Disease Propagation Research Articles

Dystrophic microglia are associated with neurodegenerative disease and not healthy aging in the human brain

Loss of physiological microglial function may increase the propagation of neurodegenerative diseases. Cellular senescence is a hallmark of aging; thus, we hypothesized age could be a cause of dystrophic microglia. Stereological counts were performed for total microglia, 2 microglia morphologies (hypertrophic and dystrophic) across the human lifespan. An age-associated increase in the number of dystrophic microglia was found in the hippocampus and frontal cortex. However, the increase in dystrophic microglia was proportional to the age-related increase in the total number of microglia. Thus, aging alone does not explain the presence of dystrophic microglia. We next tested if dystrophic microglia could be a disease-associated microglia morphology. Compared with controls, the number of dystrophic microglia was greater in cases with either Alzheimer's disease, dementia with Lewy bodies, or limbic-predominant age-related TDP-43 encephalopathy. These results demonstrate that microglia dystrophy, and not hypertrophic microglia, are the disease-associated microglia morphology. Finally, we found strong evidence for iron homeostasis changes in dystrophic microglia, providing a possible molecular mechanism driving the degeneration of microglia in neurodegenerative disease.

To Assess the Knowledge Regarding HIV/AIDS among New Entrant of the Nursing Institute in Wardha District

Introduction: One of the world’s most significant public health issues has been HIV infections and AIDS(HIV / AIDS). The 1 percent of deaths worldwide are responsible for HIV / AIDS Knowledge of HIV /AIDS among students is crucial to the elimination of disease propagation. Besides, young adult universitystudents may start their own families someday and some may be health care providers in the future. Dataabout Knowledge, regarding HIV/AIDS are needed to sensitize and create awareness among nursingstudents as they were becoming health care administrators and providers in the future. Aim: To determinethe knowledge regarding HIV/AIDS among new entrants of the nursing institute. Materials and Methods:A cross-sectional study was carried out among new entrants of the nursing institute in Wardha district. Thestudents were chosen randomly. This cross-sectional descriptive research was carried out by pre-designed,pre-tested, and semi-structured surveys of eligible 296 nursing students in their first year of life. First-yearBasic Bsc nursing100 students, GNM100 students, ANM 96students in the age-group of 17-19 years on 1st,4th, and 7th Oct’ 2016, respectively. After the study purpose was clarified, the students received 45 minuteswithout shared consultation under the supervision of the investigator, to obtain informed verbal consent andmaintain confidentiality. Students have been given the right to withdraw without fear or obligation fromthe study at any time during their data collection, but none declined to take part. The research variablesincluded demographic variables such as sex, professional education, the difference between HIV / AIDS,routes of transmission, prevention strategies, category of behavioral risk, and antiretroviral therapy, etc.Result: Out of 296 total students, 123 (51.9%) were women and 103 (44.0%) were rural native students.The mean knowledge score for the professional education of ANM was 17.35± 3.59, for GNM it was 13.00±3.61and for B.Sc. Nursing it was 17.37±4.31. The majority of the lot was aware of all four routes of infectiontransmission and methods of prevention. When the mean data score for the three new entrants is compared, nomajor statistical difference except ANM (P=.999) is noted.

Dynamic characterization of a stochastic SIR infectious disease model with dual perturbation

Environmental perturbations are unavoidable in the propagation of infectious diseases. In this paper, we introduce the stochasticity into the susceptible–infected–recovered (SIR) model via the parameter perturbation method. The stochastic disturbances associated with the disease transmission coefficient and the mortality rate are presented with two perturbations: Gaussian white noise and Lévy jumps, respectively. This idea provides an overview of disease dynamics under different random perturbation scenarios. By using new techniques and methods, we study certain interesting asymptotic properties of our perturbed model, namely: persistence in the mean, ergodicity and extinction of the disease. For illustrative purposes, numerical examples are presented for checking the theoretical study.

An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19

This paper stresses its base contribution on a new SIR-type model including direct and fomite transmission as well as the effect of distinct household structures. The model derivation is modulated by several mechanistic processes inherent from typical airborne diseases. The notion of minimum contact radius is included in the direct transmission, facilitating the arguments on physical distancing. As fomite transmission heavily relates to former-trace of sneezes, the vector field of the system naturally contains an integral kernel with time delay indicating the contribution of undetected and non-quarantined asymptomatic cases in accumulating the historical contamination of surfaces. We then increase the complexity by including the different transmission routines within and between households. For airborne diseases, within-household interactions play a significant role in the propagation of the disease rendering countrywide effect. Two steps were taken to include the effect of household structure. The first step subdivides the entire compartments (susceptible, exposed, asymptomatic, symptomatic, recovered, death) into the household level and different infection rates for the direct transmission within and between households were distinguished. Under predefined conditions and assumptions, the governing system on household level can be raised to the community level. The second step then raises the governing system to the country level, where the final state variables estimate the total individuals from all compartments in the country. Two key attributes related to the household structure (number of local households and number of household members) effectively classify countries to be of low or high risk in terms of effective disease propagation. The basic reproductive number is calculated and its biological meaning is invoked properly. The numerical methods for solving the DIDE-system and the parameter estimation problem were mentioned. Our optimal model solutions are in quite good agreement with datasets of COVID-19 active cases and related deaths from Germany and Sri Lanka in early infection, allowing us to hypothesize several unobservable situations in the two countries. Focusing on extending minimum contact radius and reducing the intensity of individual activities, we were able to synthesize the key parameters telling what to practice.

Modelling and Solution of Infectious Diseases Using the Extended Laplace Adomian Decomposition Techniques

The use of Mathematical models to describe the transmission of infectious diseases has attracted a lot of interest over the years and serious worldwide effort is accelerating the developments in the establishment of a global efforts for combating pandemics of infectious diseases. Scientists from different fields have teamed up for rapid assessment of potentially immediate situations. Toward this aim, mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. The recent outbreak of covid 19 pandemic had increased the curiosity for the formulation of Mathematical models to describe and analyze the propagation of the disease. This paper focuses on the modeling and analysis of an infectious diseases model using the extended Laplace Adomian Decomposition (LAD) method. The method is used to obtain solutions in the form of infinite series. The result of the research with the aid of MAPLE indicates that physical contact with an infected person is the major cause of the propagation of any infectious disease in the absence of pharmaceutical and non pharmaceutical safety protocols such as the proper use of face mask, physical and social distancing. It becomes vital to subject the infected persons in isolation and adhere to the necessary protocols by relevance agencies and this will significantly flattened the curve of the spread of the infectious disease.

Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 (COVID-19) in Madagascar, with all its specificities. With the strategy of the Malagasy state, which consists of isolating all suspected cases and hospitalized confirmed case, we get an epidemic model with seven compartments: susceptible (S), Exposed (E), Infected (I), Asymptomatic (A), Hospitalized (H), Cured (C) and Death (D). In addition to the classical deterministic models used in epidemiology, the stochastic model offers a natural representation of the evolution of the COVID-19 epidemic. We inferred the models with the official data provided by the COVID-19 Command Center (CCO) of Madagascar, between March and August 2020. The basic reproduction number R0 and the other parameters were estimated with a Bayesian approach. We developed an algorithm that allows having a temporal estimate of this number with confidence intervals. The estimated values are slightly lower than the international references. Generally, we were able to obtain a simple but effective model to describe the spread of the disease.

Insidious Vectors of Disease: Legacies of Conspiracy, Misinformation, Distrust in the Propagation of Infectious Disease. An Examination of Ebola, HIV/AIDS, and COVID-19

Conspiracies surrounding pandemics have existed for centuries, if not millennia.17 Some of the most dramatic and sensationalized pandemics—namely Ebola, HIV/AIDS, and Covid-19—have taken the world by storm. And the conspiracies and mis-information campaigns surrounding these pandemics have persisted in a similar vein. Notably, while there are distinct differences between the terms conspiracy, misinformation, and disinformation, this article does not seek to disentangle these terms and their separate effects. Instead, it treats them all as informational distortions—or infodemics—that all have profound consequences on the propagation of infectious diseases. Thus, the three aforementioned pandemics are worth examining as they are illustrative of the following: the entrenched nature of conspiracy within the human mind and societies; how infectious diseases are perpetuated and exacerbated beyond their inherent infectivity and transmission modalities with spreading infodemics; and why the realm of public health must develop a sophisticated arsenal to cope with these bête noires.

The emerging world of microbiome in autoimmune disorders: Opportunities and challenges

Trillions of commensal bacteria colonizing humans (microbiome) have emerged as essential player(s) in human health. The alteration of the same has been linked with diseases including autoimmune disorders such as multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, and ankylosing spondylitis. Gut bacteria are separated from the host through a physical barrier such as skin or gut epithelial lining. However, the perturbation in the healthy bacterial community (gut dysbiosis) can compromise gut barrier integrity, resulting in translocation of bacterial contents across the epithelial barrier (leaky gut). Bacterial contents such as lipopolysaccharide and bacterial antigens can induce a systemic inflammatory environment through activation and induction of immune cells. The biggest question in the field is whether inflammation causes gut dysbiosis or dysbiosis leads to disease induction or propagation, i.e., it is inside out or outside in or both. In this review, we first discuss the microbiome profiling studies in various autoimmune disorders, followed by a discussion of potential mechanisms through which microbiome is involved in the pathobiology of diseases. A better understanding of the role of the microbiome in health and disease will help us harness the power of commensal bacteria for the development of novel therapeutic agents to treat autoimmune disorders.

α– Linolenic acid modulates phagocytosis and endosomal pathways of extracellular Tau in microglia

ABSTRACT Microglia, the resident immune cells, were found to be activated to inflammatory phenotype in Alzheimer’s disease (AD). The extracellular burden of amyloid-β plaques and Tau seed fabricate the activation of microglia. The seeding effect of extracellular Tau species is an emerging aspect to study about Tauopathies in AD. Tau seeds enhance the propagation of disease along with its contribution to microglia-mediated inflammation. The excessive neuroinflammation cumulatively hampers phagocytic function of microglia reducing the clearance of extracellular protein aggregates. Omega-3 fatty acids, especially docosahexaenoic acid and eicosapentaenoic acid, are recognized to induce anti-inflammatory phenotype of microglia. In addition to increased cytokine production, omega-3 fatty acids enhance phagocytic receptors expression in microglia. In this study, we have observed the phagocytosis of extracellular Tau in the presence of α-linolenic acid (ALA). The increased phagocytosis of extracellular Tau monomer and aggregates have been observed upon ALA exposure to microglia cells. After internalization, the degradation status of Tau has been studied with early and late endosomal markers Rab5 and Rab7. Further, the lysosome-mediated degradation of internalized Tau was studied with LAMP-2A, a lysosome marker. The enhanced migratory ability in the presence of ALA could be beneficial for microglia to access the target and clear it. The increased migration of microglia was found to induce the microtubule-organizing center repolarization. The data indicate that the dietary fatty acids ALA could significantly enhance phagocytosis and intracellular degradation of internalized Tau. Our results suggest that microglia could be influenced to reduce extracellular Tau seed with dietary fatty acids.

Numerical Analysis of Novel Coronavirus (2019-nCov) Pandemic Model with Advection

Recently, the world is facing the terror of the novel corona-virus, termed as COVID-19. Various health institutes and researchers are continuously striving to control this pandemic. In this article, the SEIAR (susceptible, exposed, infected, symptomatically infected, asymptomatically infected and recovered) infection model of COVID-19 with a constant rate of advection is studied for the disease propagation. A simple model of the disease is extended to an advection model by accommodating the advection process and some appropriate parameters in the system. The continuous model is transposed into a discrete numerical model by discretizing the domains, finitely. To analyze the disease dynamics, a structure preserving non-standard finite difference scheme is designed. Two steady states of the continuous system are described i.e., virus free steady state and virus existing steady state. Graphical results show that both the steady states of the numerical design coincide with the fixed points of the continuous SEIAR model. Positivity of the state variables is ensured by applying the M-matrix theory. A result for the positivity property is established. For the proposed numerical design, two different types of the stability are investigated. Nonlinear stability and linear stability for the projected scheme is examined by applying some standard results. Von Neuman stability test is applied to ensure linear stability. The reproductive number is described and its pivotal role in stability analysis is also discussed. Consistency and convergence of the numerical model is also studied. Numerical graphs are presented via computer simulations to prove the worth and efficiency of the quarantine factor is explored graphically, which is helpful in controlling the disease dynamics. In the end, the conclusion of the study is also rendered.

ANALYSIS OF A STOCHASTIC SIS EPIDEMIC MODEL WITH TRANSPORT-RELATED INFECTION

<abstract> In this paper, we consider a stochastic SIS epidemic model with transport-related infection, which is proposed to investigate the dynamics of disease propagation between two regions. Firstly, we show that the model has a unique global positive solution. Next, the properties of the solution are studied. Especially, by constructing a suitable positive-definite decrescent radially unbounded function and stopping times, we show that the differences between susceptible populations or infected populations in two regions will disappear with probability one. Then we show that the diseases in each region is extinct and the susceptible in each region is stable in the mean. Moreover, we prove that the model has a stationary distribution and the solution has the ergodic property. At last, some numerical simulations are introduced to justify the analytical results. </abstract>

Not All Covid-19 Waves are Similar: Origins, Detection and Mitigation Strategies for Simultaneous Waves

Background: The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. Methods: We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. Results: We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora.Conclusions: Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

Covid-19 in West &amp;amp; East Africa, a Geographical Weighted Regression Exploration with http://mygeoffice.org/

Understanding the dynamics that affect the spread of Covid-19 is critical for the development of government measures to stop and reverse this nowadays disease propagation. Like in any epidemiological study, it is essential to analyze the spatial data to account for the inherent spatial heterogeneity within the data (spatial autocorrelation). This paper uses Geographically Weighted Regression (GWR) to identify the factors that influence the outbreak of Covid-19 in Western and Eastern countries of Africa. The analyses include traditional linear regression (including descriptive statistics, hierarchical clustering and correlations were not forgotten either) to reveal the importance of eight risk factors (population density, median age, aged over 65 years, GDP per capita, cardiovascular death rates, diabetes prevalence, female and male smokers) regarding Covid-19 dissemination. It is believed that this is the first attempt to explore possible causes associated with the spread of the Covid-19 pandemic in these disadvantage countries, where some intriguing clues are presented for further research such as the positive relationship between the financial purchase power of nations and the total number of infected people or the smoker’s gender impact on Covid-19.

Pandemic Policy Design Via Feedback: A Modelling Study

BackgroundThe COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve in many jurisdictions, effectively creating a closed-loop dynamic system. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision making.MethodsStarting with a SEEIQR epidemiological model, we illustrate how feedback control can be incorporated into pandemic management using a simple design (proportional-integral or PI control), which couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We then analyse a closed-loop system between the SEEIQR model and the designed feedback controller to illustrate the potential benefits of pandemic policy design that incorporates feedback.FindingsWe first explored a feedback design that responded to hospital measured infections, demonstrating robust ability to control a pandemic despite simulating large uncertainty in reproduction number R 0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). The second design compared responding to hospital occupancy to responding to case counts, showing that shorter delays reduced both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing public compliance to public health directives, and to systemic changes associated with new variants of concern and with the introduction of a vaccination program.InterpretationThe negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. This creates a closed-loop system that better represents the coupled dynamics of a disease and public health responses. Importantly, we show that feedback control is robust to delays in both measurements and responses, and to uncertainty in model parameters and the efficacy of control measures.

Trajectory analysis of the coronavirus pandemic and the impact of precautionary measures in the Kingdom of Bahrain

The Kingdom of Bahrain announced its first case of COVID-19 infection on February 24, 2020. Since that time, the government has imposed several restrictions such as closures, banning gatherings, and closing border crossings to limit the spread of the pandemic and relieve pressure on the healthcare system. This article provides an overview of the current trajectory of the pandemic in the Kingdom of Bahrain. In addition, the article introduces and applies a methodology to analyze the impact of the interventions and precautionary measures enforced by the government to limit the COVID-19 disease propagation. The results show that most of the enforced precautionary measures were effective in reducing the spread of the disease by a percentage ranging from 20.2% to 41.8%. A religious occasion in Bahrain—involving large gatherings—had increased the spread of the disease by 28.7%. Not enough evidence is found to conclude that reopening interventions had caused the disease to spread again.

Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation

Machine learning methods are increasingly applied for medical data analysis to reduce human efforts and improve our understanding of disease propagation. When the data is complicated and unstructured, shallow learning methods may not be suitable or feasible. Deep learning neural networks like multilayer perceptron (MLP) and convolutional neural network (CNN), have been incorporated in medical diagnosis and prognosis for better health care practice. For a binary outcome, these learning methods directly output predicted probabilities for patient’s health condition. Investigators still need to consider appropriate decision threshold to split the predicted probabilities into positive and negative regions. We review methods to select the cut-off values, including the relatively automatic methods based on optimization of the ROC curve criteria and also the utility-based methods with a net benefit curve. In particular, decision curve analysis (DCA) is now acknowledged in medical studies as a good complement to the ROC analysis for the purpose of decision making. In this paper, we provide the R code to illustrate how to perform the statistical learning methods, select decision threshold to yield the binary prediction and evaluate the accuracy of the resulting classification. This article will help medical decision makers to understand different classification methods and use them in real world scenario.

WITHDRAWN: A new model for epidemic prediction: COVID-19 in kingdom saudi arabia case study

Coronavirus disease-2019 (COVID-19) is a viral infection that rose in a city in the Chinese province of Hubei, Wuhan. The world did not wait too long until the virus spread to reach Europe, Africa, and America to be a global pandemic. Due to the lack of information about the behaviour of the virus, several prediction models are in use all over around the world for decision making and taking precautionary actions. Therefor, in this paper, a new model named MSIR based on SIR model is proposed. The model is used to predict the spread of the disease in three cities Riyadh, Hufof and Jeddah in the kingdom of Saudi Arabia. Also the estimation of disease propagation with and without containment measure is carried out. We think that the results could be used to enhance the predictability of the pandemic outbreaks in other cities and to build long term artificial intelligence prediction model.

Coronavirus Disease Pandemic: A Public Health Perspective

The new novel coronavirus was discovered by a Scottish virologist in London in 1964. As is usual with new discoveries, it seems the article she wrote about this discovery, was rejected by a peer reviewed journal. Dr Almeida wrote to the prestigious journal Nature (Nature 220, 1968), outlining her findings and proposed the name “coronavirus” for the new family of viruses. The name referred to the “crown like” appearance, she first observed on these viruses by electron microscopy. While SARS CoV-2 infection seems to be in control in China (less than 100,000 for a population of over billion), where it originated, the epidemic has moved briskly to the rest of the world. What seemed to be impossible to achieve, - imposing and enforcing strict quarantine of people, -is now a reality in majority of the countries. Success of preventive efforts is related, to how best the key principles of prevention strategies are followed; testing for infection, contact tracing, social distancing, wearing masks, and containment of the infected individuals. In December of 2019, several people in Wuhan, China, developed pneumonia and respiratory failure, like what happened during SARS epidemic of 2003. This virus is easily transmissible by symptomatic as well as asymptomatic individuals. As early as January 2020, SARS CoV-2 virus was found to spread during workshops, company meetings. Hospitals seem to provide a favorable environment for the propagation of coronavirus disease(Covid-19). Long-term care facilities are high-risk settings for infections of respiratory diseases. In the long-term care facilities, majority of the senior citizens, seem to have pre-existing conditions, such as hypertension, obesity, type-2 diabetes or cardiovascular diseases, which puts them at high-risk associated with Covid-19 severity. Several mass gatherings have been associated with explosive outbreaks of Covid-19, including political rallies, protests, sports and entertainment events. The possible role of children in transmission of the coronavirus is still not clear. Several individuals who had recovered from the COVID-19 have tested positive again at a later date, suggesting that the infection has been reactivated. These observations raise question about immunity in covid-19 patients for future infections, as well as the ‘herd immunity’ that we all are hoping for. In the absence of an evidence-based cure, the only choice we have of preventing infection is social distancing, wearing masks where needed, hand washing, contact tracing, and containment. SARS CoV-2 virus spreads through a receptor called angiotensin(ACE 2), which is expressed on many cells including the nasopharyngeal epithelial cells, by attaching to these receptors via its spike like external projections.In view of this observation, there is considerable interest in interventions, that may prevent these interactions including vaccines. The mRNA-1273 Group members have published their preliminary report in NEJM (July 14, 2020) about the successful completion of a phase-1 study of 45 healthy adults, who received two vaccines containing stabilized perfusion of SARS CoV-2 spike protein. The vaccines seem to have induced the SARS CoV-2 immune response in all participants. These preliminary findings, support and encourage the development of such novel vaccines, as well as drugs that interfere with the host receptor and virus interaction

CONOCIMIENTOS PREVIOS SOBRE TRANSMISIÓN Y PREVENCIÓN DE ENFERMEDADES EN COMUNIDADES INDÍGENAS.

La Educación para la salud tiene entre sus propósitos, hacer que los ciudadanos se apropien del conocimiento sobre las medidas para mantener la salud individual y colectiva. Evitando hasta cierto punto, la aparición y propagación de enfermedades. Conscientes de la importancia que reviste el conocimiento de la población sobre la forma de transmisión de una enfermedad, para aplicar medidas preventivas eficaces, se propuso indagar los conocimientos previos sobre la transmisión y prevención de enfermedades contagiosas en estudiantes indígenas de Educación Media General, así como comparar estos conocimientos entre estudiantes de zonas rurales y zonas urbanas. Metodológicamente, se trata de una investigación descriptiva, de campo, no experimental. Para recoger los datos se diseñó una prueba de conocimiento que se aplicó a 415 estudiantes indígenas. Entre las conclusiones se destaca que aunque existen aciertos en ambos contextos, en la mayoría de los casos los estudiantes desconocen cómo se transmiten muchas de estas enfermedades, y en relación a las medidas preventivas seleccionadas por los chicos, estas se corresponden a medidas tanto eficaces como ineficaces. Se recomienda diseñar un plan de intervención educativa, iniciando con las enfermedades más comunes en cada contexto.

Linear programming with fuzzy parameters for inventory routing problem in effective management of personal protective equipment: a case study of corona virus disease 2019

ABSTRACT Coronavirus disease 2019 (COVID-19) brings about severe disruptions for human living, economics, and environmental balance. This pandemic has boosted the demand for personal protective equipment (PPE) and thus, medical wastes. Thus, a research hypothesis is stated whether it would be possible to provide more efficient distribution and inventory planning to optimize the PPE availability in a fuzzy environment. To confirm the hypothesis, in addition to addressing a scientific novelty considering integration of linear programming with fuzzy parameters, named as the Jimenez’s method and clustering heuristic methods, the practicality of these approaches is provided on a real case study and generated instances from Turkey. The computational results reveal that clustering the 32% of the hospitals ensures optimum total cost and solution time while feasibility degree changes between 0, 4, and 1 values of the Jimenez are not more effective on the results. Improved cost results reveal the economic importance of the healthcare supply chain management and optimal PPE distribution and inventory planning can break down the disease propagation and reduce the detrimental impacts on human life. The proposed approach, for the first time, ensures practical and theoretical insights of distribution and storage decisions across disciplines, namely, engineering and health-care organization authorities.