Simulation of COVID-19 Epidemic from Potential Viral Loads in Saudi Arabian Wastewater Treatment Plants (preprint)

SARS-CoV-2 is a contagious respiratory virus that has been discovered in sewage, human waste, and wastewater treatment facilities. Wastewater surveillance has been considered one of the lowest-cost means of testing for tracking the COVID-19 outbreak in communities. This paper highlights the dynamics of the virus's infection, persistence, and occurrence in wastewater treatment plants. Our aim is to develop and implement a mathematical model to infer the epidemic dynamics from the possible density of SARS-CoV-2 viral load in wastewater. We present a long-normal model and fractional order of susceptible-exposed-infected-recovery (SEIR) epidemic model for predicting the spread of the COVID-19 disease from the wastewater data. We study the dynamic properties of the fractional order SEIR model with respect to the fractional ordered values. The model is used to comprehend how the coronavirus spreads through wastewater treatment plants in Saudi Arabia. Our modeling approach can help with wastewater surveillance for early prediction and cost-effective monitoring of the epidemic outbreak in a situation of low testing capacity.

Updated results from MOMENTUM: Momelotinib vs. Danazol in JAK inhibitor-experienced, symptomatic, and anaemic myelofibrosis patients

The phase 3 MOMENTUM study (NCT04173494) of the ACVR1/JAK1/JAK2 inhibitor momelotinib (MMB) vs. danazol (DAN) in patients with myelofibrosis (MF) previously treated with a JAK inhibitor (JAKi) met the primary endpoint and all key secondary endpoints at week 24 (W24). We provide updated results from week 48 assessments. Eligible patients had primary or post-ET/ PV MF;DIPSS high, Int-2, or Int-1 risk;Total Symptom Score (TSS) >=10;haemoglobin (Hb) <10 g/dL;platelets >=25 x 109/L;prior JAKi for >=90 days (>=28 days if red blood cell [RBC] transfusions >=4 units in 8 weeks or grade 3/4 thrombocytopenia/anaemia/ hematoma);and palpable spleen >=5 cm. Randomisation was 2:1 to MMB 200 mg/day or DAN 600 mg/day for 24 weeks, followed by open-label (OL) MMB. Week 48 endpoints included durations of response (TSS, transfusion independence [TI], splenic) and overall and leukaemia-free survival (OS, LFS). As of 17 May 2022, 93/130 (72%) MMB -> MMB and 41/65 (63%) DAN -> MMB patients received OL MMB;mean MMB durations were 48 weeks and 24 weeks, respectively. Analyses for W24 responders showed the following: of TSS responders, 31/32 (97%) MMB -> MMB and 6/6 DAN -> MMB patients had TSS < baseline;of TI responders, 36/40 (90%) and 10/13 (77%) had no RBC transfusions or Hb <8 g/dL;and of spleen responders, all patients had splenic volume < baseline. In the OL phase, the most common grade >=3 treatment-emergent adverse events (TEAEs) were thrombocytopenia (MMB -> MMB, 9%;DAN -> MMB, 15%) and anaemia (MMB -> MMB, 9%;DAN -> MMB, 2%). Grade >=3 infections occurred in 19% of MMB -> MMB and 10% of DAN -> MMB patients, including grade >=3 (nonfatal) COVID-19. Peripheral neuropathy (PN) occurred in 2% of patients in each arm, and none discontinued MMB due to PN. TEAEs led to MMB discontinuation in 18% (MMB -> MMB) vs. 10% (DAN -> MMB). A trend towards improved OS up to W24 was previously observed with MMB vs. DAN (hazard ratio [HR], 0.506;p = 0.0719);after all patients crossed over to OL MMB, OS and LFS curves for both arms converged (HR, 0.945, 95% CI, 0.528-1.693;HR, 0.830, 95% CI, 0.473-1.4555). Sixty of 81 (74%) MMB -> MMB and 29 of 43 (67%) DAN -> MMB patients with baseline platelets <=150 x 109/L entered the OL phase. Efficacy and safety results in thrombocytopenic subgroups in the OL period were consistent with the intent-to- treat (ITT) population. OL MMB maintained symptom, TI, and spleen responses with continued good survival and safety in the ITT and low platelet populations. MMB may address an unmet need in anaemic patients with MF.

Advanced Nonlinear Robust Approach for Controlling COVID-19 System Based on Vaccination Campaign

This study proposes a new approach for controlling COVID-19 through vaccination, where an adequately descriptive mathematical model is created for COVID-19 using the susceptible exposed infectious recovered (SEIR) model of epidemic diseases. The presented control approach is synthesized using a combination of feedback linearization and H∞ control, and incorporates model reference control to achieve optimal time responses with the aid of the black hole optimization (BHO) algorithm. The effectiveness of the designed control law is evaluated using data from the lombardy region of Italy. The results of the simulation show that the proposed control approach is able to effectively control the COVID-19 outbreak by accurately implementing the desirable vaccination, while effectively addressing nonlinearity and uncertainty in the COVID-19 system with a desirable control action. The control method has achieved the required immunity of 6.6 million individuals after approximately 25 days with a transmission rate reduced to zero in a short time, and a vaccination rate of 170 thousand people per day © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

Psychometric properties of the environmental factors' questionnaire for undergraduate medical students taking online learning during COVID-19 pandemic: a cross-sectional study.

BACKGROUND: Environmental factors are important for students' learning during online classes, especially during a pandemic, such as COVID-19. This study aimed to validate the environmental factors' questionnaire during online learning. METHODS: A total of 218 undergraduate medical students at the Health Campus, Universiti Sains Malaysia, participated in a cross-sectional study that involved an online survey. Environmental factor scales were assessed with the nine-item lighting, noise, and temperature (LNT) scale and the six-item technology scale. Analysis was performed using confirmatory factor analysis (CFA). RESULTS: The English version of the LNT scale with nine items and three factors showed a good fit to the data, with no item deleted. For LNT, the composite reliability (CR) was 0.81, 0.81, and 0.84, respectively, while the average variance extracted (AVE) was 0.61, 0.59, and 0.6, respectively. The English version of the technology scale, with six items and one factor, also showed a good fit to the data, with no item deleted. The CR was 0.84, and the AVE was 0.51. CONCLUSIONS: The results provide psychometric evidence for environmental questionnaire scales in evaluating the factors associated with online learning among Malaysian university medical students. All items were retained and confirmed to fit the sample data.

Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd

Respiratory viruses: New frontiers-a Keystone Symposia report.

Respiratory viruses are a common cause of morbidity and mortality around the world. Viruses like influenza, RSV, and most recently SARS-CoV-2 can rapidly spread through a population, causing acute infection and, in vulnerable populations, severe or chronic disease. Developing effective treatment and prevention strategies often becomes a race against ever-evolving viruses that develop resistance, leaving therapy efficacy either short-lived or relevant for specific viral strains. On June 29 to July 2, 2022, researchers met for the Keystone symposium "Respiratory Viruses: New Frontiers." Researchers presented new insights into viral biology and virus-host interactions to understand the mechanisms of disease and identify novel treatment and prevention approaches that are effective, durable, and broad.

Interleukin-39 is a novel cytokine associated with type 2 diabetes mellitus and positively correlated with body mass index.

INTRODUCTION: It is suggested that cytokines play a key role in the pathogenesis of type 2 diabetes mellitus (T2DM). Therefore, this study explored two recently discovered cytokines, interleukin (IL)-37 (anti-inflammatory) and IL-39 (pro-inflammatory), in T2DM due to limited data in this context. METHODS: Serum IL-37 and IL-39 levels were determined in 106 T2DM patients and 109 controls using enzyme-linked immunosorbent assay kits. RESULTS: Serum levels (median and interquartile range) of IL-37 (79 [47-102] vs. 60 [46-89] ng/L; probability [p] = .04) and IL-39 (66 [59-69] vs. 31 [19-42] ng/L; p < .001) were significantly elevated in T2DM patients compared to controls. As indicated by the area under the curve (AUC), IL-39 (AUC = 0.973; p < .001) was more predictable for T2DM than IL-37 (AUC = 0.582; p = .039). Elevated levels of IL-39 were significantly associated with T2DM (odds ratio = 1.30; p < .001), while IL-37 did not show this association. Classification of IL-37 and IL-39 levels by demographic and clinical characteristics of patients revealed some significant differences including gender (IL-39), body mass index (BMI; IL-37 and IL-39) and diabetic neuropathy (IL-39). BMI was positively correlated with IL-39 (correlation coefficient [rs ] = 0.27; p = .005) and glycosylated haemoglobin (rs  = 0.31; p = .001), and negatively correlated with age at onset (rs  = -0.24; p = .015). CONCLUSIONS: IL-37 and IL-39 levels were elevated in the serum of T2DM patients. Besides, IL-39 is proposed to be a novel cytokine associated with T2DM and positively correlated with BMI.

Determining the Effect of COVID-19 on the Menstrual Cycle Among Women of Reproductive Age Group in the Jazan Region: A Cross-Sectional Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has gravely affected the world in various ways. COVID-19 is a major health crisis, with long-term physical and mental health consequences. Many women reported menstrual irregularities during and after the pandemic. The study aimed to assess the effects of COVID-19 on menstrual cycles in females of reproductive age in the Jazan region. METHODOLOGY: A descriptive cross-sectional research design was utilized to conduct the study in Jazan, Saudi Arabia. A structured questionnaire was used to collect data from 346 women aged 18-44 years who had normal menstrual cycles for more than a year before the outbreak and had a history of COVID-19 infection. RESULT: The questionnaire was completed by 346 women. Only 144 (41.6%) of the study's respondents were aged 25-34 years. Of the respondents, 283 (81.8%) were university students, and 219 (63.3%) were married. The majority of women (337, 97.4%) were vaccinated against COVID-19. A total of 301 (87.0%) were healthy. Before being infected with COVID-19, 19.70% of the responders had irregular periods, which increased to 59.50% during infection and 33.20% after getting better. There was a relationship between the regularity of menstrual periods during COVID-19 infection and the duration of menstrual periods after COVID-19 (p = 0.035); the frequency of menstrual periods before (p = 0.001), during (p = 0.009), and after (p = 0.001) COVID-19; menstrual period regularity before (p = 0.001) and after (p = 0.001) COVID-19 infection; and pain severity level during (p = 0.001) and after (p = 0.004) COVID-19 infection. Regarding the perception of the impact of COVID-19 on menstrual changes, there was an association between COVID-19 infection and variation in days during two consecutive menstrual cycles (p = 0.001), changes in the duration of menstrual cycles (p = 0.022), delayed or absent menstruation (p = 0.019), and menstruation stopping (p = 0.023). CONCLUSION: The research demonstrated the COVID-19 pandemic is an international health problem that affects women, leading to changes in regularity, duration, frequency, and severity of pain. These changes may have a long-term impact on women's reproductive health.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

The selection of potential suppliers has recently become a big challenge for the manufacturing industries due to the rapid spread of covid-19 and the escalating frequency of natural calamities such as earthquakes and floods. When decision-makers (DMs) consider quantity discounts from multiple sources, things get much more complicated. Although previous studies have looked at selecting suitable suppliers from economic and environmental aspects, no one has considered foreign transportation risks while evaluating the textile industry's global green suppliers. In this regard, for the first time, this study combines economic and environmental factors with the foreign transportation risk criterion to develop a holistic model for global green supplier selection and order allocation (SS&OA) in the textile industry under all-unit quantity discounts. Initially, the fuzzy analytical hierarchy process (FAHP) method is used to calculate the relative weights of the criteria. Second, a multi-objective linear programming (MOLP) model is developed to reduce the total procurement cost, quality rejection rate, delivery lateness rate, greenhouse gas emissions from product procurement, and foreign transportation risks. Subsequently, the developed MOLP model is transformed into a fuzzy compromise programming (FCP) model to obtain order allocation quantities among selected suppliers with their offered quantity discount rates. A real-life case study of the Pakistani textile industry is presented to validate the proposed methodology's applicability by determining the optimal order allocation quantities among multiple suppliers based on two decision-making attitudes of DMs (neutral and risk-averse). Finally, sensitivity and comparative analyses are carried out to guarantee that the proposed technique produces accurate and optimal solutions. The final results of the proposed methodology show that it can effectively manage data uncertainties during SS&OA compared to other existing approaches. The suggested integrated methodology's outcomes can assist the supplier organization in overcoming its current shortcomings and developing a long-term relationship with the buyer organization. © 2023 Elsevier Ltd

A proposed IoNT-based system to monitoring the validity of Covid-19 vaccine

The issue of protecting and monitoring the validity of the vaccine until its use is a problem that affects people's lives, especially in countries that lack adequate facilities to ensure the validity of the vaccine, as these vaccines can lose their effects if exposed to conditions outside the recommended restrictions during storage or transportation. In this article, the authors propose an effective system for monitoring the validity of Covid-19 vaccine and warning the users in real-time in case the vaccine becomes out of the supply chain conditions. The proposed system consists of three layers: the first layer is an Internet of Nano Things (IoNT) network that contains nano sensors to monitor the conditions of the vaccine vial;The second layer is the facilities of communication that achieve contact between the vaccine vial and the user;The third layer, is the users who represent the vaccine sponsor, state healthcare center and local healthcare providers. © 2023 Author(s).

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection. © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

The artificial intelligence (AI) role for tackling against COVID-19 pandemic.

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection.

Immunological imprinting: Understanding COVID-19.

Immunological imprinting generically refers to the effects prior exposures have on subsequent immune responses to, and eventually protection against, antigenically related viruses. Here, Koutsakos and Ellebedy explain different concepts and terms around imprinting and the fundamental immunological principles behind it. They also discuss the potential role imprinting may have in the context of COVID-19 vaccines.

Rural-Urban Differences in Vaccination and Hesitancy Rates and Trust: US COVID-19 Trends and Impact Survey on a Social Media Platform, May 2021-April 2022.

Objectives. To analyze rural-urban differences in COVID-19 vaccination uptake, hesitancy, and trust in information sources in the United States. Methods. We used data from a large survey of Facebook users. We computed the vaccination, hesitancy, and decline rates and the trust proportions among individuals hesitant toward COVID-19 information sources for rural and urban regions in each state from May 2021 to April 2022. Results. In 48 states with adequate data, on average, two thirds of states showed statistically significant differences in monthly vaccination rates between rural and urban regions, with rural regions having a lower vaccination rate at all times. Far fewer states showed statistically significant differences when comparing monthly hesitancy and decline rates for urban versus rural regions. Doctors and health professionals received the highest level of trust. Friends and family were also among the most trusted sources in rural areas where the vaccination uptake was low. Conclusions. Rural-urban difference in hesitancy rates among those still unvaccinated was much smaller than the rural-urban difference in vaccination rates, suggesting that access to vaccines may be another contributor to the lower vaccination rates in rural areas. (Am J Public Health. 2023;113(6):680-688. https://doi.org/10.2105/AJPH.2023.307274).

Software Implementation of a Smart Bracelet Prototype to Monitor Vital Signs, Locate, and Track COVID-19 Patients in Quarantine Zone

This paper describes the design and software implementation of a wearable prototype that allows users to monitor the vital signs of COVID-19 patients in quarantine areas. This prototype consists of two parts, the bracelet, andthe Base control unit (BCU). The bracelet is built with ESP8266 and sensors as main components, as well as the battery and other parts needed to fulfill the system's purpose (monitoring the vital signs of COVID-19 patients). At the same time, the Raspberry Pi (SCB) single board computer and GSM/GPRS/HAT are the main components of the Basic Control Unit (BCU). The current work describes the main parts of the pseudocode, as well as the activity diagram for the microcontroller and Raspberry Pi. This paper describes the mechanism of sending alert messages, whereby the system's ability to configure two types of alert messages;(1) physician Messages (these Messages will be sent to the physicianassociated with the patient if one or more vital signs reach a critical value;these messages contain all measurements of a patient's vital signs);(2) Authorize messages (these messages will be sent if the quarantine rules are violated;the patient's location will be sent to the authorized person as a Google Mapslink). Also, this paper describes the graphical user interface for communication, management,. and interaction between the users of the system. © 2022 IEEE.

Impact of COVID-19 on Corporations in Malaysia from a Corporate Restructuring and Insolvency Law Perspective

The COVID-19 pandemic has caused economic stagnancy that triggered many companies to suffer from financial distress and close down. The pandemic also sparks the practicality of the present laws on corporate restructuring. This chapter outlines the Malaysian government's intervention through fiscal injection and statutes amendments. Further, it addresses the existing laws governing directors' duties at a critical time within the insolvency zone. Besides, the discussion focuses on the applicability of restructuring measures available under the Malaysian Companies Act 2016. Methodologically, the chapter draws on the current updates for Malaysia on the issue through reliable secondary sources, employs doctrinal analysis to the relevant statutory provisions, and, whenever relevant, compares them with the UK and Australian positions. In the end, the discussion finds that Malaysia has adequate laws to cater to the restructuring needs of the impacted companies during the pandemic. Not to mention some changes were hastily introduced. The situation benefits from statutory rescue mechanisms, apart from the informal ones. The courts are mindful of protecting creditors yet are willing to get the ailing companies through the predicament. In addition, the law has many options to keep up with the demanding situations. Unfortunately, the restructuring mechanisms in place are too secured-creditors oriented. Hence, it should set out more rescue-friendly measures for stakeholders' broader interests, especially the entrepreneurship community, unsecured creditors and employees. The finding also reveals weaknesses in fraudulent trading provisions and lack of risk management as best practices in corporate governance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

Prediction Student Performance in E-Learning Environment: Challenge and Opportunity

With advancements in e-learning technology, students may power them by interacting with the eLearning environment, such that the teacher is no longer the gatekeeper of instruction. This research attempts to examine students' prediction performance based on their interaction with educational activities in MOODLE and MOOCs;this was accomplished via the use of student log files and some extra data about the specific student. In order to discover the best approach for the student's prediction, the performance prediction was explored using Decision Tree (C4.5 algorithm), Artificial Neural Network, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithm techniques. Furthermore, log file analysis shows that the rate of interaction with the e-learning context has a substantial influence on their performance, with students with the highest interaction on the MOODLE performing better than someone with low interactivity rates. According to the data, students are more interested on e-learning MOODLE than MOOCs, and as a result, they are missing out on the benefits of the available resources on MOOCs, such as viewing lecture videos and participating in quizzes, which may help them with their studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Effectiveness of Applying Coughing Exercises during Prostration in Improvement of Pulmonary Parameters among COVID-19 Patients Using a Face Mask for Oxygen

Background: Applying coughing exercises and patient's position alteration are considered non-pharmacological nursing managements which are helping to enhancement exchange of gases also respiratory status, this is of benefit to some pulmonary indicators for the COVID-19 patients. Method: a clinical trials study done from October, 11th, 2021, to May, 17th, 2022. To determine the Effectiveness of applying Coughing Exercises during Prostration in improvement of pulmonary Parameters on the sixty patients who were randomly selected. Results: more than 60% of the patient was within the age of more than 60 years, 61% of study group was males and 46.7% for control group as well as, more than 36 % of the patients did not suffering from any chronic diseases, but 30% of them had asthma. 40% of patients had effect in bilateral peripheral part and central area. Additionally, the mean of the pulmonary parameters in the study group reported a statistically significant difference, whereas the control groups did not. Conclusion: The COVID-19 patients' respiratory parameters were improved by the coughing exercises during prostration, and the beneficial effects persisted to more than 4 hours © 2023, Journal for ReAttach Therapy and Developmental Diversities.All Rights Reserved.

sIL-2R and sIL-2R/lymphocyte ratio as indicators of severity in COVID-19 pediatric patients

Objectives: To determine the role of sIL-2R and sIL-2R/lymphocyte ratio as indicators of COVID-19 severity and predictors of clinical progression among children and adolescents. Patients and Methods: This observational cross-sectional study enrolled 76 pediatric patients [40 (52.6%) males and 36 (47.4%) females] with confirmed COVID-19. Patients were classified into two groups;mild to moderate and severe to critical according to WHO classification of severity and were assessed using COVID-19 severity assessment score and COVID-19 severity index. Soluble IL-2R (sIL-2R) concentrations were measured using a commercial enzyme-linked immunosorbent assay and sIL-2R/lymphocyte ratio was calculated for each patient. Results: Receiver-operating characteristic (ROC) curve analysis showed that sIL-2R has a significantly higher discriminative power between patients in both groups (AUC=0.955) as compared to sIL-2R/lymphocyte ratio (AUC=0.711) (p value<0.0001). At an associated criterion of >140 ng/l, the sensitivity and specificity of sIL-2R were 81.4.% and 100%, respectively. Soluble IL-2R also showed better performance in predicting the need for supplemental oxygen [threshold>140 ng/l, AUC=0.904 (0.814 to 0.960)], ICU admission [threshold>140 ng/l, AUC=0. 935 (0.854 to 0.979)], and mechanical ventilation [threshold>180 ng/l, AUC=0. 892 (0.799 to 0.951)]. Conclusion: Soluble IL-2R can play a potential role as a feasible indicator of COVID-19 severity in children and adolescents, thus informing healthcare providers to direct care to patients who may require intensive or critical care. © 2020 The author (s).