As one of the most neglected zoonotic diseases, brucellosis has posed a serious threat to public health worldwide. This study is purposed to apply different machine learning models to improve the prediction accuracy of human brucellosis (HB) in Shaanxi, China from 2008 to 2020, under livestock husbandry intensification from a spatiotemporal perspective. We quantitatively evaluated the performance and suitability of ConvLSTM, RF, and LSTM models in epidemic forecasting, and investigated the spatial heterogeneity of how different factors drive the occurrence and transmission of HB in distinct sub-regions by using Kernel Density Analysis and Shapley Additional Explanations. Our findings demonstrated that ConvLSTM network yielded the best predictive performance with the lowest average RMSE of 13.875 and MAE values of 18.393. RF model generated an underestimated outcome while LSTM model had an overestimated one. In addition, climatic conditions, intensification of livestock keeping and socioeconomic status were identified as the dominant factors that drive the occurrence of HB in Shaanbei Plateau, Guanzhong Plain, and Shaannan Region, respectively. This work provided a comprehensive understanding of the potential risk of HB epidemics in Northwest China driven by both anthropogenic activities and natural environment, which can support further practice in disease control and prevention.

Prestigious journals are widely admired for publishing quality scholarship, yet the primary indicators of journal prestige (i.e., impact factors) do not directly assess audience admiration. Moreover, the publication landscape has changed substantially in the last 20 years, with electronic publishing changing the way we consume scientific research. Given that it has been 18 years since the publication of the last journal prestige survey of SIOP members, the authors conducted a new survey and used these results to reflect on changing practices within industrial and organizational (I-O) psychology. SIOP members (n = 557) rated the prestige and relevance of I-O and management journals. Responses were analyzed according to job setting, and were compared to a survey conducted by Zickar and Highhouse (2001) in 2000. There was considerable consistency in prestige ratings across settings (i.e., management department vs. psychology department; academic vs. applied), especially among the top journals. There was considerable variance, however, in the perceived usefulness of different journals. Results also suggested considerable consistency across the two time periods, but with some increases in prestige among OB-oriented journals. Changes in the journal landscape are discussed, including the rise of OHP as a topic of concentration in I-O. We suggest that I-O programs will continue to attract the top researchers in talent management and OHP, which should result in the use of a broader set of journals for judging I-O program impact.