UV Disinfection Robots: A Review.

The novel coronavirus (COVID-19) pandemic has completely changed our lives and how we interact with the world. The pandemic has brought about a pressing need to have effective disinfection practices that can be incorporated into daily life. They are needed to limit the spread of infections through surfaces and air, particularly in public settings. Most of the current methods utilize chemical disinfectants, which can be laborious and time-consuming. Ultraviolet (UV) irradiation is a proven and powerful means of disinfection. There has been a rising interest in the implementation of UV disinfection robots by various public institutions, such as hospitals, long-term care homes, airports, and shopping malls. The use of UV-based disinfection robots could make the disinfection process faster and more efficient. The objective of this review is to equip readers with the necessary background on UV disinfection and provide relevant discussion on various aspects of UV robots.

Real-time contamination zoning in water distribution networks for contamination emergencies: a case study.

Contamination of urban water distribution systems (WDS) is a critical issue due to the number of victims that might be impacted in a short period of time. Any effective rapid emergency response plan for reducing the number of sick people or deaths among those drinking the polluted water requires a reliable forecast of the water contamination zoning map (CZM). The water CZM is a visual representation of the spread of contamination at the time of contamination detection. This study presents a novel methodology based on the rough set theory (RST) for real-time forecasting of the CZM caused by simultaneous multi-point contamination injection in WDS. Our proposed methodology consists of (i) a Monte Carlo simulation model to capture the uncertainties in a multi-point deliberate contamination, (ii) a numerical simulation model for simulating pipe flow, and (iii) a rough set-based technique for real-time CZM for a WDS equipped with a set of monitoring stations. The proposed methodology can be used for any type of random contamination of WDSs as well as emergencies in deliberate contamination of water distribution networks.

Incidence of invasive breast cancer in the presence of competing mortality: the Canadian National Breast Screening Study.

Mortality due to causes other than breast cancer is a potential competing risk which may alter the incidence probability of breast cancer and as such should be taken into account in predictive modelling. We used data from the Canadian National Breast Screening Study (CNBSS), which consist of two randomized controlled trials designed to evaluate the efficacy of mammography among women aged 40-59. The participants in the CNBSS were followed up for incidence of breast cancer and mortality due to breast cancer and other causes; this allowed us to construct a breast cancer risk prediction model while taking into account mortality for the same study population. In this study, we use 1980-1989 as the study period. We exclude the prevalent cancers from the CNBSS to estimate the probability of developing breast cancer, given the fact that women were cancer-free at the beginning of the follow-up. By the end of 1989, from 89,434 women, 944 (1.1 %) were diagnosed with invasive breast cancer, 922 (1.0 %) died from causes other than breast cancer, and 87,568 (97.9 %) were alive and not diagnosed with invasive breast cancer. We constructed a risk prediction model for invasive breast cancer based on 39 risk factors collected at the time of enrolment or the initial physical examination of the breasts. Age at entry (HR 1.07, 95 % CI 1.05-1.10), lumps ever found in left or right breast (HR 1.92, 95 % CI 1.19-3.10), abnormality in the left breast (HR 1.26, 95 % CI 1.07-1.48), history of other breast disease, family history of breast cancer score (HR 1.01, 95 % CI 1.00-1.01), years menstruating (HR 1.02, 95 % CI 1.01-1.03) and nulliparity (HR 1.70, 95 % CI 1.23-2.36) are the model's predictors. We investigated the effects of time-dependent factors. The model is well calibrated with a moderate discriminatory power (c-index 0.61, 95 % CI 0.59-0.63); we use it to predict the 9-year risk of developing breast cancer for women of different age groups. As an example, we estimated the probability of invasive cancer at 5 years after enrolment to be 0.00448, 0.00556, 0.00691, 0.00863, and 0.01034, respectively, for women aged 40, 45, 50, 55, and 59, all of whom had never noted lumps in their breasts, had 32 years of menstruating, 1-2 live births, no other types of breast disease and no abnormality found in their left breasts. The results of this study can be used by clinicians to identify women at high risk of breast cancer for screening intervention and to recommend a personalized intervention plan. The model can be also utilized by a woman as a breast cancer risk prediction tool.

Predictors of competing mortality to invasive breast cancer incidence in the Canadian National Breast Screening study.

BACKGROUND: Evaluating the cost-effectiveness of breast cancer screening requires estimates of the absolute risk of breast cancer, which is modified by various risk factors. Breast cancer incidence, and thus mortality, is altered by the occurrence of competing events. More accurate estimates of competing risks should improve the estimation of absolute risk of breast cancer and benefit from breast cancer screening, leading to more effective preventive, diagnostic, and treatment policies. We have previously described the effect of breast cancer risk factors on breast cancer incidence in the presence of competing risks. In this study, we investigate the association of the same risk factors with mortality as a competing event with breast cancer incidence. METHODS: We use data from the Canadian National Breast Screening Study, consisting of two randomized controlled trials, which included data on 39 risk factors for breast cancer. The participants were followed up for the incidence of breast cancer and mortality due to breast cancer and other causes. We stratified all-cause mortality into death from other types of cancer and death from non-cancer causes. We conducted separate analyses for cause-specific mortalities. RESULTS: We found that "age at entry" is a significant factor for all-cause mortality, and cancer-specific and non-cancer mortality. "Menstruation length" and "number of live births" are significant factors for all-cause mortality, and cancer-specific mortality. "Ever noted lumps in right/left breasts" is a factor associated with all-cause mortality, and non-cancer mortality. CONCLUSIONS: For proper estimation of absolute risk of the main event of interest common risk factors associated with competing events should be identified and considered.

Medical resource allocation planning by integrating machine learning and optimization models.

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.

Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II.

BACKGROUND: Modeling breast cancer progression and the effect of various risk is helpful in deciding when a woman should start and end screening, and how often the screening should be undertaken. METHODS: We modeled the natural progression of breast cancer using a hidden Markov process, and incorporated the effects of covariates. Patients are women aged 50-59 (older) and 40-49 (younger) years from the Canadian National Breast Screening Studies. We included prevalent cancers, estimated the screening sensitivities and rates of over-diagnosis, and validated the models using simulation. RESULTS: We found that older women have a higher rate of transition from a healthy to preclinical state and other causes of death but a lower rate of transition from preclinical to clinical state. Reciprocally, younger women have a lower rate of transition from a healthy to preclinical state and other causes of death but a higher rate of transition from a preclinical to clinical state. Different risk factors were significant for the age groups. The mean sojourn times for older and younger women were 2.53 and 2.96 years, respectively. In the study group, the sensitivities of the initial physical examination and mammography for older and younger women were 0.87 and 0.81, respectively, and the sensitivity of the subsequent screens were 0.78 and 0.53, respectively. In the control groups, the sensitivities of the initial physical examination for older and younger women were 0.769 and 0.671, respectively, and the sensitivity of the subsequent physical examinations for the control group aged 50-59 years was 0.37. The upper-bounds for over-diagnosis in older and younger women were 25% and 27%, respectively. CONCLUSIONS: The present work offers a basis for the better modeling of cancer incidence for a population with the inclusion of prevalent cancers.

Tumor size distribution of invasive breast cancers and the sensitivity of screening methods in the Canadian National Breast Screening Study.

INTRODUCTION: This study set out to explore if breast cancers of different sizes are detected with varying sensitivity. In addition, we attempt to determine the effect of tumor size on screening detectability. SUBJECTS AND METHODS: Data arising from the Canadian National Breast Screening Study (CNBSS) was used to perform all analyses. The CNBSS consists of two randomized controlled trials, which includes data on detection methods, age, and allocation groups. We stratified tumor size by 5 mm; age into 40-49 and 50-59 years age groups; and cancer detection or presentation methods into mammography only, physical breast examination only, both mammography and physical breast examination, interval cancers, and incident cancers. RESULTS: This study revealed that a difference in tumor size exists for age (smaller tumor sizes are found in older women) and breast cancer detection or presentation modes. More specifically, breast cancers detected by mammography screening are statistically smaller than those detected by physical breast examination or those presenting as incident or interval cancers. This study also found that tumor size affects screening detectability for women in their 50's but not in their forties. That is, a statistically significant difference between mammography screening alone and physical examination alone was observed for women between the ages of 50-59 for tumor sizes up to 20 mm, including prevalent cases, and up to 15 mm when prevalent cases were excluded. CONCLUSION: The results of this study suggest that smaller breast cancers are more likely to be detected among women in their 50s.

Optimizing Canadian breast cancer screening strategies: a perspective for action.

While controversies regarding optimal breast cancer screening modalities, screening start and end ages, and screening frequencies continue to exist, additional population-based randomized trials are unlikely to be initiated to examine these concerns. Simulation models have been used to evaluate the efficacy and effectiveness of various breast cancer screening strategies, however these models were all developed using US data. Currently, there is a need to examine the optimal screening and treatment policies in the Canadian context. In this commentary, we discuss the current controversies pertaining to breast cancer screening, and describe the fundamental components of a simulation model, which can be used to inform breast cancer screening and treatment policies.