A downstream manufacturer can procure high‐quality inputs from its upstream raw material supplier to produce finished products with high quality. The manufacturer may also have the option to source alternative cheaper low‐quality inputs to partially replace the inputs from its supplier to produce finished products of lower quality. That is, the manufacturer can use a mixture of the supplier's inputs and alternative ones in product design such that product quality depends on the proportion of each input used in products. We endogenize this product quality decision in a simple analytical model to study the impact of this option on the supplier's profits. Intuitively, the option of sourcing alternative inputs could hurt the supplier in two ways: It sells fewer inputs, and it needs to set a lower wholesale price due to competitive pressure. However, our study reveals an opposite finding: The supplier can benefit not only by selling more inputs but also by setting a higher wholesale price when the manufacturer has the option of sourcing alternative low‐quality inputs. This interesting finding is due to the wholesale price push‐up effect: The upstream supplier may deliberately raise the wholesale price when sourcing alternative inputs is an option for the downstream manufacturer. This increased wholesale price encourages the manufacturer to use a higher proportion of alternative cheaper inputs in its product design; as a result, the manufacturer's marginal cost is reduced, which leads to a decrease in its retail price. Therefore, consumer demand for the manufacturer's products becomes higher, which in turn results in higher manufacturer demand for the supplier's inputs. Moreover, we show that despite this higher wholesale price, the manufacturer can earn higher profits by optimally charging a lower retail price. Furthermore, we find that the wholesale price of the supplier's inputs can exceed the manufacturer's retail price, thereby leading to a negative profit margin for the manufacturer from using the supplier's inputs. Finally, we demonstrate the robustness of our findings under several model extensions.
Background: COVID-19 is spreading worldwide. No specific medicine has been used for the treatment of coronavirus infections. The aim of this study is to establish a new risk predictive model to screen potential critical patients for early intervention. Methods: In this study, Clinical characteristics were collected and analyzed from 317 confirmed cases of COVID-19. A total of 175 of the 317 cases with detailed examination results were included to establish models for predicting the risk of disease progression. Major independent risk factors were incorporated into MuLBSTA model to establish new models for predicting critical risk. We further tracked 25 mild or moderate patients with COVID-19 to research dynamic changes of the major independent risk factors in COVID-19 progression. Results: The average age of all of the 317 patients was 47.76 (SD 17.22). A total of 48 (15.14%) were diagnosed with mild disease with a median age of 34(39.29±13.04), 116(36.59%) were diagnosed with moderate disease with a median age of 34(38.78±12.32), 38(11.99%) were diagnosed as severe with a median age of 56(58.24±15.12), and 115(36.28) were diagnosed as critical with a median age of 59(56.89+17.09). The most common symptom at onset of illness were fever(211[66.56%] patients). Age>50, CK>64, CD4≤461, and CD8≤241 were predicted to be major independent risk factors that could promote COVID-19 progression. Compared with the MuLBSTA model, the predictive ability of the CD4-CD8-MuLBSTA model and the CD4-MuLBSTA model were improved by 11.87% and 11.79%, respectively. In the prospective study, CK value began to show significant differences from day13. The average CD4 in Severe Group began to decline significantly on the fourth day, and the CD8 maintained a relatively low level in the Severe Group after day13. Conclusions: Severe COVID-19 patients were significantly older than non-severe patients. Immune systems of severe COVID-19 patients were significantly suppressed, and advanced age(>50 years), low levels of CD4(≤461) or CD8(≤241) was important clinical manifestations of rapid deterioration. CK values in severe COVID-19 patients were significantly higher than in no severe patients. CD4 and CD8 were incorporated into the MuLBSTA to establish a new model, which is an ideal risk prediction model for COVID-19 patients. 4 Background Coronavirus disease 2019(COVID-19) is a respiratory illness caused by a new virus(SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus2), which was first reported in December 2019 in Wuhan, Hubei Province, China. 1-5 Symptoms range from a mild cough to pneumonia, even with no symptoms. 3,6 There is evidence that it spreads from person to person. 7 Whole genome sequencing showed that COVID-19 is a beta coronavirus similar to human severe acute respiratory syndrome(SARS) and middle eastern respiratory syndrome(MERS). This new coronavirus claded from SARS and MERS requires enhanced surveillance and further investigation. 8 Like other several coronaviruses, SARS-CoV-2 initially causes mild or moderate symptoms i...
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