Background In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies. Methods To explore the coevolution dynamics between science and policy (SAP), this study explored the changes in, and development of, COVID-19 research in the early period of the COVID-19 outbreak in China, from 30 December 2019 to 26 June 2020. In this study, VOSviewer was adopted to calculate the link strength of items extracted from scientific publications, and machine learning clustering analysis of scientific publications was carried out to explore dynamic trends in scientific research. Trends in relevant policies that corresponded to changing trends in scientific research were then traced. Results The study observes a salient change in research content as follows: an earlier focus on “children and pregnant patients”, “common symptoms”, “nucleic acid test”, and “non-Chinese medicine” was gradually replaced with a focus on “aged patients”, “pregnant patients”, “severe symptoms and asymptomatic infection”, “antibody assay”, and “Chinese medicine”. “Mental health” is persistent throughout China’s COVID-19 research. Further, our research reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. The average issuance time of relevant COVID-19 policies in China is 8.36 days after the launching of related research. Conclusions In the early stage of the outbreak in China, the formulation of research-driven-COVID-19 policies and related scientific research followed a similar dynamic trend, which is clearly a manifestation of a coevolution model (CEM). The results of this study apply more broadly to the formulation of policies during public health emergencies, and provide the foundation for future EBPM research.

The dynamics of research are a prism that reflects interactions between science, the societal environment, and government policies. Against the backdrop of the ongoing global COVID-19 pandemic, this study explored the changes in, and development of, COVID-19 research in the period from December 30, 2019 to April 27, 2020. The study observes a salient change in research content: an earlier focus on “all patients”, “common symptoms”, and “nucleic acid test” was gradually replaced by a focus on “children”, “pregnant patients”, “severe symptoms”, and a combination of “nucleic acid test” and “antibody assay”. Some topics such as “vaccine R&D”, “knowledge, attitude, and practice” (KAP), and “mental health” were persistent throughout the recent history of China’s COVID-19 research. This study also reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. In the early stage of the outbreak in China, the formulation of COVID-19 policies followed a rapidly-progressing co-evolutionary model (CEM). The results of this study apply more broadly to the formulation of policies in public health emergencies, especially in the early stages.

The rapid response of COVID-19 scientific research played a significant role in pandemic prevention and control but failed to block the spread of the pandemic rapidly. Besides the complexity of the virus, the effectiveness of control and prevention measures, and other factors, the adaptation of the mode of conducting scientific research is also crucial for the prevention and control of COVID-19. In this study, a parallel model was used to explore the effects of the rapid scientific response on COVID-19 to assess why pandemics continue to spread under rapid response. Analysis: This study presents the response of scientific research based on country/region and publication dimensions after analyzing COVID-19 studies in the Web of Science and PubMed databases. Co-occurrence analysis of items was used to determine the generation rate of COVID-19 research under different topics to identify the reflected innovation model. Results: More manifestations on rapid response of COVID-19 research, especially compared with the linear model of SARS research, showed that the COVID-19 research followed a parallel or concurrent innovation model. Conclusion:Early multi-stakeholder partnership, convenient information sharing, and improved research competence promote the parallel model in COVID-19. Meanwhile, the uncertainty of the COVID-19 virus and the adverse effect of rapid response may limit the time efficiency of the parallel model. In conclusion, the rapid prevention and control of the pandemic cannot fully rely on scientific research but requires more combined effort under an uncertain global setting.

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