https://orcid.org/0000-0002-7952-6659
Figures
Abstract
Wastewater and environmental surveillance has been promoted as a communicable disease surveillance tool because it overcomes inherent biases in laboratory-based communicable disease surveillance. Yet, little empirical evidence exists to support this notion, and it remains largely an intuitive, though highly plausible hypothesis. Our interdisciplinary study uses WES data to show evidence for underreporting of SARS-CoV-2 in the context of measurable and statistically significant associations between economic conditions and SARS-CoV-2 incidence and testing rates. We obtained geolocated, anonymised, laboratory-confirmed SARS-CoV-2 cases, wastewater SARS-CoV-2 viral load data and socio-demographic data for Gauteng Province, South Africa. We spatially located all data to create a single dataset for sewershed catchments served by two large wastewater treatment plants. We conducted epidemiological, persons infected and principal component analysis to explore the relationships between variables. Overall, we demonstrate the co-contributory influences of socio-economic indicators on access to SARS-CoV-2 testing and cumulative incidence, thus reflecting that apparent incidence rates mirror access to testing and socioeconomic considerations rather than true disease epidemiology. These analyses demonstrate how WES provides valuable information to contextualise and interpret laboratory-based epidemiological data. Whilst it is useful to have these associations established for SARS-CoV-2, the implications beyond SARS-CoV-2 are legion for two reasons, namely that biases inherent in clinical surveillance are broadly applicable across pathogens and all pathogens infecting humans will find their way into wastewater albeit in varying quantities. WES should be implemented to strengthen surveillance systems, especially where economic inequalities limit interpretability of conventional surveillance data.
Citation: Maree G, Els F, Naidoo Y, Naidoo L, Mahamuza P, Macheke M, et al. (2025) Wastewater surveillance overcomes socio-economic limitations of laboratory-based surveillance when monitoring disease transmission: The South African experience during the COVID-19 pandemic. PLoS ONE 20(2): e0311332. https://doi.org/10.1371/journal.pone.0311332
Editor: Dhammika Leshan Wannigama, Yamagata University Faculty of Medicine: Yamagata Daigaku Igakubu Daigakuin Igakukei Kenkyuka, JAPAN
Received: September 17, 2024; Accepted: January 22, 2025; Published: February 25, 2025
Copyright: © 2025 Maree et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: An anonymised deidentified data set to replicate our findings has been made available on figshare: https://doi.org/10.6084/m9.figshare.28227206.v1 GCRO Quality of Life data is an open data source and the dataset is freely available under the CC BY 4.0 license from the DataFirst service: https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/GCRO/about Wastewater data is available from the NICD here: www.wastewater.nicd.ac.za Clinical and laboratory data on SARS-COV-2 cannot be shared publicly because of health data restrictions in South Africa and per ethics approval requirements. Data is managed and available via the NICD at www.nicd.ac.za All ethics approval for clinical data has been included in this submission.
Funding: We acknowledge the Water Research Commission (2020.2021 - 00669), the Bill and Melinda Gates foundation (INV050051 and INV049314) for funding this research. The National Institute for Communicable Diseases, and the Gauteng City Regional Observatory contributed in-kind with staff complements.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Surveillance is a core component of the International Health Regulations, and central to the Global Health Security Agenda [1]. World Health Assembly member states are obliged to detect, assess, notify and report events, and to assess their capacity to do so using the Core Capacities document [2]. During the COVID-19 pandemic, which led to an estimated 775 million laboratory-confirmed cases to date [3], testing of individual patient clinical material (usually nasopharyngeal swabs by PCR) and indicators based on these data (including testing rate, incidence rate and proportion testing positive) were the major epidemiological tools used to support monitoring of the pandemic and government decision-making.
SARS-CoV-2 is transmitted by droplet and airborne transmission. Factors predisposing to transmission include crowded conditions, proximity and duration of contact, particularly in the absence of mask-wearing [4]. The clustering of these factors in low socioeconomic households and communities has been shown to exacerbate disease transmission and lead to higher disease incidence [5,6]. However, from the earliest days of the pandemic, it was observed that countries and regions with poorer socioeconomic status reported fewer cases of SARS-CoV-2 [7,8]. Most importantly, a reason for this was limited access to testing, evidenced at a global scale by the African region reporting the fewest SARS-CoV-2 cases across the globe alongside the least number of SARS-CoV-2 tests per capita [8].
South Africa, a country with a population of over 62 million persons resident in nine provinces and 52 health districts, experienced five reported waves of SARS-CoV-2 [9] each caused by different genetic variants. Following initial detection, the first (ancestral strain) and second waves (Beta variant) of the pandemic occurred between March-June 2020, and from November 2020 to February 2021 [10]. The third and fourth waves occurred from May to September 2021 (Delta variant) [10] and from November 2021 to January 2022 (Omicron BA.1 variant) [11]. A subsequent resurgence in cases was responsible for a fifth wave dominated by the Omicron lineage BA.4 and BA.5 [10]. The impact of the COVID-19 pandemic was devastating. Disease transmission levels across South Africa were high, with seroprevalence data after the third wave of SARS-CoV-2, suggesting that infections were likely 7.8 times higher than the number of laboratory-confirmed cases [12]. Excess deaths greatly exceeded reported deaths, indicating that official statistics underestimated the death rate [13]. In spite of active case finding [14] and the availability of a comprehensive laboratory network, these data suggested extensive under diagnosis and under-reporting of cases.
These observations foregrounded the intrinsic shortcomings of traditional approaches to laboratory-based communicable disease surveillance programmes. Patient factors (such as health care acceptability and accessibility, financial means to procure testing and the presence and severity of symptoms); health system factors (such as clinician propensity to test, test availability, financial support for testing), and laboratory factors (clerical errors, inherent test performance characteristics) mediate laboratory testing and in turn impact the sensitivity, quality and representivity of surveillance data. Traditional case-based surveillance methods thus underestimate disease burden.
Since the COVID-19 pandemic, wastewater and environmental surveillance (WES) has increasingly been implemented as a complementary surveillance modality that has potential to overcome these limitations [15]. WES has proven utility in supporting polio surveillance by providing highly sensitive data on the presence of poliovirus in communities, material for genomic sequencing and by supporting identification of chains of transmission [16]. These data have greatly enabled polio risk assessments and decision-making regarding public health interventions including the need for vaccination campaigns. During the COVID-19 pandemic, multiple advantages of WES for SARS-CoV-2 became apparent. WES provided first evidence of importation of the virus into new geographical regions [17], heralded the onset of new waves of infection, illustrated disease transmission patterns and allowed inference of relative population burden during endemic phases [18]. WES also provided material for genotyping that demonstrated the presence of a broader range of variants than seen clinically [19]. In light of this global experience, the WHO issued updated guidance for countries conducting WES (WHO guidelines), citing the ability of WES to overcome the limitations of laboratory-based surveillance. However, despite these advantages and recommendations, there is a paucity of evidence integrating socioeconomic factors and disease epidemiology based on clinical testing data with wastewater surveillance in order to substantiate the claims that WES overcomes clinical testing limitations. Thus, definitive evidence for the ability of WES to overcome the limitations of laboratory-based surveillance is urgently required. We used clinical, wastewater surveillance, demographic and socioeconomic data in two different socio-economic contexts to explain and quantify the relationship between these variables and burden of SARS-CoV-2 disease, thus substantiating the use of WES as a necessary surveillance tool that enables interpretation of clinical surveillance data.
Methods
Conceptual framework
We developed a conceptual framework (Fig 1) to demonstrate the relationships between wastewater concentrations of SARS-CoV-2 and SARS-CoV-2 disease indicators in sewered communities. The population burden of SARS-CoV-2 infections determines the levels of SARS-CoV-2 in wastewater. However, clinical indicators (including incidence rate, testing rate and proportion testing positive) reflect the distribution of testing, health care availability and accessibility. As these factors may be influenced by socio-economic conditions, reported case rates may not accurately reflect the true burden of infections. By determining the inter-relationships between wastewater surveillance data, social determinants of health and reported SARS-CoV-2 cases, the role of WES may be better understood.
The green arrow indicates the research question posed by this work.
Study setting
The study took place in two sewersheds (sewershed D and sewershed O) in different metropolitan areas of Gauteng Province, South Africa. These sewersheds were purposively selected on the basis of geographical representativeness of city populations, namely sewershed D (City of Tshwane), to the north of the Gauteng Province, and sewershed O (City of Ekurhuleni) in the east of Gauteng Province (Fig 2). Sewershed D is residential with formal housing in the west and central areas, and the metropolitan central business district to the east. A small area of informal housing exists to the far west. Sewershed O is mostly residential with low density, low-rise housing with areas of industrial and manufacturing activity to the north east. Informal settlements and backyard shacks are present in most neighbourhoods in the central areas, whilst a wealthier community of gated estates with a low population density is present in the northernmost section.
Data sources and study period
Clinical SARS-CoV-2 laboratory testing and data management.
In South Africa public and private laboratories were legally mandated to report all SARS-CoV-2 test results and patient data including residential address via the National Institute for Communicable Diseases (NICD). These datasets had been geocoded by NICD as part of outbreak response activities and all cases geocoded to Gauteng were extracted. Following ethics review and written approval for this study, anonymised and deidentified geocoded data for positive and negative SARS-CoV-2 PCR test results were supplied for the study period (1 June 2021 (epidemiological week 22 of 2021) to 18 March 2022 (epidemiological week 11 of 2022)) and the data was obtained on 23 May 2023.
Wastewater SARS-CoV-2 laboratory testing and data management.
Routine SARS-CoV-2 wastewater surveillance data (obtained from laboratory processing of one litre grab-samples collected weekly as previously described [9]) from wastewater treatment plant (WWTP) ‘D’ and ‘O’ were identified for the study period. We obtained wastewater flow rates (in ML per day) from WWTP managers.
Quality of life survey- data.
The Quality of Life (QoL) survey is a biennial household survey produced by the Gauteng City-Region Observatory (GCRO) which covers a range of topics including demographics, access to services and perceptions of residents. The survey is weighted to ensure representativity to ward level, a geopolitical division of municipalities, developed by the Municipal Demarcation Board and the smallest unit for which demographic and socioeconomic data are provided in South Africa. We obtained data from the 2020/21 (QoL6) survey (n = 13,616 respondents) pertaining to 207 and 781 respondents from sewersheds D and O respectively [21].
Maps of sewershed reticulation methods.
Data for the sewage networks and pipelines were obtained from the City of Tshwane and the City of Ekurhuleni, respectively. The City of Tshwane provided a comprehensive dataset including sewershed areas, manholes, and distribution pipelines. The City of Ekurhuleni consulting engineers provided shapefiles of the sewershed. ArcGIS 10.6.2 software was used for all mapping and spatial analysis.
Data synthesis and analysis
General approach.
We overlaid sewershed shape files, hexagonal population data, geolocated SARS-CoV-2 cases and geolocated QoL respondents in order to create a dataset comprising these elements for sewersheds D and O. To minimise the effect of the Modifiable Area Unit Problem (MAUP) that is encountered when polygon values require changing because of a change in the shape (zoning effect) or overall area (size effect) of the polygon, we scrutinised our datasets to determine the most viable polygon layer for all spatial datasets to allow for aggregation and comparability without disaggregation of polygon data. Ultimately we manipulated all data to the ward level allowing for comparative analysis over space and time. We conducted these analyses using SPSS software version 29.0.2.0 (20).
Data extraction and aggregation to determine population size, socio-economic and epidemiological parameters by sewershed.
To determine population size and age structure within wards and sewersheds, we extracted population data from the GeoTerraImage (GTI) dataset for hexagons whose centroids fell within the sewershed and ward boundaries, and aggregated population data into four broad classes, namely children (0–4 years), adolescents (5–19), adults (20–59) and elderly (≥60 years). We geolocated QoL respondents within ward and sewershed boundaries and extracted and aggregated relevant socioeconomic and health fields for these individuals. Geolocated positive and negative SARS-CoV-2 PCR test data were aggregated by ward and sewershed level, and used together with population denominator data for each spatial unit to determine overall and weekly incidence, positivity rate and testing rate per 100,000. We created epidemiologic curves using Microsoft Excel 365 using incidence, positivity and testing rates together with SARS-CoV-2 concentration in wastewater for each catchment [22].
Principal Component Analysis (PCA).
We conducted principal component analysis (PCA) on the socioeconomic, demographic and clinical variables for each sewershed, using ward-level aggregated data as the unit of analysis to ascertain which variables relate most to epidemiological indicators in each sewershed. Using R (RStudio v4.0.2) and for each sewershed, we generated 1) a correlation coefficient (r) matrix for inter-variable correlation analysis; 2) a Scree plot for ascertaining the dimensions contributing the most to the explained variance as a percentage; 3) a variable loading graph for ascertaining the variables which contribute to the identity of the prominent dimensions; 4) and a biplot for plotting the variables as vectors in 2D space against the two most prominent dimensions.
Determination of theoretical infectious case load using mass balance equations and comparison with laboratory-confirmed cases by epidemiological week.
We estimated the theoretical number of persons infected in each sewershed by epidemiological week using mass balance equations from Acheampeong et al [23], and Hoffman et al [24] as follows:
where A = RNA per L of wastewater (natural scale); B = estimated flow (L/day) obtained from the wastewater treatment authorities; C = grams of faeces/ person-day, estimated at 128g [25]; D = SARS-CoV-2 RNA per gram faeces, estimated at 2.58 × 10^8 gene copies shed per day per infected person [24]. We assumed a linear regression to determine the number of persons infected per 100 laboratory-confirmed cases by epidemiological week within each sewershed.
While WBE is useful as a tool to estimate infection prevalence and captures asymptomatic and pre-symptomatic cases that are missed from traditional clinical surveillance, the limited data in our study places limitations on its use. Particularly, local data is needed on differentiated shedding rates [26–28] as well as better wastewater flow information [23,27].
Ethical statement
This study was reviewed and approved by the University of the Witwatersrand Human Research Ethics Committee (HREC), M220904. In addition, the National Institute for Communicable Diseases obtained Ethics Approval for essential communicable disease surveillance and outbreak and response activities including SARS-CoV-2 (M210752).
Results
Demographic, socioeconomic and health characteristics
Sewershed O (land area = 120 km2, population = 905,996 persons) has around four times the number of people and just under double the population density of sewershed D (Table 1). Both sewersheds have similar age profiles and are dominated by younger, working age cohorts, with lower proportions of persons over 60 years of age. The proportion of households earning < USD90, together with the proportion relying on public transport, suggest that households in sewershed O are poorer. Most of the population in Sewershed D (98%) and Sewershed O (87%) are connected to the formal sewer network indicating that the wastewater surveillance data encapsulates most of the communities within the study areas [21]. The remaining population live within informal areas and use alternative sanitation options like chemical toilets and ventilated pit latrines. As many as 13% of households in sewershed O share sanitation, compared with around 2% in sewershed D. Regarding health care, responses suggested that up to 10% of households in sewershed O vs 4% in sewershed D struggled to access health care during 2020–2021 period. Up to 35% of households in Sewershed O were unable to maintain SARS-CoV-2 non-pharmaceutical measures.
SARS-CoV-2 clinical testing, incidence rates and percentage test positive (PTP)
During the study period (1 June 2021 to 18 March 2022), 78% and 60% of positive and negative test results respectively were successfully geolocated. The SARS-CoV-2 clinical testing rate per 100,000 in sewershed D (23.7) was over twice the rate in sewershed O (10.6). Despite higher absolute numbers of SARS-CoV-2 cases, sewershed O had a cumulative incidence of laboratory-confirmed SARS-CoV-2 cases around three times lower than sewershed D (15,293 total cases and 1,688/100,000, vs 11,026 total cases and 4,483/100,000) (Table 1 and Fig 3).
Fig 3 illustrates changes in testing, incidence and proportion testing positive (PTP) by epidemiological week during the two waves of infection that occurred during the study period, namely Delta (between epidemiological weeks 15 and 40 in 2021) and Omicron (epidemiological weeks 46 - 51 in 2021). Whilst all indicators follow the similar trends, rates are lower in sewershed O.
Relationship between clinical case data and socioeconomic status
Correlation matrices for sewersheds D and O reveal that clinical indicators (PTP, cumulative incidence and testing rates) do not exhibit consistent relationships with demographic, or socio-economic indicators within and between sewersheds (Fig 4). In sewershed O, PTP correlates with indicators associated with poverty (access to and use of shared sanitation, % of the population with income below USD 90, and COVID-19 index (% unable to implement COVID-19 preventive measures)), whilst this relationship is not evident in the more affluent sewershed D. In sewershed D, it appears that PTP and cumulative incidence are correlated with quality of life, use of public transport and proportion of persons with income below USD 90, suggesting that economic conditions influence access to testing, in turn leading to apparently low cumulative incidence. Taken together, in areas with poorer socio-economic conditions, testing rates and cumulative incidence may not reflect disease burden, whilst PTP better reflects disease risk.
Scree plots for sewershed D and O demonstrate that 76.7% and 77.5% of data are explained by dimensions 1 and 2 (S1 Fig). The variable loading plot for sewershed D indicates that access to shared sanitation, reliance on non-sewered toilets and population groups under 60 years of age contributed the most to composition of dimensions 1 and 2 (S2a Fig) whereas in sewershed O, population group under 60 years of age, testing rate and cumulative incidence rate contributed the most to the composition of dimension 1 and 2 (S2b Fig). In both sewersheds, testing rate and cumulative incidence contributed to dimensions 1 and 2, but the proportion test positive and refused COVID testing variables contributed minimally.
The biplots (Fig 5) demonstrate that SARS-COV-2 testing and incidence rates contribute equivalent influence and spatial distribution on socioeconomic factors for both sewersheds, whilst the proportion test positive indicator was much less affected by socioeconomic factors. In the biplot for sewershed D, access to healthcare and refused access to COVID testing are more associated with cumulative incidence and testing rates, while only refused access to COVID test is associated with the two clinical variables in sewershed O. In sewershed D, the clustering of the Quality of Life score and the greater than 60 years age group as well as the clustering of COVID-19 Index with income below USD90 indicated these variables are influenced by and can inform one another. In sewershed O, struggled to access health care and Quality of Life score illustrated similar clustering with the COVID-19 Index and access to public transport.
In the biplots, the magnitude and colouring of the vectors are related to the variable loading scores, while the vector direction and quadrant location is informed by the interrelationship between variables and their contribution to dimensions 1 and 2.
Quantitative SARS-CoV-2 surveillance in wastewater
Wastewater concentrations of SARS-CoV-2 and geolocated laboratory-confirmed SARS-CoV-2 cases in sewersheds D and O were significantly correlated in each sewershed, though less so in sewershed O (Spearman’s correlation coefficient 0.723 (p < 0.001)), 0.476 (p = 0.020) for sewersheds D and O respectively). In each sewershed, higher concentrations and case-loads were observable during the Delta and Omicron waves that occurred during the study period (Fig 6) and both reached an ebb during weeks 40–42 of 2021. Wastewater concentrations (measured in log genome copies per millilitre) for both sewersheds ranged between 0.5 and 3.5 log copies/mL and were at similar concentrations in the same epidemiological week. Weekly laboratory-confirmed case-counts followed similar trends despite different population sizes in the two catchments.
Comparison of estimated and actual SARS-CoV-2 case burden
Regression analysis by sewershed of laboratory-confirmed SARS-CoV-2 cases versus theoretical number of infections indicated that for each 100 reported cases, sewershed O likely had over 63,000 infections compared with sewershed D, with 2,700 cases (Fig 7).
Discussion
In our transdisciplinary spatial analysis of clinical and environmental data during two large COVID-19 pandemic waves in sewersheds with differing socioeconomic conditions, we observed that despite different population sizes, the concentrations of SARS-CoV-2 in wastewater and the absolute numbers of SARS-CoV-2 cases by epidemiological week were similar. In the light of assumed equivalent excretion rates of SARS-CoV-2 in infected individuals and identical demographic profiles in each sewershed, equivalent wastewater concentrations suggest vast under-reporting of cases in the poorer sewershed. Our socioeconomic analysis demonstrated negative correlations between income and SARS-CoV-2 cumulative incidence and testing rates in the poorer sewershed. Overall, we demonstrated the co-contributory influences of socio-economic indicators on access to SARS-CoV-2 testing and cumulative incidence, thus reflecting that apparent incidence rates mirror access to testing and socioeconomic considerations rather than true disease epidemiology. These analyses demonstrate how WES provides valuable information to contextualise and interpret laboratory-based epidemiological data.
Laboratory-based surveillance systems under-represent the true burden of disease due to a combination of asymptomatic infection, individual and cultural practices regarding health seeking, quality of health care and socio-economic factors that impair access to testing. Furthermore, these same socio-economic factors including education and poverty, are associated with higher SARS-CoV-2 rates [6]. Underreporting of SARS-CoV-2 cases in South Africa was evident through excess mortality reports which indicated over 70,000 excess deaths vs 28,000 reported COVID-19 deaths during 2020 [13]. Households in lower income groups, those who rely on public health care, and Black African and Coloured population groups were more likely to have struggled to access healthcare and testing facilities [29]. Our data, demonstrating that cumulative incidence and testing rate in sewershed O was negatively correlated with low income ( <USD 90 household income per month) suggest that clinical testing was missing this population segment.
Few studies have triangulated clinical testing, wastewater surveillance data and socio-economic factors. Using WES data in contrast to clinical testing data, Lancaster et al [30] identified specific communities (Black, poor) that were more vulnerable to SARS-CoV-2. Saingam et al [31] demonstrated that a machine learning model to predict COVID-19 and post-infectious sequelae is strengthened when including WES data together with socio-demographic parameters. Rogawski McQuade et al [32] in Bangladesh, using purposively selected sampling sites across the economic spectrum, demonstrated equivalent SARS-CoV-2 levels in wastewater, but 23 and 70 times the number of clinical tests conducted in the wealthier vs middle and poor income areas. Our interdisciplinary study is the first to use WES data to show evidence for underreporting of SARS-CoV-2 in the context of measurable and statistically significant associations between economic conditions and SARS-CoV-2 incidence and testing rates, something which has been intuitively speculated but not empirically demonstrated [33].
Several limitations exist in our data collection and interpretation. A small proportion of SARS-CoV-2 cases were not successfully geolocated, however non-geolocated cases are more likely to have originated outside the province, or from persons resident in informal settings thus not contributing to wastewater levels of SARS-CoV-2. The absence of wastewater sampling points within wards precluded inclusion of SARS-CoV-2 levels in PCA analyses. Whilst our wastewater data was generated from urban sewersheds, similar findings in non-sewered areas suggest that our findings are generalisable across sewered and non-sewered settings [32]. The persons infected calculations are indicative and limited by the availability of daily wastewater flow data as well as detailed viral shedding rates [23]. These limitations point to key areas for improvement as the field of WES matures within South Africa.
The addition of WES data to national and global surveillance systems will strengthen sensitivity of event detection for outbreak and pandemic disease, monitoring of endemic disease trends, and will jointly provide material for genomic epidemiology [19]. An evaluation of detection of H5N1 avian influenza by six northern hemisphere surveillance systems in 2010 demonstrated an increase in sensitivity of detection using a combination of data inputs. Authors concluded that the range of surveillance methodologies and variation in system designs created synergy between systems, led to improved data quality and validity, and allowed data to converge on event detection [34]. In a low-middle income country, the need for multiple surveillance systems is even more necessary, as data quality and completeness from single modality surveillance systems may vary, leading to challenges in decision making during a crisis.
Our findings provide evidence to support intuitive thinking that WES overcomes testing biases particularly in situations with socio-economic disparities and weaker clinical disease surveillance programmes for SARS-CoV-2. Our findings are likely broadly applicable to all communicable disease surveillance programmes, as biases affecting clinical surveillance programmes are not disease-specific, and all pathogens infecting humans are likely to find their way into wastewater albeit in varying quantities. As such, our findings strengthen the case for investment in implementation of WES. Ongoing implementation of WES will allow public health authorities to determine optimal configurations of WES surveillance systems for each pathogen and public health use-case. Further research to determine optimal sample collection, processing and testing methods is needed. Interpretive frameworks or mathematical models will support integration and interpretation of WES data with clinical surveillance data.
Supporting information
S1 Fig. Principal Component Analysis Scree plots indicating the percentage of variance explained by principal components.
Dimension 1 and 2 account for 76.7% in sewershed D (a) and 77.5% sewershed O (b).
https://doi.org/10.1371/journal.pone.0311332.s001
(TIF)
S2 Fig. Variable loading bar plots of the variables contributing to the composition of dimensions 1 and 2 for (a) sewershed D and (b) sewershed O.
Note: Colour ramp matches the PCA plots and the darker the shade the lower the loading value.
https://doi.org/10.1371/journal.pone.0311332.s002
(TIF)
References
- 1. Wolicki SB, Nuzzo JB, Blazes DL, Pitts DL, Iskander JK, Tappero JW. Public health surveillance: at the core of the global health security agenda. Health Secur. 2016;14(3):185–8. pmid:27314658
- 2.
World Health Organisation. Checklist and indicators for monitoring progress in the development of IHR core capacities in states parties, April 2013. UHC Healthier Populations (HEP); 2013. Report No.: WHO/HSE/GCR/2013.2. Available from: https://www.who.int/publications-detail-redirect/who-hse-gcr-2013-2
- 3.
World Health Organisation. WHO Coronavirus (COVID-19) dashboard> Deaths [Dashboard]. 2023. Available from: https://data.who.int/dashboards/covid19/cases
- 4. Yazdani S, Heydari M, Foroughi Z, Jabali H. Factors affecting COVID-19 transmission and modelling of close contact tracing strategies. Iran J Public Health. 2021;50(10):2121–31. pmid:35223580
- 5. Karmakar M, Lantz PM, Tipirneni R. Association of social and demographic factors with COVID-19 incidence and death rates in the US. JAMA Netw Open. 2021;4(1):e2036462. pmid:33512520
- 6. De Figueiredo AM, De Figueiredo DCMM, Gomes LB, Massuda A, Gil-García E, De Vianna RPT, et al. Social determinants of health and COVID-19 infection in Brazil: an analysis of the pandemic. Rev Bras Enferm. 2020;73(Suppl 2):e20200673. pmid:33206820
- 7. Bwire G, Ario AR, Eyu P, Ocom F, Wamala JF, Kusi KA, et al. The COVID-19 pandemic in the African continent. BMC Med. 2022;20(1):167. pmid:35501853
- 8. Wamai RG, Hirsch JL, Van Damme W, Alnwick D, Bailey RC, Hodgins S, et al. What could explain the lower COVID-19 burden in Africa despite considerable circulation of the SARS-CoV-2 virus? Int J Environ Res Public Health. 2021;18(16):8638. pmid:34444386
- 9.
National Institute for Communicable Diseases. SARS-COV-2 sequencing update. Network for Genomic Sequencing. 2023. Available from: https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/sars-cov-2-genomic-surveillance-update/.
- 10. Tegally H, Wilkinson E, Giovanetti M, Iranzadeh A, Fonseca V, Giandhari J, et al. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature. 2021;592(7854):438–43. pmid:33690265
- 11. Karim SSA, Karim QA. Omicron SARS-CoV-2 variant: a new chapter in the COVID-19 pandemic. Lancet (London, England). 2021;398(10317):2126–8. pmid:34871545
- 12. Mutevedzi PC, Kawonga M, Kwatra G, Moultrie A, Baillie V, Mabena N, et al. Estimated SARS-CoV-2 infection rate and fatality risk in Gauteng Province, South Africa: a population-based seroepidemiological survey. Int J Epidemiol. 2022;51(2):404–17. pmid:34718591
- 13. Bradshaw D, Dorrington R, Laubscher R, Groenewald P, Moultrie T. COVID-19 and all-cause mortality in South Africa – the hidden deaths in the first four waves. South Afr J Sci. 2022;118(5/6).
- 14. Modisenyane M, Madikezela L, Mngemane S, Ramadan OP, Matlala M, McCarthy K, et al. COVID-19 response in South African communities: screening, testing, tracing and movement modelling. S Afr Med J. 2022;112(5b):366–70. pmid:35747392
- 15. Clark EC, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to public health surveillance methods due to the COVID-19 pandemic: scoping review. JMIR Public Health Surveill. 2024;10:e49185. pmid:38241067
- 16. O’Reilly KM, Verity R, Durry E, Asghar H, Sharif S, Zaidi SZ, et al. Population sensitivity of acute flaccid paralysis and environmental surveillance for serotype 1 poliovirus in Pakistan: an observational study. BMC Infect Dis. 2018;18(1):176. pmid:29653509
- 17. Ahmed W, Angel N, Edson J, Bibby K, Bivins A, O’Brien JW, et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: a proof of concept for the wastewater surveillance of COVID-19 in the community. Sci Total Environ. 2020;728:138764. pmid:32387778
- 18. Smith T, Holm RH, Keith RJ, Amraotkar AR, Alvarado CR, Banecki K, et al. Quantifying the relationship between sub-population wastewater samples and community-wide SARS-CoV-2 seroprevalence. Sci Total Environ. 2022;853:158567. pmid:36084773
- 19. Yousif M, Rachida S, Taukobong S, Ndlovu N, Iwu-Jaja C, Howard W, et al. SARS-CoV-2 genomic surveillance in wastewater as a model for monitoring evolution of endemic viruses. Nat Commun. 2023;14(1):6325. pmid:37816740
- 20.
Municipal Demarcation Board (MDB) of South Africa. MDB Wards 2016. Available from: https://dataportal-mdb-sa.opendata.arcgis.com/datasets/cfddb54aab5f4d62b2144d80d49b3fdb
- 21.
Gauteng City-Region Observatory. Quality of Life Survey 2020-2021, Round 6. [object Object]. 2021. Available from: https://doi.org/10.25828/WEMZ-VF31
- 22. Iwu-Jaja C, Ndlovu NL, Rachida S, Yousif M, Taukobong S, Macheke M, et al.; SACCESS network. The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making. Sci Total Environ. 2023;903:165817. pmid:37506905
- 23. Acheampong E, Husain AA, Dudani H, Nayak AR, Nag A, Meena E, et al. Population infection estimation from wastewater surveillance for SARS-CoV-2 in Nagpur, India during the second pandemic wave. PLoS ONE. 2024;19(5):e0303529. pmid:38809825
- 24. Hoffmann T, Alsing J. Faecal shedding models for SARS-CoV-2 RNA among hospitalised patients and implications for wastewater-based epidemiology. J R Stat Soc Series C Appl Stat. 2023;72(2):330–45.
- 25. Rose C, Parker A, Jefferson B, Cartmell E. The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit Rev Environ Sci Technol. 2015;45(17):1827–79. pmid:26246784
- 26. Li W-T, Zhang Y, Liu M, Liu Y-Q, Ma X. Prolonged viral shedding in feces of children with COVID-19: a systematic review and synthesis of data. Eur J Pediatr. 2022;181(12):4011–7. pmid:36114833
- 27. Laicans J, Dejus B, Dejus S, Juhna T. Precision and accuracy limits of wastewater-based epidemiology—lessons learned from SARS-CoV-2: a scoping review. Water. 2024;16(9):1220.
- 28. McMahan CS, Self S, Rennert L, Kalbaugh C, Kriebel D, Graves D, et al. COVID-19 wastewater epidemiology: a model to estimate infected populations. Lancet Planet Health. 2021;5(12):e874–81. pmid:34895497
- 29.
Maree G, Fatti CC, Götz G, Hamann C, Parker A. Effects of the COVID-19 pandemic on the Gauteng City-region: Findings from the GCRO’s Quality of Life Survey 6 (2020/21). 2021.
- 30. Lancaster E, Byrd K, Ai Y, Lee J. Socioeconomic status correlations with confirmed COVID-19 cases and SARS-CoV-2 wastewater concentrations in small-medium sized communities. Environ Res. 2022;215(Pt 2):114290. pmid:36096171
- 31. Saingam P, Jain T, Woicik A, Li B, Candry P, Redcorn R, et al. Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications. Water Res. 2024;254:121415. pmid:38479175
- 32. Rogawski McQuade ET, Blake IM, Brennhofer SA, Islam MO, Sony SSS, Rahman T, et al. Real-time sewage surveillance for SARS-CoV-2 in Dhaka, Bangladesh versus clinical COVID-19 surveillance: a longitudinal environmental surveillance study (December, 2019-December, 2021). Lancet Microbe. 2023;4(6):e442–51. pmid:37023782
- 33. Layton BA, Kaya D, Kelly C, Williamson KJ, Alegre D, Bachhuber SM, et al. Evaluation of a wastewater-based epidemiological approach to estimate the prevalence of SARS-CoV-2 infections and the detection of viral variants in disparate oregon communities at city and neighborhood scales. Environ Health Perspect. 2022;130(6):67010. pmid:35767012
- 34. Barboza P, Vaillant L, Mawudeku A, Nelson NP, Hartley DM, Madoff LC, et al; Early Alerting Reporting Project Of The Global Health Security Initiative. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events. PLoS ONE. 2013;8(3):e57252. pmid:23472077