Novel coronavirus pneumonia (NCP) caused by COVID-19 is an infectious disease with the widest global transmission recorded in human history. With the prevalence of variants of concern (VOC) such as Alpha, Beta, Gamma, Delta, and Omicron strains, COVID-19 has been showing a trend of weakening virulence[1] but increasing transmission[2]. According to statistics released by WHO, 600,443,074 people have been infected worldwide, and 6,485,216 have died until August 31, 2022. Under such circumstances, controlling the transmission of COVID-19 remains a critical public health issue.
In the face of the continuing spread of COVID-19, three main types of control measures have been adopted around the world: nucleic acid testing (NAT), vaccination and restrictions on population mobility, the implementation of which has had a profound impact on social production and economic construction[3]. Analysing the dynamic transmissibility of COVID-19 and quantifying the effectiveness of control measures can help to find a balance between global public health and economic development, and is therefore essential to mitigate the negative impact of novel coronavirus pneumonia.
Since the novel coronavirus pneumonia pandemic, there has been a lack of research based on a global perspective to quantify the impact of multiple control measures. In 2020 researchers first described the transmissibility of COVID-19 using basic regeneration number (R0)[4–6], and initially clarified the effectiveness of restrictions on population mobility[3]. In 2021, following the widespread adoption of nucleic acid testing and vaccination, single-factor studies demonstrated that nucleic acid testing limited the transmission of COVID-19[6] while vaccines had limited effectiveness due to immune escape[7]. In 2022, researchers focused on the dynamic transmissibility of variants of concern, and have begun to quantify the effectiveness of multiple control measures in localised areas[8]. Due to the limitations of localised data sources or the singularity of control measures, the lack of generalisability makes it difficult to compare the effectiveness regardingdifferent COVID-19 control measures.
In our research, we first describe the dynamic transmissibility and spatio-temporal characteristics of COVID-19. On this basis we construct a Dynamic Bayesian Network (DBN) to quantify the effectiveness of the main COVID-19 control measures and compare scenarios under strict and loose control. The innovation of our research is threefold: Firstly, our research covers the time horizon of the pandemic from January 2020 to August 2022 and the spatial horizon of 176 countries and territories, which removes the temporal and spatial limitations of the analysis; Secondly, we quantifies the effectiveness of three control measures by incorporating them into the same formula, which ensures the availability regarding compare the effectiveness of every control measures and combinations of such control measures; And thirdly, our research can be used to predict the dynamics transmissibility of COVID-19 control measures, which could be a significant reference for predicting the outbreaks of novel coronavirus pneumonia and for the development of policies for restrictions globally. The findings of our research are generalisable and provide a global reference for the evaluation of control measures for novel coronavirus pneumonia, which will help to promote global health equity and reduce the negative impact of COVID-19 on economic globalisation.