The role of time-varying viral shedding in modelling environmental surveillance for public health: revisiting the 2013 poliovirus outbreak in Israel

Review of Poliovirus Transmission and Economic Modeling to Support Global Polio Eradication: 2020-2024

Continued investment in the development and application of mathematical models of poliovirus transmission, economics, and risks leads to their use in support of polio endgame strategy development and risk management policies. This study complements an earlier review covering the period 2000-2019 and discusses the evolution of studies published since 2020 by modeling groups supported by the Global Polio Eradication Initiative (GPEI) partners and others. We systematically review modeling papers published in English in peer-reviewed journals from 2020-2024.25 that focus on poliovirus transmission and health economic analyses. In spite of the long-anticipated end of poliovirus transmission and the GPEI sunset, which would lead to the end of its support for modeling, we find that the number of modeling groups supported by GPEI partners doubled and the rate of their publications increased. Modeling continued to play a role in supporting GPEI and national/regional policies, but changes in polio eradication governance, decentralized management and decision-making, and increased heterogeneity in modeling approaches and findings decreased the overall impact of modeling results. Meanwhile, the failure of the 2016 globally coordinated cessation of type 2 oral poliovirus vaccine use for preventive immunization and the introduction of new poliovirus vaccines and formulation, increased the complexity and uncertainty of poliovirus transmission and economic models and policy recommendations during this time.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

Longitudinal and quantitative fecal shedding dynamics of SARS-CoV-2, pepper mild mottle virus, and crAssphage

Wastewater-based epidemiology (WBE) emerged during the coronavirus disease 2019 (COVID-19) pandemic as a scalable and broadly applicable method for community-level monitoring of infectious disease burden. The lack of high-resolution fecal shedding data for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) limits our ability to link WBE measurements to disease burden. In this study, we present longitudinal, quantitative fecal shedding data for SARS-CoV-2 RNA, as well as for the commonly used fecal indicators pepper mild mottle virus (PMMoV) RNA and crAss-like phage (crAssphage) DNA. The shedding trajectories from 48 SARS-CoV-2-infected individuals suggest a highly individualized, dynamic course of SARS-CoV-2 RNA fecal shedding. Of the individuals that provided at least three stool samples spanning more than 14 days, 77% had one or more samples that tested positive for SARS-CoV-2 RNA. We detected PMMoV RNA in at least one sample from all individuals and in 96% (352/367) of samples overall. CrAssphage DNA was detected in at least one sample from 80% (38/48) of individuals and was detected in 48% (179/371) of all samples. The geometric mean concentrations of PMMoV and crAssphage in stool across all individuals were 8.7 × 104 and 1.4 × 104 gene copies/milligram-dry weight, respectively, and crAssphage shedding was more consistent for individuals than PMMoV shedding. These results provide us with a missing link needed to connect laboratory WBE results with mechanistic models, and this will aid in more accurate estimates of COVID-19 burden in sewersheds. Additionally, the PMMoV and crAssphage data are critical for evaluating their utility as fecal strength normalizing measures and for source-tracking applications. IMPORTANCE This research represents a critical step in the advancement of wastewater monitoring for public health. To date, mechanistic materials balance modeling of wastewater-based epidemiology has relied on SARS-CoV-2 fecal shedding estimates from small-scale clinical reports or meta-analyses of research using a wide range of analytical methodologies. Additionally, previous SARS-CoV-2 fecal shedding data have not contained sufficient methodological information for building accurate materials balance models. Like SARS-CoV-2, fecal shedding of PMMoV and crAssphage has been understudied to date. The data presented here provide externally valid and longitudinal fecal shedding data for SARS-CoV-2, PMMoV, and crAssphage which can be directly applied to WBE models and ultimately increase the utility of WBE.

Temporal and spatial relationships of CrAssphage and enteric viral and bacterial pathogens in wastewater in North Carolina

Enteric disease remains one of the most common concerns for public health, particularly when it results from human exposure to surface and recreational waters contaminated with wastewater. Characterizing the temporal and spatial variation of enteric pathogens prevalent in wastewater is critical to develop approaches to mitigate their distribution in the environment. In this study, we aim to characterize pathogen variability and test the applicability of the human-associated wastewater indicator crAssphage as an indicator of enteric viral and bacterial pathogens. We conducted weekly samplings for 14 months from four wastewater treatment plants in North Carolina, USA. Untreated wastewater samples were processed using hollow fiber ultrafiltration, followed by secondary concentration methods. Adenovirus, norovirus, enterovirus, Salmonella, Shiga toxin 2 (stx2), Campylobacter, and crAssphage were measured by quantitative polymerase chain reaction (qPCR) and reverse transcriptase (rt)-qPCR. Our results revealed significant correlations between crAssphage and human adenovirus, enterovirus, norovirus, Salmonella, and Campylobacter (p<0.01). Pathogens and crAssphage concentrations in untreated wastewater showed distinct seasonal patterns, with peak concentrations of crAssphage and viral pathogens in fall and winter, while bacterial pathogens showed peaked concentrations in either winter (Campylobacter), fall (Salmonella), or summer (stx2). This study enhances the understanding of crAssphage as an alternative molecular indicator for both bacterial and viral pathogens. The findings of this study can also inform microbial modeling efforts for the prediction of the impact of wastewater pathogens on surface waters due to increased flooding events and wastewater overflows associated with climate change.

Assessing the effectiveness of environmental sampling for surveillance of foot-and-mouth disease virus in a cattle herd

The survival of foot-and-mouth disease virus (FMDV) in the environment provides an opportunity for indirect transmission, both within and between farms. However it also presents the possibility of surveillance and detection via environmental sampling. This study assesses the effectiveness of environmental sampling strategies in the event of an outbreak, using a previous model for transmission of FMDV in a cattle herd that had been parameterized using data from transmission experiments and outbreaks. We show that environmental sampling can be an effective means of detecting FMDV in a herd, but it requires multiple samples to be taken on multiple occasions. In addition, environmental sampling can potentially detect FMDV in a herd more quickly than clinical inspection. For example, taking 10 samples every 3 days results in a mean time to detection of 6 days, which is lower than the mean time to detection estimated for the 2001 UK epidemic (8 days). We also show how environmental sampling could be used in a herd considered to be at risk as an alternative to pre-emptive culling. However, because of the time taken for virus to accumulate at the start of an outbreak, a reasonable level of confidence (&gt; 99%) that an at-risk herd is indeed free from infection is unlikely to be achieved in less than 1 week.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 â€" Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds ( R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.