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The COVID-19 pandemic has resulted in over 340 million infection cases (as of 21 January 2022) and more than 5.57 million deaths globally. In reaction, science, technology and innovation communities across the globe have organised themselves to contribute to national responses to COVID-19 disease. A significant contribution has been from the establishment of wastewater-based epidemiological (WBE) surveillance interventions and programmes for monitoring the spread of COVID-19 in at least 55 countries. Here, we examine and share experiences and lessons learnt in establishing such surveillance programmes. We use case studies to highlight testing methods and logistics considerations associated in scaling the implementing of such programmes in South Africa, the Netherlands, Turkey and England. The four countries were selected to represent different regions of the world and the perspective based on the considerable progress made in establishing and implementing their national WBE programmes. The selected countries also represent different climatic zones, economies, and development stages, which influence the implementation of national programmes of this nature and magnitude. In addition, the four countries’ programmes offer good experiences and lessons learnt since they are systematic, and cover extensive areas, disseminate knowledge locally and internationally and partnered with authorities (government). The programmes also strengthened working relations and partnerships between and among local and global organisations. This paper shares these experiences and lessons to encourage others in the water and public health sectors on the benefits and value of WBE in tackling SARS-CoV-2 and related future circumstances. [ABSTRACT FROM AUTHOR]

Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater (WW) can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Data from 44 sewage sites in England, covering 31% of the population, are used in this analysis where samples are available from July 2020 to present day. Samples include the raw SARS-CoV-2 gene copy number and associated meta-data. To establish the sensitivity and specificity of the WW data, we compare to population representative prevalence surveys available across England (the ONS Covid Infection Survey - CIS). The WW data were mapped to sub-regional data of the CIS and fitted using mathematical modelling. First, a phenomenological model was developed to model how infected individuals shed SARS-CoV-2 into WW and how the markers may degrade in time and compare this to the data. Second, we develop a model to estimate SARS-CoV-2 prevalence directly from WW data which is trained on the CIS data. Data from 44 sewage sites in England, shows that SARS-CoV-2 prevalence is estimated to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, differences between sampled sites, particularly the WW flow rate, influence prevalence estimation and require careful interpretation. SARS-CoV-2 signals in WW appear 4-5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that WW surveillance can be a leading indicator for asymptomatic viral infections. Wastewater-based epidemiology complements and strengthens traditional surveillance, with significant implications for public health. Using WW to quantify infection prevalence requires knowledge of additional meta-data and outbreak detection needs to account for unexplained aberrations in WW data to improve reliability [ABSTRACT FROM AUTHOR]

Two recent association mapping studies in Parkinson disease (PD) reported three candidate genes for the PARK10 locus: EIF2B3 as a modifier of age-at-onset of PD (min P= 0.0004) and HIVEP3 as a PD risk gene (P ≤ 0.006) ( Oliveira et al. 2005 ); and LOC200008 ( CDCP2) identified by the whole genome association (WGA) study of PD of Maraganore et al. (2005) . However, evaluation of the on-line PD WGA results revealed two significant SNPs in HIVEP3 in the two datasets, Tier 1 and Tier 2, used by Maraganore et al. (P ≤ 0.008 for Tier 1 and P=0.03 for Tier 2 dataset). Here, we revisited both the HIVEP3 and CDCP2 loci by examining 47 SNPs, mostly tagging, in an expanded PD family dataset (293 multiplex and 467 singleton families). A discordant sibpair (DSP) dataset (one DSP per family), with similar data structure as the WGA Tier 1 dataset, was also tested. We confirmed our and other previous negative findings for CDCP2. However, five significant SNPs in HIVEP3 (min P=0.004) were observed, although the two significant HIVEP3 SNPs from the PD WGA study were not significant in our datasets. Even though the sets of significant HIVEP3 markers differ between studies, these findings strongly support HIVEP3 as a candidate for PARK10. Further testing of HIVEP3 by other groups is encouraged. [ABSTRACT FROM AUTHOR]