Number Of Infected Cases Research Articles

Do Investors Prefer State-Owned over Other Listed Pharmaceutical Companies During COVID-19? Evidence from Indonesian Stock Exchange

This study aims to examine the preference of investors of pharmaceutical companies, including companies that produce herbal medicines, listed in the Indonesian Stock Exchange from 2020 to 2021. By using the descriptive analysis method and reviewing the daily stock prices of Indofarma (INAF), Kimia Farma (KAEF), Kalbe Farma (KLBF), and Sido Muncul (SIDO) in that period, it was found that there were unusual stock prices increases for state-owned pharmaceutical enterprises during those times. However, a similar occurrence did not occur with the other listed pharmaceutical companies, including one herbal medicine manufacturer. In comparing stock price movement trends, the researcher used Microsoft Excel software. The researcher also reviewed the monthly stock closing prices and the news published at that moment. It was found that related events and news existed for every significant increase in stock prices, which might influence investors' perceptions. In addition, the researcher also examined the data to test whether there was a correlation between the number of infected cases and stock prices using the ARCH model estimation. It was found that the relationship between both of them was insignificant. The researcher expects that the findings of this research not only will be a discussion topic in academic groups but also will be a reference for capital market investors and the government as the policyholders and controlling shareholders of these state-owned enterprises. Further studies on listed pharmaceutical companies in the capital markets of other countries are needed to complete the findings in this research, which may find different facts due to different policies in handling the COVID-19 pandemic.

An Overview of Neurological and Psychiatric Complications During Post-COVID Period: A Narrative Review.

The coronavirus disease 2019 (COVID-19), induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a multi-organ and multi-system disease with high morbidity and mortality in severe cases due to respiratory failure and severe cardiovascular events. However, the various manifestations of neurological and psychiatric (N/P) systems of COVID-19 should not be neglected. Some clinical studies have reported a high risk of N/P disorders in COVID-19 and post-COVID-19 patients and that their outcomes were positively associated with the disease severity. These clinical manifestations could attribute to direct SARS-CoV-2 invasion into the central nervous system (CNS), which is often complicated by systemic hypoxia, the dysfunctional activity of the renin–angiotensin system and other relevant pathological changes. These changes may remain long term and may even lead to persistent post-COVID consequences on the CNS, such as memory, attention and focus issues, persistent headaches, lingering loss of smell and taste, enduring muscle aches and chronic fatigue. Mild confusion and coma are serious adverse outcomes of neuropathological manifestations in COVID-19 patients, which could be diversiform and vary at different stages of the clinical course. Although lab investigations and neuro-imaging findings may help quantify the disease’s risk, progress and prognosis, large-scale and persistent multicenter clinical cohort studies are needed to evaluate the impact of COVID-19 on the N/P systems. However, we used “Boolean Operators” to search for relevant research articles, reviews and clinical trials from PubMed and the ClinicalTrials dataset for “COVID-19 sequelae of N/P systems during post-COVID periods” with the time frame from December 2019 to April 2022, only found 42 in 254,716 COVID-19-related articles and 2 of 7931 clinical trials involved N/P sequelae during post-COVID periods. Due to the increasing number of infected cases and the incessant mutation characteristics of this virus, diagnostic and therapeutic guidelines for N/P manifestations should be further refined.

Estimating unconfirmed COVID-19 infection cases and multiple waves of pandemic progression with consideration of testing capacity and non-pharmaceutical interventions: A dynamic spreading model

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has unique epidemiological characteristics that include presymptomatic and asymptomatic infections, resulting in a large proportion of infected cases being unconfirmed, including patients with clinical symptoms who have not been identified by screening. These unconfirmed infected individuals move and spread the virus freely, presenting difficult challenges to the control of the pandemic. To reveal the actual pandemic situation in a given region, a simple dynamic susceptible-unconfirmed-confirmed-removed (D-SUCR) model is developed taking into account the influence of unconfirmed cases, the testing capacity, the multiple waves of the pandemic, and the use of non-pharmaceutical interventions. Using this model, the total numbers of infected cases in 51 regions of the USA and 116 countries worldwide are estimated, and the results indicate that only about 40% of the true number of infections have been confirmed. In addition, it is found that if local authorities could enhance their testing capacities and implement a timely strict quarantine strategy after identifying the first infection case, the total number of infected cases could be reduced by more than 90%. Delay in implementing quarantine measures would drastically reduce their effectiveness.

Application of fractional optimal control theory for the mitigating of novel coronavirus in Algeria

In this paper, we investigate the dynamics of novel coronavirus infection (COVID-19) using a fractional mathematical model in Caputo sense. Based on the spread of COVID-19 virus observed in Algeria, we formulate the model by dividing the infected population into two sub-classes namely the reported and unreported infective individuals. The existence and uniqueness of the model solution are given by using the well-known Picard–Lindelöf approach. The basic reproduction number R0 is obtained and its value is estimated from the actual cases reported in Algeria. The model equilibriums and their stability analysis are analyzed. The impact of various constant control parameters is depicted for integer and fractional values of α. Further, we perform the sensitivity analysis showing the most sensitive parameters of the model versus R0 to predict the incidence of the infection in the population. Further, based on the sensitivity analysis, the Caputo model with constant controls is extended to time-dependent variable controls in order obtain a fractional optimal control problem. The associated four time-dependent control variables are considered for the prevention, treatment, testing and vaccination. The fractional optimality condition for the control COVID-19 transmission model is presented. The existence of the Caputo optimal control model is studied and necessary condition for optimality in the Caputo case is derived from Pontryagin’s Maximum Principle. Finally, the effectiveness of the proposed control strategies are demonstrated through numerical simulations. The graphical results revealed that the implantation of time-dependent controls significantly reduces the number of infective cases and are useful in mitigating the infection.

Evaluation of Attitude and Emotions towards Sudden Closure of Educational Institution during Covid-19 among Medical Student: A Cross-Sectional Survey

In reaction to an alarming increase in the number of infected cases and fatalities due to new coronavirus infections in 2019, all academic institutions, including primary and secondary schools, were closed. Objective: To assess medical students' attitudes and emotions concerning a sudden shutdown of an educational institution during COVID-19. Methods: After meeting the requirements for 139 patients, data was collected from medical students at The University of Lahore and medical students at Allama Iqbal Medical College in Lahore, and a Performa was affixed for this purpose. Convenient sampling was employed by employing a different questionnaire to collect data from public and private patients in order to obtain consent to proceed with this data Results: The mean and standard deviation for the achievement feelings questionnaire total score were found to be 80.76±4.128 on the histogram with a normal curve. The mean and standard deviation for achievement emotions questionnaire categorization were determined to be 67.30±3.44 on the histogram with a normal curve. The mean and standard deviation for the achievement emotions questionnaire good emotions were determined to be 79.47±6.039 on the histogram with a normal curve. The mean and standard deviation for achievement feelings questionnaire negative emotions were determined to be 55.88±3.801 on the histogram with a normal curve. Conclusions: In conclusion, children scored higher on positive emotions (31/40 versus 25/55, respectively). Students are typically delighted with the return of school activities, according to the study. This could be viewed as an indication of the student's future curiosity and drive to study. According to the findings of this study, aging has both an inverse and direct association with good and negative emotions.

High Level of SARS-CoV-2 Infection in Young Population Is a Predictor for Peak Incidence.

South Korea adopted stringent preventive measures against Coronavirus virus disease 2019, resulting in three small and one large outbreaks until January 15, 2022. The fatality rate was 2.5-fold higher during peak transmission periods than in base periods. As new variants of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) are continuously emerging, the need for understanding their epidemic potential remains necessary. In South Korea, the epidemiologic data obtained from mass diagnostic testing enabled investigation of the true number of infected cases, exact incidence, and fatality numbers. Analysis found a similarity between estimated infection rates and confirmed cases. This suggested that the number of confirmed cases had an influence on the fatality rate as a quantitative parameter. The fatality rate decreased even as infection with SARS-CoV-2 variants rose. In comparative analysis, the confirmed cases in young people (ages 20–29) increased prior to every outbreak peak and marked the tipping point in infection spread. These results indicate that a high level of SARS-CoV-2 infection in young population drives peak incidence and mortality across all age groups.

Projection of the Epidemics Trend of COVID-19 in Qom, Iran: A Modeling Study

Background: Coronavirus is one of the major pathogens of the human respiratory system and a major threat to the human health. Objectives: This modeling study aimed to project the epidemics trend of coronavirus disease 2019 (COVID-19) in Qom, Iran Methods: This study projected the COVID-19 outbreak in Qom using a modified susceptible-exposed-infectious-recovered (SEIR) compartmental model by the end of December 2020. The model was calibrated based on COVID-19 epidemic trend in Qom from 1 January to 11 July. The number of infected, hospitalized, and death cases were projected by 31 December. A Monte Carlo uncertainty analysis was applied to obtain 95% uncertainty interval (UI) around the estimates. Results: According to the results, the reduced contact rate and increased isolation rate were effective in reducing the size of the epidemic in all scenarios. By reducing the contact rate from eight to six, the number of new cases on the peak day, as well as the total number of cases admitted to the hospital by the end of the period (31 December), decreased. For example, in Scenario A, compared to Scenario E, with a decrease in contact rate from eight to six, the number of new cases on peak days decreased from 15,700 to 1,100. The largest decrease in the number of new cases on peak days was related to Scenario F with 270 cases. Also, the total number of cases decreased from 948,000 to 222,000 between the scenarios, and the largest decrease in this regard was related to Scenario F, with 188,000 cases. Conclusions: The parameters of contact rate and isolation rate can reduce the number of infected cases and prevent the outbreak, or at least delay the onset of the peak. This can help health policymakers and community leaders to upgrade their health care systems.

Psycho-behavioral responses of Nigerian health workers to an initial human-to-human transmission of the coronavirus disease.

Previous pandemics have had significant impact on psychological well-being of front-line health care workers. Issues such as fear of contracting the disease, high workload as a result of high numbers of infected cases, increased job stress and unavailability of personal protective equipment have been implicated in development of psychological distress in this subset of individuals. The aim of the present paper is to describe psychobehavioral responses of health care workers and potential predictors of emotional response at onset of COVID- 19 outbreak in Nigeria.Cross-sectional web-based survey and 7-item Generalized Anxiety Disorder Questionnaire (GAD-7) were administered anonymously to 444 respondents comprising various categories of frontline healthcare workers. Stepwise multiple linear regression was used to determine predictors of anxiety scores. Participants were mostly young adults (mean age 38 years), females (57%), living with a partner (78.2%) and medical doctors (56.8%). Restrictions in clinical activities and use of hand sanitizers were commonest precautionary behaviors. Commonest emotional responses were anger and despair (27.0% and 25.7%), respectively. About 42.8% had clinically significant anxiety symptoms with highest burden among nurses. Perception of likelihood of 2nd wave (p=0.03), self-preparedness (p=0.04), gender (p=0.01) and cadre (p=0.02) were significant predictors of emotional response of anxiety.Study findings highlighted diverse psychological reactions of health care workers with a large proportion screening positive for significant anxiety symptoms. This has implications for planning a comprehensive psychosocial response to COVID-19 pandemic and for future pandemics among frontline health care workers in lowresource settings.

Design and Implementation of an Injury Detection System for Corona Tracker

Today, the trends are the robotics field since it is used in too many environments that are very important in human life. Covid 19 disease is now the deadliest disease in the world, and most studies are being conducted to find solutions and avoid contracting it. The proposed system senses the presence according to a specific injury to warn of it and transfer it to the specialist doctor. This system is designed to work in service departments such as universities, institutes, and all state departments serving citizens. This system consists of two parts: the first is fixed and placed on the desk and the other is mobile within a special robot that moves to perform the required task. This system was tested at the University of Basrah within the college of engineering, department of electrical Engineering, on teaching staff, students, and staff during the period of final academic exams. The presence of such a device is considered a warning according to a specific condition and isn’t a treatment for it, as the treatment is prescribed by the specialist doctor. It is found that the average number of infected cases is about 3% of the total number of students and the teaching staff and the working staff. The results were documented in special tables that go to the dean of the college with the attendance tables to know the daily health status of the students.

Covid-19 data modelling using hybrid conjugate gradient method

Optimization problem is a type of mathematical problem with several uses in real life. As one of the commonly applied methods for solving optimization problem, the conjugate gradient (CG) method has undergone numerous modifications in order to improve its efficiency. The hybrid CG is one of the best CG methods which can also be applied for data fitting [1]. Nowadays, Corona Virus Disease (Covid-19) has been extensively studied by many researchers worldwide due to the rising number of infected cases and deaths resulting from contracting the virus. In 2020, the Malaysian government enforced the first ‘Movement Control Order (MCO)’ in order to significantly lower the infection rate. However, the number of cases rose again at the end of year 2020, leading to the necessity to implement the second MCO in January 2021. In this study, a new hybrid conjugate gradient method is introduced to analyse the Covid-19 cases in Malaysia before MCO 2.0. This new hybrid method yields a better numerical result compared to classical and other hybrid CG methods.

A Global Sharing Mechanism of Resources: Modeling a Crucial Step in the Fight against Pandemics.

To face crises like the COVID-19 pandemic, resources such as personal protection equipment (PPE) are needed to reduce the infection rate and protect those in close contact with patients. The increasing demand for those products can, together with pandemic-related disruptions in the global supply chain, induce major local resource scarcities. During the first phase of the COVID-19 pandemic, we witnessed a reflex of ‘our people first’ in many regions. In this paper, however, we show that a cooperative sharing mechanism can substantially improve the ability to face epidemics. We present a stylized model in which communities share their resources such that each can receive them whenever a local epidemic flares up. Our main finding is that cooperative sharing can prevent local resource exhaustion and reduce the total number of infected cases. Crucially, beneficial effects of sharing are found for a large range of possible community sizes and cooperation combinations, not only for small communities being helped by large communities. Furthermore, we show that the success of sharing resources heavily depends on having a sufficiently long delay between the onsets of epidemics in different communities. These results thus urge for the pairing of a global sharing mechanism with measures to slow down the spread of infections from one community to the other. Our work uses a stylized model to convey an important and clear message to a broad public, advocating that cooperative sharing strategies in international resource crises are the most beneficial strategy for all. It stresses essential underlying principles of and contributes to designing a resilient global supply chain mechanism able to deal with future pandemics by design, rather than being subjected to the coincidental and unequal distribution of opportunities per community that we see at present.

Estimating Time-Series Changes in Social Sensitivity for COVID-19 @ Twitter in Japan

The global outbreak of COVID-19 is now putting enormous pressure on society to change our traditional social behavior. Government officials are also forced to make short-term decisions based on limited information for public health, and investor sentiment about the infection situation in each country has a significant impact on the stock market. In this paper, we attempt to visualize the time series of indexed social sentiment in Japan under the COVID-19 pandemic by using a neural network approach, and clarify changes in the sensitivity of citizens to the coronavirus. The sentiment was classified for Twitter tweets that matched the keywords for which the government was asked to restrict action, and sentiment trends were identified for the period from before the outbreak to the fifth wave of infection in Tokyo, Sapporo, Osaka, and Fukuoka. The indices obtained show a correlation with the number of infected cases by region and with national and local events, and in global cities such as Tokyo and Osaka as they experienced waves of infections and emergency declarations, sensitivity gradually became paralyzed, and parallel trends in sentiment waveforms were observed among regions.

Modeling and analysis of SEIRS epidemic models using homotopy perturbation method: A special outlook to 2019-nCoV in India

Considering the prevailing situations, the mathematical modeling and dynamics of novel coronavirus (2019-nCoV) particularly in India are studied in this paper. The goal of this work is to create an effective SEIRS model to study about the epidemic. Four different SEIRS models are considered and solved in this paper using an efficient homotopy perturbation method. A clear picture of disease spreading can be obtained from the solutions derived using this method. We parametrized the model by considering the number of infection cases from 1 April 2020 to 30 June 2020. Finally, numerical analysis and graphical representations are provided to interpret the spread of virus.

Bifurcation Analysis of a Mathematical Model for the Covid-19 Infection among Pregnant and Non-Pregnant Women

A mathematical study and analysis of the covid-19 disease is essential in the control and eradication of the dreadful covid-19 infection. This research work is focused on the covid-19 infection among the women population. The women population was divided into seven compartments. These compartments were built into a deterministic model presented as a system of ordinary differential equations. Basic mathematical analyses such as positivity of solution, the disease-free equilibrium and the basic reproduction number, were performed on the model. The model assumes only positive solutions and the total population size is bounded. The next generation matrix approach was employed in generating the basic reproduction number R0. The sensitivity analysis revealed that among the most sensitive parameters of R0 are the effective contact rate for Covid-19 transmission, the modification parameter accounting for increased susceptibility to covid-19 infection by pregnant women, and the transmission coefficient of the infectious pregnant women. The bifurcation analysis revealed a forward bifurcation which guarantees the stability of the disease-freeequilibrium when R0 is lowered below unity. Numerical simulations, using Mathematica Version 12.0 package, are given to show the effects of the most sensitive parameters of the basic reproduction number on the number of infectious cases of covid-19.

Estimating the Number of COVID-19 Cases and Impact of New COVID-19 Variants and Vaccination on the Population in Kerman, Iran: A Mathematical Modeling Study.

COVID-19 is spreading all over Iran, and Kerman is one of the most affected cities. We conducted this study to predict COVID-19-related deaths, hospitalization, and infected cases under different scenarios (scenarios A, B, and C) by 31 December 2021 in Kerman. We also aimed to assess the impact of new COVID-19 variants and vaccination on the total number of COVID-19 cases, deaths, and hospitalizations (scenarios D, E, and F) using the modified susceptible-exposed-infected-removed (SEIR) model. We calibrated the model using deaths reported from the start of the epidemic to August 30, 2021. A Monte Carlo Markov Chain (MCMC) uncertainty analysis was used to estimate 95% uncertainty intervals (UI). We also calculated the time-varying reproductive number (Rt) following time-dependent methods. Under the worst-case scenario (scenario A; contact rate = 10, self‐isolation rate = 30%, and average vaccination shots per day = 5,000), the total number of infections by December 31, 2021, would be 1,625,000 (95% UI: 1,112,000–1,898,000) with 6,700 deaths (95% UI: 5,200–8,700). With the presence of alpha and delta variants without vaccine (scenario D), the total number of infected cases and the death toll were estimated to be 957,000 (95% UI: 208,000–1,463,000) and 4,500 (95% UI: 1,500–7,000), respectively. If 70% of the population were vaccinated when the alpha variant was dominant (scenario E), the total number of infected cases and deaths would be 608,000 (95% UI: 122,000–743,000) and 2,700 (95% UI: 700–4,000), respectively. The Rt was ≥1 almost every day during the epidemic. Our results suggest that policymakers should concentrate on improving vaccination and interventions, such as reducing social contacts, stricter limitations for gathering, public education to promote social distancing, incensing case finding and contact tracing, effective isolation, and quarantine to prevent more COVID-19 cases, hospitalizations, and deaths in Kerman.

Epidemiologic and clinical characteristics of COVID-19 patients admitted in a tertiary care hospital in Khulna city

Background & objective: The outbreak of coronavirus disease 2019 (COVID-19) has become a large threat to global health due to its high contagious nature and varied mortality. The number of infected cases and deaths are rising exponentially all over the world, including Bangladesh. This study aims to investigate the epidemiological and clinical features of COVID-19 patients admitted in Gazi Medical College Hospital (GMCH), Khulna, Bangladesh. Methods: In this study, we included 110 RT-PCR positive COVID-19 cases at GMCH, Khulna, Bangladesh from 1 July, 2021 to 31 August, 2021. Demographic, clinical, laboratory and radiological data of the patients were recorded and analyzed. Results: Among 110 study subjects, 51.8% were male. The mean age was 52.6 years, with majority (43.6%) belonging to the age group of 40-59 years. Upon admission, the most frequent symptoms were fever (94.5%), cough (86.4%) and dyspnea (79.1%). Diabetes mellitus (42.7%) and hypertension (33.6%) were the most common comorbidities. Regarding laboratory parameters, neutrophilia and lymphocytopenia were observed in 72.7% and 58.2% cases, respectively. The mean D-dimer (2.9 mg/l) and CRP (61.7 mg/l) levels were above their normal limits. In high resolution CT scan of the chest (HRCT chest), bilateral lung involvement was present in 85.5% cases. Ground-glass appearance was the most frequent (89.1%) radiologic pattern. Among the study subjects, empirical antibiotic, antiviral (remdesivir) drug, and systemic glucocorticoid were given to 97.3%, 83.6% and 56.4% patients, respectively. Total 15 (13.6%) patients were severely affected with COVID-19 and admitted to the ICU, requiring mechanical ventilation. During our study, 12 patients (10.9%) died either due to multiple organ dysfunction syndrome or cardio-respiratory failure. Conclusion: The study provides key information about clinical characteristics and general management of COVID-19 patients, which may help physicians to identify the factors associated with adverse outcomes in this disease. Mediscope 2022;9(1): 9-15

Optimal control of epidemic spreading in the presence of social heterogeneity.

The spread of COVID-19 has been thwarted in most countries through non-pharmaceutical interventions. In particular, the most effective measures in this direction have been the stay-at-home and closure strategies of businesses and schools. However, population-wide lockdowns are far from being optimal, carrying heavy economic consequences. Therefore, there is nowadays a strong interest in designing more efficient restrictions. In this work, starting from a recent kinetic-type model which takes into account the heterogeneity described by the social contact of individuals, we analyse the effects of introducing an optimal control strategy into the system, to limit selectively the mean number of contacts and reduce consequently the number of infected cases. Thanks to a data-driven approach, we show that this new mathematical model permits us to assess the effects of the social limitations. Finally, using the model introduced here and starting from the available data, we show the effectiveness of the proposed selective measures to dampen the epidemic trends. This article is part of the theme issue 'Kinetic exchange models of societies and economies'.

EPIDEMIOLOGICAL CHARACTERISTICS OF VARICELLA IN KON TUM PROVINCE IN 2018

Abstract: Background: In Vietnam, the number of people suffered from Varicella was high, accounting for 25.000 – 40.000 cases and children were dominant. In Kon Tum, the annual number of Varricella cases remains high, the average 5-year period from 2013-2017 was 779 cases per year. Objective: The study aimed to describe the epidemiological characteristics of Varicella in Kon Tum province in 2018. Methodology: The retrospective description was conducted among 368 Varicella patients in Kon Tum Province from January to December 2018. Data collection was based on the Varicella monthly reporting form of the Kon Tum Center for Disease Control. Results: The cumulative prevalance of Varicella/100.000 people was 68.8. The distribution of Varicella in region III was highest, accounting for 52.7%. The Varicella experienced all months of the year, and in March had the highest number of infectious cases, accounting for 51%. The cumulative prevalence of Varicella/100.000 people among women was 78.6, the figure for was 60,0. The incidence of Varicella/100.000 people in the group under 5 years old was the highest (286.6). The cumulative prevalemce of Varicella/100.000 people in ethnic minorities was nearly 3 times higher than that of the Kinh people. The distribution of Varicella in young children accounted for the highest proportion (45.6%). Conclusion: There was a need in further strengthening health communication and education for people on preventing and controlling Varicella, especially for ethnic minorites. Key words: epidemiological characteristics, varicella, Kon Tum, Vietnam

The Association Between the Albumin and Viral Negative Conversion Rate in Patients Infected with Novel Coronavirus Disease 2019 (COVID-19).

PurposeThe novel coronavirus disease 2019 (COVID-19) epidemic is the severe global pandemic with large numbers of infected cases and deaths in recent decades. The previous studies were all about the influence of albumin (ALB) for the severity and mortality of in-patients infected with COVID-19. But few studies exist about the influence factors to achieve viral negative conversion. Therefore, this study conducted an exploratory study to investigate the effect of albumin on negative conversion rate.MethodsAmong the 190 hospitalized patients with moderate COVID-19 who had a course of disease longer than 30 days, 102 achieved viral negative conversion in 30–45 days and 88 not after 45 days. Taking other variables as concomitant variable, Cox proportional hazard regression model was applied to explore the influence of albumin to negative conversion rate under various factors.ResultsBy comparing patients who could and could not achieve the finally viral negative conversion, a possible nonlinear relationship between the continuous variables and clinical outcomes was examined by a restricted cubic spline regression model. An association was found between albumin levels and hazard ratio of viral negative conversion rate (P = 0.027). The increase of albumin was accompanied with decreases of hazard ratio of viral negative conversion rate (the value of albumin <38 g/L). But when the value of albumin was higher than 38 g/L, the hazard ratio of viral negative conversion rate approached 1, it means that albumin is not a risk factor for the viral negative conversion rate of COVID-19 disease.ConclusionFor patients with COVID-19, albumin is a common and observed laboratory parameter. It is associated with final viral negative conversion rate although its underlying mechanism and relationship with the viral negative conversion rate still need to be clarified.

Order Assignment and Scheduling for Personal Protective Equipment Production During the Outbreak of Epidemics

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic <xref ref-type="algorithm" rid="alg2">algorithm II</xref> (NSGA-II) in obtaining high-quality Pareto solutions. <i>Note to Practitioners</i>&#x2014;The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions.