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Abstract Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini‐based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly‐available benchmark data for the default value, the Gini coefficient method, and the elbow method. 

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component‐based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component‐based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly‐shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in thesmercR package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems. 

The detection of disease clusters in spatial data analysis plays a crucial role in public health, while the circular scan method is widely utilized for this purpose, accurately identifying non-circular (irregular) clusters remains challenging and reduces detection accuracy. To overcome this limitation, various extensions have been proposed to effectively detect arbitrarily shaped clusters. In this paper, we combine the strengths of two well-known methods, the flexible and elliptic scan methods, which are specifically designed for detecting irregularly shaped clusters. We leverage the unique characteristics of these methods to create candidate zones capable of accurately detecting irregularly shaped clusters, along with a modified likelihood ratio test statistic. By inheriting the advantages of the flexible and elliptic methods, our proposed approach represents a practical addition to the existing repertoire of spatial data analysis techniques. 

The National Institute of Allergy and Infectious Diseases organized a symposium in June 2022, to facilitate discussion of the environmental risks for nontuberculous mycobacteria exposure and disease. The expert researchers presented recent studies and identified numerous research gaps. This report summarizes the discussion and identifies six major areas of future research related to culture-based and culture independent laboratory methods, alternate culture media and culturing conditions, frameworks for standardized laboratory methods, improved environmental sampling strategies, validation of exposure measures, and availability of high-quality spatiotemporal data. 

Nontuberculous mycobacteria are ubiquitous environmental bacteria that frequently cause disease in persons with cystic fibrosis (pwCF). The risks for NTM infection vary geographically. Detection of high-risk areas is important for focusing prevention efforts. In this study, we apply five cluster detection methods to identify counties with high NTM infection risk. Four clusters were detected by at least three of the five methods, including twenty-five counties in five states. The geographic area and number of counties in each cluster depended upon the detection method used. Identifying these clusters supports future studies of environmental predictors of infection and will inform control and prevention efforts. 

Rationale: The prevalence of nontuberculous mycobacterial (NTM) pulmonary disease varies geographically in the United States (U.S.). Previous studies indicate that the presence of certain water-quality constituents in source water increase NTM infection risk. Objective: To identify water-quality constituents that influence the risk of NTM pulmonary infection in persons with cystic fibrosis (pwCF) in the U.S. Methods: We conducted a population-based case-control study using NTM incidence data collected from the Cystic Fibrosis Foundation Patient Registry (CFFPR) during 2010-2019. We linked patient zip code to county and associated patient county of residence with surface water data extracted from the Water Quality Portal. We used logistic regression models to estimate odds of NTM infection as a function of water-quality constituents. We modeled two outcomes: pulmonary infection due to Mycobacterium avium complex (MAC) and Mycobacterium abscessus species. Results: We identified 484 MAC cases, 222 M. abscessus cases and 2816 NTM-negative CF controls resident in 11 states. In multivariable models, we found that for every 1-standardized unit increase in the log concentration of sulfate and vanadium in surface water at the county level, the odds of infection increased by 39% and 21%, respectively, among pwCF with MAC compared with CF-NTM-negative controls. When modeling M. abscessus as the dependent variable, every 1-standardized unit increase in the log concentration of molybdenum increased the odds of infection by 36%. Conclusions: These findings suggest that naturally-occurring and anthropogenic water-quality constituents may influence the NTM abundance in water sources that supply municipal water systems, thereby increasing MAC and M. abscessus infection risk.