Real-Time Use of a Dynamic Model To Measure the Impact of Public Health Interventions on Measles Outbreak Size and Duration - Chicago, Illinois, 2024.

Measles is a highly infectious, vaccine-preventable disease that can cause severe illness, hospitalization, and death. A measles outbreak associated with a migrant shelter in Chicago occurred during February-April 2024, in which a total of 57 confirmed cases were identified, including 52 among shelter residents, three among staff members, and two among community members with a known link to the shelter. CDC simulated a measles outbreak among shelter residents using a dynamic disease model, updated in real time as additional cases were identified, to produce outbreak forecasts and assess the impact of public health interventions. As of April 8, the model forecasted a median final outbreak size of 58 cases (IQR = 56-60 cases); model fit and prediction range improved as more case data became available. Counterfactual analysis of different intervention scenarios demonstrated the importance of early deployment of public health interventions in Chicago, with a 69% chance of an outbreak of 100 or more cases had there been no mass vaccination or active case-finding compared with only a 1% chance when those interventions were deployed. This analysis highlights the value of using real-time, dynamic models to aid public health response, set expectations about outbreak size and duration, and quantify the impact of interventions. The model shows that prompt mass vaccination and active case-finding likely substantially reduced the chance of a large (100 or more cases) outbreak in Chicago.

Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Nationwide Trends in COVID-19 Cases and SARS-CoV-2 RNA Wastewater Concentrations in the United States.

Wastewater-based epidemiology has emerged as a promising technology for population-level surveillance of COVID-19. In this study, we present results of a large nationwide SARS-CoV-2 wastewater monitoring system in the United States. We profile 55 locations with at least six months of sampling from April 2020 to May 2021. These locations represent more than 12 million individuals across 19 states. Samples were collected approximately weekly by wastewater treatment utilities as part of a regular wastewater surveillance service and analyzed for SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA concentrations were normalized to pepper mild mottle virus, an indicator of fecal matter in wastewater. We show that wastewater data reflect temporal and geographic trends in clinical COVID-19 cases and investigate the impact of normalization on correlations with case data within and across locations. We also provide key lessons learned from our broad-scale implementation of wastewater-based epidemiology, which can be used to inform wastewater-based epidemiology approaches for future emerging diseases. This work demonstrates that wastewater surveillance is a feasible approach for nationwide population-level monitoring of COVID-19 disease. With an evolving epidemic and effective vaccines against SARS-CoV-2, wastewater-based epidemiology can serve as a passive surveillance approach for detecting changing dynamics or resurgences of the virus.

Uses of mathematical modeling to estimate the impact of mass drug administration of antibiotics on antimicrobial resistance within and between communities.

BACKGROUND: Antibiotics are a key part of modern healthcare, but their use has downsides, including selecting for antibiotic resistance, both in the individuals treated with antibiotics and in the community at large. When evaluating the benefits and costs of mass administration of azithromycin to reduce childhood mortality, effects of antibiotic use on antibiotic resistance are important but difficult to measure, especially when evaluating resistance that "spills over" from antibiotic-treated individuals to other members of their community. The aim of this scoping review was to identify how the existing literature on antibiotic resistance modeling could be better leveraged to understand the effect of mass drug administration (MDA) on antibiotic resistance. MAIN TEXT: Mathematical models of antibiotic use and resistance may be useful for estimating the expected effects of different MDA implementations on different populations, as well as aiding interpretation of existing data and guiding future experimental design. Here, strengths and limitations of models of antibiotic resistance are reviewed, and possible applications of those models in the context of mass drug administration with azithromycin are discussed. CONCLUSIONS: Statistical models of antibiotic use and resistance may provide robust and relevant estimates of the possible effects of MDA on resistance. Mechanistic models of resistance, while able to more precisely estimate the effects of different implementations of MDA on resistance, may require more data from MDA trials to be accurately parameterized.

Infectious Disease Modeling: Recommendations for Public Health Decision-Makers.

Infectious disease modeling plays an important role in the response to infectious disease outbreaks, perhaps most notably during the coronavirus disease 2019 (COVID-19) pandemic. In our experience working with state and local governments during COVID-19 and previous public health crises, we have observed that, while the scientific literature focuses on models' accuracy and underlying assumptions, an important limitation on the effective application of modeling to public health decision-making is the ability of decision-makers and modelers to work together productively. We therefore propose a set of guiding principles, informed by our experience, for working relationships between decision-makers and modelers. We hypothesize that these guidelines will improve the utility of infectious disease modeling for public health decision-making, irrespective of the particular outbreak in question and of the precise modeling approaches being used.

Pilot study of autologous fecal microbiota transplants in nursing home residents: Feasibility and safety.

Introduction: Antibiotic resistant bacterial infections (ARBIs) are extremely common in nursing home residents. These infections typically occur after a course of antibiotics, which eradicate both pathological and beneficial organisms. The eradication of beneficial organisms likely facilitates subsequent ARBIs. Autologous fecal microbiota transplant (aFMT) has been proposed as a potential treatment to reduce ARBIs in nursing home residents. Our objective was to determine the feasibility and safety of aFMT in a nursing home population. Methods: Pilot clinical trial. We evaluated feasibility as total number of stool samples collected for aFMT production and safety as the number and relatedness of serious (SAE) and non-serious adverse events (AE). Results: We screened 468 nursing home residents aged ≥18 years for eligibility; 67 enrolled, distributed among three nursing homes. Participants were 62.7% female and 35.8% Black. Mean age was 82.2 ± 8.5 years. Thirty-three participants underwent successful stool collection. Seven participants received antibiotics; four participants underwent aFMT. There were 40 SAEs (17 deaths) and 11 AEs. In the aFMT group, there were 3 SAEs (2 deaths) and 10 AEs. All SAEs and AEs were judged unrelated to the study intervention. Conclusions: In this pilot study of aFMT in nursing home residents, less than half were able to provide adequate stool samples for aFMT. There were no related SAEs or AEs during the study. In sum, we conclude aFMT has limited feasibility in a nursing home population due to logistic and technical challenges but is likely safe. Trial registration: ClinicalTrials.gov Identifier: NCT03061097.

Analysis of multiple bacterial species and antibiotic classes reveals large variation in the association between seasonal antibiotic use and resistance.

Understanding how antibiotic use drives resistance is crucial for guiding effective strategies to limit the spread of resistance, but the use-resistance relationship across pathogens and antibiotics remains unclear. We applied sinusoidal models to evaluate the seasonal use-resistance relationship across 3 species (Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae) and 5 antibiotic classes (penicillins, macrolides, quinolones, tetracyclines, and nitrofurans) in Boston, Massachusetts. Outpatient use of all 5 classes and resistance in inpatient and outpatient isolates in 9 of 15 species-antibiotic combinations showed statistically significant amplitudes of seasonality (false discovery rate (FDR) < 0.05). While seasonal peaks in use varied by class, resistance in all 9 species-antibiotic combinations peaked in the winter and spring. The correlations between seasonal use and resistance thus varied widely, with resistance to all antibiotic classes being most positively correlated with use of the winter peaking classes (penicillins and macrolides). These findings challenge the simple model of antibiotic use independently selecting for resistance and suggest that stewardship strategies will not be equally effective across all species and antibiotics. Rather, seasonal selection for resistance across multiple antibiotic classes may be dominated by use of the most highly prescribed antibiotic classes, penicillins and macrolides.

Wastewater network infrastructure in public health: Applications and learnings from the COVID-19 pandemic.

Accurate estimates of COVID-19 burden of infections in communities can inform public health strategy for the current pandemic. Wastewater based epidemiology (WBE) leverages sewer infrastructure to provide insights on rates of infection by measuring viral concentrations in wastewater. By accessing the sewer network at various junctures, important insights regarding COVID-19 disease activity can be gained. The analysis of sewage at the wastewater treatment plant level enables population-level surveillance of disease trends and virus mutations. At the neighborhood level, WBE can be used to describe trends in infection rates in the community thereby facilitating local efforts at targeted disease mitigation. Finally, at the building level, WBE can suggest the presence of infections and prompt individual testing. In this critical review, we describe the types of data that can be obtained through varying levels of WBE analysis, concrete plans for implementation, and public health actions that can be taken based on WBE surveillance data of infectious diseases, using recent and successful applications of WBE during the COVID-19 pandemic for illustration.

Viral dynamics of acute SARS-CoV-2 infection and applications to diagnostic and public health strategies.

SARS-CoV-2 infections are characterized by viral proliferation and clearance phases and can be followed by low-level persistent viral RNA shedding. The dynamics of viral RNA concentration, particularly in the early stages of infection, can inform clinical measures and interventions such as test-based screening. We used prospective longitudinal quantitative reverse transcription PCR testing to measure the viral RNA trajectories for 68 individuals during the resumption of the 2019-2020 National Basketball Association season. For 46 individuals with acute infections, we inferred the peak viral concentration and the duration of the viral proliferation and clearance phases. According to our mathematical model, we found that viral RNA concentrations peaked an average of 3.3 days (95% credible interval [CI] 2.5, 4.2) after first possible detectability at a cycle threshold value of 22.3 (95% CI 20.5, 23.9). The viral clearance phase lasted longer for symptomatic individuals (10.9 days [95% CI 7.9, 14.4]) than for asymptomatic individuals (7.8 days [95% CI 6.1, 9.7]). A second test within 2 days after an initial positive PCR test substantially improves certainty about a patient's infection stage. The effective sensitivity of a test intended to identify infectious individuals declines substantially with test turnaround time. These findings indicate that SARS-CoV-2 viral concentrations peak rapidly regardless of symptoms. Sequential tests can help reveal a patient's progress through infection stages. Frequent, rapid-turnaround testing is needed to effectively screen individuals before they become infectious.

Making waves: Defining the lead time of wastewater-based epidemiology for COVID-19.

Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may shed the virus in stool before developing symptoms, suggesting that measurements of SARS-CoV-2 concentrations in wastewater could be a "leading indicator" of COVID-19 prevalence. Multiple studies have corroborated the leading indicator concept by showing that the correlation between wastewater measurements and COVID-19 case counts is maximized when case counts are lagged. However, the meaning of "leading indicator" will depend on the specific application of wastewater-based epidemiology, and the correlation analysis is not relevant for all applications. In fact, the quantification of a leading indicator will depend on epidemiological, biological, and health systems factors. Thus, there is no single "lead time" for wastewater-based COVID-19 monitoring. To illustrate this complexity, we enumerate three different applications of wastewater-based epidemiology for COVID-19: a qualitative "early warning" system; an independent, quantitative estimate of disease prevalence; and a quantitative alert of bursts of disease incidence. The leading indicator concept has different definitions and utility in each application.

Stool Banking for Fecal Microbiota Transplantation: Methods and Operations at a Large Stool Bank.

Objectives: Fecal microbiota transplantation (FMT) is a recommended therapy for recurrent Clostridioides difficile infection and is being investigated as a potential therapy for dozens of microbiota-mediated indications. Stool banks centralize FMT donor screening and FMT material preparation with the goal of expanding access to FMT material while simultaneously improving its safety, quality, and convenience. Although there are published consensuses on donor screening guidelines, there are few reports about the implementation of those guidelines in functioning stool banks. Methods: To help inform consensus standards with data gathered from real-world settings and, in turn, to improve patient care, here we describe the general methodology used in 2018 by OpenBiome, a large stool bank, and its outputs in that year. Results: In 2018, the stool bank received 7,536 stool donations from 210 donors, a daily average of 20.6 donations, and processed 4,271 of those donations into FMT preparations. The median time a screened and enrolled stool donor actively donated stool was 5.8 months. The median time between the manufacture of an FMT preparation and its shipment to a hospital or physician was 8.9 months. Half of the stool bank's partner hospitals and physicians ordered an average of 0.75 or fewer FMT preparations per month. Conclusions: Further knowledge sharing should help inform refinements of stool banking guidelines and best practices.

16S rRNA sequencing of samples from universal stool bank donors.

OBJECTIVES: Universal stool banks provide stool to physicians for use in treating recurrent Clostridioides difficile infection via fecal microbiota transplantation. Stool donors providing the material are rigorously screened for diseases and disorders with a potential microbiome etiology, and they are likely healthier than the controls in most microbiome datasets. 16S rRNA sequencing was performed on samples from a selection of stool donors at a large stool bank, OpenBiome, to characterize their gut microbial community and to compare samples across different timepoints and sequencing runs. DATA DESCRIPTION: 16S rRNA sequencing was performed on 200 samples derived from 170 unique stool donations from 86 unique donors. Samples were sequenced on 11 different sequencing runs. We are making this data available because rigorously screened, likely very healthy stool donors may be useful for characterizing and understanding microbial community differences across different populations and will help shed light into the how the microbiome community promotes health and disease.

Re-Evaluating the Evidence for Faecal Microbiota Transplantation 'Super-Donors' in Inflammatory Bowel Disease.

BACKGROUND: Faecal microbiota transplantation [FMT] is a recommended treatment for recurrent Clostridioides difficile infection, and there is promise that FMT may be effective for conditions such as inflammatory bowel disease [IBD]. Previous FMT clinical trials have considered the possibility of a 'donor effect', that is, that FMT material from different donors has different clinical efficacies. METHODS: Here we re-evaluate evidence for donor effects in published FMT clinical trials for IBD. RESULTS: In ten of 12 published studies, no statistically significant donor effect was detected when rigorously re-evaluating the original analyses. One study showed statistically significant separation of microbiota composition of pools of donor stool when stratified by patient outcome. One study reported a significant effect but did not have underlying data available for re-evaluation. When quantifying the uncertainty on the magnitude of the donor effect, confidence intervals were large, including both zero donor effects and very substantial donor effects. CONCLUSION: Although we found very little evidence for donor effects, the existing data cannot rule out the possibility that donor effects are clinically important. Large clinical trials prospectively designed to detect donor effects are probably needed to determine if donor effects are clinically relevant for IBD.

Shiga Toxin-Producing Escherichia coli Transmission via Fecal Microbiota Transplant.

Fecal microbiota transplantation (FMT) is recommended therapy for multiply recurrent Clostridioides difficile infection. We report adverse events in 7 patients who received FMT from a stool donor who was colonized with Shiga toxin-producing Escherichia coli (STEC). No patients died of FMT-transmitted STEC. Improved screening can likely avoid future transmission.

Fecal Microbiota Transplants Annually and Their Positive Clinical Impact.

INTRODUCTION: Although fecal microbiota transplantation (FMT) is a recommended, clinically efficacious, and cost-effective treatment for recurrent Clostridioides difficile infection (CDI), the scale of FMT use in the United States is unknown. METHODS: We developed a population-level CDI model. RESULTS: We estimated that 48,000 FMTs could be performed annually, preventing 32,000 CDI recurrences. DISCUSSION: Improving access to FMT could lead to tens of thousands fewer C. difficile episodes per year.

Power calculations for detecting differences in efficacy of fecal microbiota donors.

Fecal microbiota transplantation (FMT) is a recommended therapy for recurrent Clostridioides difficile infection and is being investigated as a potential therapy for dozens of other indications, notably inflammatory bowel disease. The immense variability in human stool, combined with anecdotal reports from FMT studies, have suggested the existence of "donor effects", in which stool from some FMT donors is more efficacious than stool from other donors. In this study, simulated clinical trials were used to estimate the number of patients that would be required to detect donor effects under a variety of study designs. In most cases, reliable detection of donor effects required more than 100 patients treated with FMT. These results suggest that previous reports of donor effects need to be verified with results from large clinical trials and that patient biomarkers may be the most promising route to robustly identifying donor effects.

Modeling Donor Screening Strategies to Reduce the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission via Fecal Microbiota Transplantation.

The potential for transmission of severe acute respiratory syndrome coronavirus 2 shed in stool via fecal microbiota transplantation is not yet known, and the effectiveness of various testing strategies to prevent fecal microbiota transplantation-based transmission has also not yet been quantified. In this study, we use a mathematical model to simulate the utility of different testing strategies.

The role of "spillover" in antibiotic resistance.

Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use-resistance association is complicated by "spillover," in which one population's level of antibiotic use affects another population's level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen-antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.