ABSTRACT
The SARS-CoV-2 pandemic highlighted the need for novel tools to promote health equity. There has been a historical legacy around the location and allocation of public facilities (such as health care) focused on efficiency, which is not attainable in rural, low-density, United States areas. Differences in the spread of the disease and outcomes of infections have been observed between urban and rural populations throughout the COVID-19 pandemic. The purpose of this article was to review rural health disparities related to the SARS-CoV-2 pandemic while using evidence to support wastewater surveillance as a potentially innovative tool to address these disparities more widely. The successful implementation of wastewater surveillance in resource-limited settings in South Africa demonstrates the ability to monitor disease in underserved areas. A better surveillance model of disease detection among rural residents will overcome issues around the interactions of a disease and social determinants of health. Wastewater surveillance can be used to promote health equity, particularly in rural and resource-limited areas, and has the potential to identify future global outbreaks of endemic and pandemic viruses.
ABSTRACT
Hepatitis E virus (HEV) is an emerging zoonotic pathogen that exhibits great host diversity. The primary means of transmission of the virus in low- and middle-income countries is contaminated water, often due to a lack of access to proper sanitation, which leads to faecal contamination of water sources. Environmental surveillance is an important tool that can be used to monitor virus circulation and as an early warning system for outbreaks. This study was conducted to determine the prevalence and genetic diversity of HEV in wastewater, surface water (rivers and standpipe/ablution water), and effluent from a piggery in South Africa. A total of 536 water samples were screened for HEV using real-time reverse transcription-polymerase chain reaction. Overall, 21.8% (117/536) of the wastewater, river, and ablution water samples tested positive for HEV, whereas 74.4% (29/39) of the samples from the piggery tested positive. Genotyping revealed sequences belonging to HEV genotypes 3 (98%, 53/54) and 4 (2%, 1/54), with subtypes 3c, 3f, and 4b being identified.
ABSTRACT
The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.