Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen
{"title":"估计废水的有效再生数(Rt):一种方法的比较","authors":"Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen","doi":"10.1016/j.epidem.2025.100839","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The effective reproduction number (R<sub>t</sub>) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R<sub>t</sub>.</div></div><div><h3>Methods</h3><div>We review wastewater-based methods to estimate the R<sub>t</sub> for SARS-CoV-2 based on similarity to the reference case-based R<sub>t</sub>, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater R<sub>t</sub> models identified from the literature. Each model is compared to the R<sub>t</sub> estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.</div></div><div><h3>Results</h3><div>We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R<sub>t</sub> from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R<sub>t</sub> values than county R<sub>t</sub> values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).</div></div><div><h3>Conclusions</h3><div>While not all methods that estimate R<sub>t</sub> from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100839"},"PeriodicalIF":3.0000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating the effective reproduction number from wastewater (Rt): A methods comparison\",\"authors\":\"Dustin T. Hill , Yifan Zhu , Christopher Dunham , E. Joe Moran , Yiquan Zhou , Mary B. Collins , Brittany L. Kmush , David A. Larsen\",\"doi\":\"10.1016/j.epidem.2025.100839\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The effective reproduction number (R<sub>t</sub>) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R<sub>t</sub>.</div></div><div><h3>Methods</h3><div>We review wastewater-based methods to estimate the R<sub>t</sub> for SARS-CoV-2 based on similarity to the reference case-based R<sub>t</sub>, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater R<sub>t</sub> models identified from the literature. Each model is compared to the R<sub>t</sub> estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.</div></div><div><h3>Results</h3><div>We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R<sub>t</sub> from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R<sub>t</sub> values than county R<sub>t</sub> values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).</div></div><div><h3>Conclusions</h3><div>While not all methods that estimate R<sub>t</sub> from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.</div></div>\",\"PeriodicalId\":49206,\"journal\":{\"name\":\"Epidemics\",\"volume\":\"52 \",\"pages\":\"Article 100839\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Epidemics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1755436525000271\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1755436525000271","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Estimating the effective reproduction number from wastewater (Rt): A methods comparison
Background
The effective reproduction number (Rt) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the Rt.
Methods
We review wastewater-based methods to estimate the Rt for SARS-CoV-2 based on similarity to the reference case-based Rt, ease of use, and computational requirements. Using wastewater data collected between August 1, 2022, and February 20, 2024, from 205 wastewater treatment plants across New York State, we fit eight wastewater Rt models identified from the literature. Each model is compared to the Rt estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels.
Results
We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the Rt from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed Rt values than county Rt values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77).
Conclusions
While not all methods that estimate Rt from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation.
期刊介绍:
Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.