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Wastewater monitoring can anchor global disease surveillance systems.
Keshaviah, Aparna; Diamond, Megan B; Wade, Matthew J; Scarpino, Samuel V.
  • Keshaviah A; Mathematica, Princeton, NJ, USA.
  • Diamond MB; The Rockefeller Foundation, New York, NY, USA.
  • Wade MJ; Analytics & Data Science Directorate, UK Health Security Agency, London, UK.
  • Scarpino SV; Institute for Experiential AI, Network Science Institute, Department of Health Sciences, and Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA; Santa Fe Institute, Santa Fe, NM, USA; Vermont Complex Systems Center, University of Vermont, Burlington, VT, USA. Electronic ad
Lancet Glob Health ; 11(6): e976-e981, 2023 06.
Article in English | MEDLINE | ID: covidwho-2316005
ABSTRACT
To inform the development of global wastewater monitoring systems, we surveyed programmes in 43 countries. Most programmes monitored predominantly urban populations. In high-income countries (HICs), composite sampling at centralised treatment plants was most common, whereas grab sampling from surface waters, open drains, and pit latrines was more typical in low-income and middle-income countries (LMICs). Almost all programmes analysed samples in-country, with an average processing time of 2·3 days in HICs and 4·5 days in LMICs. Whereas 59% of HICs regularly monitored wastewater for SARS-CoV-2 variants, only 13% of LMICs did so. Most programmes share their wastewater data internally, with partnering organisations, but not publicly. Our findings show the richness of the existing wastewater monitoring ecosystem. With additional leadership, funding, and implementation frameworks, thousands of individual wastewater initiatives can coalesce into an integrated, sustainable network for disease surveillance-one that minimises the risk of overlooking future global health threats.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Wastewater / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: Lancet Glob Health Year: 2023 Document Type: Article Affiliation country: S2214-109X(23)00170-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Wastewater / COVID-19 Type of study: Observational study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: Lancet Glob Health Year: 2023 Document Type: Article Affiliation country: S2214-109X(23)00170-5