Outbreak-Based Giardia Dose-Response Model Using Bayesian Hierarchical Markov Chain Monte Carlo Analysis

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.

Bayesian Estimation of Supply Chain Innovation Path

With the transformation of the supply chain from factor driven to investment driven and to innovation driven, supply chain innovation has attracted more and more attention. This paper obtains the Bayesian prior probability of innovation and studies the supply chain innovation path from the perspective of strategy and behavior. The model divides the overall innovation capability of the supply chain into specific node tasks and assets (Capability Set), and the innovation demand of the supply chain is expressed in the form of the conditional probability of market demand. Under the conditions of minimum risk, minimum cost, and rapid market response, it determines who should lead the supply chain innovation first and what type of supply chain innovation should be carried out first. Finally, taking the supply chain of the professional market in Zhejiang Province as an example, this paper verifies the theoretical model of supply chain innovation decision-making. The innovation of the supply chain of Zhejiang's specialized market is decomposed into the capability set of each enterprise node of the supply chain. This paper transforms the group innovation ability of the supply chain in the professional market into the individual innovation ability of a single enterprise node and reveals the starting point and intensity of the demand innovation of products/services in the supply chain.

Statewide Quantitative Microbial Risk Assessment for Waterborne Viruses, Bacteria, and Protozoa in Public Water Supply Wells in Minnesota

Infection risk from waterborne pathogens can be estimated via quantitative microbial risk assessment (QMRA) and forms an important consideration in the management of public groundwater systems. However, few groundwater QMRAs use site-specific hazard identification and exposure assessment, so prevailing risks in these systems remain poorly defined. We estimated the infection risk for 9 waterborne pathogens based on a 2-year pathogen occurrence study in which 964 water samples were collected from 145 public wells throughout Minnesota, USA. Annual risk across all nine pathogens combined was 3.3 × 10^-1 (95% CI: 2.3 × 10^-1 to 4.2 × 10^-1), 3.9 × 10^-2 (2.3 × 10^-2 to 5.4 × 10^-2), and 1.2 × 10^-1 (2.6 × 10^-2 to 2.7 × 10^-1) infections person^-1 year^-1 for noncommunity, nondisinfecting community, and disinfecting community wells, respectively. Risk estimates exceeded the U.S. benchmark of 10^-4 infections person^-1 year^-1 in 59% of well-years, indicating that the risk was widespread. While the annual risk for all pathogens combined was relatively high, the average daily doses for individual pathogens were low, indicating that significant risk results from sporadic pathogen exposure. Cryptosporidium dominated annual risk, so improved identification of wells susceptible to Cryptosporidium contamination may be important for risk mitigation.

Quantitative Microbial Risk Assessment for Contaminated Private Wells in the Fractured Dolomite Aquifer of Kewaunee County, Wisconsin

BACKGROUND

Private wells are an important source of drinking water in Kewaunee County, Wisconsin. Due to the region's fractured dolomite aquifer, these wells are vulnerable to contamination by human and zoonotic gastrointestinal pathogens originating from land-applied cattle manure and private septic systems.

OBJECTIVE

We determined the magnitude of the health burden associated with contamination of private wells in Kewaunee County by feces-borne gastrointestinal pathogens.

METHODS

This study used data from a year-long countywide pathogen occurrence study as inputs into a quantitative microbial risk assessment (QMRA) to predict the total cases of acute gastrointestinal illness (AGI) caused by private well contamination in the county. Microbial source tracking was used to associate predicted cases of illness with bovine, human, or unknown fecal sources.

RESULTS

Results suggest that private well contamination could be responsible for as many as 301 AGI cases per year in Kewaunee County, and that 230 and 12 cases per year were associated with a bovine and human fecal source, respectively. Furthermore, Cryptosporidium parvum was predicted to cause 190 cases per year, the most out of all 8 pathogens included in the QMRA.

DISCUSSION

This study has important implications for land use and water resource management in Kewaunee County and informs the public health impacts of consuming drinking water produced in other similarly vulnerable hydrogeological settings. https://doi.org/10.1289/EHP7815.