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Monitoring human arboviral diseases through wastewater surveillance: Challenges, progress and future opportunities.
Lee, Wei Lin; Gu, Xiaoqiong; Armas, Federica; Leifels, Mats; Wu, Fuqing; Chandra, Franciscus; Chua, Feng Jun Desmond; Syenina, Ayesa; Chen, Hongjie; Cheng, Dan; Ooi, Eng Eong; Wuertz, Stefan; Alm, Eric J; Thompson, Janelle.
  • Lee WL; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore.
  • Gu X; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore.
  • Armas F; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore.
  • Leifels M; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore.
  • Wu F; Department of Epidemiology, Human Genetics, and Environmental Sciences, Center for Infectious Disease, University of Texas School of Public Health, Houston, TX, USA.
  • Chandra F; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore.
  • Chua FJD; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore.
  • Syenina A; Program in Emerging Infectious Diseases, Duke-NUS Medical School, Singapore 169857, Singapore; Viral Research and Experimental Medicine Centre (ViREMiCS), SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore.
  • Chen H; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore.
  • Cheng D; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore.
  • Ooi EE; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Program in Emerging Infectious Diseases, Duke-NUS Medical Sch
  • Wuertz S; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.
  • Alm EJ; Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Center for Microbiome Informatics and Therapeutics, Massachus
  • Thompson J; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore
Water Res ; 223: 118904, 2022 Sep 01.
Article in English | MEDLINE | ID: covidwho-1956371
ABSTRACT
Arboviral diseases are caused by a group of viruses spread by the bite of infected arthropods. Amongst these, dengue, Zika, west nile fever and yellow fever cause the greatest economic and social impact. Arboviral epidemics have increased in frequency, magnitude and geographical extent over the past decades and are expected to continue increasing with climate change and expanding urbanisation. Arboviral prevalence is largely underestimated, as most infections are asymptomatic, nevertheless existing surveillance systems are based on passive reporting of loosely defined clinical syndromes with infrequent laboratory confirmation. Wastewater-based surveillance (WBS), which has been demonstrated to be useful for monitoring diseases with significant asymptomatic populations including COVID19 and polio, could be a useful complement to arboviral surveillance. We review the current state of knowledge and identify key factors that affect the feasibility of monitoring arboviral diseases by WBS to include viral shedding loads by infected persons, the persistence of shed arboviruses and the efficiency of their recovery from sewage. We provide a simple model on the volume of wastewater that needs to be processed for detection of arboviruses, in face of lower arboviral shedding rates. In all, this review serves to reflect on the key challenges that need to be addressed and overcome for successful implementation of arboviral WBS.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Arbovirus Infections / Arboviruses / Zika Virus / Zika Virus Infection / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Water Res Year: 2022 Document Type: Article Affiliation country: J.watres.2022.118904

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Arbovirus Infections / Arboviruses / Zika Virus / Zika Virus Infection / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Humans Language: English Journal: Water Res Year: 2022 Document Type: Article Affiliation country: J.watres.2022.118904