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Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.

ABSTRACT: Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.

SUBMITTER: Li X

PROVIDER: S-EPMC8141262 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The abuse of legal and illegal drugs is a global public health problem, also affecting the social and economic well-being of the population. Thus, there is a significant interest in monitoring drug consumption. Relevant epidemiological information on lifestyle habits can be obtained from the chemical analysis of urban wastewater. In this work, passive sampling using polar organic chemical integrative samplers (POCIS) was used to quantify licit and illicit drugs biomarkers in wastewater for the application of wastewater-based epidemiology (WBE). In this WBE study, a small urban community of approximately 1179 inhabitants was monitored from 18 March 2020 to 3 March 2021, covering the mobility restriction and flexibilization periods of the COVID-19 pandemic in Brazil. Consumption was estimated for amphetamine, caffeine, cocaine, MDMA, methamphetamine, nicotine, and THC. The highest estimated consumption among illicit drugs was for THC (2369 ± 1037 mg day-1 1000 inh-1) followed by cocaine (353 ± 192 mg day-1 1000 inh-1). There was a negative correlation between consumption of caffeine, cocaine, MDMA, nicotine, and THC with human mobility, expressed by cellular phone mobility reports (P-value = 0.0094, 0.0019, 0.0080, 0.0009, and 0.0133, respectively). Our study is the first long-term drug consumption evaluation during the COVID-19 pandemic, with continuous sampling for almost a whole year. The observed reduction in consumption of both licit and illicit drugs is probably associated with stay-at-home orders and reduced access, which can be due to the closure of commercial facilities during some time of the evaluated period, smaller drug supply, and reduced income of the population due to the shutdown of companies and unemployment. The assay described in this study can be used as a complementary and cost-effective tool to the long-term monitoring of drug use biomarkers in wastewater, a relevant epidemiological strategy currently limited to short collection times.

Project description:BackgroundWastewater-based epidemiology provides an opportunity for near real-time, cost-effective monitoring of community-level transmission of SARS-CoV-2. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods for estimating the numbers of infected individuals on the basis of wastewater RNA concentrations are inadequate.MethodsThis is a wastewater-based epidemiology study using wastewater samples that were collected weekly or twice a week from three sewersheds in South Carolina, USA, between either May 27 or June 16, 2020, and Aug 25, 2020, and tested for SARS-CoV-2 RNA. We developed a susceptible-exposed-infectious-recovered (SEIR) model based on the mass rate of SARS-CoV-2 RNA in the wastewater to predict the number of infected individuals, and have also provided a simplified equation to predict this. Model predictions were compared with the number of confirmed cases identified by the Department of Health and Environmental Control, South Carolina, USA, for the same time period and geographical area.FindingsWe plotted the model predictions for the relationship between mass rate of virus release and numbers of infected individuals, and we validated this prediction on the basis of estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The rate of unreported COVID-19 cases, as estimated by the model, was approximately 11 times that of confirmed cases (ie, ratio of estimated infections for every confirmed case of 10·9, 95% CI 4·2-17·5). This rate aligned well with an independent estimate of 15 infections for every confirmed case in the US state of South Carolina.InterpretationThe SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed on the basis of the mass rate of RNA copies released per day. This approach overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to inform policy decisions.FundingClemson University, USA.

Project description:The growth of COVID-19 pandemic throughout more than 213 countries around the world have put a lot of pressures on governments and health services to try to stop the rapid expansion of the pandemic. During 2009, H1N1 Influenza pandemic, statistical and mathematical methods were used to track how the virus spreads around countries. Most of these models that were developed at the beginning of the XXI century are based on the classical susceptible-infected-recovered (SIR) model developed almost a hundred years ago. The evolution of this model allows us to forecast and compute basic and effective reproduction numbers (R t and R 0 ), measures that quantify the epidemic potential of a pathogen and estimates different scenarios. In this study, we present a traditional estimation technique for R0 with statistical distributions by best fitting and a Bayesian approach based on continuous feed of prior distributions to obtain posterior distributions and computing real time Rt . We use data from COVID-19 officially reported cases in Ecuador since the first confirmed case on February 29th. Because of the lack of data, in the case of R0 we compare two methods for the estimation of these parameters below exponential growth and maximum likelihood estimation. We do not make any assumption about the evolution of cases due to limited information and we use previous methods to compare scenarios about R0 and in the case of Rt we used Bayesian inference to model uncertainty in contagious proposing a new modification to the well-known model of Bettencourt and Ribeiro based on a time window of m days to improve estimations. Ecuadorian R0 with exponential growth criteria was 3.45 and with the maximum likelihood estimation method was 2.93. The results show that Guayas, Pichincha and Manabí were the provinces with the highest number of cases due to COVID-19. Some reasons explain the increased transmissibility in these localities: massive events, population density, cities dispersion patterns, and the delayed time of public health actions to contain pandemic. In conclusion, this is a novel approach that allow us to measure infection dynamics and outbreak distribution when not enough detailed data is available. The use of this model can be used to predict pandemic distribution and to implement data-based effective measures.

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Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.

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