  While computer modeling and simulation are crucial for understanding
scientometrics, their practical use in literature remains somewhat limited. In
this study, we establish a joint coauthorship and citation network using
preferential attachment. As papers get published, we update the coauthorship
network based on each paper's author list, representing the collaborative team
behind it. This team is formed considering the number of collaborations each
author has, and we introduce new authors at a fixed probability, expanding the
coauthorship network. Simultaneously, as each paper cites a specific number of
references, we add an equivalent number of citations to the citation network
upon publication. The likelihood of a paper being cited depends on its existing
citations, fitness value, and age. Then we calculate the journal impact factor
and h-index, using them as examples of scientific impact indicators. After
thorough validation, we conduct case studies to analyze the impact of different
parameters on the journal impact factor and h-index. The findings reveal that
increasing the reference number N or reducing the paper's lifetime {\theta}
significantly boosts the journal impact factor and average h-index. On the
other hand, enlarging the team size m without introducing new authors or
decreasing the probability of newcomers p notably increases the average
h-index. In conclusion, it is evident that various parameters influence
scientific impact indicators, and their interpretation can be manipulated by
authors. Thus, exploring the impact of these parameters and continually
refining scientific impact indicators are essential. The modeling and
simulation method serves as a powerful tool in this ongoing process, and the
model can be easily extended to include other scientific impact indicators and
scenarios.
