An Operations Research-Based Teaching Unit for Grade 10: The ROAR Experience, Part I

We introduce "Ricerca Operativa Applicazioni Reali" (ROAR;in English, "Real Applications of Operations Research"), a three-year project for higher secondary schools. Its main aim is to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. ROAR offers examples and problems closely connected with students' everyday life or with the industrial reality, balancing mathematical modeling and algorithmics. The project is composed of three teaching units, addressed to grades 10, 11, and 12. The implementation of the first teaching unit took place in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). In particular, in this paper, we provide a full description of this first teaching unit in terms of objectives, prerequisites, topics and methods, organization of the lectures, and digital technologies used. Moreover, we analyze the feedback received from students and teachers involved in the experimentation, and we discuss advantages and disadvantages related to distance learning that we had to adopt because of the COVID-19 pandemic.

Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department.

Introduction An "unscheduled absence" refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department's modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID­19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P [Formula: see text] 0.0002. For example, there was a 1% unscheduled absence rate among anesthesiologists working Mondays and Fridays early in the pandemic when the prevalence of COVID-19 among asymptomatic patients was 1.3%. At a 1% unscheduled absence rate, 67 would be the minimum scheduled to maintain a <5.0% risk for being unable to run all 65 anesthetizing locations. In contrast, there was a 3% unscheduled absence rate among nurse anesthetists working Mondays and Fridays during the Omicron variant surge when the prevalence was 4.5%. At a 3% unscheduled absence rate, 70 would be the minimum scheduled to maintain the same risk of not being able to run 65 rooms. Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

What makes a health system good? From cost-effectiveness analysis to ethical improvement in health systems.

Fair allocation of scarce healthcare resources has been much studied within philosophy and bioethics, but analysis has focused on a narrow range of cases. The Covid-19 pandemic provided significant new challenges, making powerfully visible the extent to which health systems can be fragile, and how scarcities within crucial elements of interlinked care pathways can lead to cascading failures. Health system resilience, while previously a key topic in global health, can now be seen to be a vital concern in high-income countries too. Unfortunately, mainstream philosophical approaches to the ethics of rationing and prioritisation provide little guidance for these new problems of scarcity. Indeed, the cascading failures were arguably exacerbated by earlier attempts to make health systems leaner and more efficient. This paper argues that health systems should move from simple and atomistic approaches to measuring effectiveness to approaches that are holistic both in focusing on performance at the level of the health system as a whole, and also in incorporating a wider range of ethical concerns in thinking about what makes a health system good.

Towards Predicting Supplier Resilience: A Tree-Based Model Approach

With looming uncertainties and disruptions in today's global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions. © 2022 IEEE Computer Society. All rights reserved.

Antibiotic consumption in secondary and tertiary hospitals in Colombia: national surveillance from 2018-2020.

Objective: To assess the compliance in secondary and tertiary level hospitals with monthly reporting of antibiotic consumption to the Colombian National Public Health Surveillance System (SIVIGILA-INS), and to describe reported antibiotic consumption during 2018-2020. Methods: This study involved a secondary analysis of antibiotic consumption data reported to SIVIGILA-INS. Frequency of hospital reporting was assessed and compared against expected reports, disaggregated by intensive care units (ICU)/non-ICU wards and geographical regions. Consumption was expressed as defined daily dose (DDD) per 100 occupied beds for seven antibiotics. Results: More than 70% of hospitals reported antibiotic consumption at least once in each of the three years (79% in ICU and 71% in non-ICU wards). Of these, ICU monthly reporting was complete (12 monthly reports per year) for 59% in the period 2018-2019 but only 4% in 2020. Non-ICU reporting was complete for 52% in 2019 and for 2% in 2020. Most regions had an overall decrease in reporting in 2020. Analysis of antibiotic consumption showed an increase for piperacillin/tazobactam, ertapenem, and cefepime from 2019 to 2020. Conclusions: There were gaps in the consistency and frequency of reporting. Efforts are needed to improve compliance with monthly reporting, which declined in 2020, possibly due to the COVID-19 pandemic. Non-compliance on reporting and data quality issues should be addressed with the hospitals to enable valid interpretation of antibiotic consumption trends.

A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption.

Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying [Formula: see text]-greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.

Operation policy of multi-capacity logistic robots in hotel industry

Purpose: This paper aims to propose an operation policy of multi-capacity room service robots traveling within a hotel. As multi-capacity robots can serve many requests in a single trip, improved operation policy can reduce the investment cost of robots. Design/methodology/approach: Using a mathematical model-based optimization technique, an optimal set of robots with minimum installation cost is derived while serving the entire room service demands. Through testing a variety of scenarios by changing the price and function of robots to be installed, insights that consider the various situations are offered. Findings: Though the increase in capacity saves much time for room service at a lower capacity level, the amount of time saved gradually decreases as the capacity increases. Besides, the installation strategy is divided into two cases depending on the purchase cost of robots. Research limitations/implications: Currently, the studies focusing on the adoption of service robots from an operations view are rarely be found. To reduce the burden of investment cost, this study takes the unique approach to improve the operation policy of service robots by using the multi-capacity robots. Practical implications: This study guides the hotel to install an adequate set of robots. The result confirms that the optimal installation set of robots is affected by various factors, such as the room service information, the hotel structure and the unit execution cycle. Originality/value: After the outbreak of COVID-19, people avoid face-to-face contact and interest in non-contact service is growing. This paper deals with the efficient way to implement non-contact delivery through logistic robots, a timely and important topic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

An Exercise for Teaching Transportation Problem Using Spatial Data

We present an exercise for teaching the transportation problem using a mix of spatial and randomly generated data. It illustrates the potential of using qualitative and quantitative data and is suitable for undergraduate or introductory business school courses on operations research (OR), logistics, and supply chain management. It poses two challenges: (i) given the demand locations and volume, open a certain number of warehouses to ensure customer responsiveness and (ii) given those warehouses with capacity limits, determine an optimal distribution plan that minimizes the total distribution cost. This exercise is developed with the active participation of MBA students in an introductory OR course. The participants, attending the class online from different parts of India during the COVID-19 pandemic, helped generate realistic customer locations by sharing their location data. Visualizing this spatial data (after masking) in Google My Maps helps the students decide on suitable warehouse locations by considering the proximity to customers as well as diverse socioeconomic, political, and environmental factors. Then, using these warehouse and customer data, the optimal distribution plan is obtained by employing Open- Solver. Students appreciate the exposure-starting from data set generation to deriving an optimal solution-offered by this data-driven decision-making exercise. © 2022 The Author(s).

Vaccine Allocation and Distribution: A Review with a Focus on Quantitative Methodologies and Application to Equity, Hesitancy, and COVID-19 Pandemic

This review focuses on vaccine distribution and allocation in the context of the current COVID-19 pandemic. The implications discussed are in the areas of equity in vaccine distribution and allocation (at a national level as well as worldwide), vaccine hesitancy, game-theoretic modeling to guide decision-making and policy-making at a governmental level, distribution and allocation barriers (in particular in low-income countries), and operations research (OR) mathematical models to plan and execute vaccine distribution and allocation. To conduct this review, we adopt a novel methodology that consists of three phases. The first phase deploys a bibliometric analysis;the second phase concentrates on a network analysis;and the last phase proposes a refined literature review based on the results obtained by the previous two phases. The quantitative techniques utilized to conduct the first two phases allow describing the evolution of the research in this area and its potential ramifications in future. In conclusion, we underscore the significance of operations research (OR)/management science (MS) research in addressing numerous challenges and trade-offs connected to the current pandemic and its strategic impact in future research. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Analytics Saves Lives During the COVID-19 Crisis in Chile

During the COVID-19 crisis, the Chilean Ministry of Health and the Ministry of Sciences, Technology, Knowledge and Innovation partnered with the Instituto Sistemas Complejos de Ingeniería (ISCI) and the telecommunications company ENTEL, to develop innovative methodologies and tools that placed operations research (OR) and analytics at the forefront of the battle against the pandemic. These innovations have been used in key decision aspects that helped shape a comprehensive strategy against the virus, including tools that (1) provided data on the actual effects of lockdowns in different municipalities and over time;(2) helped allocate limited intensive care unit (ICU) capacity;(3) significantly increased the testing capacity and provided on-the-ground strategies for active screening of asymptomatic cases;and (4) implemented a nationwide serology surveillance program that significantly influenced Chile's decisions regarding vaccine booster doses and that also provided information of global relevance. Significant challenges during the execution of the project included the coordination of large teams of engineers, data scientists, and healthcare professionals in the field;the effective communication of information to the population;and the handling and use of sensitive data. The initiatives generated significant press coverage and, by providing scientific evidence supporting the decision making behind the Chilean strategy to address the pandemic, they helped provide transparency and objectivity to decision makers and the general population. According to highly conservative estimates, the number of lives saved by all the initiatives combined is close to 3,000, equivalent to more than 5% of the total death toll in Chile associated with the pandemic until January 2022. The saved resources associated with testing, ICU beds, and working days amount to more than 300 million USD.

A comparative analysis of outpatient nutrition clinic scheduling outcomes based on in-person and telehealth patient care delivery modalities.

During the start of the global COVID-19 pandemic in March 2020, patient care modalities changed from in-person to telehealth to comply with physical distancing guidelines. Our study uniquely examines operations data from three distinct periods: before the transition to telehealth, early transition from in-person care to telehealth, and the eventual adoption of telehealth. We present a comparative analysis of outpatient nutrition clinic scheduling outcomes based on care delivery modality. We used descriptive statistics to report means and variance and frequencies. We used inferential statistics to make comparisons: categorical data were compared using chi-square analysis with post-hoc comparisons using a z-test with alpha at 0.05. Means of continuous variables were compared using ANOVA with Tukey HSD post-hoc analysis. We found patient demographics remained widely unchanged across the three distinct periods as the demand for telehealth visits increased, with a notable rise in return patient visits, signaling both adaptability across the patient population and acceptance of the telehealth modality. These analyses along with evidence from the included literature review point to many the benefits of telehealth, thus telehealth as a healthcare delivery modality is here to stay. Our work serves as a foundation for future studies in this field, provides information for decision-makers in telehealth-related strategic planning, and can be utilized in advocacy for the extension of telehealth coverage.

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework.

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

Innovative solutions in last mile delivery: concepts, practices, challenges, and future directions

In the last decade, e-commerce has been growing consistently. Fostered by the covid pandemic, online retail has grown exponentially, particularly in industries including food, clothing, groceries, and many others. This growth in online retailing activities has raised critical logistic challenges, especially in the last leg of the distribution, commonly referred to as the Last Mile. For instance, traditional truck-based home delivery has reached its limit within metropolitan areas and can no longer be an effective delivery method. Driven by technological progress, several other logistic solutions have been deployed as innovative alternatives to deliver parcels. This includes delivery by drones, smart parcel stations, robots, and crowdsourcing, among others. In this setting, this paper aims to provide a comprehensive review and analysis of the latest trends in last-mile delivery solutions from both industry and academic perspectives (see Figure 1 for overview). We use a content analysis literature review to analyse over 80 relevant publications, derive the necessary features of the latest innovation in the last mile delivery, and point out their different maturity levels and the related theoretical and operational challenges. (Figure presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

A partial coverage hierarchical location allocation model for health services

We consider a hierarchical maximal covering location problem (HMCLP) to locate health centres and hospitals so that the maximum demand is covered by two levels of services in a successively inclusive hierarchy. We extend the HMCLP by introducing the partial coverage and a new definition of the referral. The proposed model may enable an informed decision on the healthcare system when dynamic adaptation is required, such as a COVID-19 pandemic. We define the referral as coverage of health centres by hospitals. A hospital may also cover demand through referral. The proposed model is solved optimally for small problems. For large problems, we propose a customised genetic algorithm. Computational study shows that the GA performs well, and the partial coverage substantially affects the optimal solutions. [Submitted: 20 January 2021;Accepted: 15 January 2022]

Exploring Social Value Prospects of Australia's Construction Industry Towards the Aboriginal Communities, Under COVID-19 Recovery Efforts

The COVID-19 pandemic has exacerbated pre-existing economic and social challenges throughout the world. The unemployment rate in Australia has skyrocketed due to the country's first recession in 40 years. Australia's governments have responded by investing heavily in construction projects that stimulate the economy and can also be expanded into modern public procurement policies targeting specific groups, such as the conservative Aboriginal communities. However, a number of asymmetries between policy makers and practitioners regarding the implementation of social procurement policies. By surveying Aboriginal construction workers and correlating the obtained responses with their social value expectations and employer preferences, the paper argues that private corporations can build social value prospects for conservative employees when they proactively introduce employment policies (such as rewarding remuneration and career development attributes) and cultural benefit strategies (such as all-inclusive and ethnically diverse workplaces). To ensure a rapid recovery from the economic setbacks caused by the pandemic, it is essential to carry out such studies to estimate how enhanced infrastructure spending in Australia can contribute to sustainable social progress. © 2022 Seventh Sense Research Group®

Interpretable Operations Research for High-Stakes Decisions: Designing the Greek COVID-19 Testing System

In the summer of 2020, in collaboration with the Greek government, we designed and deployed Eva-the first national-scale, reinforcement learning system for targeted COVID-19 testing. In this paper, we detail the rationale for three major design/algorithmic elements: Eva's testing supply chain, estimating COVID-19 prevalence, and test allocation. Specifically, we describe the design of Eva's supply chain to collect and process thousands of biological samples per day with special emphasis on capacity procurement. Then, we propose a novel, empirical Bayes estimation strategy to estimate COVID-19 prevalence among various passenger types with limited data and showcase how these estimates were instrumental in making a variety of downstream decisions. Finally, we propose a novel, multiarmed bandit algorithm that dynamically allocates tests to arriving passengers in a nonstationary environment with delayed feedback and batched decisions. All our design and algorithmic choices emphasize the need for transparent reasoning to enable human-in-the-loop analytics. Such transparency was crucial to building trust and acceptance among policymakers and public health experts in a period of global crisis.

Introduction: 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

The judges for the 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics. The prestigious Wagner Prize-awarded for achievement in implemented operations research, management science, and advanced analytics-emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year's winning application describes the design and deployment of Eva, the Greek COVID-19 testing system used as Greece was opening up for tourism in 2020. The remaining four papers describe the stochastic modeling and mixed-integer programming system used to optimize the Atlanta police patrol zones for better police balance and reduced response time to emergency calls;Lyft's new priority dispatch system, which solves the ride-sharing productivity paradox whereby increases in efficiency do not benefit the drivers;the application of advanced analytics to assist local and federal law enforcement organizations in their efforts to disrupt sex-trafficking networks;and the development of a new after-sales service concept, which increases chip availability for ASML's customers.

Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England.

The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England's National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England.

Accessible location of mobile labs for COVID-19 testing.

In this study, we address the problem of finding the best locations for mobile labs offering COVID-19 testing. We assume that people within known demand centroids have a degree of mobility, i.e., they can travel a reasonable distance, and mobile labs have a limited-and-variable service area. Thus, we define a location problem concerned with optimizing a measure representing the accessibility of service to its potential clients. In particular, we use the concepts of classical, gradual, and cooperative coverage to define a weighted sum of multiple accessibility indicators. We formulate our optimization problem via a mixed-integer linear program which is intractable by commercial solvers for large instances. In response, we designed a Biased Random-Key Genetic Algorithm to solve the defined problem; this is capable of obtaining high-quality feasible solutions over large numbers of instances in seconds. Moreover, we present insights derived from a case study into the locations of COVID-19 testing mobile laboratories in Nuevo Leon, Mexico. Our experimental results show that our optimization approach can be used as a diagnostic tool to determine the number of mobile labs needed to satisfy a set of demand centroids, assuming that users have reduced mobility due to the restrictions because of the pandemic.

A study to critically analyze Revised National Tuberculosis Control Program norms implementation at a medical college-level facility

Background: Tuberculosis (TB) control activities are implemented in the country for more than 50 years. The countrywide lockdown in 2020 adversely impacted routine health-care services including those for the management of TB. Operational research is needed to know whether Revised National Tuberculosis Control Program (RNTCP) (National Tuberculosis Elimination Program) is heading in the right direction as far as the pace and quality of implementation of the program are concerned. Aims and Objectives: The aim of the present study was to investigate the strength, weaknesses, and opportunities of RNTCP. An analysis of RNTCP was done to identify competencies and gaps. Materials and Methods: The present retroprospective and observational study was carried out at the RNTCP facility of a Government Medical College in the Central India in Madhya Pradesh during the year 2019–20. Samples of 238 patients registered under RNTCP for anti-tubercular treatment were taken in the study. Data were collected using a structured schedule from the RNTCP center and tabulated in a Microsoft Excel sheet, to assess the compliance of RNTCP norms in the management of TB. Results: The most commonly affected age was 16–49 years and the male: female ratio was 3:2. The most common basis of diagnosis was microbiological (60.92%). Follow-up sputum testing was done on time in 64.71% of patients. Adherence to anti-tubercular treatment (ATT) was regular in 78.57% of patients. All patients were telephoned while 43.14% of patients were home visited as a default action. After default action, 35.29% of patients return to regular ATT. Out of all registered patients initiated on ATT, 81.09% were treatment success, while 7.14% lost to follow-up, 2.1% became defaulters, and 4.62% patients died. Conclusion: We conclude that treatment success of TB unit was near the RNTCP norm of 85% which is below the national 88%. The probable reasons for the higher default rate and loss to follow-up rate during the study period could be the ongoing COVID-19 pandemic. Background: Tuberculosis (TB) control activities are implemented in the country for more than 50 years. The countrywide lockdown in 2020 adversely impacted routine health-care services including those for the management of TB. Operational research is needed to know whether Revised National Tuberculosis Control Program (RNTCP) (National Tuberculosis Elimination Program) is heading in the right direction as far as the pace and quality of implementation of the program are concerned. Aims and Objectives: The aim of the present study was to investigate the strength, weaknesses, and opportunities of RNTCP. An analysis of RNTCP was done to identify competencies and gaps. Materials and Methods: The present retroprospective and observational study was carried out at the RNTCP facility of a Government Medical College in the Central India in Madhya Pradesh during the year 2019–20. Samples of 238 patients registered under RNTCP for anti-tubercular treatment were taken in the study. Data were collected using a structured schedule from the RNTCP center and tabulated in a Microsoft Excel sheet, to assess the compliance of RNTCP norms in the management of TB. Results: The most commonly affected age was 16–49 years and the male: female ratio was 3:2. The most common basis of diagnosis was microbiological (60.92%). Follow-up sputum testing was done on time in 64.71% of patients. Adherence to anti-tubercular treatment (ATT) was regular in 78.57% of patients. All patients were telephoned while 43.14% of patients were home visited as a default action. After default action, 35.29% of patients return to regular ATT. Out of all registered patients initiated on ATT, 81.09% were treatment success, while 7.14% lost to follow-up, 2.1% became defaulters, and 4.62% patients died. Conclusion: We conclude that treatment success of TB unit was near the RNTCP norm of 85% which is below the national 88%. The probable reasons for the higher default rate and loss to follow-up rate during the study period could be the ongoing COVID 19 pandemic. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)