Objective: The outbreak of infectious diseases has a negative influence on public health and the economy. The prediction of infectious diseases can effectively control large-scale outbreaks and reduce transmission of epidemics in rapid response to serious public health events. Therefore, experts and scholars are increasingly concerned with the prediction of infectious diseases. However, a knowledge mapping analysis of literature regarding the prediction of infectious diseases using rigorous bibliometric tools, which are supposed to offer further knowledge structure and distribution, has been conducted infrequently. Therefore, we implement a bibliometric analysis about the prediction of infectious diseases to objectively analyze the current status and research hotspots, in order to provide a reference for related researchers. Methods: We viewed “infectious disease*” and “prediction” or “forecasting” as search theme in the core collection of Web of Science from inception to 1 May 2020. We used two effective bibliometric tools, i.e., CiteSpace (Drexel University, Philadelphia, PA, USA) and VOSviewer (Leiden University, Leiden, The Netherlands) to objectively analyze the data of the prediction of infectious disease domain based on related publications, which can be downloaded from the core collection of Web of Science. Then, the leading publications of the prediction of infectious diseases were identified to detect the historical progress based on collaboration analysis, co-citation analysis, and co-occurrence analysis. Results: 1880 documents that met the inclusion criteria were extracted from Web of Science in this study. The number of documents exhibited a growing trend, which can be expressed an increasing number of experts and scholars paying attention to the field year by year. These publications were published in 427 different journals with 11 different document types, and the most frequently studied types were articles 1618 (83%). In addition, as the most productive country, the United States has provided a lot of scientific research achievements in the field of infectious diseases. Conclusion: Our study provides a systematic and objective view of the field, which can be useful for readers to evaluate the characteristics of publications involving the prediction of infectious diseases and for policymakers to take timely scientific responses.

Live-streaming e-commerce is a popular promotional method for attracting more customers in the era of the platform economy. This study investigates a better strategy for live-streaming sales for a manufacturer in an e-platform-based supply chain. Specifically, the manufacturer has two options—self-broadcasting or key opinion leader (KOL) live-streaming—to sell products on the e-platform, which acts as a reseller and agency seller in the dual-channel supply chain. We established three Stackelberg models for the supply chain and derived the equilibrium decisions and profits of supply-chain members under three scenarios: without live-streaming, KOL live-streaming, and manufacturer self-broadcasting. After a comparative analysis, we obtained the following key results: First, even if the influence level of the KOL is within a low range, the manufacturer should change the strategy from no live-streaming e-commerce to KOL live-streaming, with a decrease in the agency fee charged from the e-platform. However, with the continued decline in agency fee, the manufacturer should change the strategy from KOL live-streaming to self-broadcasting. Furthermore, we found a win–win zone such that both supply-chain members are better off under the KOL scenario when both the agency fee of the e-platform and the influence level of the KOL are in higher ranges. Our study not only provides a theoretical basis for the enterprise to choose the appropriate live-streaming mode but also helps it to further increase profits and reduce operational risks. In the future, we will further incorporate theoretical analyses into empirical study to discover more interesting results.