Commodity Costs Research Articles

President's Page: Layoff recovery: An unadvertised benefit of SEG membership

Staff reductions, commonly referred to as layoffs, are an unfortunate reality in the energy sector. The dynamics of supply and demand significantly influence oil and gas prices. Decisions made by oil producers, such as those from the Organization of the Petroleum Exporting Countries (OPEC), further affect these prices. Natural disasters can disrupt oil production and supply chains, while political instability can also influence availability and pricing. Additionally, economic fluctuations play a crucial role in the rise and fall of this commodity's cost. Mergers often lead to overlapping positions that become redundant. While these factors are largely beyond our control, they remind us that working in this rewarding yet unpredictable industry comes with its challenges, which we can mitigate by preparing for such occurrences.

Cost of delivering childhood RSV prevention interventions to the health system in Kenya: a prospective analysis

ObjectivesTo evaluate the cost of delivering childhood respiratory syncytial virus (RSV) prevention interventions to the health system in Kenya.DesignA prospective (cost projection) activity-based costing study.SettingKenya, national introduction of interventions.ParticipantsNot applicable.InterventionsA...

МАРКЕТИНГОВІ ДОСЛІДЖЕННЯ ВПЛИВУ ЦІНОВОЇ ПОЛІТИКИ НА ПОВЕДІНКУ СПОЖИВАЧІВ

Abstract Introduction. Economic terms "marketing pricing policy" are considered in the article, "behavior of consumers", interpretation different foreign and home scientists, it is given their description. The role of marketing pricing policy is investigational for companies and consumers of products or services. It is considered types of consumer behavior and their reaction on a price. The purpose of the article. Research of approaches in relation to determination of concept "marketing pricing policy" their value and terms of pricing, consideration of term "behavior of consumers", research of types of behavior of consumers and their reaction on a price. Methods. During research the methods of comparison, analysis, synthesis, abstraction, theoretical, historical and logical generalization are used. The dialectical method of research became methodological basis. Results. It is exposed essence of economic term "marketing pricing policy" and "behavior of consumers". The conducted research showed that a competent price policy is an important component of the company's marketing strategy, because the price is one of the key factors taken into account by consumers when making purchase decisions. The study of consumer behavior is a priority area in the activity of any company, because consumers, after purchasing goods, bring profit to it. Therefore, for the successful operation of the company, customers must be in the center of attention. By studying consumers, their motives, wishes, price categories, and purchase processes, companies try to offer buyers exactly what they need. Conclusiоns. A study of theoretical and practical aspects of the marketing policy of pricing and consumer behavior was conducted, the types of consumer behavior and their reaction to the price were investigated. It was determined that the influence of marketing price policy on consumers is a key factor in making a purchase decision. Price politics determines the cost of commodities and services, that influences on perception of general utility of product consumers. The question of the factors that influence the behavior of consumers that most encourage the purchase of goods or services requires further research. Defining the role and place of price in making a purchase.

Optimisation of open pit slope design considering groundwater effects using particle swarm optimisation and scaled boundary finite element method

Slopes are a crucial structures in open pit mines. Their design has implications on the economic, safety and environmental operation of the mining industry. Designing stable slopes can be challenging due to the complexities introduced by the stratigraphy and hydrology of the strata. With rising commodity costs and inflation rates, mining operating costs are increasing. Reducing operational costs is necessary for mining industries to remain competitive. While steepening the pit slope can decrease stripping materials and save money, it also increases the risk associated with slope surges. Therefore, optimising slopes is crucial for both financial and safety reasons. Numerical models such as the finite element method experience challenges in mesh generation of heterogeneous systems characterised by varying material properties and stratigraphies. Moreover, the need for repetitive geometry update necessitates recursive mesh regeneration that increases the computational burden. Moreover, previous slope optimisation studies focus solely on dry conditions. To consider the complex condition of hydrology along with heterogeneity in the soil stratigraphy, this study develops an optimisation procedure by combining the particle swarm optimisation algorithm and the scaled boundary finite element with an image-based meshing technique to optimise slopes with groundwater and achieve the desired factor of safety (FoS). The method changes the slope design parameters and the phreatic surface of groundwater simultaneously, considering user-defined parameters while iteratively re-meshing the optimisation processes. Several cases are presented, demonstrating the optimisation of bench width, bench angle, backfill parameters, and groundwater pumping levels.

Molecular hydrogen: a sustainable strategy for agricultural and food production challenges

The world is confronting numerous challenges, including global warming, health epidemics, and population growth, each presenting significant threats to the stability and sustainability of our planet’s ecosystems. Such issues have collectively contributed to a reduction in agricultural productivity, corresponding with an increase in demand and costs of essential commodities. This critical situation requires more sustainable environmental, social, and technological solutions. Molecular hydrogen (H2) has been suggested as a “green” solution for our energy needs and many health, agricultural, and food applications. H2 supplementation in agriculture may represent a novel and low-carbon biotechnological strategy applicable to the abundant production of crops, vegetables, and fruits in agri-food chains. H2 is a potential green alternative to conventional chemical fertilizers. The use of a hydrogen-rich water irrigation system may also provide other health-related advantages, i.e., decreasing the heavy metal accumulation in crops. By adopting a H2 strategy, crop producers, food processors, and decision-makers can contribute to sustainable solutions in the face of global challenges such as climate change, communicable disease epidemics, and a growing population. The versatile applications of H₂ in agriculture and the wider food industry position it as a uniquely suitable approach to address today’s significant challenges, potentially fostering better crop production and positively impacting the agri-food chain. The present review is timely in combining the latest knowledge about the potential applications of H2 in the agriculture and food industry, from farm to fork.

Inventory Optimization Model with Quadratic Demand Pattern and Non-instantaneous Deterioration Rate with Mixed Cash and Advance Payment Scheme

Objectives: To develop a mathematical model for cost minimization by examining a scenario involving a seasonal product retail market, evaluating the outcome, and providing solutions to unanswered queries of existing models are the highlights of this present work. Methods: 1. Using dynamic differential equations with initial and boundary conditions, we mathematically model a retail market scenario for perishable commodities and determine the retailer's total cost. 2. We consider a quadratic function of time to precisely describe the maintenance cost and demand function. Also, a combination of cash and advance payment schemes is implemented in our model. 3. Our model was numerically formulated using a dataset deduced from earlier research. We have included a sensitivity analysis and a numerical example with a graphical depiction of the total cost function that highlights the most and least influential factors. 4. Based on the Hessian matrix condition and the calculus derivative approach, we developed an algorithm to calculate the optimal cost. Findings: This model provides retailers with insights for effective stock management while optimizing the cost of retailing perishable commodities with non-linear demand patterns. Sensitivity analysis showed that the two factors that have the biggest impact on retailers' overall costs are non-linear demand and holding cost parameters. The smaller optimal inventory cycle time and the large value of time at which a shortage happens raise the overall cost. Additionally, because of higher demand, the time-dependent deterioration rate has the least impact on overall cost. Additionally, a three-dimensional graph of cost minimization is offered to illustrate the convexity of total cost. Novelty: Implementation of Quadratic time-dependent demand and non-linear holding costs under a mixed cash advance payment structure, along with time-dependent deterioration and partial backlog shortages. Keywords: Quadratic Demand pattern, Non­instantaneous decay rate, Cash and Advance Payment scheme, Partial Backlog Shortages

African Swine Fever Disease Risk Assessment Using Multi-Criteria Decision Analysis: An Input for GIS-Based Risk Mapping

African Swine Fever (ASF) has severely impacted the Philippines' pig industry, caused socioeconomic losses, and affected income, supply, demand, and prices. There needs to be more understanding regarding the risk factors associated with ASF introduction into pig farms and the level of risk each farm faces. Accordingly, using a quantitative research method, this study used the Analytical Hierarchy Process (AHP) to categorize ASF risk factors into biosecurity and spatial risk factors. Twenty-five (25) respondents were selected using a purposive participatory approach to rank the importance level of each risk factor as per the two risk factor categories. The AHP analysis revealed that the highest-risk biosecurity factors are the "absence of protocols for changing clothes, separate entry and exit, disinfection of objects, restriction on food introduction, and external individuals accessing the farms." In the spatial risk factor ranking, the analysis showed that the “distance to pig farms utilizing swill feeding" was the highest risk factor, indicating its significant contribution to the overall risk of farms. A farm risk assessment was also performed based on the AHP results and the level of compliance of each farm on the different risk factors. The study was conducted on selected pig farms in the municipality of Echague by evaluating their compliance with the identified risk factors and determining the level of risk they posed. The risk assessment results for African swine fever on farms reveal a concerning scenario. With 70% of farms assessed as "high risk" in terms of biosecurity and 74% classified as "medium risk" in spatial vulnerability, the overall assessment indicates that 84% of farms are at a moderate risk level. This suggests a widespread need for improved biosecurity practices and disease monitoring to prevent the introduction and spread of African swine fever. The 16% of farms deemed "high risk" pose a significant threat, requiring immediate action to prevent disease outbreaks. These findings emphasize the importance of implementing stringent biosecurity measures, enhancing surveillance efforts, and raising awareness to safeguard the pig industry from the devastating impacts of the ASF disease, like the rising cost of meat and pork-based commodities. Furthermore, these show the importance of considering the biosecurity and spatial risk factors for a more comprehensive risk assessment. The AHP ranking and risk assessment process is crucial in developing a GIS-based risk mapping and surveillance system. This offers government authorities a valuable decision-making tool to proactively prevent the introduction of African swine fever (ASF) and mitigate the necessity for widespread culling of pigs. By implementing targeted interventions informed by the study results, the government can work towards safeguarding the pig industry.

Organizational Resilience and the Attention-Based View of the Firm—Empirical Evidence from German SMEs

The need for organizations to increase resilience is still growing, not least because of economic crises, such as the 2007–2009 global financial crisis, the COVID-19 pandemic, and supply shortages in raw material procurement combined with rising commodity costs and energy prices. We use an attention-based view approach as a theoretical lens to investigate the shift in management’s attention due to a crisis, with a particular focus on working capital management (WCM) as a decisive finance tool. In addition, we examine how management’s attention shifts to organizational resilience (OR). We also analyze how managers’ cognitive flexibility (CF), which reflects their ability to anticipate and quickly adapt, moderates the change in attention. By surveying 307 top and middle managers in German small- and medium-sized enterprises, we measure the crisis effect on the shift in managerial attention to WCM and OR. In addition, we measure managers’ CF and examine the contribution of these individual characteristics in the context of WCM and OR. Our mediation model reveals a positive, indirect-only effect of the crisis on the shift in management attention to OR mediated by the shift in management attention to WCM. In addition, our results show that managers’ CF negatively moderates the effect between managerial focus shifts to WCM and to OR. Our study’s results deepen the understanding of the importance of managers’ CF in times of crisis and the impact of CF on sales development, suggesting that managers’ CF may influence the link between the focal characteristics of WCM and OR.

Analyzing Crisis Dynamics: How metal-energy Markets influence green electricity investments

The world continues to face major economic challenges due to crises that have a considerable impact on energy consumption. Since the global energy crisis, energy commodity costs have risen, and economic slowdowns in various regions continue to influence electricity market trends. Clean energy markets, propelled by the growing interest of economic entities and investors, are now interconnected with several commodity markets. In this context, energy metals play a crucial role in influencing the dynamics of the clean energy and particularly electricity markets. This study takes a close look at the complex interconnections between clean energy indices and the metals market, focusing on volatility dynamics. Drawing on advanced methodologies, including the Diebold and Yilmaz method, we unveil the complex relationships that link these markets. Our results reveal distinct patterns and interactions, highlighting the nuanced nature of the connection between clean energy indices and metal prices. The insights revealed have significant implications for policymakers and investors seeking to align their strategies with sustainable energy transitions and ensure financial stability. By enhancing our understanding of the interdependencies between clean energy indices and metals markets, this research provides valuable guidance for navigating the changing clean energy investment landscape.

ANTECEDENTS OF REVISIT INTENTIONS ON HOSPITAL CHOICE IN THE DEVELOPING COUNTRY: A SEM ANALYSIS

In today's rapidly evolving healthcare landscape, understanding the factors that drive patient decision-making regarding hospital revisit intention is critical for hospitals to remain competitive and thrive. Despite the availability of world-class private hospitals, many patients from developing countries such as Bangladesh routinely travel beyond for medical treatment. Hence, developing countries are taking the initiative to strengthen their healthcare industries to increase GDP. This study aims to shed light on this issue by exploring the antecedents that affect hospitals in a developing country, Bangladesh. Data were collected by interviews at private hospitals in Chattrogram from November 2023 to December 2023 from 417 individuals using a randomized block design, and AMOS 24 was used for SEM analysis. The process in both EFA and CFA confirmed construct, convergent, and discriminant validity. The findings of this study demonstrate that the brand image of private hospitals affects patient revisit intent both directly and indirectly through service quality and patient satisfaction. A brand image does not influence the probability of returning to the same hospital. Moreover, commodity costs have a negative impact on revisit intention—service quality is associated with patient satisfaction which is moderated by the brand image of selected hospitals in Bangladesh. Management of hospitals can better position themselves to meet patients’ expectations, government may contribute to improve health outcomes and academicians may develop extensions of theory for the developing countries.

Analysis of Calculation of Cost of Production Using the Full Costing Method

The goal of the study is to investigate and demonstrate that, for Micro, Small, and Medium-Sized Businesses, applying the complete costing method for calculating manufacturing costs results in a more accurate computation. This study is crucial for entrepreneurs since it helps them determine the precise cost of manufacturing things and determines how much profit they should make. This study employs qualitative descriptive methods. The results of this study's comprehensive costing technique calculation of manufacturing costs for commodities are more accurate for use in micro, small, and medium-sized enterprises.

Empirical Analysis of African Aubergine (Solanum gilo) – Marketing and Income Disparity among Traders in Owerri, Imo State, Nigeria

African Aubergine is an indigenous tropical crop cultivated in Nigeria. Agricultural marketing creates incentives that accelerate the promotion of further production and consumption of harvested produce. Therefore, this study analysed African Aubergine marketing and income disparity among traders in Owerri, Imo State, Nigeria. Primary data collected via multistage sampling from ninety-five (95) respondents was analysed using descriptive statistics, marketing margin and efficiency analysis, Ordinary Least Square regression and Gini Coefficient index. The results show that the mean age, years spent in school, household size, trading experience and quantity of bags sold were 37 years, 8 years, 7 people, 9 years and 8 bags per month, respectively. The estimated marketing margin and efficiency index were ₦1,250 and 0.36, respectively. The estimated coefficients of Ordinary Least Square regression (R2) were 0.773. Thus, the variables (marketing experience, cost price, quantity sold and marketing cost) in the regression model accounted for 77% of the variation in the marketing margin of African Aubergine traders in the study area. The estimated Gini Coefficient was 0.59, indicating a moderate level of income disparity (inequality) in the sales income of the respondents. Several marketing constraints were observed in the study area. Therefore, this study recommends improved credit access and market information dissemination, commodity cost subsidisation, storage technology adoption, market infrastructure development and interventions, commodity price control and policy modification that regulates market activities.

Industrial Technologies for CO2 Reduction Applicable to Glass Furnaces

In recent years, the European Union’s legislation about sustainable development has promoted the gradual decarbonization of all industrial sectors, pushing towards the final goal of a carbon-neutral European glass industry in 2050. Moreover, the COVID-19 pandemic, the war in Ukraine and the consequent natural gas supply crisis have led to significant increases in the costs of traditional energy commodities and CO2 emission allowances. In this scenario, the European glass industry, which is both an energy-intensive sector and a large emitter of CO2, needs to reduce its specific energy consumption, change its energy sources and decarbonize its production process. In order to understand and support this metamorphosis of the glass industry, the follwing questions must be answered: are the technologies reported in scientific publications merely theoretical exercises, or can they be adopted by the industry? In what timeframe can they be adopted? The aim of this study is to review consolidated and emerging technologies applicable to the glass industry and investigate which ones can be implemented in the short or medium term to reduce energy consumption and CO2 emissions related to the glass production process. This study is based on a review of the literature, the materials presented in technical conferences and the opinions of interviewed experts. This study showed that the literature is not very substantial, lacking detailed information on technologies and their effects in terms of energy savings or emissions. More information can be found in the proceedings of selected specialist conferences. This study found that, on one hand, some technologies are mature and only adopted when economically viable, and appropriate boundary conditions are available; the state of the art regarding these technologies was already extensively covered in past publications (e.g., cullet pre-heating). On the other hand, there are many promising technologies in the research or testing phase (i.e., steam methane reforming, process electrification, use of hydrogen); in-depth studies about them are limited due to the novelty of the solutions that they propose or not available due to industrial secrecy issues. In addition to periodicals and specialized conferences, interviews were carried out with managers and technicians from industry, as well as technicians from the Italian glass research institute and industrial machinery producers (especially melting furnaces). The interviews represent added value of this publication, useful in helping us to truly understand the state of the art and degree of readiness of the technologies identified. In addition, the production values of the glass industry were studied: our research confirmed that the most important sub-sectors are flat and container glass, as well as the largest glass-producing nations/continents. Finally, a review of specific energy consumption and CO2 emissions indexes was carried out.

Gold and Silver: Relative Values in the Ancient Past

We have documented more than 200 relative values of gold and silver across almost 3000 years (2500 bce–400 ce) to establish value benchmarks for essentially pure metal. Our aim is to improve understanding of ancient economies by enabling regional and temporal comparisons of these relative values. First, we establish silver as an early, reliable benchmark for valuing gold of varying purity before implementation of parting. Whilst purity accounted for two to threefold variation in the value of gold, we conclude that availability was more influential. Access to Nubian gold until about 1100 bce seems an important influence on gold-silver value ratios in Egypt and the Near East, which increased significantly following loss of this source. This investigation yields a suite of relative values for essentially pure gold and silver, subdivided by regions and intervals from 2500 bce–400 ce. These will enable future comparisons of precious metal-denominated costs of labour and commodities, including with today.

Modeling and Forecasting Volatility in USD/GBP Exchange Rate

Rate changes can occur hourly, daily, or in large incremental shifts. These changes may impact firms by changing the cost of commodities imported from other countries and the demand for their goods among foreign consumers. Therefore, it is essential to forecast exchange rates to manage this business effect. This study aims to determine the best model for predicting volatility in the exchange rate between USD and GBP. In particular, we analyze exchange rates using the Autoregressive Integrated Moving Average (ARIMA) model and the volatility or variance model by Generalized Autoregressive Conditional Heteroscedasticity (GARCH). To determine the best model, the performance of each model is evaluated with several criteria, namely Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that EGARCH(1,1) has the best forecasting performance in the out-sample section because it can better capture out-sample data patterns with minimum RMSE, MAE, and MAPE.

Dynamic linkages between real exchange rates and real unit labour costs: evidence from 18 economies

ABSTRACT The article examines the long-run behaviour of real exchange rates based on the premises of classical political economy. Specifically, it investigates the dynamics between real exchange rates and real unit labour costs of tradable commodities for 18 developed and developing economies. The analysis is carried out by applying recent methods of panel data, while the short- and long-run dynamics are estimated based on the cross-sectional autoregressive distributed lag model. Our findings confirm the presence of short- and long-run effects of real unit labour costs on real exchange rates. Consequently, new and more effective foreign exchange rate policies may be designed.

Is the future sustainable? Analysis of Generation Z’s social awareness of sustainable development in Poland

Abstract Nowadays, the pace of social and economic development is constantly growing, which has led to many negative effects. In order to stop this negative impact, reference should be made to the assumptions of sustainable development (SD), and young people – Generation Z – should be considered its main promoters. The main objective of this article is to present Generation Z’s perception of sustainable development in Poland. A Computer-assisted web interviewing survey (CAWI, N=1172) was conducted to understand the basic demographics, general issues, SD in a daily context, and SD-related knowledge in 936 cities of Poland. The results have shown that Generation Z in Poland recognises the social and inclusive role of SD. Cost of commodities, social disparities and conflict, and disrupted economic growth were identified as the emerging challenges for Sustainable Development Goals (SDGs) in Polish cities.

Securing accelerated access to long-acting injectable cabotegravir for HIV prevention in low- and middle-income countries.

Reductions in HIV acquisition have slowed, and the global community is significantly off track from global goals. Oral pre-exposure prophylaxis (PrEP) alone cannot address the diverse needs of the millions of people at risk of HIV acquisition. Long-acting injectable cabotegravir (CAB-LA) received United States Food and Drug Administration approval for HIV prevention in December 2021. When studied, CAB-LA demonstrated high effectiveness, provides months of protection versus daily use, is preferred by some users and has the potential to achieve commodity cost reduction. These factors position CAB-LA to catalyse transformation in HIV prevention. Significant work must be undertaken to ensure at-scale uptake in low- and middle-income countries. Leveraging decades of product introduction experience, Clinton Health Access Initiative (CHAI) has developed an innovative roadmap to support equitable CAB-LA introduction, comprising tightly executed market-shaping, product development, regulatory, and programmatic and implementation action. Proven models exist (e.g. long-acting reversible contraceptives, paediatric tuberculosis treatment and antiretrovirals (ARVs), such as paediatric dolutegravir and tenofovir disoproxil fumarate, lamivudine, and dolutegravir) for partnership-driven, accelerated, impactful product introduction. Based on learnings from these models and needs in the prevention space, CHAI developed a roadmap to maximize the near-term impact of CAB-LA and accelerate the development of, access to and impact of quality-assured, low-cost generic CAB-LA. This roadmap is intended to inform introduction planning and investment decision-making across a range of stakeholders, including donors, governments, manufacturers and other partners working in the HIV prevention space. Elements include (1) ensuring coordination and alignment across partners, and avoiding redundancy experienced during oral PrEP introduction; (2) preparing national programmes and providing support to maximize impact, including the development of national policies, guidelines and introduction plans; system strengthening; quantification and procurement; and addressing evidence needs, among other areas; (3) supporting community engagement, ensuring that demand generation and delivery approaches are person-centred and community-led; (4) incentivizing generic product development through, for example, milestone-based commercialization incentives and product development cost-sharing; and (5) expediting regulatory reviews. Accelerating access to affordable, generic CAB-LA can transform progress towards HIV epidemic control. This vision of impact at scale in prevention is achievable, if informed by results-backed approaches to introduction.

Green split multiple-commodity pickup and delivery vehicle routing problem

This paper studies a new version of the green split multiple-commodity pickup and delivery vehicle routing problem, in which the multiple commodity demands of each retail store could be split and each retail store could be visited multiple times. In addition, the reallocation of items collected from other retail stores, traffic congestion and time-windows for convenient pickup and delivery were considered when formulating the model. To solve this complex model, a two-stage search quantum particle swarm optimization (TSQPSO) algorithm is proposed. The two-stage search strategy of TSQPSO helps to find the optimization direction quickly. Meanwhile, the three designed neighbourhood structures help to prevent the algorithm from falling into local optimal solutions. We compared the results obtained by the GAMS optimization solver and our algorithm. The average gap between the results is 0.86%, but our algorithm can obtain the optimal solution much faster. The computational results show that the model is capable of offering more reasonable and economical solutions. Furthermore, we found that with the lower unit operating commodity cost at the depot, the total cost is lower when the commodities are allowed to distribute from the depot. Conversely, when it is higher, the total cost is lower when the commodities collected from the supply retail stores are reallocated.

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19.