Medical diagnostic tests to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) for individuals in the United States were initially limited to people who were traveling or symptomatic to track disease ...Medical diagnostic tests to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) for individuals in the United States were initially limited to people who were traveling or symptomatic to track disease incidence due to the cost of providing testing for all people in a community on a routine basis. As an alternative to randomly sampling large groups of people to track disease incidence at significant cost, wastewater-based epidemiology (WBE) is a well-established and cost-effective technique to passively measure the prevalence of disease in communities without requiring invasive testing. WBE can also be used as a forecasting tool since the virus is shed in individuals prior to developing symptoms that might otherwise prompt testing. This study applied the WBE approach to understand its effectiveness as a possible forecasting tool by monitoring the SARS-CoV-2 levels in raw wastewater sampled from sewer lift stations at a large public university campus setting including dormitories, academic buildings, and athletic facilities. The WBE analysis was conducted by sampling from building-specific lift stations and enumerating target viral copies using RT-qPCR analysis. The WBE results were compared with the 7-day rolling averages of confirmed infected individuals for the following week after the wastewater sample analysis. In most cases, changes in the WBE outcomes were followed by similar trends in the clinical data. The positive predictive value of the applied WBE approach was 86% for the following week of the sample collection. In contrast, positive correlations between the two data with Spearmen correlation (rs) ranged from 0.16 to 0.36. A stronger correlation (rs = 0.18 to 0.51) was observed when WBE results were compared with COVID-19 cases identified on the next day of the sampling events. The P value of 0.007 for Dorm A suggests high significance, while moderate significance was observed for the other dormitories (B, C, and D). The outcomes of this investigation demonstrate that WBE can be a valuable tool to track the progression of diseases like COVID-19 seven days before diagnostic cases are confirmed, allowing authorities to take necessary measures in advance and also enable authorities to decide to reopen a facility after a quarantine.展开更多
Wastewater-based epidemiology(WBE)offers a unique window into the health and habits of communities through the analysis of pollutants and biomarkers in sewage.Traditionally focused on small molecules,such as pharmaceu...Wastewater-based epidemiology(WBE)offers a unique window into the health and habits of communities through the analysis of pollutants and biomarkers in sewage.Traditionally focused on small molecules,such as pharmaceuticals and illegal drugs,recent advances in environmental proteomics have expanded WBE to include large biomolecules such as proteins.Notably,novel sampling methods using polymeric probes and high-resolution mass spectrometry have facilitated the detection of human and animal proteins,both soluble and in particulate material,linking them to specific populations and industrial activities.An immunological dimension to this approach is fundamental to include the recognition of host immunoglobulins,immune-response proteins,and pathogen antigens in wastewater,potentially serving as indicators of community immune status,infection prevalence,and vaccination coverage.This review consolidates the latest advancements in environmental proteomics as applied to WBE,emphasizing an immunological perspective as a comprehensive tool for assessing population health and environmental conditions to bridge environmental monitoring,public health,and clinical diagnostics.展开更多
Wastewater-based epidemiology is a new approach to monitor drug abuse. It involves collecting wastewater, analysis of residues of drugs or its metabolites in wastewater, and back-calculation of drug consumption by tak...Wastewater-based epidemiology is a new approach to monitor drug abuse. It involves collecting wastewater, analysis of residues of drugs or its metabolites in wastewater, and back-calculation of drug consumption by taking into account wastewater flow, stability of drug target residues in wastewater, and excretion rates of drugs/metabolites. Wastewater-based epidemiology has the advantages of being inexpensive and yielding more consistent and near real-time results. It has the great potential to supplement the existing drug monitoring methods. It can be used to build large-scale(regional, national, or even continental) monitoring networks that would yield spatial patterns and temporal trends in drug abuse. This paper described in detail the principle and procedures of this wastewater-based approach. Application of this approach across the globe was also reviewed. The uncertainties involved in the approach and knowledge gaps were identified. Finally, necessity, benefits, and feasibility to set up nation or province-wide monitoring networks based on wastewater analysis in China were discussed.展开更多
Wastewater-based surveillance serves as a supplementary approach to clinical surveillance of COVID-19 during the epidemic.This study aimed to track the prevalence of the disease and the viral genetic variability throu...Wastewater-based surveillance serves as a supplementary approach to clinical surveillance of COVID-19 during the epidemic.This study aimed to track the prevalence of the disease and the viral genetic variability through wastewater-based surveillance in the post-epidemic era.Between January to December 2023,samples were collected from the influent lines of two wastewater treatment plants(WWTPs),concentrated using PEG8000,and subjected to detection of the target genes ORF 1ab and N of SARS-CoV-2 via reverse transcriptional quantitative PCR(RT-qPCR).For next-generation sequencing(NGS),high-quality samples from both wastewater and clinical patients were selected.Weekly analysis were performed using R software to evaluate the correlation between the SARSCoV-2 RNA concentrations in wastewater and positive rate of reported cases,indicating a positive correlation.Genetic diversity patterns of SARS-CoV-2 in wastewater resembled those in the patient source based on Principal Coordinates Analysis(PCoA)with three clusters for different stages.The rise of RNA concentration in wastewater indicates the growth of cases and the emergence of new variants,serving as an early warning of potential viral mutations,disease outbreaks even possible epidemics.Furthermore,the genomic surveillance of wastewater could help identify new variants that may not be captured through population monitoring,especially when sample sizes are insufficient.Consequently,surveillance of SARS-CoV-2 in municipal wastewater has emerged as a reliable,earlywarning monitoring system for COVID-19 in the post-epidemic era.展开更多
Wastewater-based epidemiology(WBE)has emerged as an effective environmental surveillance tool in monitoring fecal-oral pathogen infections within a community.Congruently,SARS-Co V-2,the etiologic agent of COVID-19,has...Wastewater-based epidemiology(WBE)has emerged as an effective environmental surveillance tool in monitoring fecal-oral pathogen infections within a community.Congruently,SARS-Co V-2,the etiologic agent of COVID-19,has been demonstrated to infect the gastrointestinal tissues,and be shed in feces.In the present study,SARS-Co V-2 RNA was concentrated from wastewater,sludge,surface water,ground water,sediment,and soil samples of municipal and hospital wastewater systems and related environments in Wuhan during the COVID-19 middle and low risk periods,and the viral RNA copies quantified using reverse transcription quantitative polymerase chain reaction(RT-q PCR).From the findings of this study,during the middle risk period,one influent sample and three secondary effluents collected from waste water treatment plant 2,as well as two samples from Jinyintan Hospital wastewater system influent were SARS-Co V-2 RNA positive.One sludge sample collected from Guanggu Branch of Tongji Hospital,which was obtained during the low risk period,was also positive for SARS-Co V-2 RNA.These study findings demonstrate the significance of WBE in continuous surveillance of SARS-Co V-2 at the community level,even when the COVID-19 prevalence is low.Overall,this study can be used as an important reference for contingency management of wastewater treatment plants and COVID-19 prevention and control departments of Wuhan.展开更多
Objective:This study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater.Method:The study collected data from January to...Objective:This study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater.Method:The study collected data from January to August 2023,including the number of reported cases,the positive ratio of nucleic acid samples in sentinel hospitals,the incidence rate of influenza-like symptoms in students,and the positive ratio of wastewater samples in different counties and districts in Shangrao City.Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h.The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient.Results:A total of 2797 wastewater samples were collected.The wastewater monitoring study,based on sampling point distribution,was divided into two phases.Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases,reaching peak values of 91.67%and 100%respectively.The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators.The correlation analysis revealed a strong linear correlation across all four types of data,with Spearman correlation coefficients ranging from 0.783 to 0.977,all of which were statistically significant.Conclusion:The positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023.This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals,supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.展开更多
Wastewater analysis offers objective and complementary information to illicit drug agencies by monitoring patterns of illicit drug consumption.In this study,wastewater samples from three different wastewater treatment...Wastewater analysis offers objective and complementary information to illicit drug agencies by monitoring patterns of illicit drug consumption.In this study,wastewater samples from three different wastewater treatment plants in Sydney,Australia were collected in March 2016.Ten targeted drugs were analysed and temporal and geographical analyses were performed to obtain a better understanding of the type and amount of illicit drugs consumed in Sydney in comparison with similar studies conducted around Australia and in Europe.Among the targeted drugs,methamphetamine was consumed the most,followed by cocaine and 3,4-methylenedioxymethamphetamine(MDMA).Weekly patterns were observed where a peak during the weekend was present.The geographical analysis showed differences between the regions targeted.This observation may be related to socio-demographic aspects.The comparison of our study to other data sources from Australia showed a high consumption of methamphetamine in Sydney and Western Australia.The comparison between Sydney and different European cities revealed a difference in consumption,which is in line with traditional market indicators.The information obtained through wastewater analysis provides complementary information regarding illicit drug consumption,the size,and the evolution of the illicit drug market.This,ultimately,will assist authorities in making informed decisions.展开更多
Background:The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems.The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate ...Background:The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems.The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19.We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.Methods:Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties,we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29,2020 to June 30,2022.We included covariates such as COVID-19 vaccine coverage in the county,comorbidities,demographic variables,and holiday gatherings.Findings:Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission.Models that included wastewater had higher predictive power than models that included clinical cases only,increasing the accuracy of the model by 15%.Predicted hospital admissions correlated highly with observed admissions(r¼0.77)with an average difference of 0.013 hospitalizations per 100,000(95%CI¼[0.002,0.025])Interpretation:Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone.The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.展开更多
Although epidemiological surveillance of COVID-19 has been gradually downgraded globally,the transmission of COVID-19 continues.It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets ...Although epidemiological surveillance of COVID-19 has been gradually downgraded globally,the transmission of COVID-19 continues.It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data.Herein,we propose a comprehensive method for estimating the effective reproduction number using wastewater data.The wastewater virus concentration data,which were collected twice a week,were analyzed using daily COVID-19 incidence data obtained from Takamatsu,Japan between January 2022 and September 2022.We estimated the shedding load distribution(SLD)as a function of time since the date of infection,using a model employing the delay distribution,which is assumed to follow a gamma distribution,multiplied by a scaling factor.We also examined models that accounted for the temporal smoothness of viral load measurement data.The model that smoothed temporal patterns of viral load was the best fit model(WAIC=2795.8),which yielded a mean estimated distribution of SLD of 3.46 days(95%CrI:3.01–3.95 days).Using this SLD,we reconstructed the daily incidence,which enabled computation of the effective reproduction number.Using the best fit posterior draws of parameters directly,or as a prior distribution for subsequent analyses,we first used a model that assumed temporal smoothness of viral load concentrations in wastewater,as well as infection counts by date of infection.In the subsequent approach,we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting.Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data.Adding weekly case count data reduced the uncertainty of the effective reproduction number.We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19,and that inferential performance is enhanced when those data are combined with weekly incidence data.展开更多
Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical ...Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.展开更多
文摘Medical diagnostic tests to detect Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) for individuals in the United States were initially limited to people who were traveling or symptomatic to track disease incidence due to the cost of providing testing for all people in a community on a routine basis. As an alternative to randomly sampling large groups of people to track disease incidence at significant cost, wastewater-based epidemiology (WBE) is a well-established and cost-effective technique to passively measure the prevalence of disease in communities without requiring invasive testing. WBE can also be used as a forecasting tool since the virus is shed in individuals prior to developing symptoms that might otherwise prompt testing. This study applied the WBE approach to understand its effectiveness as a possible forecasting tool by monitoring the SARS-CoV-2 levels in raw wastewater sampled from sewer lift stations at a large public university campus setting including dormitories, academic buildings, and athletic facilities. The WBE analysis was conducted by sampling from building-specific lift stations and enumerating target viral copies using RT-qPCR analysis. The WBE results were compared with the 7-day rolling averages of confirmed infected individuals for the following week after the wastewater sample analysis. In most cases, changes in the WBE outcomes were followed by similar trends in the clinical data. The positive predictive value of the applied WBE approach was 86% for the following week of the sample collection. In contrast, positive correlations between the two data with Spearmen correlation (rs) ranged from 0.16 to 0.36. A stronger correlation (rs = 0.18 to 0.51) was observed when WBE results were compared with COVID-19 cases identified on the next day of the sampling events. The P value of 0.007 for Dorm A suggests high significance, while moderate significance was observed for the other dormitories (B, C, and D). The outcomes of this investigation demonstrate that WBE can be a valuable tool to track the progression of diseases like COVID-19 seven days before diagnostic cases are confirmed, allowing authorities to take necessary measures in advance and also enable authorities to decide to reopen a facility after a quarantine.
基金supported by the Spanish Ministry of Science and Innovation(MICINN,Spain)(Project nos.PID2020-114065RB-C22 and PID2020-114065RB-C21).
文摘Wastewater-based epidemiology(WBE)offers a unique window into the health and habits of communities through the analysis of pollutants and biomarkers in sewage.Traditionally focused on small molecules,such as pharmaceuticals and illegal drugs,recent advances in environmental proteomics have expanded WBE to include large biomolecules such as proteins.Notably,novel sampling methods using polymeric probes and high-resolution mass spectrometry have facilitated the detection of human and animal proteins,both soluble and in particulate material,linking them to specific populations and industrial activities.An immunological dimension to this approach is fundamental to include the recognition of host immunoglobulins,immune-response proteins,and pathogen antigens in wastewater,potentially serving as indicators of community immune status,infection prevalence,and vaccination coverage.This review consolidates the latest advancements in environmental proteomics as applied to WBE,emphasizing an immunological perspective as a comprehensive tool for assessing population health and environmental conditions to bridge environmental monitoring,public health,and clinical diagnostics.
基金supported by the National Natural Science Foundation of China(Grant Nos.41371442&41671492)
文摘Wastewater-based epidemiology is a new approach to monitor drug abuse. It involves collecting wastewater, analysis of residues of drugs or its metabolites in wastewater, and back-calculation of drug consumption by taking into account wastewater flow, stability of drug target residues in wastewater, and excretion rates of drugs/metabolites. Wastewater-based epidemiology has the advantages of being inexpensive and yielding more consistent and near real-time results. It has the great potential to supplement the existing drug monitoring methods. It can be used to build large-scale(regional, national, or even continental) monitoring networks that would yield spatial patterns and temporal trends in drug abuse. This paper described in detail the principle and procedures of this wastewater-based approach. Application of this approach across the globe was also reviewed. The uncertainties involved in the approach and knowledge gaps were identified. Finally, necessity, benefits, and feasibility to set up nation or province-wide monitoring networks based on wastewater analysis in China were discussed.
基金supported by the Key Technology Research and Development Program of the Science and Technology Department of Sichuan Province(China)(2021YFS0064)the Youth Innovation Research Project by the Sichuan Medical Association(China)(Q22017)the Science Foundation of the Health and Family Planning Commission of Chengdu(China)(22056).
文摘Wastewater-based surveillance serves as a supplementary approach to clinical surveillance of COVID-19 during the epidemic.This study aimed to track the prevalence of the disease and the viral genetic variability through wastewater-based surveillance in the post-epidemic era.Between January to December 2023,samples were collected from the influent lines of two wastewater treatment plants(WWTPs),concentrated using PEG8000,and subjected to detection of the target genes ORF 1ab and N of SARS-CoV-2 via reverse transcriptional quantitative PCR(RT-qPCR).For next-generation sequencing(NGS),high-quality samples from both wastewater and clinical patients were selected.Weekly analysis were performed using R software to evaluate the correlation between the SARSCoV-2 RNA concentrations in wastewater and positive rate of reported cases,indicating a positive correlation.Genetic diversity patterns of SARS-CoV-2 in wastewater resembled those in the patient source based on Principal Coordinates Analysis(PCoA)with three clusters for different stages.The rise of RNA concentration in wastewater indicates the growth of cases and the emergence of new variants,serving as an early warning of potential viral mutations,disease outbreaks even possible epidemics.Furthermore,the genomic surveillance of wastewater could help identify new variants that may not be captured through population monitoring,especially when sample sizes are insufficient.Consequently,surveillance of SARS-CoV-2 in municipal wastewater has emerged as a reliable,earlywarning monitoring system for COVID-19 in the post-epidemic era.
基金supported by the Wuhan Bureau of Science and Technology(No.202002020101010022)China Geological Survey(No.DD20190282)the support team from the National Biosafety Laboratory in Wuhan,China,for the support they extended to us。
文摘Wastewater-based epidemiology(WBE)has emerged as an effective environmental surveillance tool in monitoring fecal-oral pathogen infections within a community.Congruently,SARS-Co V-2,the etiologic agent of COVID-19,has been demonstrated to infect the gastrointestinal tissues,and be shed in feces.In the present study,SARS-Co V-2 RNA was concentrated from wastewater,sludge,surface water,ground water,sediment,and soil samples of municipal and hospital wastewater systems and related environments in Wuhan during the COVID-19 middle and low risk periods,and the viral RNA copies quantified using reverse transcription quantitative polymerase chain reaction(RT-q PCR).From the findings of this study,during the middle risk period,one influent sample and three secondary effluents collected from waste water treatment plant 2,as well as two samples from Jinyintan Hospital wastewater system influent were SARS-Co V-2 RNA positive.One sludge sample collected from Guanggu Branch of Tongji Hospital,which was obtained during the low risk period,was also positive for SARS-Co V-2 RNA.These study findings demonstrate the significance of WBE in continuous surveillance of SARS-Co V-2 at the community level,even when the COVID-19 prevalence is low.Overall,this study can be used as an important reference for contingency management of wastewater treatment plants and COVID-19 prevention and control departments of Wuhan.
基金supported by the Guangzhou Laboratory Project(SRPG22-007)the Guangzhou Laboratory Project(GZNL2024A01004)the National Key Research and Development Program of China(2021YFC2301604).
文摘Objective:This study aims to assess the feasibility of evaluating the COVID-19 epidemic trend through monitoring the positive percentage of SARS-CoV-19 RNA in wastewater.Method:The study collected data from January to August 2023,including the number of reported cases,the positive ratio of nucleic acid samples in sentinel hospitals,the incidence rate of influenza-like symptoms in students,and the positive ratio of wastewater samples in different counties and districts in Shangrao City.Wastewater samples were obtained through grabbing and laboratory testing was completed within 24 h.The data were then normalized using Z-score normalization and analyzed for lag time and correlation using the xcorr function and Spearman correlation coefficient.Results:A total of 2797 wastewater samples were collected.The wastewater monitoring study,based on sampling point distribution,was divided into two phases.Wuyuan County consistently showed high levels of positive ratio in wastewater samples in both phases,reaching peak values of 91.67%and 100%respectively.The lag time analysis results indicated that the peak positive ratio in all wastewater samples in Shangrao City appeared around 2 weeks later compared to the other three indicators.The correlation analysis revealed a strong linear correlation across all four types of data,with Spearman correlation coefficients ranging from 0.783 to 0.977,all of which were statistically significant.Conclusion:The positive ratio of all wastewater samples in Shangrao City accurately reflected the COVID-19 epidemic trend from January to August 2023.This study confirmed the lag effect of wastewater percent positive and its strong correlation with the reported incidence rate and the positive ratio of nucleic acid samples in sentinel hospitals,supporting the use of wastewater percent positive monitoring as a supplementary tool for infectious disease surveillance in the regions with limited resources.
基金Marie Morelato would like to acknowledge the UTS Chancellor’s Postdoctoral Research Fellowship.Frederic Been acknowledges the Swiss National Science Foundation[SNSF_P2LAP2_164892]the Research Foundation-Flanders[FWO,project 12Y8518N]for his postdoctoral fellowshipas well as the INTERWASTE[grant number 734522]project funded by the European Commission[grant number Horizon 2020].
文摘Wastewater analysis offers objective and complementary information to illicit drug agencies by monitoring patterns of illicit drug consumption.In this study,wastewater samples from three different wastewater treatment plants in Sydney,Australia were collected in March 2016.Ten targeted drugs were analysed and temporal and geographical analyses were performed to obtain a better understanding of the type and amount of illicit drugs consumed in Sydney in comparison with similar studies conducted around Australia and in Europe.Among the targeted drugs,methamphetamine was consumed the most,followed by cocaine and 3,4-methylenedioxymethamphetamine(MDMA).Weekly patterns were observed where a peak during the weekend was present.The geographical analysis showed differences between the regions targeted.This observation may be related to socio-demographic aspects.The comparison of our study to other data sources from Australia showed a high consumption of methamphetamine in Sydney and Western Australia.The comparison between Sydney and different European cities revealed a difference in consumption,which is in line with traditional market indicators.The information obtained through wastewater analysis provides complementary information regarding illicit drug consumption,the size,and the evolution of the illicit drug market.This,ultimately,will assist authorities in making informed decisions.
基金supported by the CDC’s ELC Program,NYS Unique Federal Award Number NU50CK000516 (NYS Epidemiology and Laboratory Capacity for Prevention and Control of Emerging Infectious Diseases).
文摘Background:The public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems.The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19.We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data.Methods:Using county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties,we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29,2020 to June 30,2022.We included covariates such as COVID-19 vaccine coverage in the county,comorbidities,demographic variables,and holiday gatherings.Findings:Wastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission.Models that included wastewater had higher predictive power than models that included clinical cases only,increasing the accuracy of the model by 15%.Predicted hospital admissions correlated highly with observed admissions(r¼0.77)with an average difference of 0.013 hospitalizations per 100,000(95%CI¼[0.002,0.025])Interpretation:Using wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone.The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.
基金Y.O.received funding from the SECOM Science and Technology Foundation,The Kyoto University Foundation,and Fujiwara Memorial Foundation.H.N.received funding from Health and Labour Sciences Research Grants(grant numbers 20CA2024,21HB1002,21HA2016,and 23HA2005)the Japan Agency for Medical Research and Development(grant numbers JP23fk0108612 and JP23fk0108685)+3 种基金JSPS KAKENHI(grant numbers 21H03198 and 22K19670)the Environment Research and Technology Development Fund(grant number JPMEERF20S11804)of the Environmental Restoration and Conservation Agency of Japan,Kao Health Science Researchthe Daikin GAP Fund of Kyoto University,the Japan Science and Technology Agency SICORP program(grant numbers JPMJSC20U3 and JPMJSC2105)the RISTEX program for Science,Technology,and Innovation Policy(grant number JPMJRS22B4)。
文摘Although epidemiological surveillance of COVID-19 has been gradually downgraded globally,the transmission of COVID-19 continues.It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data.Herein,we propose a comprehensive method for estimating the effective reproduction number using wastewater data.The wastewater virus concentration data,which were collected twice a week,were analyzed using daily COVID-19 incidence data obtained from Takamatsu,Japan between January 2022 and September 2022.We estimated the shedding load distribution(SLD)as a function of time since the date of infection,using a model employing the delay distribution,which is assumed to follow a gamma distribution,multiplied by a scaling factor.We also examined models that accounted for the temporal smoothness of viral load measurement data.The model that smoothed temporal patterns of viral load was the best fit model(WAIC=2795.8),which yielded a mean estimated distribution of SLD of 3.46 days(95%CrI:3.01–3.95 days).Using this SLD,we reconstructed the daily incidence,which enabled computation of the effective reproduction number.Using the best fit posterior draws of parameters directly,or as a prior distribution for subsequent analyses,we first used a model that assumed temporal smoothness of viral load concentrations in wastewater,as well as infection counts by date of infection.In the subsequent approach,we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting.Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data.Adding weekly case count data reduced the uncertainty of the effective reproduction number.We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19,and that inferential performance is enhanced when those data are combined with weekly incidence data.
基金supported by the US National Science Foundation(No.1638320).
文摘Wastewater-based epidemiology(WBE)is emerging as an effective tool to provide early warnings of potential disease outbreaks within communities through detecting the presence of pathogens in wastewater before clinical cases are reported.Nevertheless,quantitative prediction of future clinical case is challenging as uncertainties of dynamic shedding and disease transmission patterns can lead to complex correlation between wastewater viral concentration and clinical cases.Such complexities,augmented by factors such as viral variant,public behavioral change,etc.,make it challenging to develop empirical models or data-driven models to provide accurate prediction of disease case for public health policy makings.To address this gap,this study developed an iterative data-driven framework utilizing Long-Short Time Memory(LSTM)neural networks for multi-timestep real-time predictions of future clinical cases based on WBE.The proposed LSTM model structure integrates both wastewater and historical clinical data as inputs.The prediction framework enables the update of LSTM model as more WBE dataset become available to enhance its adaptability to evolving pandemic stages.This framework was applied for real-time forecasting of COVID-19 clinical cases based on dataset of Ohio Wastewater Monitoring Project from July 2020 to October 2023.The developed iterative LSTM models were proven to achieve excellent performance in making clinical case predictions at different stages of COVID-19 pandemic.Early warning threshold of viral surge was defined by moving percentile method and results showed that the model achieved over 90%accuracy in future clinical case prediction and therefore demonstrated high reliability in pre-warning of potential disease outbreaks.This framework was also found to possess strong transferability across diverse geographic regions.The impacts of social policies and events on model predictions as well as the ramification of this model for future pandemics warning are discussed.
