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Monitoring changes in COVID-19 infection using wastewater-based epidemiology: A South African perspective.

ABSTRACT: Monitoring of COVID-19 infections within communities via wastewater-based epidemiology could provide a cost-effective alternative to clinical testing. This approach, however, still requires improvement for its efficient application. In this paper, we present the use of wastewater-based epidemiology in monitoring COVID-19 infection dynamics in the KwaZulu-Natal province of South Africa, focusing on four wastewater treatment plants for 14 weeks. The SARS-CoV-2 viral load in influent wastewater was determined using droplet digital PCR, and the number of people infected was estimated using published models as well as using a modified model to improve efficiency. On average, viral loads ranged between 0 and 2.73 × 10⁵ copies/100 ml, 0-1.52 × 10⁵ copies/100 ml, 3 × 10⁴-7.32 × 10⁵ copies/100 ml and 1.55 × 10⁴-4.12 × 10⁵ copies/100 ml in the four wastewater treatment plants studied. The peak in viral load corresponded to the reported COVID-19 infections within the districts where these catchments are located. In addition, we also observed that easing of lockdown restrictions by authorities corresponded with an increase in viral load in the untreated wastewater. Estimation of infection numbers based on the viral load showed that a higher number of people could potentially be infected, compared to the number of cases reported based on clinical testing. The findings reported in this paper contribute to the field of wastewater-based epidemiology for COVID-19 surveillance, whilst highlighting some of the challenges associated with this approach, especially in developing countries.

SUBMITTER: Pillay L

PROVIDER: S-EPMC8062404 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWastewater-based epidemiology provides an opportunity for near real-time, cost-effective monitoring of community-level transmission of SARS-CoV-2. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods for estimating the numbers of infected individuals on the basis of wastewater RNA concentrations are inadequate.MethodsThis is a wastewater-based epidemiology study using wastewater samples that were collected weekly or twice a week from three sewersheds in South Carolina, USA, between either May 27 or June 16, 2020, and Aug 25, 2020, and tested for SARS-CoV-2 RNA. We developed a susceptible-exposed-infectious-recovered (SEIR) model based on the mass rate of SARS-CoV-2 RNA in the wastewater to predict the number of infected individuals, and have also provided a simplified equation to predict this. Model predictions were compared with the number of confirmed cases identified by the Department of Health and Environmental Control, South Carolina, USA, for the same time period and geographical area.FindingsWe plotted the model predictions for the relationship between mass rate of virus release and numbers of infected individuals, and we validated this prediction on the basis of estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The rate of unreported COVID-19 cases, as estimated by the model, was approximately 11 times that of confirmed cases (ie, ratio of estimated infections for every confirmed case of 10·9, 95% CI 4·2-17·5). This rate aligned well with an independent estimate of 15 infections for every confirmed case in the US state of South Carolina.InterpretationThe SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed on the basis of the mass rate of RNA copies released per day. This approach overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to inform policy decisions.FundingClemson University, USA.

Project description:The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater produced interest in its use for sentinel surveillance at a community level and as a complementary approach to syndromic surveillance. With this work, we set the foundations for wastewater-based epidemiology (WBE) in Portugal by monitoring the trends of SARS-CoV-2 RNA circulation in the community, on a nationwide perspective during different epidemiological phases of the pandemic. The Charité assays (E_Sarbecco, RdRP, and N_Sarbecco) were applied to monitor, over 32-weeks (April to December 2020), the dynamics of SARS-CoV-2 RNA at the inlet of five wastewater treatment plants (WWTP), which together serve more than two million people in Portugal. Raw wastewater from three Coronavirus disease 2019 (COVID-19) reference hospitals was also analyzed during this period. In total, more than 600 samples were tested. For the first weeks, detection of SARS-CoV-2 RNA was sporadic, with concentrations varying from 103 to 105 genome copies per liter (GC/L). Prevalence of SARS-CoV-2 RNA increased steeply by the end of May into late June, mainly in Lisboa e Vale do Tejo region (LVT), during the reopening phase. After the summer, with the reopening of schools in mid-September and return to partial face-to-face work, a pronounced increase of SARS-CoV-2 RNA in wastewater was detected. In the LVT area, SARS-CoV-2 RNA load agreed with reported trends in hotspots of infection. Synchrony between trends of SARS-CoV-2 RNA in raw wastewater and daily new COVID-19 cases highlights the value of WBE as a surveillance tool, particularly after the phasing out of the epidemiological curve and when hotspots of disease re-emerge in the population which might be difficult to spot based solely on syndromic surveillance and contact tracing. This is the first study crossing several epidemiological stages highlighting the long-term use of WBE for SARS-CoV-2. Graphical abstract Unlabelled Image

Project description:The global pandemic caused by the newly described severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused worldwide suffering and death of unimaginable magnitude from coronavirus disease 2019 (COVID-19). The virus is transmitted through aerosol droplets, and causes severe acute respiratory syndrome. SARS-CoV-2 uses the receptor-binding domain of its spike protein S1 to attach to the host angiotensin-converting enzyme 2 receptor in lung and airway cells. Binding requires the help of another host protein, transmembrane protease serine S1 member 2. Several factors likely contribute to the efficient transmission of SARS-CoV-2. The receptor-binding domain of SARS-CoV-2 has a 10- to 20-fold higher receptor-binding capacity compared with previous pandemic coronaviruses. In addition, because asymptomatic persons infected with SARS-CoV-2 have high viral loads in their nasal secretions, they can silently and efficiently spread the disease. PCR-based tests have emerged as the criterion standard for the diagnosis of infection. Caution must be exercised in interpreting antibody-based tests because they have not yet been validated, and may give a false sense of security of being "immune" to SARS-CoV-2. We discuss how the development of some symptoms in allergic rhinitis can serve as clues for new-onset COVID-19. There are mixed reports that asthma is a risk factor for severe COVID-19, possibly due to differences in asthma endotypes. The rapid spread of COVID-19 has focused the efforts of scientists on repurposing existing Food and Drug Administration-approved drugs that inhibit viral entry, endocytosis, genome assembly, translation, and replication. Numerous clinical trials have been launched to identify effective treatments for COVID-19. Initial data from a placebo-controlled study suggest faster time to recovery in patients on remdesivir; it is now being evaluated in additional controlled studies. As discussed in this review, till effective vaccines and treatments emerge, it is important to understand the scientific rationale of pandemic-mitigation strategies such as wearing facemasks and social distancing, and implement them.

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Monitoring changes in COVID-19 infection using wastewater-based epidemiology: A South African perspective.

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