Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications
Copyright © 2024 Elsevier Ltd. All rights reserved..
Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:254 |
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| Enthalten in: |
Water research - 254(2024) vom: 01. Apr., Seite 121415 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Saingam, Prakit [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 and post-COVID |
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| Anmerkungen: |
Date Completed 08.04.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.watres.2024.121415 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COVID-19 and post-COVID | |
| 650 | 4 | |a Chronic kidney disease | |
| 650 | 4 | |a Demographics | |
| 650 | 4 | |a Disease prediction | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a WBE | |
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| 650 | 7 | |a Wastewater |2 NLM | |
| 700 | 1 | |a Jain, Tanisha |e verfasserin |4 aut | |
| 700 | 1 | |a Woicik, Addie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Candry, Pieter |e verfasserin |4 aut | |
| 700 | 1 | |a Redcorn, Raymond |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Sheng |e verfasserin |4 aut | |
| 700 | 1 | |a Himmelfarb, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Bryan, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Winkler, Mari K H |e verfasserin |4 aut | |
| 700 | 1 | |a Gattuso, Meghan |e verfasserin |4 aut | |
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