Number Of Available Beds Research Articles

Improving resource allocation in the precisionmedicine Era: a simulation-based approach using R.

The application of personalized medicine in developing countries is a major challenge, especially for those with poor economic status. A critical factor in improving the application of personalized medicine is the efficient allocation of resources. In healthcare systems, optimizing resource allocation without compromising patient care is paramount. This tutorial employs a simulation-based approach to evaluate the efficiency of bed allocation within a hospital setting. Utilizing a patient arrival model with an exponential distribution, we simulated patient trajectories to examine system bottlenecks, particularly focusing on waiting times. Initial simulations painted a scenario of an 'unstable'system, where waiting times and queue lengths surged due to the limited number of available beds. This research offers insights for hospital management on resource optimization leading to improved patient care.

An environmental scan of residential treatment service provision in Ontario

BackgroundOntario has one of the highest rates of substance-related harms in Canada. Residential treatment programs in the province provide a variety of in-house treatment services to support the needs of individuals with substance use disorders (SUD). However, these programs are not standardized, often varying in the type, quality, and availability of services offered, including evidence-based interventions such as Opioid Agonist Treatment (OAT). Local treatment systems are also rather fragmented and complex to navigate, creating barriers for potential services users to identify and make informed choices on available treatment options.MethodsBetween May to August 2023, we conducted an environmental scan to capture available information on all publicly-funded residential treatment programs in Ontario using the ConnexOntario service portal, a government-funded, health services information platform. Data were captured on organization name, geographical location, program description, program type (residential addictions treatment or supportive recovery programs), eligibility criteria, target population, the program’s OAT policies, number of available beds, minimum and maximum length of stay, projected wait times, funding source, and associated fees for program admission. Data were extracted and organized by geographic region, and findings were presented descriptively.ResultsA total of 102 residential addiction treatment programs and 36 residential supportive recovery programs in Ontario were identified. The scan noted substantial regional variations in program availability and wait times, along with a lack of programs tailored to unique populations such as women, youth, and Indigenous peoples. There is also a paucity of publicly-available information on program offerings, including detailed specifics on OAT policies within residential treatment programs that are crucial to ensuring that the services being offered are safe and grounded in evidence-based practice.ConclusionsFindings from the scan highlight notable gaps in program types, offerings, and availability among residential treatment programs in the province, including a lack of standardization on OAT policies across programs. Efforts should be made to ensure access to treatment-specific program information relevant to potential service users and to enhance coordinated access to residential treatment services in the province.

European’ health care indicators and pancreatic cancer incidence and mortality: A mediation analysis of Eurostat data and Global Burden of Disease Study 2019

AimTo highlight correlations existing between incidence and mortality of pancreatic cancer, and health care indicators in 36 European countries. MethodsThe Global Burden of Disease (GBD) and Eurostat databases were queried between 2004 and 2019. Incidence and mortality were age-standardized. From Eurostat, indicators regarding expenditure, hospital beds, medical technology, health personnel, physicians by medical specialty and unmet needs for medical examination were extracted. Correlations between GBD and Eurostat data were analysed through mediation analysis applying clustering for countries. ResultsIncidence increased by +0.6% per year (p = 0.001) and mortality by +0.3% (p = 0.001), being increasing for most of the European countries considered. Incidence and mortality were strongly positively correlated (p = 0.001). Higher current health expenditure, expenditure in inpatient curative care, the number of available beds, the number of computed tomography scan, magnetic resonance units, practising medical doctors were all related to higher incidence (p < 0.05), whereas the unmet need for medical examinations was related to lower incidence. When the mediator’ effect of incidence was handled, these indicators, together with expenditure on outpatient curative cares, the number of pet scanners and of radiation therapy equipment, were related to lower mortality (p < 0.05). ConclusionsHealth care environment correlates with reported incidence and mortality of pancreatic cancer. This highlights both that ameliorated socio-economic societies suffer from higher incidence but lower mortality, as well as the epidemiological bias originating from countries’ diagnostic ability.

Putting ICU triage guidelines into practice: A simulation study using observations and interviews.

The COVID-19 pandemic has prompted many countries to formulate guidelines on how to deal with a worst-case scenario in which the number of patients needing intensive care unit (ICU) care exceeds the number of available beds. This study aims to explore the experiences of triage teams when triaging fictitious patients with the Dutch triage guidelines. It provides an overview of the factors that influence decision-making when performing ICU triage with triage guidelines. Eight triage teams from four hospitals were given files of fictitious patients needing intensive care and instructed to triage these patients. Sessions were observed and audio-recorded. Four focus group interviews with triage team members were held to reflect on the sessions and the Dutch guidelines. The results were analyzed by inductive content analysis. The Dutch triage guidelines were the main basis for making triage decisions. However, some teams also allowed their own considerations (outside of the guidelines) to play a role when making triage decisions, for example to help avoid using non-medical criteria such as prioritization based on age group. Group processes also played a role in decision-making: triage choices can be influenced by the triagists' opinion on the guidelines and the carefulness with which they are applied. Intensivists, being most experienced in prognostication of critical illness, often had the most decisive role during triage sessions. Using the Dutch triage guidelines is feasible, but there were some inconsistencies in prioritization between teams that may be undesirable. ICU triage guideline writers should consider which aspects of their criteria might, when applied in practice, lead to inconsistencies or ethically questionable prioritization of patients. Practical training of triage team members in applying the guidelines, including explanation of the rationale underlying the triage criteria, might improve the willingness and ability of triage teams to follow the guidelines closely.

Demand-supply balance of disaster medical care in Osaka City based on damage estimation for a Nankai Trough megathrust earthquake: A geographic information system-based analysis.

Studies have not fully examined whether the medical care system would be able to manage the high number of casualties due to a Nankai Trough earthquake, whose probability of recurrence in the next 30 years is ~70%. This study assessed the demand-supply balance of the disaster medical care system in Osaka city by integrating the data on damage estimation and disaster coping hospitals using a geographic information system. We obtained data on the distribution of casualties in two cases, high and low rates of evacuation from the tsunami, and available beds in Osaka city calculated from operating data of each disaster coping hospital. We expanded these data on a geographic information system and investigated the balance of medical care. The total number of available beds in the disaster medical care facilities was 5,559, and the shortage with evacuation rates being either low or high, would be 47,631 and 1,487, respectively. With a low evacuation rate, bed shortage is a common occurrence in coastal areas. With a high evacuation rate, bed shortage decreases, and problems with medical care arise in the eastern area of Osaka city. In the case of a low evacuation rate, greater bed shortage was found along the coast of Osaka Bay where the probabilities of flooding are high. In the case of a high evacuation rate, however, the number of casualties was much lower. A shortage of medical care did not occur along the coast, but in the eastern part of Osaka city.

The Tourism Infrastructure of Sălaj County

"The Tourism Infrastructure of Sălaj County. The paper represents the third part of a larger study concerning tourism in Sălaj County, taking into consideration the tourism infrastructure in the county. The paper analysed statistical data regarding the accommodation establishments and their capacity, available online from the National Institute of Statistics (TEMPO-ONLINE database) for a period of 33 years, between 1990 and 2022. The total number of accommodation establishments dropped from 23 in 1990 to 16 in 2007, but then increased significantly, reaching 127 accommodation units in 2022. Most of these recently emerged units, such as bungalows and guesthouses, have however a small capacity. Therefore, the evolution in terms of the total number of available beds in certified accommodation units has not been equally spectacular. There was the same decreasing trend, from 1922 beds in 1990, to 754 beds in 2008, followed by an increase, which nevertheless reached a maximal value of 1923 beds in 2016. Since then, the overall capacity has slightly decreased. Most of the beds are currently available in accommodation establishments located in towns (mainly in Zalău) and spa resorts, especially in hotels, hostels, and guesthouses. The general trend in recent years has been to improve the degree of comfort, which in Zalău gave rise to several 4-star hotels, in addition to the existent 3-star hotels. Recreational facilities are mainly available in the two spa resorts, Băile Boghiș and Bizușa-Băi, but there are some similar facilities in Zalău, Jibou and Șimleu Silvaniei, while most of the seats in public catering facilities are located in Zalău, Boghiș and Ip. The positive trend in terms of tourism infrastructure is expected to be maintained in the following years, as tourism is the economic activity most likely to be strategically important for the sustainable development of Sălaj County in the future. Keywords: Sălaj County, tourism infrastructure, accommodation units, recreational facilities, Romania. "

Prediction of COVID-19 Infections for Municipalities in the Netherlands: Algorithm Development and Interpretation.

BackgroundCOVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities.ObjectiveWe aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques.MethodsWe collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands.ResultsThe final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household.ConclusionsData about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic

Many challenges lie ahead when dealing with COVID-19, not only related to the acceleration of the pandemic, but also to the prediction of personal protective equipment sets consumption to accommodate the explosive demand. Due to this situation of uncertainty, hospital administration encourages the excess stock of these materials, over-stocking products in some hospitals, and provoking shortages in others. The number of available personal protective equipment sets is one of the three main factors that limit the number of patients at a hospital, as well as the number of available beds and the number of professionals per shift. In this scenario, we developed an easy-to-use expert system to predict the demand for personal protective equipment sets in hospitals during the COVID-19 pandemic, which can be updated in real-time for short term planning. For this system, we propose a naive statistical modeling which combines historical data of the consumption of personal protective equipment sets by hospitals, current protocols for their uses and epidemiological data related to the disease, to build predictive models for the demand for personal protective equipment in Brazilian hospitals during the pandemic. We then embed this modeling in the free Safety-Stock system, which provides useful information for the hospital, especially the safety-stock level and the prediction of consumption/demand for each personal protective equipment set over time. Considering our predictions, a hospital may have its needs related to specific personal protective equipment sets estimated, taking into account its historical stock levels and possible scheduled purchases. The tool allows for adopting strategies to control and keep the stock at safety levels to the demand, mitigating the risk of stock-out. As a direct consequence, it also enables the interchange and cooperation between hospitals, aiming to maximize the availability of equipment during the pandemic.

An Innovative Telemedicine Initiative From Sri Lanka: The Value of Volunteerism in a Resource-Constrained Scenario During the COVID-19 Pandemic.

The unprecedented rise in COVID-19 cases in Sri Lanka since July 2021 led to a situation where the health system was getting overwhelmed. The priority shifted toward triaging patients and identifying those who need immediate medical intervention and managing the rest in home settings. An integrated patient management system where patients could access a medical professional through a short messages service (SMS) and calling system was established. This service provided telephone triage, patient advice, and coordinated with the national ambulance system to evacuate ill patients. This integrated helpline system had a major impact on the management of the recent surge of COVID-19 pandemic in Sri Lanka by patients needing urgent care were directed for hospitalization and the rest managed at home with support, reassurance, and guidance. The numbers of oxygen-dependent patients and deaths declined rapidly and the number of available beds increased. The system played a major role in bringing the crisis under control. Despite many challenges, this innovative integrated system is a unique example of medical volunteerism. The pandemic catalyzed the utilization of information and communication technologies effectively by providing healthcare with a reduction of the burden on healthcare institutions and professionals.

Intensive care unit strain and mortality risk in patients admitted from the ward in Australia and New Zealand.

ICU strain (low number of available beds) may be associated with a delay and altered threshold for ICU admission and adverse patient outcomes. We aimed to investigate the impact of ICU strain on hospital mortality in critically ill patients admitted from wards across Australia and New Zealand. Ward patient admitted to ICU and ICU bed data at 137 hospitals were accessed between January 2013 and December 2016. ICU strain was classified as low (≤0.5 patients admitted per available ICU bed in a 6-h block), medium (0.5 to ≤1) or high (>1). Logistic regression models were used to examine the relationship between ICU strain and hospital mortality. 57,844 ICU admissions were analysed, with the majority (64.4%) admitted to medium-strain ICUs. Those admitted to high-strain ICUs spent longer in hospital prior to ICU than medium-strain or low-strain ICUs. After adjusting for confounders those admitted to high-strain ICUs [OR 1.24 (95%CI 1.14-1.35)] or medium-strain ICUs [OR 1.18 (95%CI 1.09-1.27)], (p < 0.001) had a higher risk of death compared low-strain ICUs. ICU strain is associated with longer times in hospital prior to ICU admission and was associated with increased risk of death in patients admitted from ward.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENYERAPAN TENAGA KERJA PADA SEKTOR PERHOTELAN DI PROVINSI MALUKU DENGAN MENGGUNAKAN METODE REGRESI DATA PANEL

The hotel sector is one of the sectors that has made a major contribution to employment and economic development in Maluku Province. The more the population, the more difficult it is to find work. The purpose of this study is to analyze the effect of the variable number of accommodation, number of occupancy, number of available beds and minimum wage of employees on labor absorption in the hotel sector in Maluku Province. One method that can be used to solve this problem is the Panel Data Regression method. Panel data regression is a combination of time series data and cross section data. This method can be used to determine the relationship between two or more variables that are quantitative in nature, so that one variable can be predicted from the other variables. The Panel Data Regression model used in this study is the Fixed Effect Model (FEM) with the results obtained, namely that there are three variables that affect labor absorption in the hotel sector in Maluku Province, namely the number of accommodation, the number of occupancy and the minimum wage of employees. Meanwhile, the number of available beds has no effect on labor absorption in the hotel sector in Maluku Province.

ANALISIS TINGKAT EFISIENSI DAN OUTCOME PELAYANAN DI RS AISYAH SITI FATIMAH SIDOARJO SELAMA PANDEMI COVID-19

Background: Hospitals during the COVID-19 pandemic are trying hard to reduce the number of deaths and losses of COVID-19. Analysis of hospital efficiency and results of hospital services is needed to improve and assess the quality of hospital services provided to patients. In measuring the efficiency of the services provided by the hospital, an analysis of bed use was used through the Barber Johnson chart. To assess the outcome of a service through mortality and morbidity rates. Subjects and Method: This research were descriptive research with data collection by interview to health workers which includes Professional Care Providers (PPA) document study. The research was conducted during August-December 2021. The data obtained were processed into percentage data and Barber Johnson charts. Results: Research Results The mortality rate in June was 24.2% and July 41.7%. Johnson's barber chart is in an inefficient area. BTO in June and July 2021 is 5 and 3, TOI in June and July 2021 is 2.6 and 6.9. Conclusion: Aisyiyah Siti Fatimah Hospital Sidoarjo has not yet entered the efficient area of ??the Barber-Johnson Graph. Mortality and morbidity rates are also high due to the COVID-19 pandemic. Strategies that can be carried out are by increasing the number of available beds during the COVID-19 pandemic as an effort to mitigate the hospital disaster, and improving the addition of supporting equipment for medical services related to COVID-19 so that inpatient services are efficient. Keywords: BOR, Barber Johnson, Mortality, Morbidity

Emergency Preparedness and Management of Mobile Cabin Hospitals in China During the COVID-19 Pandemic.

The healthcare systems in China and globally have faced serious challenges during the coronavirus disease (COVID-19) pandemic. The shortage of beds in traditional hospitals has exacerbated the threat of COVID-19. To increase the number of available beds, China implemented a special public health measure of opening mobile cabin hospitals. Mobile cabin hospitals, also called Fangcang shelter hospitals, refer to large-scale public venues such as indoor stadiums and exhibition centers converted to temporary hospitals. This study is a mini review of the practice of mobile cabin hospitals in China. The first part is regarding emergency preparedness, including site selection, conversion, layout, and zoning before opening the hospital, and the second is on hospital management, including organization management, management of nosocomial infections, information technology support, and material supply. This review provides some practical recommendations for countries that need mobile cabin hospitals to relieve the pressure of the pandemic on the healthcare systems.

Internal Regulation Center in hospitals: Repercussions of its implementation on the health services' indicators.

ObjectiveTo evaluate the hospital indicators and their repercussions on the number of monthly admissions to a public university hospital, before and after implementing the Internal Regulation Center.MethodAn evaluative research study, of the Case Study type, developed in a public university hospital. A total of 28 indicators related to structure, production, productivity and quality were measured, which are part of internal Benchmarking. The data were analyzed by means of descriptive statistics and multiple regression to identify the independent factors and those associated with the number of monthly hospitalizations with 95% confidence intervals.ResultsImplementation of the Center significantly increased (p<0.001) the number of discharges, the bed utilization factor and the bed renewal rate, emergency hospitalization, bed occupancy percentage, surgical procedures performed and the patient-day mean value (p=0.027). There was a reduction (p<0.001) in the number of visits to the medical, obstetric and orthopedic emergency room, in the rates of in-hospital infection and infant mortality, as well as a mean reduction of 0.81/day, approximately one day less of hospitalization per patient, or a gain of 40 available beds per month.ConclusionAlthough the number of available beds was lower in the post-implementation period, the bed replacement interval was reduced, representing an increase of 40 more beds per month due to the reduction in the patients’ length of stay in the institution.

Predictive analytics for hospital discharge flow determination

In recent years, hospitals around the world are faced with large patient flows, which negatively affect the quality of patient care and become a crucial factor to consider in inpatient management. The main objective of this management is to maximize the number of available beds, using efficient planning. Intensive Care Units (ICU) are hospital units with a higher monetary consumption, and the importance of indicators that allow the achievement of useful information for a correct management is critical. This study allowed the prediction of the Length of Stay (LOS) based on their demographic data, information collected at the time of admission and clinical conditions, which can help health professionals in conducting a more assertive planning and a better quality service. The results obtained show that Machine Learning (ML) models, using demographic information simultaneously with the patient's pathway, as well as clinical data, drugs, tests and analysis, introduce a greater predictive ability for LOS.

An Approach to the Modeling and Simulation of Intra-Hospital Diseases

This publication presents an approach to a simulator to recreate a large number of scenarios and to make agile decisions in the planning of a real emergency room system. A modeling and simulation focused on the point prevalence of intrahospital infections in an emergency room and how it is affected by different factors related to hospital management. To carry out the simulator modeling, the Agent-based Modeling and Simulation (ABMS) paradigm was used. Thus, different intervening agents in the emergency room environment — patients and doctors, among others— were classified. The user belonging to the health system has different data to configure the simulation, such as the number of patients, the number of available beds, etc.&#x0D; Based on the tests carried out and the measurements obtained, it is concluded that the disease propagation model relative to the time and contact area of the patients has greater precision than the purely statistical model of the intensive care unit.

Flexibility and Bed Margins of the Community of Madrid's Hospitals during the First Wave of the SARS-CoV-2 Pandemic.

Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM’s health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: “bed margin” (available beds minus occupied beds, expressed as a percentage) and “flexibility” (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions: This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital’s management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.

Predictable country-level bias in the reporting of COVID-19 deaths

We examine whether a country's management of the COVID-19 pandemic relate to the downward biasing of the number of reported deaths from COVID-19. Using deviations from historical averages of the total number of monthly deaths within a country, we find that the probability of underreporting of COVID-related deaths for countries with the most stringent policies was 58.6%, compared to a 28.2% for countries with the least stringent policies. Countries with the lowest ex ante healthcare capacity in terms of number of available beds underreport deaths by 52.5% on average, compared to 23.1% for countries with the greatest capacity.

Dependence between mortality in regions and prevalence of active SARS-COV2 carriers and resources available to public healthcare organizations

The paper dwells on certain mathematical models showing how epidemics develop, namely, logistic ones, SIR-model, and some others. There is also a review of articles that focus on such models showing dynamics of incidence with COVID-19 infection. These models are often successfully applied for data collected in a whole country but on a regional level there are difficulties due to peculiarities of calculating mortality figures in Russia. In this case regression models can be useful with their obvious advantage at the initial stage in an epidemic process. They also include exogenous variables that influence mortality, for example, a number of doctors and nurses per a hospital, how well hospitals are equipped with ALV devices, and a number of available beds in them. Our research goal was to build up a linear regression model that could be used as a basis for estimating regional mortality caused by COVID-19 as well as for more efficient distribution of all the resources mentioned above. The model is built as per a set of resource parameters including data on «active cases». Preliminary three variables that showed data on resources available to communicable diseases departments in hospitals were transformed into a new single one via linear transformation. Then the model was tested on a training sample containing an endogenous variable on mortality and four factor ones including prevalence of active virus carriers. Regions were included into training data with different lags; they were included into such daily samples when death cases were registered rarely. Then the estimated model was applied with other values. It turned out to be quite efficient in estimating COVID-induced mortality for regions from trainings samples as well as for several others (for certain intervals). As a result, we built a regression model and estimated its precision; the model showed a relation between mortality in a region and prevalence of active SARS-CoV2 carriers and availability of resources to hospitals in it. It can be useful when these resources are distributed. It can also be used to build SIRD, SEIR, and SEIRF models at a regional level when choosing parameters in them related to mortality. A methodology itself that can be similarly applied for other epidemic processes also deserves certain attention

Зависимость уровня смертности в регионах от распространенности активных носителей SARS-CoV-2 и ресурсов организаций здравоохранения

The paper dwells on certain mathematical models showing how epidemics develop, namely, logistic ones, SIR-model, and some others. There is also a review of articles that focus on such models showing dynamics of incidence with COVID-19 infection. These models are often successfully applied for data collected in a whole country but on a regional level there are difficulties due to peculiarities of calculating mortality figures in Russia. In this case regression models can be useful with their obvious advantage at the initial stage in an epidemic process. They also include exogenous variables that influence mortality, for example, a number of doctors and nurses per a hospital, how well hospitals are equipped with ALV devices, and a number of available beds in them. Our research goal was to build up a linear regression model that could be used as a basis for estimating regional mortality caused by COVID-19 as well as for more efficient distribution of all the resources mentioned above. The model is built as per a set of resource parameters including data on «active cases». Preliminary three variables that showed data on resources available to communicable diseases departments in hospitals were transformed into a new single one via linear transformation. Then the model was tested on a training sample containing an endogenous variable on mortality and four factor ones including prevalence of active virus carriers. Regions were included into training data with different lags; they were included into such daily samples when death cases were registered rarely. Then the estimated model was applied with other values. It turned out to be quite efficient in estimating COVID-induced mortality for regions from trainings samples as well as for several others (for certain intervals). As a result, we built a regression model and estimated its precision; the model showed a relation between mortality in a region and prevalence of active SARS-CoV2 carriers and availability of resources to hospitals in it. It can be useful when these resources are distributed. It can also be used to build SIRD, SEIR, and SEIRF models at a regional level when choosing parameters in them related to mortality. A methodology itself that can be similarly applied for other epidemic processes also deserves certain attention