ABSTRACT
Purpose: Scholars have concentrated their efforts on COVID-19's impact on industries worldwide in order to manage timely supply chain disruptions. Epidemic outbursts are a unique supply chain risk that is distinguished by prolonged disruption propagation, disruption existence, and high uncertainty. The purpose of this study was to investigate the role of R&D investment and firm performance in mediating the relationship between disruption risk and supply chain performance in Pakistani manufacturing industries and supply chain employees during the recovery phase of the COVID-19 pandemic via application of dynamic capability theory. Methodology: From July 21 to August 23, 2020, three hundred and eighteen employees from supply chains of manufacturing industries in Rawalpindi and Islamabad, Pakistan, participated in this cross-sectional online web-based survey. The four standard research scales were used to examine the research and development, disruption risk, firm, and supply chain performance. The response link was distributed to respondents via Facebook, WhatsApp, and email. The data was analyzed using structural equation modelling and a partial least squares technique in the study. Results: The study's findings suggest that disruption risk, research and development investment, and firm performance all improve supply chain performance, but the mediation effect is unsupported by the data. These measures help to plan a better supply chain in the face of disruption risk, and they provide one of the timely empirical conclusions on the role of R&D investment in mitigating risk disruptions and improving supply chain performance
ABSTRACT
An increase in number of Coronavirus disease 2019 (COVID-19) cases will lead to more cluster discovery in Malaysia. Furthermore, with the increasing population, city growth, workplace income needs, high-risk groups, and other relevant factors can contribute to the formation of the new clusters. The cluster distribution of the disease could be seen by mapping and spatial analysis to understand their spatial phenomena of the disease dynamics. The purpose of the study is to analyse the spatial distribution of COVID-19 cluster cases in Selangor for year 2020. Two objectives of the study are i) to determine the hotspot location of the COVID- 19 cluster, and ii)to examine the spatial distribution of the factors affecting the COVID-19 cluster. The data processing was conducted using hotspot analysis and ordinary least squares (OLS) in ArcGIS Pro and Microsoft Excel to explore the local disease phenomena. TheCOVID-19 cases was most prevalent in the Petaling district, followed by Hulu Langat and Klang. The virus had the least impact in Sabak Bernam, Hulu Selangor, Kuala Selangor, Sepang, Kuala Langat, and Gombak. Three environmental factors of population density, the effects of urbanisation, and workplace cases were influential variables at the local clusters. These findings could help the local agencies to facilitate and control the spread mode of the virus in a spatial human environment.
ABSTRACT
The COVID-19 pandemics challenges governments across the world. To develop adequate responses, they need accurate models for the spread of the disease. Using least squares, we fitted Bertalanffy-Pütter (BP) trend curves to data about the first wave of the COVID-19 pandemic of 2020 from 49 countries and provinces where the peak of the first wave had been passed. BP-models achieved excellent fits (R-squared above 99%) to all data. Using them to smoothen the data, in the median one could forecast that the final count (asymptotic limit) of infections and fatalities would be 2.48 times (95% confidence limits 2.42-2.6) and 2.67 times (2.39-2.765) the total count at the respective peak (inflection point). By comparison, using logistic growth would evaluate this ratio as 2.00 for all data. The case fatality rate, defined as the quotient of the asymptotic limits of fatalities and confirmed infections, was in the median 4.85% (confidence limits 4.4%-6.5%). Our result supports the strategies of governments that kept the epidemic peak low, as then in the median fewer infections and fewer fatalities could be expected.