An Analytical Approach for Dispatch Operations of Emergency Medical Services: A Case Study of COVID-19

: Emergency medical services (EMS) aims to deliver timely ambulatory care to incidents in communities. However, the operations of EMS may contend with suddenly increasing demands resulting from unexpected disasters such as disease outbreaks (e.g., COVID-19) or hurricanes. To this end, it usually requires better strategical decisions to dispatch, allocate, and reallocate EMS resources to meet the demand changes over time in terms of demographic and geographic distribution of incidents. In this study, we focus on the operation of the EMS resources (i.e., ambulance dispatch) in response to a demand disruption amid the COVID-19 pandemic. Specifically, we present a analytical framework to (1) analyze the underlying demographic and geographic patterns of emergency incidents and EMS resources;(2) develop a mathematical programming model to identify potential demand gaps of EMS coverage across different districts;and (3) provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. The proposed method is validated with emergency response incident data in New York City for the first COVID-19 surge from March to April 2020. We found that it takes a long incident response time to scenes which reflects unexpected incident demands during COVID-19 surge. To cover such disruptive demands, ambulances need to be reallocated between service districts while meeting the response time standard. The proposed framework can be potentially applied to similar disruptive scenarios in the future and other operational systems disrupted by other disasters. Highlights: We propose an analytical framework using optimization modeling and simulation techniques for EMS resource allocation in response to a demand disruption amid the COVID-19 pandemic.We propose mathematical programming models to identify potential demand gaps of EMS coverage across different districts.We provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. © 2023, The Author(s).

Design of Folate-Containing Liposomal Nucleic Acid Delivery Systems for Antitumor Therapy

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.

Emergency Material Scheduling Optimization Method Using Multi-Disaster Point Distribution Approach

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies. © 2023 by the authors.

Study of emergency supplies based dispatch in a persistent epidemic

The continued outbreak of the novel coronavirus pneumonia (COVID-19) has had a huge impact on people's lives. In the context of the ongoing epidemic and the limited distribution capacity due to the multi-regional epidemic closure, it has become an urgent reality to minimise the damage caused to people's daily lives under the epidemic and other emergencies, and to implement safe, fair and economical dispatch of emergency supplies for the epidemic area. The problem. Based on this, a mixed integer linear programming model is constructed to maximise the fairness and minimise the transportation cost of emergency material dispatch. © 2023 SPIE.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution (VREMD) is one of the core issues of emergency management, which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events. To summarize the latest research progress, we collected 511 VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software. Subsequently, we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords, research gaps, and future works. The results show that do -mestic scholars and research organizations held an unqualified advantage regarding the number of published papers. However, these organizations with the most publications performed poorly regarding the number of literature citations. China and the US have contributed the vast majority of the literature, and there are close collaborations between researchers from both countries. The optimization model of VREMD can be divided into single-, multi-, and joint-objective models. The shortest travel time is the most common optimization objective in the single-objective optimization model. Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously. In recent literature, scholars have focused on the impact of uncertainty and special events (e.g., COVID-19) on VREMD. Moreover, some scholars focus on joint optimization models to optimize vehicle routes and central locations (or material allocation) simultaneously. So-lution algorithms can be divided into two primary categories, i.e., mathematical planning methods and intelligent evolutionary algorithms. The branch and bound algorithm is the most dominant mathematical planning algorithm, while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms. It is shown that the nondominated sorting genetic algorithm II (NSGA-II) can effectively solve the multiobjective model of VREMD. To further improve the algorithm's performance, re-searchers have proposed improved hybrid intelligent algorithms that combine the ad-vantages of NSGA-II and certain other algorithms. Scholars have also proposed a series of optimization algorithms for specific scenarios. With the development of new technologies and computation methods, it will be exciting to construct optimization models that consider uncertainty, heterogeneity, and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.(c) 2022 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Machine Learning based Automated Product Billing and Inventory

Due to the COVID-19 pandemic restrictions were imposed to stop the spread of the virus. As a result, the shopping malls, retail stores and grocery stores had to be shut down leading to significant losses. Though online shopping is always an option, buying daily groceries online is not a feasible option due to delivery times. As the world is now recovering from the pandemic, people have now started visiting malls, retail stores and other places for buying grocery items. In such situation it then becomes very crucial to help people shop more efficiently to reduce the buying time. This will help to keep the crowd under control without jeopardizing the safety and social distance norms. The major problem with most top retail stores is that the customer must wait in long queues after buying their products for getting them billed and payment. This puts pressure on the management and billing staff as well as makes the process of shopping a time consuming and unpleasant experience for the customer. The other issue is that most retail stores waste a significant area of their shop in setting up the billing counters and furthermore also must recruit people for running the billing counters. This project proposes a way of solving the above-mentioned problems and issues with the help of a smart shopping cart. A cart with built in billing and product scanning system which will prepare the bill as the customer is shopping, thus saving customer's valuable time, area of the shop and eliminating the need for recruiting employees for billing. © 2022 IEEE.

Contribution of Live Video to Physicians' Remote Assessment of Suspected COVID-19 Patients in an Emergency Medical Communication Centre: A Retrospective Study and Web-Based Survey.

The COVID-19 pandemic had a major impact on emergency medical communication centres (EMCC). A live video facility was made available to second-line physicians in an EMCC with a first-line paramedic to receive emergency calls. The objective of this study was to measure the contribution of live video to remote medical triage. The single-centre retrospective study included all telephone assessments of patients with suspected COVID-19 symptoms from 01.04.2020 to 30.04.2021 in Geneva, Switzerland. The organisation of the EMCC and the characteristics of patients who called the two emergency lines (official emergency number and COVID-19 number) with suspected COVID-19 symptoms were described. A prospective web-based survey of physicians was conducted during the same period to measure the indications, limitations and impact of live video on their decisions. A total of 8957 patients were included, and 2157 (48.0%) of the 4493 patients assessed on the official emergency number had dyspnoea, 4045 (90.6%) of 4464 patients assessed on the COVID-19 number had flu-like symptoms and 1798 (20.1%) patients were reassessed remotely by a physician, including 405 (22.5%) with live video, successfully in 315 (77.8%) attempts. The web-based survey (107 forms) showed that physicians used live video to assess mainly the breathing (81.3%) and general condition (78.5%) of patients. They felt that their decision was modified in 75.7% (n = 81) of cases and caught 7 (7.7%) patients in a life-threatening emergency. Medical triage decisions for suspected COVID-19 patients are strongly influenced by the use of live video.

Ladybug Beetle Optimization algorithm: application for real-world problems.

In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.

[Use of emergency services for suicide attempts during the SARS-CoV-2 pandemic-a prevalence study]. / Inanspruchnahme des Rettungsdiensts bei Suizidversuchen im Verlauf der SARS-CoV-2-Pandemie.

Background: The pandemic has caused several changes in the emergency care system. The deployment figures in emergency medical services have shown atypical fluctuations. This has also been explained by changes in behavior and an increased sense of stress among the population. Existing research provides hints for the increased incidence of mental health symptoms in emergency care during ongoing pandemics. Objective: In this context, this paper examines the occurrence of emergency medical services calls related to the keyword suicide in relation to total calls. Methods: This is a retrospective cross-sectional study based on routine documentation from a fire and rescue dispatch center with descriptive and exploratory data analyses. The data are divided by settlement-structural county types and compared with incidences and pandemic phases. Results: Phase 1 and 2a show a decrease in the number of dispatches during the pandemic. In addition, there is a shift in the number of dispatch cases with a context of suicide by structure types in phase 3. A decreased dispatch rate in the sparsely populated rural county is offset by an increase in the metropolitan area. Changes made to the control center system resulted in an increase in the number of dispatch cases in the context of suicide. Conclusion: Continuous mental health surveillance, including data collected by emergency medical services, can provide valuable insight. The study also highlights the need for standardization of emergency dispatch center data.

Is Use Of An Interactive Voice Server In An Emergency Medical Dispatch Center An Efficient And Safe Measure?

Introduction: In the first wave of COVID-19 pandemic, Emergency Medical Dispatch Centers (EMDC) faced an influx of calls. During this time, with the scope of handling emergency calls more quickly, it was decided to use an Interactive Voice Server (IVS). The objective of this study was to identify whether the implementation of an IVS is efficient and safe. Method(s): From 20/03/19 until 20/04/26, an IVS was activated between 8 AM and midnight. IVS offered the caller to choose either 1-press the 'zero' key for Coronavirus Syndrome with no respiratory difficulties;or 2-stay on line for any other reason. If the caller typed 'zero', the call was directed to a 'crisis dispatcher' specially trained to handle COVID cases. If he stayed on line, his call was placed in the same queueing list as all emergency calls and handled by a "conventional dispatcher". All medical dispatch files picked up during IVS activation period were included and classified in 2 groups: "IVS Yes" if caller pressed 'zero' and "IVS No" if not. Patient's age, gender and profile of the caller (patient or third party) were collected. The level of severity of the patients was assessed upon the dispatcher' decision ranging from sending an Advanced Life Support ambulance (ALS), a Basic Life Support ambulance (BLS) or no transport. Data were compared between the 2 groups with Chi-square tests. Result(s): 2846 callers were in the group "IVS Yes" and 12111 in "IVS No". Main results are in table 1. Conclusion(s): IVS allowed almost 15% of calls to be directed to a specialized provider where they waited to be processed by staff trained within a few days to deal exclusively with COVID cases. This has led to decrease the number of calls handled by the conventional dispatch and allowed more time to respond to severe emergency calls. Moreover, because only 0.07% "IVS Yes" needed an ALS ambulance, we can assume that the use of IVS is safe. IVS is therefore an effective tool, which allows safe triage of less serious patients and frees up time to answer to severe calls.

Optimal model for fair dispatch of emergency materials considering regional disaster classification

In order to achieve fair dispatch of emergency materials at different disaster sites, inspired by the idea of accurate prevention and control in different regions and grades, a new fair scheduling model considering differentiated disaster classification was constructed with the aim of minimizing scheduling time and maximizing fairness of emergency materials distribution considering the minimum dissatisfaction of emergency materials at each disaster site at the same level as a benchmark to design fairness measurement index. Then, the model was solved by genetic algorithm, and simulation was conducted for the case of Hubei Province, the worst-hit area of the COVID-19 epidemic. The results show that this model can effectively solve the problem of fair material dispatch in differentiated disaster situations under the condition of material shortage. and while ensuring priority and focus of emergency materials distribution in worst-hit areas, it also takes into account the fairness of material dispatch at all levels of disaster areas. © 2022, Editorial Department of China Safety Science Journal. All rights reserved.

Optimization of Aircraft Taxiing Strategies to Reduce the Impacts of Landing and Take-Off Cycle at Airports

The increasing attention of opinion towards climate change has prompted public authorities to provide plans for the containment of emissions to reduce the environmental impact of human activities. The transport sector is one of the main ones responsible for greenhouse emissions and is under investigation to counter its burdens. Therefore, it is essential to identify a strategy that allows for reducing the environmental impact produced by aircraft on the landing and take-off cycle and its operating costs. In this study, four different taxiing strategies are implemented in an existing Italian airport. The results show advantageous scenarios through single-engine taxiing, reduced taxi time through improved surface traffic management, and onboard systems. On the other hand, operating towing solutions with internal combustion cause excessive production of pollutants, especially HC, CO, NOX, and particulate matter. Finally, towing with an electrically powered external vehicle provides good results for pollutants and the maximum reduction in fuel consumption, but it implies externalities on taxiing time. Compared to the current conditions, the best solutions ensure significant reductions in pollutants throughout the landing and take-off cycle (−3.2% for NOx and −44.2% for HC) and economic savings (−13.4% of fuel consumption).

Solution of Solar Integrated Economic Load Dispatch Problems using COVID-19 Algorithm

Electrical power dispatch at a minimum cost of operation has been a challenging issue for thermal power stations and has research work has been carried out for decades. It has been observed that day by day resources of conventional energy are depleting so, the world is shifting towards renewable energy sources. This paper presents a novel technique COVID-19 Optimizer Algorithm (CVA) for solving the economic load dispatch problem of solar generation systems and thermal generating plants of a power system. The proposed method can be considered for solving the various types of economic load dispatch (ELD) problem considering numerous constraints viz. ramp rate limit & prohibited operating zones. Simulation results proved that the technique proposed performs way better than other modern optimization algorithms both in terms of quality of result obtained as well as computational efficiency. The robust nature of the CVA technique in solving solar integrated ELD problems can be inferred from the results. © 2022 IEEE.

MicroGrid Designer: user-friendly design, operation and control assist tools for resilient microgrid and autonomous community

During this decade, many countries have experienced natural and accidental disasters, such as typhoons, floods, earthquakes, and nuclear plant accidents, causing catastrophic damage to infrastructures. Since the end of 2019, all countries of the world are struggling with the COVID-19 and pursuing countermeasures, including inoculation of vaccine, and changes in our lifestyle and social structures. All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous, so that vital lifelines are secured during calamities. A paradigm shift has been taking place toward reorganizing the energy social service management in many countries, including Japan, by effective use of sustainable energy and new supply schemes. However, such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service. Therefore, new social infrastructures and novel management systems to supply energy and social service will be required. In this paper, user-friendly design, operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance. © 2022

Construction Plan for Backup Dispatch of Regional Power Grid

Emergencies such as the COVID-19 and natural disasters have brought severe ordeals to the current grid emergency dispatch system, and there is an urgent need to improve and consummate the existing backup dispatch system. This paper firstly analyzes the existing three kinds of backup dispatch systems and their advantages and disadvantages, and then compares in detail the construction of national dispatch, provincial dispatch, and prefectural dispatch, and points out several existing problems of backup dispatch at all levels under the current emergency system. In order to gradually solve these problems, a backup dispatch system combining emergency and disaster recovery has been proposed based on the two-place three-center mode, it gradually realizes the prevention of risks from social security incidents such as public health incidents and serious natural disasters. © 2022 IEEE.

Maximization of Reactive Power Reserve in wind integrated power system using CHIO approach

The Reactive Power Reserve (RPR) is an important indicator to plan stable and secure power system operations. The evaluation of RPR is very important to analyze the performance of wind integrated power systems. The RPR should be kept as high as possible for a stable and secure operation of a power system. However, it is difficult to maintain adequate RPR in wind integrated power systems owing to the uncertainty associated with it. A scenario-based nonlinear, complex RPR maximization problem is proposed in this work to ensure the voltage stability of the wind integrated power systems. A newly developed 'Coronavirus Herd Immunity Optimizer (CHIO)' is utilized to solve the proposed problem. Programs are developed in MATLAB and tested on IEEE 30 bus system. The voltage controllers present in the power system are adjusted continuously during the optimization for optimality. Further, the free parameters of CHIO are also tuned through sensitivity analysis. The proficiency of CHIO is verified through various case studies and comparisons with other methods. Copyright (C) 2022 The Authors.

Coronavirus Herd Immunity Optimizer (CHIO) for Transmission Expansion Planning

This paper presents a novel mathematical model to simultaneously tackle the economic dispatch (ED) problem considering valve point effect, load uncertainty, distributed generation (DG) uncertainty, incentive-based demand response, and plug-in electric vehicle into the transmission expansion planning (TEP) problem to minimize the total cost of the system. Monte-Carlo is employed to consider the uncertain characteristic of DGs and loads. Considering ED problem in solving TEP problem with uncertain aspects of DGs and loads, made the problem so complicated. So, to overcome this complicity, a new meta-heuristic coronavirus herd immunity optimizer (CHIO) algorithm is utilized. The presented methodology is verified on an IEEE 24-bus test system. Finally, to evaluate the CHIO algorithm efficiency, a comparison is made between the results obtained by CHIO and Branch and Bound (B&B) algorithm. Numerical results show the efficiency of the newly presented methodology in solving TEP and ED problems simultaneously.

A Fuzzy Logic Based Optimal Network System for the Delivery of Medical Goods via Drones and Land Transport in Remote Areas

Land transport in rural areas are characterized by the abundance of road bends, varied road conditions, elevated terrains, and the far distance between each station. Such situations have rendered full-path drone delivery impossible, and thus deliveries using drones must inevitably be combined with land transportation in the whole process. Ever since the Covid-19 outbreak, there has been an unprecedentedly high demand for efficiency in the delivery of medical goods such as vaccines and medicines, especially in rural areas. Such measures prove indispensable in preventing the spread of the disease among all citizens. Conventionally, the abundance of road conditions, the length, and width of paths, and the characteristics of road bends, are considered and analyzed by human staff qualitatively using their experience and personal judgment before deciding on the best delivery path and the optimal network. To overcome the shortcomings of conventional methods, this article proposes a machine learning-based algorithm that considers all the different road conditions as well as the terrain elevations systematically and quantitatively to determine the best delivery path and construct the optimal delivery network system. When combined with drone delivery, our algorithm will also yield the most feasible position for the drone to be deployed and stationed to deliver the goods to the intended destinations, thereby creating a more comprehensive delivery network system. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Effect of a prioritization score on the inter-hospital transfer time management of severe COVID-19 patients: a quasi-experimental intervention study.

BACKGROUND: The overburdening of the healthcare system during the coronavirus disease 19 (COVID-19) pandemic is driving the need to create new tools to improve the management of inter-hospital transport for patients with a severe COVID-19 infection. OBJECTIVE: The aim of this study was to analyse the usefulness of the application of a prioritization score (IHTCOVID-19) for inter-hospital transfer of patients with COVID-19 infection. METHODS: The study has a quasi-experimental design and was conducted on the Medical Emergency System, the pre-hospital emergency department of the public company belonging to the Autonomous Government of Catalonia that manages urgent healthcare in the region. Patients with a severe COVID-19 infection requiring inter-hospital transport were consecutively included. The pre-intervention period was from 1 to 31 March 2020, and the intervention period with the IHTCOVID-19 score was from 1 to 30 April 2020 (from 8 am to 8 pm). The prioritization score comprises four priority categories, with Priority 0 being the highest and Priority 3 being the lowest. Inter-hospital transfer (IHT) management times (alert-assignment time, resource management time and total central management time) and their variability were evaluated according to whether or not the IHTCOVID-19 score was applied. RESULTS: A total of 344 IHTs were included: 189 (54.9%) in the pre-intervention period and 155 (45.1%) in the post-intervention period. The majority of patients were male and the most frequent age range was between 50 and 70 years. According to the IHTCOVID-19 score, 12 (3.5%) transfers were classified as Priority 0, 66 (19.4%) as Priority 1, 247 (71.8%) as Priority 2 and 19 (5.6%) as Priority 3. Overall, with the application of the IHTCOVID-19 score, there was a significant reduction in total central management time [from 112.4 (inter-quartile range (IQR) 281.3) to 89.8 min (IQR 154.9); P = 0.012]. This significant reduction was observed in Priority 0 patients [286.2 (IQR 218.5) to 42.0 min (IQR 58); P = 0.018] and Priority 1 patients [130.3 (IQR 297.3) to 75.4 min (IQR 91.1); P = 0.034]. After applying the IHTCOVID-19 score, the average time of the process decreased by 22.6 min, and variability was reduced from 618.1 to 324.0 min. CONCLUSION: The application of the IHTCOVID-19 score in patients with a severe COVID-19 infection reduces IHT management times and variability.

Heatstroke-related ambulance dispatch risk before and during COVID-19 pandemic: Subgroup analysis by age, severity, and incident place.

BACKGROUND: In summer 2020 under the COVID-19 pandemic, the Ministry of Health, Labour and Welfare has made public warnings that specific preventive measures such as maskwearing and stay-at-home orders, may increase heatstroke risk. In our previous work, we found a lower risk of heatstroke-related ambulance dispatches (HSAD) during the COVID-19 period, however, it is uncertain whether similar risk reductions can be observed in different vulnerable subgroups. This study aimed to determine the HSAD risk during the COVID-19 pandemic by age, severity, and incident place subgroups. METHOD: A summer-specific (June-September), time-series analysis was performed, using daily HSAD and meteorological data from 47 Japanese prefectures from 2017 to 2020. A two-stage analysis was applied to determine the association between HSAD and COVID-19 pandemic, adjusting for maximum temperature, humidity, seasonality, and relevant temporal adjustments. A generalized linear model was utilized in the first stage to estimate the prefecture-specific effect estimates. Thereafter, a fixed effect meta-analysis in the second stage was implemented to pool the first stage estimates. Subsequently, subgroup analysis via an interaction by age, severity, and incident place was used to analyze the HSAD risk among subgroups. RESULTS: A total of 274,031 HSAD cases was recorded across 47 Japanese prefectures. The average total number of HSAD in the pre-COVID-19 period was 69,721, meanwhile, the COVID-19 period was 64,869. Highest reductions in the risks was particularly observed in the young category (ratio of relative risk (RRR) = 0.54, 95% Confidential Interval (CI): 0.51, 0.57) compared to the elderly category. Whereas highest increment in the risks were observed in severe/death (RRR = 1.25, 95% CI: 1.13, 1.37) compared to the mild category. CONCLUSION: COVID-19 situation exhibited a non-uniform change in the HSAD risk for all subgroups, with the magnitude of the risks varying by age, severity, and incident place.