Spatiotemporal Surveillance of SARS-CoV-2 in the Sewage of Three Major Urban Areas in Peru: Generating Valuable Data Where Clinical Testing Is Extremely Limited.
Braulio Pardo-FigueroaElias Mindreau-GanozaAlonso Reyes-CalderonSonia P YufraIsabel M Solorzano-OrtizAlberto J Donayre-TorresClaudia AntoniniJose Miguel RenomAntonio Marty QuispeCesar R MotaCarlos Augusto de Lemos ChernicharoMax A CarbajalMónica C Santa-MaríaPublished in: ACS ES&T water (2022)
Peru has been severely affected by the COVID-19 pandemic. By January 2022, Peru had surpassed 200 000 COVID-19 deaths, constituting the highest death rate per capita worldwide. Peru has had several limitations during the pandemic: insufficient testing access, limited contact tracing, a strained medical infrastructure, and many economic hurdles. These limitations hindered the gathering of accurate information about infected individuals with spatial resolution in real time, a critical aspect of effectively controlling the pandemic. Wastewater monitoring for SARS-CoV-2 RNA offered a promising alternative for providing needed population-wide information to complement health care indicators. In this study, we demonstrate the feasibility and value of implementing a decentralized SARS-CoV-2 RNA wastewater monitoring system to assess the spatiotemporal distribution of COVID-19 in three major cities in Peru: Lima, Callao, and Arequipa. Our data on viral loads showed the same trends as health indicators such as incidence and mortality. Furthermore, we were able to identify hot spots of contagion within the surveyed urban areas to guide the efforts of health authorities. Viral decay in the sewage network of the cities studied was found to be negligible (<2%). Overall, our results support wastewater monitoring for SARS-CoV-2 as a valuable and cost-effective tool for monitoring the COVID-19 pandemic in the Peruvian context.
Keyphrases
- sars cov
- healthcare
- respiratory syndrome coronavirus
- public health
- health information
- wastewater treatment
- mental health
- coronavirus disease
- electronic health record
- risk factors
- big data
- anaerobic digestion
- cardiovascular disease
- type diabetes
- antibiotic resistance genes
- quality improvement
- cardiovascular events
- health promotion
- data analysis
- climate change
- health insurance
- human health
- deep learning
- nucleic acid