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Artificial sweeteners have sparked a heated debate worldwide due to their ambiguous impacts on public and environmental health and food safety and quality. Many studies on artificial sweeteners have been conducted; however, none scientometric studies exist in the field. This study aimed to elaborate on the knowledge creation and development of the field of artificial sweeteners and predict the frontiers of knowledge based on bibliometrics. In particular, this study combined VOSviewer, CiteSpace, and Bibliometrix to visualize the mapping of knowledge production, covered 2389 relevant scientific publications (1945–2022), and systematically analyzed articles and reviews (n = 2101). Scientific publications on artificial sweeteners have been growing at an annual rate of 6.28% and globally attracting 7979 contributors. Susan J. Brown with total publications (TP) of 17, average citation per article (AC) of 36.59, and Hirsch (h)-index of 12 and Robert F. Margolskee (TP = 12; AC = 2046; h-index = 11) were the most influential scholars. This field was clustered into four groups: eco-environment and toxicology, physicochemical mechanisms, public health and risks, and nutrition metabolism. The publications about environmental issues, in particular, "surface water," were most intensive during the last five years (2018–2022). Artificial sweeteners are gaining importance in the monitoring and assessment of environmental and public health. Results of the dual-map overlay showed that the future research frontiers tilt toward molecular biology, immunology, veterinary and animal sciences, and medicine. Findings of this study are conducive to identifying knowledge gaps and future research directions for scholars. [Display omitted] • Developments in the field of artificial sweeteners (ASs) were assessed via bibliometrics. • The main productive nations of articles were USA, China, and UK. • The aquatic environment was intensively focused on. • Future frontiers included molecular biology, medicine, and immunology. • ASs have received attention for monitoring environmental and public health. [ABSTRACT FROM AUTHOR]

A reasonable prediction of infectious diseases' transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, a single intervention is effective in disease control but at huge expense, while combined interventions would be more efficient, among which, enhancing detection number is crucial in the control strategy for COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection numbers in a real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata; it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimic the effect of multiple factors in infectious disease control.