Characteristics of MD Matriculation in American Universities
In: Advances in Applied Sociology: AASoci, Band 11, Heft 5, S. 262-274
ISSN: 2165-4336
7 Ergebnisse
Sortierung:
In: Advances in Applied Sociology: AASoci, Band 11, Heft 5, S. 262-274
ISSN: 2165-4336
In: Environmental science and pollution research: ESPR, Band 29, Heft 53, S. 81063-81075
ISSN: 1614-7499
In: SEPPUR-D-22-00541
SSRN
In: CHEM97242
SSRN
In: Risk analysis: an international journal, Band 43, Heft 10, S. 1946-1961
ISSN: 1539-6924
AbstractCOVID‐19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision‐making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision‐making in the context of COVID‐19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID‐19 at the national level.
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Band 238, S. 113584
ISSN: 1090-2414
In: STOTEN-D-22-00024
SSRN