Impact of terminal cleaning in rooms previously occupied by patients with healthcare-associated infections.

Healthcare associated infections (HAIs) are costly but preventable. A limited understanding of the effects of environmental cleaning on the riskiest HAI associated pathogens is a current challenge in HAI prevention. This project aimed to quantify the effects of terminal hospital cleaning practices on HAI pathogens via environmental sampling in three hospitals located throughout the United States. Surfaces were swabbed from 36 occupied patient rooms with a laboratory-confirmed, hospital- or community-acquired infection of at least one of the four pathogens of interest (i.e., Acinetobacter baumannii (A. baumannii), methicillin resistant Staphylococcus aureus (MRSA), vancomycin resistant Enterococcus faecalis/faecium (VRE), and Clostridioides difficile (C. difficile)). Six nonporous, high touch surfaces (i.e., chair handrail, bed handrail, nurse call button, desk surface, bathroom counter near the sink, and a grab bar near the toilet) were sampled in each room for Adenosine Triphosphate (ATP) and the four pathogens of interest before and after terminal cleaning. The four pathogens of interest were detected on surfaces before and after terminal cleaning, but their levels were generally reduced. Overall, C. difficile was confirmed on the desk (n = 2), while MRSA (n = 24) and VRE (n = 25) were confirmed on all surface types before terminal cleaning. After cleaning, only MRSA (n = 6) on bed handrail, chair handrail, and nurse call button and VRE (n = 5) on bathroom sink, bed handrail, nurse call button, toilet grab bar, and C. difficile (n = 1) were confirmed. At 2 of the 3 hospitals, pathogens were generally reduced by >99% during terminal cleaning. One hospital showed that VRE increased after terminal cleaning, MRSA was reduced by 73% on the nurse call button, and VRE was reduced by only 50% on the bathroom sink. ATP detections did not correlate with any pathogen concentration. This study highlights the importance of terminal cleaning and indicates room for improvement in cleaning practices to reduce surface contamination throughout hospital rooms.

Development and evaluation of an intensive short course: the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III).

Quantitative microbial risk assessment (QMRA) is a growing interdisciplinary field addressing exposures to microbial pathogens and infectious disease processes. Risk science is inherently interdisciplinary, but few of the contributing disciplinary programs offer courses and training specifically in QMRA. To develop multidisciplinary training in QMRA, an annual 10-day long intensive workshop was conducted from 2015 to 2019-the Quantitative Microbial Risk Assessment Interdisciplinary Instructional Institute (QMRA III). National leaders in the fields of public health, engineering, microbiology, epidemiology, communications, public policy, and QMRA served as instructors and mentors over the course of the program. To provide cross-training, multidisciplinary teams of 5-6 trainees were created from the approximately 30 trainees each year. A formal assessment of the program was performed based on observations and surveys containing Likert-type scales and open-ended prompts. In addition, a longitudinal alumni survey was also disseminated to facilitate the future redevelopment of QMRA institutes and determine the impact of the program. Across all years, trainees experienced statistically significant increases (P < 0.05) in their perceptions of their QMRA abilities (e.g., use of specific computer programs) and knowledge of QMRA constructs (e.g., risk management). In addition, 12 publications, three conference presentations, and two research grants were derived from the QMRA III institute projects or tangential research. The success of QMRA III indicates that a short course format can effectively address many multidisciplinary training needs. Key features of QMRA III, including the inter-disciplinary training approach, hands-on exercises, real-world institute projects, and interaction through a mentoring process, were vital for training multidisciplinary teams housing multiple forms of expertise. Future QMRA institutes are being redeveloped to leverage hybrid learning formats that can further the multidisciplinary training and mentoring objectives.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Ohio Coronavirus Wastewater Monitoring Network: Implementation of Statewide Monitoring for Protecting Public Health.

CONTEXT: Prior to the COVID-19 pandemic, wastewater influent monitoring for tracking disease burden in sewered communities was not performed in Ohio, and this field was only on the periphery of the state academic research community. PROGRAM: Because of the urgency of the pandemic and extensive state-level support for this new technology to detect levels of community infection to aid in public health response, the Ohio Water Resources Center established relationships and support of various stakeholders. This enabled Ohio to develop a statewide wastewater SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) monitoring network in 2 months starting in July 2020. IMPLEMENTATION: The current Ohio Coronavirus Wastewater Monitoring Network (OCWMN) monitors more than 70 unique locations twice per week, and publicly available data are updated weekly on the public dashboard. EVALUATION: This article describes the process and decisions that were made during network initiation, the network progression, and data applications, which can inform ongoing and future pandemic response and wastewater monitoring. DISCUSSION: Overall, the OCWMN established wastewater monitoring infrastructure and provided a useful tool for public health professionals responding to the pandemic.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Fluence-based QMRA model for bacterial photorepair and regrowth in drinking water after decentralized UV disinfection.

Ultraviolet disinfection is a promising solution for decentralized drinking water systems such as communal water taps. A potential health risk is enzymatic photorepair of pathogens after UV disinfection, which can result in regrowth of pathogens. Even though photorepair is a known issue, no formal risk assessments have been conducted for photorepair after UV disinfection in drinking water. The main objective was to construct a quantitative microbial risk assessment (QMRA) of photorepair after UV disinfection of drinking water in a decentralized system. UV disinfection and photorepair kinetics for E. coli were modelled using reproducible fluence-based determinations. Impacts of water collection patterns, and wavelength-dependent water container material transmittance, sunlight intensity, and photorepair enzyme absorbance were quantified. After UV disinfection by 16 or 40 mJ/cm2 of < 5-log microorganisms per L, risk of infection did not exceed 1-in-10,000 under conditions permitting E. coli photorepair. Risk from photorepair was less than 1-in-10,000 for photorepair light exposure < 0.75 h throughout the day for UV fluence 16 mJ/cm2 or greater. UV disinfection followed by solar disinfection surpassing photoreactivation during storage reduced risk below 1-in-10,000 for photorepair light exposure > 2.5 h between modelled times of 9 AM - 3 PM. The model can be expanded to other pathogens as UV fluence and photorepair fluence response kinetics become available, and this QMRA can be used to inform the placement of community water access points to reduce risk of photorepair and ensure adequate shelf life of UV disinfected water under safe storage conditions.

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly. Highlights: We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

Safety of hybrid bronchial stents in transplant airway complications: a single center experience.

Background: Tracheobronchial stents are often used to manage lung transplant airway complications. In 2005, the Food and Drug Administration (FDA) issued a warning against the use of metallic stents for benign airway disease. Since that time, fully covered hybrid metallic stents have been developed and are increasingly used due to their ease of insertion and removal. There is limited data to support their safe utilization for transplant airway complications. This is the largest analysis to date of the safety of hybrid metallic stents for transplant airway complications. Methods: We performed a retrospective study of patients who had covered metallic stents placed for transplant airway complications between April 2016 to April 2021. Information obtained from chart review included demographics, procedure notes and stent data including indication for placement, type, size, location, duration, and complications. Results: We identified 50 patients who had a combined 376 stents placed for a total of 15,711 stent days. The most common minor complication and reason for removal was mucus plugging affecting 193 stents.There were only two cases of major stent associated complications. Among minor complications, there was a increased risk of stent fracture with Bonastent® (P=0.04). Conclusions: Our data shows that hybrid metallic stents are a safe intervention for patients with transplant airway complications. Most complications were minor and managed with repeat bronchoscopy. There were only two major complications over a 5-year period. Given the wide spectrum of possible airway complications, further research is needed to determine the optimal use of stents for patients with transplant airway complications.

Wastewater surveillance of SARS-CoV-2 in dormitories as a part of comprehensive university campus COVID-19 monitoring.

Wastewater-based epidemiology is an effective tool for monitoring infectious disease spread or illicit drug use within communities. At the Ohio State University, we conducted a SARS-CoV-2 wastewater surveillance program in the 2020-2021 academic year and compared results with the university-required weekly COVID-19 saliva testing to monitor COVID-19 infection prevalence in the on-campus residential communities. The objectives of the study were to rapidly track trends in the wastewater SARS-CoV-2 gene concentrations, analyze the relationship between case numbers and wastewater signals when adjusted using human fecal viral indicator concentrations (PMMoV, crAssphage) in wastewater, and investigate the relationship of the SARS-CoV-2 gene concentrations with wastewater parameters. SARS-CoV-2 nucleocapsid and envelope (N1, N2, and E) gene concentrations, determined with reverse transcription droplet digital PCR, were used to track SARS-CoV-2 viral loads in dormitory wastewater once a week at 6 sampling sites across the campus during the fall semester in 2020. During the following spring semester, research was focused on SARS-CoV2 N2 gene concentrations at 5 sites sampled twice a week. Spearman correlations both with and without adjusting using human fecal viral indicators showed a significant correlation (p < 0.05) between human COVID-19 positive case counts and wastewater SARS-CoV-2 gene concentrations. Spearman correlations showed significant relationships between N1 gene concentrations and both TSS and turbidity, and between E gene concentrations and both pH and turbidity. These results suggest that wastewater signal increases with the census of infected individuals, in which the majority are asymptomatic, with a statistically significant (p-value <0.05) temporal correlation. The study design can be utilized as a platform for rapid trend tracking of SARS-CoV-2 variants and other diseases circulating in various communities.

Anastomotic Suturing Techniques and Their Association With Post-Lung Transplantation Complications.

INTRODUCTION: Currently, standard practice is to use the continuous suturing technique on the bronchial anastomosis during lung transplantation. This study used a large cohort to investigate and contrast continuous and interrupted suturing techniques, comparing survival outcomes and occurrence of postoperative bronchial complications to examine if utilization of interrupted suturing has merit. METHODS: Survival outcomes of 740 single-center lung transplant recipients over 8 y (February 2012-March 2020) were compared by suturing techniques: either continuous or interrupted at the bronchial anastomosis. Clinical parameters and demographics were compared between two suturing groups, with P values < 0.05 considered significant. The groups were compared for postoperative morbidity, including need for bronchial interventions. Survival was compared using Kaplan-Meier curves and log-rank tests. Cox regression analysis was run with statistically significant variables to study association with survival. RESULTS: Of the 740 patients, 462 received the continuous suturing technique and 278 received the interrupted suturing technique. Most demographic and clinical data were not statistically significant between the two groups, and those that were significant were not associated with worse survival outcomes, with the exception of the variable diagnosis. Bronchial complications were comparable between the continuous and interrupted groups (12.6% versus 10.4%, P = 0.382). Extracorporeal membrane oxygenation (ECMO) use did not differ significantly between the two groups (P = 0.12). The Kaplan-Meier curve showed comparable survival between groups (P = 0.98), and Cox regression analysis showed that only diagnosis, bronchial complications, and ECMO utilization were associated with different survival outcomes. Chronic obstructive pulmonary disorder was shown to be associated with more favorable survival outcomes as opposed to idiopathic pulmonary fibrosis and the category "other". The need for ECMO and the occurrence of a bronchial complication were also associated with worse survival outcomes. CONCLUSIONS: Both techniques showed reasonable post-transplant outcomes, as our study demonstrated similar survival outcomes and bronchial complication rates.

Knowledge to Predict Pathogens: Legionella pneumophila Lifecycle Systematic Review Part II Growth within and Egress from a Host Cell.

Legionella pneumophila (L. pneumophila) is a pathogenic bacterium of increasing concern, due to its ability to cause a severe pneumonia, Legionnaires' Disease (LD), and the challenges in controlling the bacteria within premise plumbing systems. L. pneumophila can thrive within the biofilm of premise plumbing systems, utilizing protozoan hosts for protection from environmental stressors and to increase its growth rate, which increases the bacteria's infectivity to human host cells. Typical disinfectant techniques have proven to be inadequate in controlling L. pneumophila in the premise plumbing system, exposing users to LD risks. As the bacteria have limited infectivity to human macrophages without replicating within a host protozoan cell, the replication within, and egress from, a protozoan host cell is an integral part of the bacteria's lifecycle. While there is a great deal of information regarding how L. pneumophila interacts with protozoa, the ability to use this data in a model to attempt to predict a concentration of L. pneumophila in a water system is not known. This systematic review summarizes the information in the literature regarding L. pneumophila's growth within and egress from the host cell, summarizes the genes which affect these processes, and calculates how oxidative stress can downregulate those genes.

Modeling fomite-mediated SARS-CoV-2 exposure through personal protective equipment doffing in a hospital environment.

Self-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2-positive patient care. Staff may subconsciously become contaminated through improper glove removal; so, quantifying this exposure is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modeled using a discrete-time Markov chain for: IV-drip care, blood pressure monitoring, and doctors' rounds. Accretion of viral RNA on gloves during care was modeled using a stochastic recurrence relation. In the simulation, the HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing caseload. A parametric study was conducted to analyze the effect of: (1a) increasing patient numbers on the ward, (1b) the proportion of COVID-19 cases, (2) the length of a shift, and (3) the probability of touching contaminated PPE. The driving factors for the exposure were surface contamination and the number of surface contacts. The results simulate generally low viral exposures in most of the scenarios considered including on 100% COVID-19 positive wards, although this is where the highest self-inoculated dose is likely to occur with median 0.0305 viruses (95% CI =0-0.6 viruses). Dose correlates highly with surface contamination showing that this can be a determining factor for the exposure. The infection risk resulting from the exposure is challenging to estimate, as it will be influenced by the factors such as virus variant and vaccination rates.

Quantifying Benefits and Harms of Lung Cancer Screening in an Underserved Population: Results From a Prospective Study.

Lung cancer screening with annual low-dose computed tomography reduces lung cancer death by 20-26%. However, potential harms of screening include false-positive results, procedures from false positives, procedural complications and failure to adhere to follow-up recommendations. In diverse, underserved populations, it is unknown if benefits of early lung cancer detection outweigh harms. We conducted a prospective observational study of lung cancer screening participants in an urban, safety-net institution from September 2014 to June 2020. We measured benefits of screening in terms of cancer diagnosis, stage, and treatment. We measured harms of screening by calculating false-positive rate, procedures as a result of false positive screens, procedural complications, and failure to follow-up with recommended care. Of patients with 3-year follow up, we measured these same outcomes in addition to compliance with annual screening. Of 1509 participants, 55.6% were African American, 35.2% White, 8.1% Hispanic, and 0.5% Asian. Screening resulted in cancer detection and treatment in 2.8%. False positive and procedure as a result of a false positive occurred in 9.2% and 0.8% of participants, respectively with no major complications from diagnostic procedures or treatment. Adherence to annual screening was low, 18.7%, 3.7%, and 0.4% at 1, 2, and 3 years after baseline screening respectively. Multidisciplinary lung cancer screening in a safety-net institution can successfully detect and treat lung cancer with few harms of false-positive screens, procedure after false-positive screens and major complications. However, adherence to annual screening is poor.

Bystander chemical exposures and injuries associated with nearby plastic sewer pipe manufacture: public health practice and lessons.

Cured-in-place pipes (CIPPs) are plastic liners manufactured inside existing damaged sanitary sewer, storm sewer, and water pipes that extend the service life of host pipes. This process often is conducted in neighborhoods and near roadways. Before, during, and after plastic manufacture, waste materials that include volatile materials are released into the air. Emissions from this manufacturing process can affect outdoor air quality and indoor air quality for buildings connected to the sewer system. We identified key issues and solicited stakeholder feedback to estimate and manage public health risks of CIPP-generated chemical air pollution. A work group representing 13 U.S. agencies and public health associations provided feedback and prioritized public health issues for action. To mitigate potential public and occupational health risks, additional testing and public health educational efforts were recommended. An improved understanding of CIPP chemical exposure pathways, as well as stakeholder needs and interests, is essential.

Comparing approaches for modelling indirect contact transmission of infectious diseases.

Mathematical models describing indirect contact transmission are an important component of infectious disease mitigation and risk assessment. A model that tracks microorganisms between compartments by coupled ordinary differential equations or a Markov chain is benchmarked against a mechanistic interpretation of the physical transfer of microorganisms from surfaces to fingers and subsequently to a susceptible person's facial mucosal membranes. The primary objective was to compare these models in their estimates of doses and changes in microorganism concentrations on hands and fomites over time. The abilities of the models to capture the impact of episodic events, such as hand hygiene, and of contact patterns were also explored. For both models, greater doses were estimated for the asymmetrical scenarios in which a more contaminated fomite was touched more often. Differing representations of hand hygiene in the Markov model did not notably impact estimated doses but affected pathogen concentration dynamics on hands. When using the Markov model, losses due to hand hygiene should be handled as separate events as opposed to time-averaging expected losses. The discrete event model demonstrated the effect of hand-to-mouth contact timing on the dose. Understanding how model design influences estimated doses is important for advancing models as reliable risk assessment tools.

Respirators, face masks, and their risk reductions via multiple transmission routes for first responders within an ambulance.

First responders may have high SARS-CoV-2 infection risks due to working with potentially infected patients in enclosed spaces. The study objective was to estimate infection risks per transport for first responders and quantify how first responder use of N95 respirators and patient use of cloth masks can reduce these risks. A model was developed for two Scenarios: an ambulance transport with a patient actively emitting a virus in small aerosols that could lead to airborne transmission (Scenario 1) and a subsequent transport with the same respirator or mask use conditions, an uninfected patient; and remaining airborne SARS-CoV-2 and contaminated surfaces due to aerosol deposition from the previous transport (Scenario 2). A compartmental Monte Carlo simulation model was used to estimate the dispersion and deposition of SARS-CoV-2 and subsequent infection risks for first responders, accounting for variability and uncertainty in input parameters (i.e., transport duration, transfer efficiencies, SARS-CoV-2 emission rates from infected patients, etc.). Infection risk distributions and changes in concentration on hands and surfaces over time were estimated across sub-Scenarios of first responder respirator use and patient cloth mask use. For Scenario 1, predicted mean infection risks were reduced by 69%, 48%, and 85% from a baseline risk (no respirators or face masks used) of 2.9 × 10-2 ± 3.4 × 10-2 when simulated first responders wore respirators, the patient wore a cloth mask, and when first responders and the patient wore respirators or a cloth mask, respectively. For Scenario 2, infection risk reductions for these same Scenarios were 69%, 50%, and 85%, respectively (baseline risk of 7.2 × 10-3 ± 1.0 × 10-2). While aerosol transmission routes contributed more to viral dose in Scenario 1, our simulations demonstrate the ability of face masks worn by patients to additionally reduce surface transmission by reducing viral deposition on surfaces. Based on these simulations, we recommend the patient wear a face mask and first responders wear respirators, when possible, and disinfection should prioritize high use equipment.

Development of methods to estimate microcystins removal and water treatment resiliency using mechanistic risk modelling.

Drinking water treatment processes are capable of removing microcystins but consistent operation of processes optimized for cyanobacterial harmful algal bloom (cHAB) conditions is not fiscally feasible. Therefore, utilities must ready themselves and start the cHAB processes as a reactionary response. Predictive analytics and modelling are impactful tools to prepare water systems for cHABs, but are still in early stages of development. Until those prospective models are completed, a method to determine best actions in advance of a bloom event thus improving system resiliency is needed. In this study, an adaptation of the quantitative microbial risk analysis (QMRA) methodology was applied to develop this method. This method and resulting models were developed around the Toledo (Ohio, USA) water crisis of 2014, but being mechanistic, they are easily adaptable to other systems' process operations data. Results from this internally validated model demonstrate how rapid action using both powdered activated carbon and measured increases in chlorine dose can mitigate health risks. Our research also demonstrates the importance of modelling the cellular status of the toxins (toxins either in an intact cell or in the water from a lysed cell). Risks were characterized using hazard quotients (HQ) and at the peak of the crisis ranged from a minimum of 0.00244 to a maximum of 2.84 for adults. In simulations where cHAB-specific treatment was used this decreased to 0.00057 and 0.236 respectively. We further outline how this methodology can be used to simulate water system resiliency to likely and aberrant microbial hazard events to plan for the best interventions to protect public health. This method can be used for other hazards expected to be variable in the future, where system prepatory planning is critical to continued public health protection. Considering the water quantity and quality fluctuations occurring and likely to intensify under climate change, this type of computationally supported preparedness is vital to maintaining robust water system resiliency.

Modeling COVID-19 infection risks for a single hand-to-fomite scenario and potential risk reductions offered by surface disinfection.

We used a quantitative microbial risk assessment approach to relate log10 disinfection reductions of SARS-CoV-2 bioburden to COVID-19 infection risks. Under low viral bioburden, minimal log10 reductions may be needed to reduce infection risks for a single hand-to-fomite touch to levels lower than 1:1,000,000, as a risk comparison point. For higher viral bioburden conditions, log10 reductions of more than 2 may be needed to achieve median infection risks of less than 1:1,000,000.

A Data Simulation Method to Optimize a Mechanistic Dose-Response Model for Viral Loads of Hepatitis A.

Driven by the quantitative estimate of risk via the dose-response models, quantitative microbial risk assessment has been used successfully for public health interventions. The dose-response models are derived starting from an average exposed dose of infectious particles, this dictates the of dose data units required. Then dose-response data from animal model experiments are used to optimize these mechanistic dose-response models. For hepatitis A (Hep-A), the only available dose-response data use grams of feces for dose units. Therefore, to develop a dose-response model for Hep-A a method of converting these doses in grams of feces into infectious particles, while accounting for the uncertainty of this conversion is needed. This research develops a method to couple data simulation with the likelihood estimation method for model optimization to accomplish this. This adapted method uses data simulation to model the doses as viral particles while accounting for the within-group variability of this simulation. Then these simulated doses, coupled with the original dose-response data, are used to optimize the mechanistic dose-response models. This method results in a more computationally rigorous means of modeling these types of dose-response data. The resulting dose-response model for Hep-A is also more appropriate to use than the current option for Hep-A risk models.

COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has been used to analyze markers in wastewater treatment plant (WWTP) influent to characterize emerging chemicals, drug use patterns, or disease spread within communities. This approach can be particularly helpful in understanding outbreaks of disease like the novel Coronavirus disease-19 (COVID-19) when combined with clinical datasets. In this study, three RT-ddPCR assays (N1, N2, N3) were used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in weekly samples from nine WWTPs in southeastern Virginia. In the first several weeks of sampling, SARS-CoV-2 detections were sporadic. Frequency of detections and overall concentrations of RNA within samples increased from mid March into late July. During the twenty-one week study, SARS-CoV-2 concentrations ranged from 101 to 104 copies 100 mL-1 in samples where viral RNA was detected. Fluctuations in population normalized loading rates in several of the WWTP service areas agreed with known outbreaks during the study. Here we propose several ways that data can be presented spatially and temporally to be of greatest use to public health officials. As the COVID-19 pandemic wanes, it is likely that communities will see increased incidence of small, localized outbreaks. In these instances, WBE could be used as a pre-screening tool to better target clinical testing needs in communities with limited resources.