Factors influencing the epidemiological characteristics of pandemic COVID 19: A TISM approach

A stochastic scale conjugate neural network procedure for the SIRC epidemic delay differential system

In this study, a stochastic computing structure is provided for the numerical solutions of the SIRC epidemic delay differential model, i.e. SIRC-EDDM using the dynamics of the COVID-19. The design of the scale conjugate gradient (CG) neural networks (SCGNNs) is presented for the numerical treatment of SIRC-EDDM. The mathematical model is divided into susceptible S ( ρ ) , recovered R ( ρ ) , infected I ( ρ ) , and cross-immune C ( ρ ) , while the numerical performances have been provided into three different cases. The exactitude of the SCGNNs is perceived through the comparison of the accomplished and reference outcomes (Runge-Kutta scheme) and the negligible absolute error (AE) that are performed around 10-06 to 10-08 for each case of the SIRC-EDDM. The obtained results have been presented to reduce the mean square error (MSE) using the performances of train, validation, and test data. The neuron analysis is also performed that shows the AE by taking 14 neurons provide more accurateness as compared to 4 numbers of neurons. To check the proficiency of SCGNNs, the comprehensive studies are accessible using the error histograms (EHs) investigations, state transitions (STs) values, MSE performances, regression measures, and correlation.

Factors influencing the organisational homeostasis in healthcare organisations

PURPOSE

The purpose of this study is to identify organisational homeostasis factors in the context of healthcare organisations and to develop a conceptual model for green transformation.

DESIGN/METHODOLOGY/APPROACH

The organisational homeostasis factors were determined by review of literature study and the opinions of healthcare experts. Scheduled interviews and closed-ended questionnaires are employed to collect data for this research. This study employed "TISM methodology" and "MICMAC analysis" to better comprehend how the components interact with one another and prioritise them based on their driving and dependence power.

FINDINGS

This study identified 10 factors of organisational homeostasis in healthcare organisation. Recognition of interdependence, hormesis, strategic coalignment, consciousness on dependence of healthcare resources and cybernetic principle of regulations are the driving or key factors of this study.

RESEARCH LIMITATIONS/IMPLICATIONS

The study's primary focus was on the organisational homeostasis factors in healthcare organisations. The methodological approach and structural model are used in a healthcare organisation; in the future, these approaches can be applied to other industries as well.

PRACTICAL IMPLICATIONS

The key drivers of organisational homeostasis and the identified factors will be better comprehended and understood by academic and important stakeholders in healthcare organisations. Prioritizing the factors helps the policymakers to comprehend the organisational homeostasis for green transformation in healthcare.

ORIGINALITY/VALUE

In this study, the TISM and MICMAC analysis for healthcare is proposed as an innovative approach to address the organisational homeostasis concept in the context of green transformation in healthcare organisations.

Jingfang granules ameliorate inflammation and immune disorders in mice exposed to low temperature and high humidity by restoring the dysregulation of gut microbiota and fecal metabolites

The dramatic changes in global climate on human health have been extremely severe. The immune disorder caused by low temperature and high humidity (LTHH) have become a severe public health issue. Clinically, Jingfang granule (JF) has the effect of dispelling cold and eliminating dampness, and is widely used in the treatment of cold caused by wind and cold, autoimmune diseases, and COVID-19 with cold-dampness stagnating in the lung pattern. Our study aims to elucidate the effect of JF on LTHH-induced immune disorders in mice as well as the underlying mechanisms. In this study, JF increased the spleen index, improved fecal character, repaired the intestinal barrier and alleviated intestinal inflammatory responses. Most importantly, JF ameliorated immune disorder in LTHH mice, which was manifested primarily by the significant increase in gdT, CD8+ Tcm, and CD8+ Tem cells, as well as the decrease in TH1, TH17, CD4+ Tem1, CD4+ Tem2, immature NK, mature NK cells, and M1-like macrophages. Interestingly, the JF treatment not only regulated the gut microbiota by decreasing the abundance of harmful bacteria, as well as up-regulating the abundance of beneficial bacteria, but also ameliorated the metabolic disorders by reversing the levels of fecal metabolites to normality. The results of the correlation analysis demonstrated a significant association among gut microbiota, fecal metabolites and immune cells. In addition, JF inhibited the TLR4/NF-κB/NLRP3 pathway in LTHH mice. In conclusion, our results suggested that JF alleviated inflammation and immune disorders in LTHH mice by restoring gut microbiota and fecal metabolism.

Agility adaptability and alignment in start-ups

Purpose

When coping with uncertainties, three characteristics distinguish firms: agility, adaptability and alignment (triple-A). Based on significant field research, the triple-A highlights the significance of coordinating agility, adaptability and alignment. Start-ups are facing a lot of challenges in this turbulent environment. However, this sector is undergoing a major transformation. Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. This paper aims to identify, analyze and categorize the enablers for agility, adaptability and alignment in start-ups using the total interpretive structural modeling (TISM) approach.

Design/methodology/approach

In addition to the scheduled interview, a closed-ended questionnaire was used to collect data. To identify how the factors interact, the TISM technique is used, and the Matriced’Impacts Croises-Multipication Applique’ and Classment method is used to rank and categorize the agility, adaptability and alignment enablers.

Findings

This study identified ten agility, adaptability and alignment factors for start-ups. It has been found that the key importance should be given to management involvement, conflict management, collaboration and information integration.

Research limitations/implications

This study primarily focused on the agility, adaptability and alignment factors in start-ups.

Practical implications

This study will help academics and key stakeholders understand the aspects that lead to agility, adaptability and alignment in start-ups.

Originality/value

Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. Therefore, this is a novel attempt in this industry’s agility, adaptability and alignment.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents

Introduction

This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment.

Methods

Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities.

Results

Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city.

Discussion

Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Analysis of Country-Level Risk Factors of COVID-19 Mortality Across Countries of Asia: A Generalised Estimating Equation Approach

This research aimed to investigate the death counts from coronavirus disease (COVID-19) across Asian countries by selecting 42 countries with a nonzero death count. Several studies have assessed personal-level factors that affect mortality rates in patients with COVID-19. However, the influence of country-level factors is still debatable. The results of a generalised estimating equation confirmed that the expected death counts across the countries in the middle quartile group of gross domestic product, upper quartile group of population density and lower quartile group of hospital bed count were higher than those in the countries in other quartile groups. The results further confirmed the positive association of the percentage of the population aged ≥65 years and time (day) with the death count. Visualisations and descriptive statistics showed that the death count increased over time across the Asian countries, with maximum death count occurring in the third quarter of the year 2020. These findings support the recommendation that countries with lower number of hospital beds per 1,000 persons, higher percentage of the population aged 65 years and older and middle and lower economies should take more precautions to reduce the death count from COVID-19.

Research on the cross-contamination in the confined conference room with the radiant floor heating system integrated with the down-supply ventilation

The performance of a confined conference room under radiant heat floor combined with down-supply ventilation (RFDS) is investigated. The purpose of the study is to reveal the mechanism of indoor contaminants transmission from infected person and to protect the occupants. The exhaled contaminant dispersion was simulated using a CFD model and experimentally validated in an office with the down-supply ventilation (DS). The effects of radiant floor (RF) combined with down-supply ventilation on airflow and contaminant distribution in the room was evaluated with regard to different RF temperatures and inlet velocities. The influence of downdraft from the envelope on the airflow pattern of the room is also discussed. It is proved that the exposure risk of pollutants can be reduced by strengthening the insulation of the envelope. The simulation results showed that the proposed RFDS system could significantly reduce the level of exposure to contaminants in the breathing zone (BZ) of occupants. In this research case, the RFDS reduced the average exposure rates by more than 50% relative to the case with the only down-supply warm air heating. Furthermore, With improved envelope insulation, the probability of infection can be reduced by more than 80% only by avoiding a simultaneous inlet velocity of 0.3 m/s at RF temperatures no less than 27 °C. The results suggested an improved fresh air heating mode for passive ultra-low energy consumption buildings with good thermal insulation and air tightness.

COVID-19 medical waste transportation risk evaluation integrating type-2 fuzzy total interpretive structural modeling and Bayesian network

With the amount of medical waste rapidly increasing since the corona virus disease 2019 (COVID-19) pandemic, medical waste treatment risk evaluation has become an important task. The transportation of medical waste is an essential process of medical waste treatment. This paper aims to develop an integrated model to evaluate COVID-19 medical waste transportation risk by integrating an extended type-2 fuzzy total interpretive structural model (TISM) with a Bayesian network (BN). First, an interval type-2 fuzzy based transportation risk rating scale is introduced to help experts express uncertain evaluation information, in which a new double alpha-cut method is developed for the defuzzification of the interval type-2 fuzzy numbers (IT2FNs). Second, TISM is combined with IT2FNs to construct a hierarchical structural model of COVID-19 medical waste transportation risk factors under a high uncertain environment; a new bidirectional extraction method is proposed to describe the hierarchy of risk factors more reasonably and accurately. Third, the BN is integrated with IT2FNs to make a comprehensive medical waste transportation risk evaluation, including identifying the sensitive factors and diagnosing the event's causation. Then, a case study of COVID-19 medical waste transportation is displayed to demonstrate the effectiveness of the proposed model. Further, a comparison of the proposed model with the traditional TISM and BN model is conducted to stress the advantages of the proposed model.

Preventive Behaviors and Influencing Factors among Thai Residents in Endemic Areas during the Highest Epidemic Peak of the COVID-19 Outbreak

This research aims to investigate COVID-19 preventive behavior and influencing factors among Thai residents during the highest epidemic peak of COVID-19. Nine hundred and forty-six residents in five districts with high COVID-19 infection cases in Thailand were systematically included in this cross-sectional survey. The results showed that 87.2% and 65.2% of the residents had a high level of general knowledge and preventive measures, respectively. As to COVID-19 attitudes, poor levels of attitude among Thai residents were found in risk perception (53.6%) and mistrust issues (70.4%). Moreover, this study presents good preventive behavior (77.0%) among Thai residents. Multiple logistic regression showed that the influence factors of COVID-19 preventive behavior were the young age group (AOR 2.97, 95% CI 1.68-5.25), high income (AOR 1.38, 95% CI 1.03-1.86), and high level of general COVID-19 knowledge (AOR 2.21, 95% CI 1.64-2.96). The conclusion was that providing information on COVID-19 via social media was the key mechanism of policy action for increasing the level of COVID-19 preventive behavior during the highest epidemic peak in Thailand. In addition, the pandemic preparedness and response policy, with resident participation and involvement, could be recommended for the resilience of pandemic preparedness.

SARS-CoV-2 Infection in Captive Hippos (Hippopotamus amphibius), Belgium

Two adult female hippos in Zoo Antwerp who were naturally infected with SARS-CoV-2 showed nasal discharge for a few days. Virus was detected by immunocytochemistry and PCR in nasal swab samples and by PCR in faeces and pool water. Serology was also positive. No treatment was necessary.

Mutations in SARS-CoV-2: Insights on structure, variants, vaccines, and biomedical interventions

COVID-19 is a worldwide pandemic caused by SARS-coronavirus-2 (SARS-CoV-2). Less than a year after the emergence of the Covid-19 pandemic, many vaccines have arrived on the market with innovative technologies in the field of vaccinology. Based on the use of messenger RNA (mRNA) encoding the Spike SARS-Cov-2 protein or on the use of recombinant adenovirus vectors enabling the gene encoding the Spike protein to be introduced into our cells, these strategies make it possible to envisage the vaccination in a new light with tools that are more scalable than the vaccine strategies used so far. Faced with the appearance of new variants, which will gradually take precedence over the strain at the origin of the pandemic, these new strategies will allow a much faster update of vaccines to fight against these new variants, some of which may escape neutralization by vaccine antibodies. However, only a vaccination policy based on rapid and massive vaccination of the population but requiring a supply of sufficient doses could make it possible to combat the emergence of these variants. Indeed, the greater the number of infected individuals, the faster the virus multiplies, with an increased risk of the emergence of variants in these RNA viruses. This review will discuss SARS-CoV-2 pathophysiology and evolution approaches in altered transmission platforms and emphasize the different mutations and how they influence the virus characteristics. Also, this article summarizes the common vaccines and the implication of the mutations and genetic variety of SARS-CoV-2 on the COVID-19 biomedical arbitrations.

Green Operation Strategies in Healthcare for Enhanced Quality of Life

Healthcare services have now become a fundamental requirement for all individuals owing to rising pollution levels and shifting lifestyles brought on by fast modernization. The hospital is a specialized healthcare facility where doctors, nurses, and other medical professionals offer their services. Academics and professionals have emphasized green operation initiatives such as green design, green purchasing, green supply chain, and green manufacturing to increase public awareness of environmental problems affecting company operations associated with healthcare for the quality of life. The purpose of this research is to use total interpretive structural modeling and MICMAC (matrix cross multiplication applied to a classification) analysis to investigate and analyze the elements impacting green operations strategies in healthcare. The data are gathered using a closed-ended questionnaire together with a scheduled interview. The components' interactions are explored using the total interpretive structural modeling technique, and the MICMAC analysis is used to rank and categorize the green operation strategy variables. The study is a novel effort to address and focus on stakeholders, vision and structure, resources, and capabilities. Green operations strategies have only been the subject of a small number of studies in the past, and those studies were mostly addressed at manufacturing-specific green strategies. Thus, by promoting energy efficiency programs, green building design, alternative sources of energy, low-carbon transportation, local food, waste reduction, and water conservation, the health sector can develop multiple key strategies to become more climate-friendly with significant health, environmental, and social co-benefits for quality of life.

Identification of Papain-Like Protease inhibitors of SARS CoV-2 through HTVS, Molecular docking, MMGBSA and Molecular dynamics approach

Coronaviruses (CoVs) are a large group of enveloped positive sense single-stranded RNA viruses that can cause disease to humans. These are zoonotic having potential to cause large-scale outbreaks of infections widely causing morbidity and mortality. Papain-Like Protease (PLpro) is a cysteine protease, essential for viral replication and proliferation, as a highly conserved enzyme it cleaves peptide linkage between Nsp1, Nsp2, Nsp3, and Nsp4. As a valid therapeutic target, it stops viral reproduction and boosts host immune response thereby halting further spread of infection. In the purpose of identifying inhibitors targeting Papain-Like Proteases (PLpro) we initiated a high throughput virtual screening (HTVS) protocol using a SuperNatural Database. The XP docking results revealed that two compounds SN00334175 and SN00162745 exhibited docking scores of -10.58 kcal/mol and -9.93 kcal/mol respectively. The Further PRIME MMGB-SA studies revealed Van der Waal energy and hydrophobic energy terms as major contributors for total binding free energy. The 100 ns molecular dynamics simulation of SN00334175/7JN2 and SN00162745/7JN2 revealed that these complexes were stabilized with ligand binding forming interactions with Gly266, Asn267, Tyr268, Tyr273, Thr301 and Asp302, Lys157, Leu162, Asp164, Arg166, Glu167, Pro248 and Tyr264.

Modeling Key Strategies for Reducing Socio-Economic and Health Crisis: Perspective from COVID-19 Pandemic

The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that "strong leadership and control" and "awareness on social media" play significant roles in reducing pandemic challenges, while "promoting online purchase behavior" and "online education" are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.

Imperfect or perfect attempts at closing COVID -19 policy and planning emergent gaps

The COVID-19 pandemic has presented a new wave of health, infrastructure and built environment challenges and opportunities. The COVID-19 pandemic induced environment presents a divide between the "new and old normal" with policy and planning implications for health, transport and general socio-economic growth and development. Multiple and complex nuanced transport matters cascade all geographic scales and pervade all sectors of the economy. The extent to which existing transport systems capacities are resilient, adaptive, and optimized for complete disaster planning, management and sustainability is questioned. This paper critically reviews how the COVID-19 pandemic has stretched the resilience and adaptive transport systems capacities in South Africa. A critical question interrogated is whether on-going policy and planning interventions constitute imperfect or perfect attempts at closing COVID -19 policy and planning emergent gaps. The paper makes use of South Africa as a case study, referencing the Disaster Management Act (No. 57 of 2002) and logical Disaster Management Act: Regulations relating to COVID-19 (Government Notice 318 of 2020), with specific reference to the transport sector lockdown regulations in unravelling policy and planning implications. Drawing from the complex systems adaptive theory (CSAT), sustainability theory (ST), innovation theory (IT), transitions theory (TT), thematic COVID -19 transport planning and policy adaptation, mitigation measures in the South African transportation sector are discussed. Emergent lessons with respect to developing and advancing a new generation of resilient, adaptive, and optimized transport proof infrastructure and services including revising transport and related policies that navigates through various waves and cycles of induced pandemic and shocks is suggested.

Does the internet help governments contain the COVID-19 pandemic? Multi-country evidence from online human behaviour

The effectiveness of social distancing and other public health interventions for containing the COVID-19 pandemic has been demonstrated. However, whether and how Internet use behaviours can lead to enhanced self-protection and reduced transmission when considered in conjunction with behavioural interventions remains unclear. This study investigated the strength of effective Internet behaviours and its interaction with global public health interventions for controlling the COVID-19 pandemic. We conducted an econometric analysis of multisource infection and policy information, Internet behaviour, and meteorological information from worldwide in a 3-month period. People's Internet behaviours may contribute crucially to pandemic containment. Furthermore, they may help enhance the effects of public health interventions, particularly behavioural interventions. We discussed plausible mechanisms through which Internet behaviours reduce epidemic spread independently or in tandem with behavioural interventions. Further investigation into the heterogeneity of the interventions demonstrates Internet behaviour's significance in heightening the effects of difficult-to-implement, primitive crisis orientation, and specific objectives of interventions. Governments should recognise the importance of the Internet and leverage it in managing social crises. Our findings serve as a reference for the formulation of global public health policy. Specifically, the insights provided herein can facilitate the implementation of strategies for containing ongoing secondary outbreaks of COVID-19 or outbreaks of other emergent infectious diseases.

Modelling of factors influencing organizational readiness for change in start-ups during the COVID-19 emergency

Purpose

The purpose of this paper is to “identify”, “analyze” and “construct” a framework to quantify the relationships between several determinants of organizational preparedness for change in the start-ups during the COVID-19 emergencies.

Design/methodology/approach

Total interpretive structural modelling (TISM) is used to find characteristics that assist in analyzing the readiness or preparedness level before initiating a change deployment process in start-ups. A cross-impact matrix multiplication applied to classification (MICMAC) analysis is performed to determine the driving and dependent elements of change in start-ups.

Findings

From literature research and an expert interview, this study selected ten variables of change preparedness to explore inner interconnections and comprehend the inner connections factors. The findings depict that clarity of mission and goals, reward system, technological advancement and motivational readiness have been considered the most important readiness factor for deploying organizational change in start-ups during the COVID-19 emergencies.

Practical implications

This research will aid the management and researchers gain a better understanding of the factors that influence change preparedness. Constant observation of current changes in the start-ups and the external environment will aid in improving the quality of products or services provided by the start-ups during the COVID-19. The start-ups can use these criteria linked to change readiness. The priority of each element is determined using MICMAC analysis and ranking using the TISM technique, which assists start-ups in ordering the enablers from highest to lowest priority.

Originality/value

There is no research regarding factors influencing organizational readiness for change in start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to organizational readiness for change in start-ups. This gap inspired the present study, which uses the “Total Interpretive Structural Modelling (TISM)” technique to uncover change determinants and investigate hierarchical interconnections among factors influencing organizational readiness to change in start-ups during the COVID-19 emergencies.

The Importance of Behavioral and Native Factors on COVID-19 Infection and Severity: Insights from a Preliminary Cross-Sectional Study

The COVID-19 pandemic has had a major impact on a global scale. Understanding the innate and lifestyle-related factors influencing the rate and severity of COVID-19 is important for making evidence-based recommendations. This cross-sectional study aims at establishing a potential relationship between human characteristics and vulnerability/resistance to SARS-CoV-2. We hypothesize that the impact of the virus is not the same due to cultural and ethnic differences. A cross-sectional study was performed using an online questionnaire. The methodology included the development of a multi-language survey, expert evaluation, and data analysis. Data were collected using a 13-item pre-tested questionnaire based on a literature review between 9 December 2020 and 21 July 2021. Data were statistically analyzed using logistic regression. For a total of 1125 respondents, 332 (29.5%) were COVID-19 positive; among them, 130 (11.5%) required home-based treatment, and 14 (1.2%) intensive care. The significant and most influential factors on infection included age, physical activity, and health status (p < 0.05), i.e., better physical activity and better health status significantly reduced the possibility of infection, while older age significantly increased it. The severity of infection was negatively associated with the acceptance (adherence and respect) of preventive measures and positively associated with tobacco (p < 0.05), i.e., smoking regularly significantly increases the severity of COVID-19 infection. This suggests the importance of behavioral factors compared to innate ones. Apparently, individual behavior is mainly responsible for the spread of the virus. Therefore, adopting a healthy lifestyle and scrupulously observing preventive measures, including vaccination, would greatly limit the probability of infection and prevent the development of severe COVID-19.

Modeling of factors affecting supplier selection on start-ups during frequent pandemic episodes like COVID-19

Purpose

In developing countries such as India, start-ups play an essential role in “industrial output,” “Gross Domestic Product ” and “employment creation.” Evidence suggests that pandemics have risen over the last century due to rising global travel and assimilation, urbanization, alterations in land use, and significantly larger exploitation of the natural environment. These trends are likely to continue and intensify. These pandemic episodes affect businesses, especially start-ups. Supplier selection is among the vital critical elements that start-ups must include in start-ups' strategy procedures during the pandemic episodes. This study's focus is to “identify,” “analyze,” and “categorize” the factors affecting supplier selection in start-ups during frequent pandemic episodes like coronavirus disease 2019 (COVID-19).

Design/methodology/approach

Through “literature review” and “experts' opinion” from various start-ups in India, ten affecting factors were identified. Total Interpretative Structural Modeling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) were employed to analyze the interrelationship among the factors affecting the supplier selection on start-ups during frequent pandemic episodes, and these factors were ranked as “autonomous,” “independent,” “linkage,” and “dependent” factors.

Findings

The findings show that “performance history,” “service levels,” “technical capability,” and “financial stability” are the most critical factors affecting the supplier selection on start-ups during frequent pandemic episodes. The next importance should be safety and environmental concern” and “quality.”

Research limitations/implications

The factors affecting supplier selection on start-ups during frequent pandemic episodes are the current focus of this study. This study is mainly performed on Indian start-ups and can be extended to other countries.

Practical implications

The start-ups can rely on this study to clearly understand the factors affecting the supplier selection on start-ups during frequent pandemic episodes.

Originality/value

There is no research regarding factors affecting supplier selection on start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to supplier selection in start-ups. This gap inspired the present study, which employs the “Total Interpretive Structural Modeling (TISM)” technique to uncover supplier selection determinants and investigate hierarchical interconnections among factors influencing/affecting supplier selection in start-ups during frequent pandemic episodes.

Geographically weighted regression model for physical, social, and economic factors affecting the COVID-19 pandemic spreading

This study aims to analyze the spatial distribution of the epidemic spread and the role of the physical, social, and economic characteristics in this spreading. A geographically weighted regression (GWR) model was built within a GIS environment using infection data monitored by the Iraqi Ministry of Health records for 10 months from March to December 2020. The factors adopted in this model are the size of urban interaction areas and human gatherings, movement level and accessibility, and the volume of public services and facilities that attract people. The results show that it would be possible to deal with each administrative unit in proportion to its circumstances in light of the factors that appear in it. So, there will not be a single treatment for all areas with different urban characteristics, which sometimes helps not to stop social and economic life due to the imposition of a comprehensive ban on movement and activities. Therefore, there will be other supportive policies other than the ban, depending on the urban indicators for each region, such as reducing external movement from it or relying on preventing public activities only.

Communicating COVID-19: Analyzing Higher Education Institutional Responses in Canada, China, and the USA

The study aims to examine how higher education institutions (HEIs) in three countries responded to the challenges of COVID-19 over a six-month period at the outbreak of the global pandemic. Employing document analysis, we examined 732 publicly available communications from 27 HEIs in Canada, China, and the USA. Through theoretical frameworks of crisis management and Situational Crisis Communication Theory (SCCT), we explore how HEIs respond to the pandemic and protect campus stakeholders. The study revealed common patterns in communication strategies during different stages of the pandemic that include accepting responsibility, emotional reassurance, and compensating victims. It also revealed key differences across social contexts and environments and distinct leadership styles. Findings offer insight into how HEIs communicated at the outset of the COVID-19 pandemic and inform the application of SCCT and crisis management theory to institutional behavior in the context of prolonged and intersecting disasters.

Readiness for lean-sustainability in start-ups during the COVID-19 era

Purpose

This study aims to “identify”, “analyse” and “categorise” the lean-sustainability enablers for start-ups during the COVID-19 epidemic using total interpretive structural modelling (TISM).

Design/methodology/approach

A closed-ended questionnaire was used to collect data in addition to the scheduled interview. The TISM methodology is used to determine how the variables interact, and the matrice d’Impacts croises-multiplication applique´ a classement (MICMAC) method is used to rank and categorise the lean-sustainability enablers.

Findings

This study identified ten lean-sustainability enablers for start-ups during the COVID-19 pandemic. The study says that the key factors are leadership and managerial commitment, implementation of employee skills and abilities, strategic need, personnel engagement and financial ability.

Research limitations/implications

The study focused primarily on lean-sustainability characteristics for start-ups.

Practical implications

This research will aid key stakeholders and academics in better understanding the factors that contribute to lean-sustainability in start-ups.

Originality/value

This study proposes the TISM technique for start-ups, which is a novel attempt in the subject of lean-sustainability in this industry.

Readiness for sustainable-resilience in healthcare organisations during Covid-19 era

Purpose

Using total interpretive structural modelling (TISM), this paper aims to “identify”, “analyse” and “categorise” the sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic.

Design/methodology/approach

To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview with each respondent. To identify how the factors interact, the TISM approach was employed and the cross-impact matrix multiplication applied to a classification method was used to rank and categorise the sustainable-resilience readiness factors.

Findings

This study identified ten sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. The study states that the major factors are environmental scanning, awareness and preparedness, team empowerment and working, transparent communication system, learning culture, ability to respond and monitor, organisational culture, resilience engineering, personal and professional resources and technology capability.

Research limitations/implications

The study focused primarily on sustainable-resilience readiness characteristics for the healthcare sector.

Practical implications

This research will aid key stakeholders and academics in better understanding the factors that contribute to sustainable-resilience in healthcare.

Originality/value

This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of readiness for sustainable-resilience in this sector. The paper proposes a framework including a mixture of factors for sustainability and resilience in the healthcare sector for operations.

Impact of air temperature and containment measures on mitigating the intrahousehold transmission of SARS-CoV-2: a data-based modelling analysis

OBJECTIVES

Air temperature has been considered a modifiable and contributable variable in COVID-19 transmission. Implementation of non-pharmaceutical interventions (NPIs) has also made an impact on COVID-19 transmission, changing the transmission pattern to intrahousehold transmission under stringent containment measures. Therefore, it is necessary to re-estimate the influence of air temperature on COVID-19 transmission while excluding the influence of NPIs.

DESIGN, SETTING AND PARTICIPANTS

This study is a data-based comprehensive modelling analysis. A stochastic epidemiological model, the ScEIQR model (contactable susceptible-exposed-infected-quarantined-removed), was established to evaluate the influence of air temperature and containment measures on the intrahousehold spread of COVID-19. Epidemic data on COVID-19, including daily confirmed cases, number of close contacts, etc, were collected from the National Health Commission of China.

OUTCOME MEASURES

The model was fitted using the Metropolis-Hastings algorithm with a cost function based on the least squares method. The LOESS (locally weighted scatterplot smoothing) regression function was used to assess the relationship between air temperature and rate of COVID-19 transmission within the ScEIQR model.

RESULTS

The ScEIQR model indicated that the optimal temperature for spread of COVID-19 peaked at 10℃ (50℉), ranging from 5℃ to 14℃ (41℉-57.2℉). In the fitted model, the fitted intrahousehold transmission rate (β') of COVID-19 was 10.22 (IQR 8.47-12.35) across mainland China. The association between air temperature and β' of COVID-19 suggests that COVID-19 might be seasonal. Our model also validated the effectiveness of NPIs, demonstrating that diminishing contactable susceptibility (Sc) and avoiding delay in diagnosis and hospitalisation (η) were more effective than contact tracing (κ and ρ).

CONCLUSIONS

We constructed a novel epidemic model to estimate the effect of air temperature on COVID-19 transmission beyond implementation of NPIs, which can inform public health strategy and predict the transmission of COVID-19.

Assessment of COVID-19 prevention and protection measures in hospitals

This paper aims to develop an assessment framework for the Covid-19 prevention and protection measures in hospitals. The conceptual model is developed by using fifty-four attributes, fifteen criteria, and three enablers. The multi-grade fuzzy approach is used to develop the assessment framework, and Importance Performance Analysis (IPA) identifies the weaker attributes in the case organization. The case hospital's preventive and safety measures assessment level is 8.05, which is 'very highly focused on protection measures,' and fourteen weaker attributes were identified. The case hospital management should focus on the guidelines of Covid-19 preventive and protection measures, strict protocols, regular audits, education and training of the staff, and active surveillance. Case hospital managers should also focus on staffing and timings, the formulation of policies, and abiding by those policies without any fail. This proposed assessment model is a new initiative in-hospital assessment in preventive and safety measures in the healthcare sector during the Covid-19 era. This framework will enable hospital managers as a continuous assessment tool to improve their Covid-19 prevention operations.

The fine-scale associations between socioeconomic status, density, functionality, and spread of COVID-19 within a high-density city

BACKGROUND

Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches.

METHODS

Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul-Oct 2020 (wave 3), and Nov 2020-Feb 2021 (wave 4), and for local and imported confirmed cases.

RESULTS

The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission.

CONCLUSIONS

The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.

SARS-CoV-2 Spike Glycoprotein and ACE2 Interaction Reveals Modulation of Viral Entry in Wild and Domestic Animals

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen causing life-threatening diseases in humans. Interaction between the spike protein of SARS-CoV-2 and angiotensin-converting enzyme 2 (ACE2) is a potential factor in the infectivity of a host. In this study, the interaction of SARS-CoV-2 spike protein with its receptor, ACE2, in different hosts was evaluated to predict the probability of viral entry. Phylogeny and alignment comparison of the ACE2 sequences did not lead to any meaningful conclusion on viral entry in different hosts. The binding ability between ACE2 and the spike protein was assessed to delineate several spike binding parameters of ACE2. A significant difference between the known infected and uninfected species was observed for six parameters. However, these parameters did not specifically categorize the Orders into infected or uninfected. Finally, a logistic regression model constructed using spike binding parameters of ACE2, revealed that in the mammalian class, most of the species of Carnivores, Artiodactyls, Perissodactyls, Pholidota, and Primates had a high probability of viral entry. However, among the Proboscidea, African elephants had a low probability of viral entry. Among rodents, hamsters were highly probable for viral entry with rats and mice having a medium to low probability. Rabbits have a high probability of viral entry. In Birds, ducks have a very low probability, while chickens seemed to have medium probability and turkey showed the highest probability of viral entry. The findings prompt us to closely follow certain species of animals for determining pathogenic insult by SARS-CoV-2 and for determining their ability to act as a carrier and/or disseminator.

Adherence to COVID-19 preventive measures among male medical students, Egypt

BACKGROUND

Adherence to COVID-19 preventive measures is essential for disease control especially given the lack of effective treatment at the time of data collection. Medical students' adherence to COVID-19 preventive measures is highly important because they are at a higher risk of infection as they spend considerable time at hospitals for training. In addition, they will be responsible for disease fighting in the future. This study aimed to identify the adherence to COVID-19 preventive measures among the studied male students of Al-Azhar Faculty of Medicine, Egypt.

METHODS

An online survey was conducted on a convenience sample of 537 male medical students of Al-Azhar University at the Cairo branch between March 2 and April 15, 2021, using an Arabic questionnaire constructed by Google form with link sharing to students' groups on WhatsApp and face book applications.

RESULTS

A total of 537 students voluntarily submitted their responses. Most of these students were aged between 18 and 20 years (62.4%), rural residents (62.9%), having family income of 5000 LE or less/month (64.2%), studying at the first, second, and sixth years (79.2%). The prevalence of adherence to COVID-19 preventive measures among studied students was 28.1% without significant difference in the students' adherence status regarding age, residence, family income, studying year, or academic score. Wearing a face mask outside the house was practiced by 58% of students, avoiding hugging or kissing others (41.3%), and keeping a distance from others (20.7%).

CONCLUSION

Students showed a low level of adherence to COVID-19 preventive measures among students. Also, there is no significant difference between students' adherence status in relation to their socio-economic or academic variables. This unhealthy behavior increases the risk of infection among students. Also, students become a source of infection for their vulnerable contacts. More studies are needed to identify the real cause of this low level of adherence.

A Glance at the Barriers and Enablers of the Nursing Role in Pandemics: An Integrative Review

Objective: to analyze the enablers and barriers to nurses’ professional role fulfillment during pandemics and disasters. Materials and methods: this was an integrative review based on the SALSA analytical method, carried out in eight databases, namely: Medline, Proquest, CINAHL, Web of Science, BVS, SpringerLink, Scopus, and Pubmed. The descriptors used were the following: ‘nurse’s role’ AND ‘pandemic’ AND ‘patient’ AND ‘coronavirus infection’, in English, Spanish and Portuguese. Filters along with inclusion and exclusion criteria were applied to the 11,031 articles found, after which the final sample consisted of a total of 32 articles. Results: considering the objectives set and the results obtained, it is noted that all actions focused on three main instances, namely: a) training and updating for role performance; b) predictors, enablers, and barriers for exercising the role in the face of a pandemic; and c) evidence of the role in the face of pandemics. Conclusion: this study allowed understanding both the barriers and the enablers that allow the fulfillment of nurses’ professional role in the face of pandemics or catastrophes. It also clarified that emerging infectious diseases must be addressed in an immediate, collaborative, and multidisciplinary way. Nurses’ experience, leadership, and professional training are key attributes that these professionals possess to enable their role to be more proactive during an emergency situation.

Modelling the resilience of start-ups during COVID-19 pandemic

Purpose

Resilience, the ability of start-ups to deal with anticipated instabilities and probable disruptions, is becoming an important success element during coronavirus disease 2019 (Covid-19). To survive in this pandemic situation, resilience is an important concept for start-ups. The present paper aims to “identify”, “analyse” and “categorize” the resilience factors for start-ups during the Covid-19 pandemic using total interpretive structural modelling (TISM).

Design/methodology/approach

The resilience elements of start-ups during Covid-19 were identified and shortlisted during the first phase, which included literature analysis and extensive interaction with experts. TISM was used in the second phase to investigate or to determine how the factors interplayed between the resilience factors of start-ups during Covid-19. The Matrice d'impacts Croises Multiplication Appliquee a un Classment (MICMAC) method is used to rank and categorize the factors. Closed-ended questionnaire with the scheduled interview was conducted to collect the data.

Findings

The first part of the study found ten resilience elements in total. The TISM digraph was constructed in the second step to show why one resilience component led to another. The MICMAC analysis divided these factors into four groups: autonomous, linkage, dependent and independent. These groups represented resilience variables based on their driving and dependent power, which assist executives and managers in proactively addressing them while using the TISM digraph as a guide.

Research limitations/implications

During the Covid-19 epidemic, this study focused primarily on resilience characteristics for Indian start-ups.

Practical implications

This study will help key stakeholders and scholars to better understand the elements that contribute to start-up's resilience.

Originality/value

The TISM method for start-up's resilience is suggested in this paper, which is a novel attempt in the field of resilience in this industry.

Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature

Coronavirus disease 2019 (COVID-19) has become a significant global public health issue resulting from SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). COVID-19 outbreak approaches an unprecedented challenge for human health, the economy, and societies. The transmission of the COVID-19 is influenced by many factors, including climatic, environmental, socioeconomic, and demographic. This study aimed to investigate the influences of climatic and sociodemographic determinants on COVID-19 transmission. The climatic variables considered herein were air temperature, relative humidity, wind speed, air pollution, and cumulative precipitation. Sociodemographic variables included population density, socioeconomic conditions, misinformation, and personal hygiene practices towards the pandemic. Review results indicated that lower temperatures and greater incidence of COVID-19 are reported in a more significant number of studies. Another factor linked to COVID-19 occurrence was the humidity. However, the results were varied; some research reported positive, and others reported negative relationships. In addition, poor air quality, along with strong winds, makes the virus more vulnerable to spreading, leading to a spike in COVID-19 cases. PM2.5, O3, and NO2 also showed a strong correlation with the recent epidemic. The findings on rainfall were inconsistent between studies. Among the non-climatic factors, population density, education, and income were credited as potential determinants for the coronavirus outbreak. Climatic and sociodemographic factors showed a significant correlation on the COVID-19 outbreak. Thus, our review emphasizes the critical importance of considering climatic and non-climatic factors while developing intervention measures. This study's core findings will support the decision-makers in identifying climatic and socioeconomic elements that influence the risks of future pandemics.

Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

How to make intelligent automation projects agile? Identification of success factors and an assessment approach

Purpose

Organizations are fast adopting new technologies such as automation, analytics and artificial intelligence, collectively called intelligent automation, to drive digital transformation. When adopting intelligent automation, there is a need to understand the success factors of these new technologies and adapt agile software development (ASD) practices to meet customer expectations. The purpose of this paper is to explore the success factors of intelligent automation and create a framework for managers and practitioners to meet dynamic business demands. Total interpretive structural modeling (TISM) framework is a suitable approach to integrate quantitative measurement with qualitative semi-structured interviews capturing the context of the individual organization environment.

Design/methodology/approach

This paper identified agility factors and their interrelationships using a TISM framework. TISM results were validated using a one-tailed t-test to confirm the interrelationships between factors. Furthermore, the agility index of a case project organization was assessed using a graph-theoretic approach (GTA) to identify both the triggering factors for agility success and improvement proposals.

Findings

Results showed that leadership vision, organization structure and program methodology were driving factors. The TISM model was validated statistically and the agility index of the intelligent automation case project organization was calculated to be79.5%. Here, a GTA was applied and the triggering factors for improvement of the agility index were identified.

Research limitations/implications

The limitations of the study are described along with the opportunities for future research as the field evolves through the rapid innovation of technology and products.

Practical implications

The increasing role of digital transformation in enterprise strategy and operations requires practitioners to understand how ASD practices must be planned, measured and/or improved over time through the implementation of automation, analytics and artificial intelligence programs. The TISM digraph provides a framework of hierarchical structure to organize the influencing factors, which assists in achieving organizational goals. This study highlights the driving factors which contribute to the success of intelligent automation projects and project organizations.

Originality/value

This is a first attempt to analyze the interrelationships among agility factors in intelligent automation projects (IAP) using TISM and the assessment of the agility index of a case IAP organization using a GTA.

An Overview on the Epidemiology and Immunology of COVID-19

Coronaviruses are a large family of viruses that cause illnesses ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and the 2019 novel coronavirus infection (COVID-19). Currently, there is no analyzed data to examine the outbreak of COVID-19 by continent and no determination of prevalence trends; this article reviews COVID-19 epidemiology and immunology. Original research, reviews, governmental databases, and treatment guidelines are analyzed to present the epidemiology and immunology of COVID-19. Reports from patients who were COVID-19 infected showed typical symptoms of neutrophilia, lymphopenia, and increased systemic inflammatory proteins of IL-6 and C reactive protein (CRP). These observations agree with the results of severe conditions of MERS or lethal cases of SARS, in which there is an increased presence of neutrophils and macrophages in the airways. Additionally, analyzed data showed that Europe (49.37%), the Americas (27.4%), and Eastern Mediterranean (10.07%) had the most cumulative total per 100,000 population confirmed cases, and Africa (6.9%), Western Pacific (3.46%), and South-East Asia (2.72%) had the lowest cumulative total per 100,000 population confirmed cases. In general, the trend lines showed that the number of confirmed cases (cumulative total) and deaths (cumulative total) would decrease eventually.

The aftermath of COVID-19 pandemic period: barriers in implementation of social distancing at workplace

Purpose

The study aims to investigate the barriers in implementing social distancing at the workplace as an aftermath of the COVID-19 pandemic period.

Design/methodology/approach

Study design consists of a review of literature, data collection and analysis. It encompasses identification, verification and analysis of the relationships among the barriers. Data have been collected from a panel of experts on matrix-type questionnaires from workplaces. Interpretive structural modeling (ISM) augmented with “Matrice d' Impacts Croise's Multiplication Appliquée a UN Classement (Cross Impact Matrix Multiplication Applied to Classification)” (MICMAC) for structural analysis.

Findings

The literature has identified twenty major barriers to implementing social distancing at the workplace. The research findings reveal/show that the barrier “matter of disrespect” occupies the bottom level in the ISM model. Therefore, it is the most critical barrier; whereas, employees with disabilities and “herding culture” are crucial as they occupy the next lowest level, therefore, are crucial. Moreover, there are ten barriers positioned in the middle of the model having moderate-severe effects, and seven falls on the top level of the model having relatively less severe effects. Results of MICMAC affirm and avow the results of ISM.

Research limitations/implications

The study will have profound theoretical and practical implications for stakeholders since it provides lot of new useful and valuable information, gives relational insights and determines priorities subject to usual limitations of survey research.

Originality/value

It is an original attempt to make some sense of practicability of social distancing for stakeholders including policymakers, frontline health workers and public at large.

Addressing the Challenges to Sustainable Initiatives in Value Chain Flexibility: Implications for Sustainable Development Goals

The value chain refers to the source of competition to facilitate organizations to maximize and sustain value for their consumers. Value chain flexibility is necessary to build sustainable initiatives in addressing ambiguity. In the literature, there is a lack of framework to highlight the challenges to sustainable initiatives in value chain flexibility. This study fills this research gap by suggesting a framework for challenges to sustainable initiatives in value chain flexibility. In this study, thirteen potential challenges to sustainable initiatives in value chain flexibility are identified and an integrated model is developed. It adopts the modified Total Interpretive Structure Model and the Cross-Impact Matrix Multiplication Applied to Classification methodology. The mixed approach is used as the modified Total Interpretive Structure Model organizes the binary interactions among the challenges, while Cross-Impact Matrix Multiplication Applied to Classification analysis organizes specific precise assessments of the driving power and dependence of the challenges. The results of the study reflect that (i) lack of supplier commitment to sustainable products and (ii) lack of knowledge toward sustainability in value chains are the challenges that achieved the highest driving power. The challenge ‘inadequate communication among the suppliers in the value chain’ is at the highest level in the analysis. The proposed framework could help government and non-government bodies to formulate policies to efficiently address challenges to sustainable initiatives in value chain flexibility.

A study on the influential factors of the last mile delivery projects during Covid-19 era

The Covid-19 has shifted the face of many markets including e-commerce and online business with many bottlenecks to be cleared. The last-mile delivery project has the greatest effect on all types of e-commerce companies because it has many consumer touchpoints as well as the Covid-19 pain points. Due to these interconnected issues, the delivery projects itself requires modern solutions. The purpose of this paper is to identify, analyse and categorize the major factors that affect the last mile delivery projects in e-commerce, food sector, retail sector and so on using total ınterpretive structural modelling approach during the Covid-19. Ten major factors are identified from literature review, and expert opinions are collected from multiple organizations that are involved in the last mile delivery projects. The results indicate that types of goods, achieving routing efficiency and meeting fulfilment timeline are the key factors for last mile delivery projects during the time of Covid-19. This study helps the managers to identify the key factors and to focus on these factors for the successful implementation of last mile delivery project.

New Metrics for Assessing the State Performance in Combating the COVID-19 Pandemic

Previous research has noted that many factors greatly influence the spread of COVID-19. Contrary to explicit factors that are measurable, such as population density, number of medical staff, and the daily test rate, many factors are not directly observable, for instance, culture differences and attitudes toward the disease, which may introduce unobserved heterogeneity. Most contemporary COVID-19 related research has focused on modeling the relationship between explicitly measurable factors and the response variable of interest (such as the infection rate or the death rate). The infection rate is a commonly used metric for evaluating disease progression and a state's mitigation efforts. Because unobservable sources of heterogeneity cannot be measured directly, it is hard to incorporate them into the quantitative assessment and decision-making process. In this study, we propose new metrics to study a state's performance by adjusting the measurable county-level covariates and unobservable state-level heterogeneity through random effects. A hierarchical linear model (HLM) is postulated, and we calculate two model-based metrics-the standardized infection ratio (SDIR) and the adjusted infection rate (AIR). This analysis highlights certain time periods when the infection rate for a state was high while their SDIR was low and vice versa. We show that trends in these metrics can give insight into certain aspects of a state's performance. As each state continues to develop their individualized COVID-19 mitigation strategy and ultimately works to improve their performance, the SDIR and AIR may help supplement the crude infection rate metric to provide a more thorough understanding of a state's performance.

The association between initial COVID-19 spread and meteorological factors in Indonesia

On March 2, 2020, the first Coronavirus Disease (COVID-19) case was reported in Jakarta, Indonesia. One and a half months later (15/05/2020), the cumulative number of infection cases was 16,496, with a total of 1076 mortalities. This study investigates the possible role of weather in the early cases of COVID-19 in six selected cities in Indonesia. Daily temperature and relative humidity data from weather stations nearby in each city were collected from March 3 to April 30, 2020, corresponding with COVID-19 incidence. Correlation tests and regression analysis were performed to examine the association of those two data series. Moreover, we analyzed the distribution of COVID-19 referring the weather data to estimate the effective range of weather data supporting the COVID-19 incidence. Our result reveals that weather data is generally associated with COVID-19 incidence. The daily average temperature (T-ave) and relative humidity (RH) present significant positive and negative correlation with COVID-19 data, respectively. However, the correlation coefficients are weak, with the strongest correlations found at the 5-day lag, i.e., 0.37 (- 0.41) for T-ave (RH). The regression analysis consistently confirmed this relation. The distribution analysis reveals that most COVID-19 cases in Indonesia occurred in the daily temperature range of 25-31 °C and relative humidity of 74-92%. Our findings suggest that COVID-19 incidence in Indonesia has a weak association with weather conditions. Therefore, non-meteorological factors seem to play a more prominent role and should be given greater consideration in preventing the spread of COVID-19.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42398-021-00202-9.

COVID-19 Prevention Practices and Determinant Factors Among Healthcare Professionals Working in Hospitals of South Gondar Zone, Northwestern Ethiopia

BACKGROUND

Coronavirus disease 2019 (COVID-19) has been a global public health crisis since December 2019. Health care professionals (HCP) are highly vulnerable for contracting the disease. Due to the absence of known treatment, prevention is the best option for controlling its transmission.

OBJECTIVE

The study aimed to assess COVID-19 prevention practices and determinant factors among HCP working in hospitals of South Gondar ZoneH, Northwestern Ethiopia.

METHODS

Institution-based cross-sectional study design was used from July 1 to 30, 2020 among 372 HCPs. The participants were selected on random sampling technique basis. Data were entered into Epi data version 3.1 and exported to Statistical Package for Social Science (SPSS V.25) software for data cleaning and analysis. Binary logistic regression analysis was used to assess the association between independent variables and prevention practice of COVID-19.

RESULTS

A total of 372 HCPs participated in the study with a response rate of 94.9%. Less than one-third 112 (30.1%) of the participants were females. About 308 (82.8%), 285 (76.6%), and 224 (60.2%) of the participants had good knowledge, positive attitude, and good prevention practice towards COVID-19, respectively. Being male (AOR =2.68; 95% CI (confidence interval): 1.50,4.81), 2-5 years working experience (AOR= 4.61; 95% CI: 1.70,12.47), greater than five years working experience (AOR= 5.86; 95% CI: 2.01,17.05), age of 31-40 years old (AOR= 2.49; 95% CI; 1.41,4.41), above 40 years (AOR= 6.94; 95% CI: 2.33-20.71), use of COVID-19 guideline (AOR= 4.79; 95% CI: 2.17-10.53), and using peers as sources of information about COVID-19 (AOR= 2.06; 95% CI: 1.07-3.94) were factors of COVID-19 prevention practices.

CONCLUSION

Less than two-thirds of the participants had good COVID-19 prevention practices. Sex, work experience, age, use of COVID-19 guideline, and using peers as sources of information were factors of COVID-19 prevention practices. Hence, continuous information dissemination and experience sharing on COVID-19 prevention should be done continuously.

Which National Factors Are Most Influential in the Spread of COVID-19?

The outbreak of the novel COVID-19, declared a global pandemic by WHO, is the most serious public health threat seen in terms of respiratory viruses since the 1918 H1N1 influenza pandemic. It is surprising that the total number of COVID-19 confirmed cases and the number of deaths has varied greatly across countries. Such great variations are caused by age population, health conditions, travel, economy, and environmental factors. Here, we investigated which national factors (life expectancy, aging index, human development index, percentage of malnourished people in the population, extreme poverty, economic ability, health policy, population, age distributions, etc.) influenced the spread of COVID-19 through systematic statistical analysis. First, we employed segmented growth curve models (GCMs) to model the cumulative confirmed cases for 134 countries from 1 January to 31 August 2020 (logistic and Gompertz). Thus, each country's COVID-19 spread pattern was summarized into three growth-curve model parameters. Secondly, we investigated the relationship of selected 31 national factors (from KOSIS and Our World in Data) to these GCM parameters. Our analysis showed that with time, the parameters were influenced by different factors; for example, the parameter related to the maximum number of predicted cumulative confirmed cases was greatly influenced by the total population size, as expected. The other parameter related to the rate of spread of COVID-19 was influenced by aging index, cardiovascular death rate, extreme poverty, median age, percentage of population aged 65 or 70 and older, and so forth. We hope that with their consideration of a country's resources and population dynamics that our results will help in making informed decisions with the most impact against similar infectious diseases.

Modeling the enablers of sourcing risks faced by startups in COVID-19 era

Purpose

Startups across all sectors are affected by the COVID-19 pandemic and are facing a formidable challenge in terms of marketing and operations. Most of the startups have experienced a downturn in demand and supply due to COVID-19 led disturbances in sourcing networks. This paper aims to identify, analyze and categorize the significant risks influencing sourcing operations in startups during the COVID-19 era in India, using the total interpretive structural modeling (TISM) approach.

Design/methodology/approach

Eight enablers were identified through literature review and expert opinions from various startups in India. This study adopted the TISM approach to analyze the inter-relationships between the enablers. Multiplication Applied to Classification (MICMAC) analysis was used to rank the sourcing risk enablers and classify them as autonomous, independent, linkage and dependent enablers.

Findings

The results indicate “insufficient fund” as the most critical enabler. Network issues and employee flexibility risk were among the other critical enablers that have a high driving power. Supplier risk, quality risk and demand risk were found to have highly dependent on other enablers for implementation.

Research limitations/implications

This study mainly focuses on the sourcing risks in startup operations in India. This study can be extended to many other countries.

Practical implications

This study will help startup industry managers and practitioners understand the interactions of enablers and identify critical enablers to mitigate risks in startup sourcing operations in the COVID-19 era.

Originality/value

The present study identifies the sourcing risk enablers in the COVID-19 era. It is the first attempt to analyze the interrelationship among sourcing risks in startups using the TISM approach.

Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19

The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the effect of the countermeasures, so we studied the early phases of the infection when countermeasures had not yet taken place. We collected the most diverse dataset of potentially relevant factors and infection metrics to date for this task. Using it, we show the importance of different factors and factor categories as determined by both statistical methods and machine learning (ML) feature selection (FS) approaches. Factors related to culture (e.g., individualism, openness), development, and travel proved the most important. A more thorough factor analysis was then made using a novel rule discovery algorithm. We also show how interconnected these factors are and caution against relying on ML analysis in isolation. Importantly, we explore potential pitfalls found in the methodology of similar work and demonstrate their impact on COVID-19 data analysis. Our best models using the decision tree classifier can predict the infection class with roughly 80% accuracy.

An approximate analytical formula for estimating the weight of factors affecting the spread of COVID-19: a case study of the first wave

COVID-19 pandemic has changed the way we live our lives for the foreseen future. To date, there have been over 113 million reported cases and 2.5 million deaths worldwide. Many studies investigated the factors affecting the number of daily cases such as weather conditions, lockdown duration and other factors. In this study, we propose a COVID-19 analytical formula for factors contributing to the number of the new coronavirus daily cases. We have also calculated values of relative weights of those factors. We focus on the first wave data that are publically available. Seven countries were considered including the UK, Italy, Spain, Canada, South Korea, Germany and France. We considered the following factors: temperature, humidity, government expenditure, lockdown hours and the number of daily tests for COVID-19 performed. The weights were calculated based on the hypothesis that a high correlation between recorded data of a given pair of countries implies a high correlation of the pair's COVID-19 proposed analytical formula. The factors are calculated using the brute-force technique. Our results showed that in five out of the seven countries; temperature, humidity, and lockdown duration were the most dominant with values of 26%, 32% and 38%, respectively. In other countries, however, humidity, government expenditure and the daily performed tests for COVID-19 were the most effective factors, with relative values of 35%, 26%, and 28%.

An analysis to identify the important variables for the spread of COVID-19 using numerical techniques and data science

Considering system theory, the socio-economic variables that constitute a society should be able to capture the system response such as the number of weekly COVID-19 cases. A numerical approach has been presented in this paper to answer two vital questions; which variables are more important and how many variables are needed to capture the dynamics of the spread. Using the theory of least squares regression, two types of problems have been set up and solved using multilinear regression (MLR) and nonlinear powered function known as NLR in this study. Numerical techniques were applied to pre- and post-process the data and the vast number of outputs. Total 43 socio-economic and meteorological variables from 31 counties in California in the United States resulted about 37.4 millions of combinations for the analysis. Results show that variables related to total population, household income, occupation, and transportation are more important than the others. The frequency of having higher correlation for a variable increases as more variables are combined with it. Similarly, correlation increases as the number of variables in a combination increases. Some 5- variable combinations can capture the dynamics of the spread with higher accuracy having correlation coefficient as high as 0.985.