leveraged for statistical analyses: 1) the weekly concentration of SARS-CoV-2 in wastewater samples collected from the schools, and 2) daily number of children absent from each of the nine schools normalized by the student enrollments per school. Relationships between school absenteeism rate and moving average of the WW-SARS-CoV-2 were examined using time-series regression analysis. Given differences in the temporal resolution between the wastewater and absenteeism data, these data were aggregated by week so that one-week of absenteeism could be matched to the week of sampling. Moreover, for COVID-19 it is generally accepted that there are time lags between infection, onset of symptoms, followed by clinical diagnosis (Medema et al. 2020; Caveny et al. 2022). The time between fecal shedding and clinical diagnosis is about a week (Medema et al., 2020; Peccia et al., 2020) or two (Randazzo et al., 2020). Since there is a time-lag between infection with COVID-19 and onset of symptoms followed by absence from the school, a weekly lagged SARS-CoV-2 exposure was computed for each school. Moreover, there were large variations in SARS-CoV-2 concentrations since wastewater samples were collected from a single point in time each week, so two-week moving averages were computed to smoothen these data. The two-week moving averages facilitated capturing wastewater contributions from infected children prior to the progression of the symptoms requiring the absence from school. In addition, we also anticipated an association between children ill enough to miss school and those children who were asymptomatic (Crowe et al. 2021). Since all variables were skewed towards low values (Figure 2), they were normalized using log-transformation. Using log-log regression, school (student) absent rate was modelled with respect to the moving average of 7-day lag and 7-day lead (or a week before and week after the week of absenteeism data as done previously in Sharkey et al. 2021) for each school separately, and all schools together.

Figure 2.

Figure 2.

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Statistical distribution of student absent rate (Panel A), and wastewater SARS-CoV-2 RNA levels log10 transformed in units of genomic copies (gc) per liter of wastewater (Panel B).

Since SARS-CoV-2 was below the detection limit in more than half of the samples, we computed 7-day lag and 7-day lead moving average, which yielded 54% of the days with greater than positive values for all school and the rest were treated as “0” in the school-specific analysis. For the aggregated analysis that considered all schools collectively, eight weeks had data that were below the detection limit for all schools. However, when moving averages were computed across weeks, all moving averages included a detectable SARS-CoV-2 level.

2.6. School District Communications

Meetings were held each week with school district leaders to review the results of the wastewater testing as well as absenteeism in each school. These meetings served as an opportunity to inform school leaders about the data, and to facilitate discussion regarding the need to plan for mitigation strategies proactively and how best to communicate results to families.

3. RESULTS

3.1. Student Health Results

The average students’ absence rate was 7.1% across all schools during Fall 2021 to Spring 2023. The student’s absence was positively skewed (Figure 2A). The students’ absence rate ranged from 4.6% for E1 to 12.5% in H1 (Table 2). The absence rate also varied from semester to semester, highest being in Spring 2022, during the second half of the school year when COVID-19 related quarantine measures were still in place.

Although a robust testing program was available at each school, few students accessed testing. Among the 7,863 students enrolled in the nine schools during the regular academic year, only 119 (less than 2% of the population) visited the school clinics (during the period of this study) with symptoms consistent with COVID-19. Among the 119 students, 103 guardians consented to COVID-19 testing, and among the 103 tested only 4 tested positive. Although well-staffed and ready to provide medical diagnosis, the school clinics were underutilized.

Despite the lack of utilization of testing resources, vaccination levels of the student population were consistent with the national averages reported by the CDC for the week ending May 11, 2024 (CDC, 2024) which was documented at 13.3% for the 5- to 11-year-old population and 17.9% for the 12- to 17-year-old population. For the study schools specifically, 9% of the 5- to 11-year-old students and a 28% of the 12 plus year old students were fully vaccinated (2 shots) as of March 21, 2022. The vaccination rates by school (Table 2) varied from 7% to 38%. The elementary schools (E1 at 4% and E2 at 7%) had the lowest rates of vaccination, whereas the middle and high schools generally had the highest rates of vaccination (16% to 38%). Of note, national average reporting started September 2023 with very low fractions reported at the initiation of the CDC national immunization study. Therefore, there was no overlap in the dates for when the vaccination records were available between the schools that were part of this study and the US national statistics. Given the time differences in reporting with the nine schools statistics corresponding to almost 2-years prior to the national statistics, we believe that the vaccination rates are consistent showing elementary school students with lower vaccination rates than high school students.

3.2. Wastewater Results and Comparison with Absenteeism

A total of 479 WW samples were collected during the study period. Of these 183 (38%) were positive for SARS-CoV-2 and 296 (62%) were below the detection limit (Table 2). The range of SARS-CoV-2 RNA from WW samples ranged from detection limits (100 gc/L) to a maximum of 10,200,000 gc/L. (Table 3). SARS-CoV-2 concentration in wastewater across all schools showed a skewed distribution, with only a few samples greater than 5 log10 (gc/L) units (105 gc/L) (Figure 2B). The median of the positive values was 5,540 gc/L. The fraction of WW samples positive for SARS-CoV-2 per school ranged from 26.2% (E1) to 55.6% (H2). The fraction of WW samples positive (31% on average) at the elementary schools were lower than middle (39% on average) and high schools (43% on average) although the differences were not statistically significant (p>0.12). The fraction of WW samples positive at schools with closed architecture (42% on average) was not statistically different than for schools with open architecture (36% on average) (p=0.49), suggesting that the small sample size and other confounding factors such as student age may have concealed any benefits that increased ventilation may have provided. The maximum WW values were all observed at the high schools (10,200,000 gc/L, 4,630,000 gc/L, and 2,280,000 gc/L). The wastewater source solely from a middle school, had the next largest value (1,300,000 gc/L). The maximum WW values for all elementary schools and the combined elementary/middle school wastewater were all less than 1,000,000 gc/L. The differences in the maximums between the elementary schools and high schools was statistically significant (p=0.04).

Table 3.

SARS-CoV-2 RNA levels (gc/L) at the 8-grade school wastewater sampling sites.

Sampling Date H1 H2 H3 M1 M2/E1 E2 E3 E4 Notes
12-Jan-22 NS NS NS NS NSa 25,300 12,514 13,020
19-Jan-22 4,800 2,200 NS 200 1,364 4,725 67,463 1,400
26-Jan-22 BDL 1,353,667 5,850 4,200 BDLb BDL BDL BDL
1-Feb-22 BDL 94,766 2,048 2,370 BDL BDL 130 BDL
8-Feb-22 BDL 4,650 BDL BDL 8,340 BDL BDL 95,520
15-Feb-22 BDL BDL BDL BDL BDL BDL BDL BDL
22-Feb-22 BDL BDL BDL BDL BDL BDL BDL 840
1-Mar-22 BDL BDL BDL BDL BDL BDL BDL BDL
8-Mar-22 BDL BDL BDL BDL BDL BDL BDL BDL
15-Mar-22 BDL 1,000 BDL BDL BDL BDL BDL 2,800
22-Mar-22 NS NS NS NS NS NS NS NS Spring Break
29-Mar-22 BDL BDL BDL BDL BDL BDL BDL BDL
5-Apr-22 BDL BDL BDL 96 173 NS BDL BDL
12-Apr-22 BDL 450 BDL BDL BDL BDL BDL BDL
19-Apr-22 323 BDL 5,240 27,900 BDL BDL BDL BDL
26-Apr-22 BDL 59,040 65,025 BDL 240 BDL BDL BDL
3-May-22 BDL BDL 2,040 5,760 BDL BDL 2,550 9,540
10-May-22 BDL 31,714 BDL 13,680 BDL BDL BDL BDL
17-May-22 BDL 23,400 2,277,975 800 BDL BDL BDL BDL
25-May-22 BDL 17,100 BDL NS 17,167 BDL BDL BDL
31-May-22 BDL 1,876,600 3,800 292,650 BDL 600 1,200 BDL
7-Jun-22 BDL 18,882 15,780 372 799 4,656 991,944 BDL
14-Jun-22 NS 1,065 97,850 NS NS NS NS NS Summer School, two schools open
21-Jun-22 NS BDL 1,200 NS NS NS NS NS
28-Jun-22 NS 36,986 1,950 152,850 390 9,525 NS NS Summer School, five schools open
5-Jul-22 NS 323 14,250 9,225 BDL BDL NS NS
12-Jul-22 NS BDL 48,150 26,700 BDL 9,150 NS NS
19-Jul-22 NS 1,080 BDL 500 1,080 1,500 NS NS
23-Aug-22 3,200 1,329 1,380 BDL BDL BDL BDL 5,000
30-Aug-22 7,500 8,310 BDL 467,300 216 BDL BDL 42,800
6-Sep-22 270 204,700 BDL BDL BDL 7,800 BDL 14,700
13-Sep-22 BDL 39,000 BDL 3,067 BDL BDL BDL BDL
20-Sep-22 BDL 4,418 BDL 4,600 420 BDL BDL BDL
27-Sep-22 NS NS NS NS NS NS NS NS Hurricane Ian
04-Oct-22 BDL 750 BDL 4,500 BDL BDL BDL BDL
11-Oct-22 BDL BDL BDL 351,600 BDL BDL 8,760 BDL
18-Oct-22 BDL 823 BDL 1,303,310 12,620 BDL 1,740 768
25-Oct-22 BDL 863 BDL 11,805 BDL BDL BDL 10,500
1-Nov-22 BDL 527 BDL 4,630 BDL 300 BDL BDL
8-Nov-22 BDL BDL BDL 6,450 BDL 3,020 BDL BDL
15-Nov-22 BDL BDL 45,750 9,300 BDL 1,650 179,520 BDL
22-Nov-22 NS NS NS NS NS NS NS NS Thanksgiving Break
29-Nov-22 471 BDL 1,840 531 BDL 10,992 5,544 BDL
6-Dec-22 71,165 2,970 27,080 3,610 BDL BDL BDL BDL
13-Dec-22 10,200 1,333 938,850 17,571 4,362 BDL BDL BDL
20-Dec-22 10,187,250 6,525 387,257 20,160 49,926 BDL 1,500 221,867
27-Dec-22 NS NS NS NS NS NS NS NS Winter break
3-Jan-23 NS NS NS NS NS NS NS NS Winter break
10-Jan-23 8,800 128,070 BDL BDL BDL BDL BDL 2,229
17-Jan-23 2,280 30,600 BDL BDL BDL BDL BDL BDL
24-Jan-23 BDL BDL 6,514 BDL BDL BDL BDL 4,350
31-Jan-23 BDL BDL 2,100 BDL BDL BDL 23,940 BDL
7-Feb-23 BDL 23,571 1,800 BDL BDL 1,680 2,160 53,850
14-Feb-23 1,000 BDL BDL BDL BDL 221,900 BDL BDL
21-Feb-23 5,133 1,600 BDL BDL BDL BDL BDL BDL
28-Feb-23 2,625 BDL 96,400 23,775 BDL BDL 149,200 1,050
7-Mar-23 BDL 4,629,343 BDL 6,100 BDL 57,525 16,800 BDL
14-Mar-23 10,800 BDL 1,425 BDL BDL 1,680 1,800 BDL
21-Mar-23 NS NS NS NS NS NS NS NS Spring Break
28-Mar-23 BDL BDL BDL BDL BDL BDL BDL BDL
4-Apr-23 14,900 BDL BDL BDL 8,931 357,100 BDL BDL
11-Apr-23 BDL BDL 1,800 BDL BDL 17,900 6,600 BDL
18-Apr-23 1620 BDL 267 4800 1200 3300 NS BDL
25-Apr-23 BDL 22,223 14925 BDL BDL BDL BDL BDL
2-May-23 BDL 5,250 BDL BDL BDL BDL BDL BDL
9-May-23 BDL BDL BDL BDL BDL BDL BDL BDL
16-May-23 BDL BDL BDL BDL BDL BDL 19,918 BDL
23-May-23 BDL BDL BDL BDL BDL 2,250 BDL 24,225
30-May-23 BDL BDL BDL BDL 3,360 BDL BDL BDL
6-Jun-23 BDL BDL BDL BDL BDL BDL 12,705 6,060
Number of Samples 61 57 61 57 60 63 62 58
Fraction Positive 29.8% 55.6% 43.5% 51.7% 26.2% 32.8% 33.3% 31.0%
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*

Shaded columns correspond to first sampling site listed in header.

a

NS = No sample

b

BDL = Below Detection Limit. Detection limit estimated at 100 gc/L.

Time series plots of WW SARS-CoV-2 concentration and students’ absence rates aggregated for all schools showed distinctive trends (Figure 3). Elevated levels of SARS-CoV-2 in wastewater were observed at the very beginning of the sampling period (January 2022), towards the end of the 2022 school year (end of May to beginning of June 2022), at the beginning of the regular school year (end of August to beginning of September 2022), right before winter break (end of December 2022), and during the late winter of 2023 (late February to early March of 2023). WW SARS-CoV-2 concentration followed absenteeism from January through June 2022 and from November 29 through December 2023.

Figure 3.

Figure 3.

Open in a new tab

Temporal association between wastewater SARS-CoV-2 and school absenteeism rate. Data corresponds to aggregated results for all schools evaluated.

For individual schools, different patterns were observed (Figure 4). Some schools such as E3 showed only short-term sporadic spikes in SARS-CoV-2, whereas M1 showed low levels during the 2022 school year (ending at the beginning of June 2022) and then more sustained levels of SARS-CoV-2 for the Fall of 2023. H3 showed consistent and sustained levels during both the end of the 2021–2022 school year (January to June 2022) and the beginning of 2022–2023 school year (August 2022 through May 2023).

Figure 4.

Figure 4.

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Time series plots of wastewater data for all school sites (panel A) and for individual school sites E3 (panel B), M1 (panel C), and H3 (panel D).

Statistical analysis of the data showed significant associations between students’ weekly absent rate and SARS-CoV-2 genomic copies in the school wastewater (Table 4). With one percent increase in wastewater SARS-CoV-2 student absent rate increased by 1.4% (β ~ 0.014; 95% confidence interval (CI) was 0.002 to 0.027; p < 0.05). However, this association varied across the selected nine schools. Wastewater SARS-CoV-2 showed the strongest association with the student absent rate in M2/E1 and H2 schools. In the M2/E1 combined wastewater system one percentage increase in wastewater SARS-CoV-2 concentration was associated with a 4.4% increase in students’ absence rate (β ~ 0.044; 95% CI = 0.021 – 0.068; p < 0.001) (Table 4). However, in four of the nine schools, namely H1, M1, E3, and E4, this association was not statistically significant.

Table 4.

Association between loge(% absent students) and loge(seven day moving average of SARS-CoV-2/L in school specific wastewater January 1, 2022 to May 2023).

School ID Regression Coefficient, β 95% CIa Observations R-squared
H1 −0.004 (−0.028 – 0.019) 77 0.002
H2 0.025*** (0.008 – 0.043) 77 0.101
H3 0.014* (−0.002 – 0.031) 77 0.039
M1 −0.001 (−0.018 – 0.016) 77 0
M2/E1 0.044** (0.021 – 0.068) 77 0.159
E2 0.021* (−0.002 – 0.043) 77 0.042
E3 0.01 (−0.011 – 0.031) 77 0.012
E4 0.002 (−0.029 – 0.033) 77 0
All Schools 0.014** (0.002 – 0.027) 77 0.066
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a

Confidence limits (CI) in parentheses;

***

p<0.01,

**

p<0.05,

*

p<0.1

4. DISCUSSION

A significant association was observed between WW SARS-CoV-2 and students’ absence rate. Such results are consistent with prior studies that correlate COVID-19 cases within school settings with wastewater SARS-CoV-2 RNA levels (Field-Miller et al. 2023; Hassard et al. 2023; Kim and Boehm 2023; Lopez Marin et al. 2023; Wolken et al. 2023). This study’s use of absenteeism is consistent with the work of Stark et al. (2023) who recommend using student absenteeism as a proxy for community-wide clinical cases. Therefore, in the absence of clinical case data, the results of this study support that absenteeism may be a suitable proxy for estimating disease prevalence, especially for communities with limited access or hesitancy in accepting clinical diagnosis.

4.1. Underserved Communities

This study supports that facilities for testing COVID-19, although available for students, were not utilized at the schools within the low income communities. Studies have shown that families in low-income neighborhoods may be incentivized to not seek healthcare and diagnosis for their children (Kullar et al. 2020; Sandoiu 2020; Kenworthy et al. 2022), especially for communities where local mandates were prohibited and where a distrust exists. This is especially critical if family members are employed by companies with self-imposed health mandates requiring time off for those in contact with positive COVID-19 individuals. A known positive test among family members could represent lost income for the family, providing an incentive to not seek diagnosis, especially for families with low economic reserves. Although clinical facilities were available within the schools, the access to these facilities were not sought by students. Additional difficulties in access to clinical diagnosis, as expressed by the school administrators, included the requirement of forms documenting guardian consent prior to medical care within the school clinic.

Regardless of the societal incentives and logistical barriers that may discourage testing, increased awareness of the possible transmission of diseases through wastewater data can potentially encourage families to prioritize mitigation (e.g., voluntary mask use) and vaccination for their children. Vaccine uptake was low among children in the US. Although families at the schools did not utilize the testing resources available, the uptake of vaccines by the population studied was consistent with rates of uptake nationwide. Awareness of impending outbreaks as identified through wastewater can also assist school administrators in implementing mitigation measures such as promotion of handwashing and social distancing, to protect students as well. The advantage of wastewater measurements is that it does not require active participation by the population but provides information that can promote awareness of disease transmission within the school which can be used to encourage behaviors that increase school safety.

4.2. Influence of grade level on SARS-CoV-2 levels in wastewater

Results show an association between grade school level with higher maximum SARS-CoV-2 levels in high schools compared to elementary schools. These differences are counter to the vaccination rates observed at the nine schools, with high school students with a higher proportion of vaccine uptake relative to the elementary school students. Thus, the higher peak levels of WW SARS-CoV-2 in high schools relative to elementary schools can be attributed to differences in infectivity and fecal shedding of SARS-CoV-2 between pediatric and adult populations. In a study among familial outbreaks, 1.3% of the children tested positive for COVID-19 in comparison to 3.5% of adults with this difference statistically significant (p=0.002) (Xu et al., 2020). Similarly, a study of 1298 COVID-19 cases from 883 families showed that incidence of infection in contacts with children (13.2%) was statistically lower than with adults (21.2%) (Hua et al., 2020). In terms of fecal shedding, a study of 71 children infected with COVID-19 showed positive detection of SARS-CoV-2 RNA in only 21% of the fecal samples (Khemiri et al., 2023), whereas studies in adults show higher proportions of detections ranging from 40% (Daou et al., 2022,) to 67% (Chen et al., 2020). Overall, studies suggest lower infectivity rates for children and lower proportions of fecal shedding. The lower levels of SARS-CoV-2 in wastewater at the elementary schools (especially the lower maximums that were statistically different) can be partially attributed to lower infectivity and lower shedding among the children who are infected.

4.3. Influence of Architecture

Results showed that the high schools with enclosed architecture had more positive SARS-CoV-2 detections in wastewater (43.5% and 55.6%) compared to the schools with an open architecture (29.8%). The design of the buildings influences the amount of ventilation. Open style buildings allow for better separation among the air conditioning systems allowing outdoor air to enter more frequently as the doors are opened, especially between class hours. In addition, hallways tend to get crowded between classes and having hallways open to the environment facilitates ventilation during times when students are changing classes. Thus, the lower fraction of positives (29.8%) may be due to better air circulation from the more open architectural design of this specific high school (See Figure 1, top left). Studies have found that improved ventilation, through increased air exchanges and regular opening of windows (Zivelonghi and Lai, 2021) at schools is associated with lower infection risks from COVID-19 (Pistochini et al., 2022). The primary drawback of improved ventilation, however, is increased energy usage and a decrease in thermal comfort of the inhabitants (Xu et al. 2023). Results from this current study comparing the architecture of the high schools and levels of SARS-CoV-2 in wastewater are consistent with the literature that shows that improved ventilation is associated with lower rates of infection. More studies are needed, however, to illustrate this point as the number of data points in the current study were not sufficient to observe statistical significance.

The architectural design of the school also appears to have Impacted the correlation between absenteeism and WW SARS-CoV-2 levels, with schools with the high correlations characterized by a “closed” architecture (e.g., M2/E1, H2) and those with low significant (E2, H3) and non-significant correlations (H1, E3, E4, and M1) characterized by an “open” architecture. The reason for this improved correlation is not quite understood. One study found that correlations between COVID-19 and SARS-CoV-2 in wastewater declined in sewers designed to accept storm water (Fahrenfeld et al., 2022). We can speculate that the schools with more closed designs are more compact with all infrastructure enclosed within a smaller footprint to encourage energy efficiency. This more compact structure of the building may result in a tighter sewer system subject to less infiltration and inflow of groundwater possibly reducing this potential confounder on the quality of the wastewater sample.

4.4. Use of Wastewater to Augment Decision-Making Information for Mitigation

One advantage of the wastewater data is the ability to observe outbreaks within small sewer sheds. The typical observation from the current study when evaluating individual schools were abrupt increases in WW SARS-CoV-2 (i.e., spikes) in wastewater with a more gradual diminishing of the spike in later weeks. Such spikes were smoothed when the data were aggregated by school. Thus school-based measurements provide the opportunity to capture these spikes early allowing for possible increases in mitigation measures earlier. However, only some schools showed associations between absenteeism and WW SARS-CoV-2 levels. This lack of consistency among schools may be due to the smaller sample sizes and the variability of the wastewater data (Amirali et al., 2024).

When data were aggregated among all schools, results showed significant positive associations between students’ weekly absent rate and SARS-CoV-2 genomic copies in the school wastewater. Such results can be used to develop messaging for administrators and families in terms of increase disease risk. Such messaging should include actionable plans in response to observed spikes. For example, a study by Corchis-Scott et al. (2023) acted on a positive detection within a university residence hall with daily measurements. Detection of SARS-CoV-2 in wastewater over three consecutive days resulted in a testing campaign among the occupants within the residence hall. As a minimum, information about a positive detection should be released to administrators and families in real-time taking advantage of possibly smart phones for rapid communication of wastewater results (Gonçalves, 2023). This would allow for mitigation measures such as increase cleaning and hygiene throughout the building, increased HVAC air exchange rates plus maintenance, encourage social distance, encourage the use of masks, and minimizing activities that would require students to come in close contact indoors. Messaging to administrator and families should be appropriate, easy to understand with an explanation of the risks and personal mitigation measures individuals can take. Messaging also needs to be sensitive to its potential interpretation for stigmatization and discrimination of the school (Honda et al., 2021), and careful thought is needed in how best to present wastewater results.

One way to get around stigmatization is by reporting results from regional wastewater treatment plants which aggregate results from a larger community, as opposed to reports by individual school. In discussion with the school administration staff, school-specific data was a major concern, from both a positive and negative perspective. From a positive perspective, families with children at schools without wastewater programs would perceive inequities in the provision of health-related information. From a negative perspective, student populations from schools with exceedingly high levels of wastewater SARS-CoV-2 could be stigmatized especially given the broad range of demographics among schools within Miami-Dade County. To avoid stigmatization or perceived inequities in information, the school administration staff recommended the utilization of regional wastewater treatment plant data to inform student families of the local potential of disease spread.

Given the success in tracking COVID-19 using SARS-CoV-2 measurements in wastewater, school administrators should consider augmenting decision-making for other illnesses with information obtained from wastewater. Wastewater surveillance should be expanded to include other communicable diseases such as Influenza A/B, RSV, human metapneumovirus, poliovirus, Candida auris, and Mpox which are now monitored at the regional wastewater treatment plant level (Babler et al., 2023b; Sharkey et al., 2023), and measles which recently emerged at an elementary school in Florida (Mogg, 2024). An increasing trend for families is to opt out of vaccinations for children. The decreased vaccination rates in the schools increases disease transmission risks in school settings (Bagcchi 2024). Wastewater epidemiology using abstenteeism as an early proxy for health effects may assist school administrators in identifying the early onset of outbreaks at schools, especially where vaccination rates for communicable diseases are low.

4.5. Limitations

Overall limitations of this study included the small number of schools evaluated, a total of 9 with 8 wastewater sampling sites. As a result, this study represents a preliminary assessment between student absenteeism and wastewater SARS-CoV-2 levels from schools. To confirm the results from this study, additional schools should be added at all levels (elementary, middle, and high schools) and with differing characteristics (e.g., open versus closed architecture) in order to better discern statistically different results. The effect of school population size should also be considered when choosing schools for future studies, as small population sizes may bias the data towards a positive or negative wastewater based upon the shedding of a small number of individuals.

Additional limitations lie in the clinical data. Limitations include the lack of absenteeism data for teachers and staff, and the lack of information about school visitors who may contribute SARS-CoV-2 to the wastewater. The association between SARS-CoV-2 and absence rate varied over time, with stronger associations during the Spring 2022 compared to Fall of 2022 and Spring of 2023. One of the reasons could have been the stringent quarantine measures in place during the Spring 2022. In addition, variations in absenteeism could be linked to other reasons for missing school, other illnesses, and community immunity that resulted from vaccination efforts and natural infections. The clinical data was also limited due to a small sample size, as only few students and staff members accessed COVID-19 testing in the school clinics. This may affect the generalizability of the findings.

Several limitations existed with the wastewater data. First, wastewater may not be representative of all building occupants as not all may use the facilities during school hours. In addition, samples at 5 of the 8 sites were collected as grab samples from flowing sewer pipes representing the characteristics of the wastewater flowing from the school at that point in time. Future efforts would benefit by collecting composite samples which provide a more representative sample of wastewater collected from the entire school day (Babler et al., 2023a; Mendoza Grijalva et al., 2022).

5. Conclusions

Students’ absence from school greatly impacts their performance as well as overall quality of life. This research makes a novel contribution by documenting the statistical association between SARS-CoV-2 and students’ absence rate. The results were statistically significant when aggregated for all eight sampling points and nine schools collectively and for a subset of the individual schools. Wastewater monitoring should therefore be considered as one of the tools available along with other control measures (improved ventilation, improved architectural design of schools, social distancing, mask use, vaccination) to minimize disease transmission. Future work is needed to operationalize wastewater-based measurements into messaging strategies for administrators and families that encourage mitigation practices (increase cleaning, increased hygiene, use of masks, encouraged vaccination, social distancing, and improved ventilation and HVAC maintenance) in grade school settings such that schools can be maintained open, avoiding losses in student education, facilitating in-person socialization, and promoting improved mental health outcomes. Operationalization should also consider school architecture and other factors that may influence the associations between prevalence of illnesses within the schools and the results from wastewater measurements.

Given the fact that children represent a vulnerable population, the difficulty in mitigating disease spread in school settings and the limitations of accessing healthcare and the uptake of certain vaccines, especially in under resourced communities, new innovative measures are needed to improve school safety. Wastewater measurements represent an important component of maintaining school safety by providing information about disease prevalence among the population in a fashion that anonymizes the health status of individuals. Such information is important, especially for underserved communities, which may be more hesitant to receive diagnoses due to the greater economic impacts that a positive diagnosis may have on those from lower income levels.

Supplementary Material

1

Highlights.

  • Wastewater SARS-CoV-2 levels at schools correlated with student absenteeism

  • Associations strongest at schools with enclosed architecture

  • One percent increase in loge WW SARS-CoV-2 associated with a 1.4% student absence rate

  • For resource limited schools, student absenteeism can be a proxy for clinical testing

  • Recommend wastewater monitoring coupled with appropriate messaging to families

Acknowledgements

This study was financially supported by The National Institutes of Health RADx-UP and RADx-rad programs under Award Number 1OT2HD108111-01-S and Award Number U01DA053941. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also supported through in-kind support provided by Miami-Dade County Public Schools including considerable effort by facilities and operations to provide access to the sampling sites and by the Director of Comprehensive Health Services who facilitated access to clinical information. We are thankful to the greater South Florida RADx-rad and RADx-UP team members who have provided input and feedback as this project progressed inclusive of the colleagues of the Shared Resources of the Sylvester Comprehensive Cancer Center and colleagues at Weill Cornell Medicine.

Footnotes

Declaration of 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.

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