Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model
Wastewater-based estimation of infectious disease prevalence in real-time assists public health authorities in developing effective responses to current outbreaks. However, wastewater-based estimation for IAV remains poorly demonstrated, partially because of a lack of knowledge about temporal variat...
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Elsevier
2025-01-01
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| Series: | Environment International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412024008055 |
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| author | Hiroki Ando Michio Murakami Masaaki Kitajima Kelly A. Reynolds |
| author_facet | Hiroki Ando Michio Murakami Masaaki Kitajima Kelly A. Reynolds |
| author_sort | Hiroki Ando |
| collection | DOAJ |
| description | Wastewater-based estimation of infectious disease prevalence in real-time assists public health authorities in developing effective responses to current outbreaks. However, wastewater-based estimation for IAV remains poorly demonstrated, partially because of a lack of knowledge about temporal variation in shedding amount of an IAV-infected person. In this study, we applied two mathematical models to previously collected wastewater and clinical data from four U.S. states during the 2022/2023 influenza season, dominated by the H3N2 subtype. First, we modeled the relationship between the detection probability of IAV in wastewater and FluA case counts, using a logistic function. The model revealed that a 50 % probability of IAV detection in wastewater corresponds to 0.53 (95 % CrI: 0.35–0.78) cases per 100,000 people, as observed in clinical surveillance over two weeks. Next, we applied the previously developed PRESENS model to IAV wastewater concentration data from California, revealing rapid and prolonged virus shedding patterns. The estimated shedding model was incorporated into an extended version of the PRESENS model to assess the variability in the relationship between IAV concentrations and case numbers across other states, including Massachusetts, New Jersey, and Utah. As a result, our analysis demonstrated the effectiveness of normalizing IAV concentrations with PMMoV (Pepper mild mottle virus) to accurately understand spatial distribution patterns of IAV prevalence. We successfully estimated FluA case counts from wastewater concentrations within a factor of two for 80 % of data from a state where 34 % of the state population was monitored by wastewater surveillance. Importantly, wastewater-based estimates provided real-time or leading insights (0–2 days) compared to clinical case detection in the three states, enabling early understanding of the incidence trends by limiting delays in data publication. These findings highlight the potential of wastewater surveillance to detect IAV outbreaks in near real-time and enhance efficiency of the infectious disease management. |
| format | Article |
| id | doaj-art-b1cc28cdbf89498db7d85563363fe1a9 |
| institution | Kabale University |
| issn | 0160-4120 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environment International |
| spelling | doaj-art-b1cc28cdbf89498db7d85563363fe1a92025-01-24T04:44:05ZengElsevierEnvironment International0160-41202025-01-01195109218Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS modelHiroki Ando0Michio Murakami1Masaaki Kitajima2Kelly A. Reynolds3Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, United StatesCenter for Infectious Disease Education and Research, Osaka University, 2-8 Yamadaoka, Suita, Osaka 565-0871, JapanResearch Center for Water Environment Technology, Graduate School of Engineering, The University of Tokyo, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, JapanMel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, United States; Corresponding author.Wastewater-based estimation of infectious disease prevalence in real-time assists public health authorities in developing effective responses to current outbreaks. However, wastewater-based estimation for IAV remains poorly demonstrated, partially because of a lack of knowledge about temporal variation in shedding amount of an IAV-infected person. In this study, we applied two mathematical models to previously collected wastewater and clinical data from four U.S. states during the 2022/2023 influenza season, dominated by the H3N2 subtype. First, we modeled the relationship between the detection probability of IAV in wastewater and FluA case counts, using a logistic function. The model revealed that a 50 % probability of IAV detection in wastewater corresponds to 0.53 (95 % CrI: 0.35–0.78) cases per 100,000 people, as observed in clinical surveillance over two weeks. Next, we applied the previously developed PRESENS model to IAV wastewater concentration data from California, revealing rapid and prolonged virus shedding patterns. The estimated shedding model was incorporated into an extended version of the PRESENS model to assess the variability in the relationship between IAV concentrations and case numbers across other states, including Massachusetts, New Jersey, and Utah. As a result, our analysis demonstrated the effectiveness of normalizing IAV concentrations with PMMoV (Pepper mild mottle virus) to accurately understand spatial distribution patterns of IAV prevalence. We successfully estimated FluA case counts from wastewater concentrations within a factor of two for 80 % of data from a state where 34 % of the state population was monitored by wastewater surveillance. Importantly, wastewater-based estimates provided real-time or leading insights (0–2 days) compared to clinical case detection in the three states, enabling early understanding of the incidence trends by limiting delays in data publication. These findings highlight the potential of wastewater surveillance to detect IAV outbreaks in near real-time and enhance efficiency of the infectious disease management.http://www.sciencedirect.com/science/article/pii/S0160412024008055Wastewater surveillanceinfluenza A virusSheddingPMMoV-normalizationPRESENS modelLogistic function |
| spellingShingle | Hiroki Ando Michio Murakami Masaaki Kitajima Kelly A. Reynolds Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model Environment International Wastewater surveillance influenza A virus Shedding PMMoV-normalization PRESENS model Logistic function |
| title | Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model |
| title_full | Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model |
| title_fullStr | Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model |
| title_full_unstemmed | Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model |
| title_short | Wastewater-based estimation of temporal variation in shedding amount of influenza A virus and clinically identified cases using the PRESENS model |
| title_sort | wastewater based estimation of temporal variation in shedding amount of influenza a virus and clinically identified cases using the presens model |
| topic | Wastewater surveillance influenza A virus Shedding PMMoV-normalization PRESENS model Logistic function |
| url | http://www.sciencedirect.com/science/article/pii/S0160412024008055 |
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