Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China

Abstract

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks.

Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny.

Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation.

Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

Keywords: Baidu search index; Bayesian structural time series; COVID-19; Granger causality test; zero-COVID policy.

Figure 1

Schematic diagram of the methodology.

Figure 2

Time series plot of BDI search terms “COVID-19 (Xin guan)”, “Pneumonia” and “Myocarditis,” as well as Nucleic acid positivity rates calculated from RT-PCR test data. This time series graph was drawn with BDI data using the y axis on the left, and positive rate of RT-PCR data using y axis on the right.

Figure 3

Distribution of positive results from internet data by human body systems in lagged Spearman correlation analysis, growth rate assessments and Granger causality examinations. The red triangle represents the positive results in Granger test of BDI data. The red circle indicates that the disease existed growth rate with statistical significance in analysis of hospital data. “r,” correlation coefficient; “G,” Granger test; “H,” hospital data. MGD, meibomian gland dysfunction; AURI, acute upper respiratory infection; ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; IPF, idiopathic pulmonary fibrosis; DKA, diabetic ketoacidosis; CAC, coronary atherosclerotic cardiopathy; DN, diabetic nephropathy.

Figure 4

Time series plot of “Pneumonia” from internet data and Hospital D inpatient data. (This time series plot was drawn with BDI data using the y axis on the left, and hospital data using y axis on the right.) (A) Displays three time series plot for pneumonia inpatient data of Hospital D, “Pneumonia” and “COVID-19 (Xin guan)” data in the BDI during the first peak period (November 2022 to February 2023). In (B), displays three time series plot for pneumonia inpatient data of Hospital D, “Pneumonia” and “Reinfection (Er yang)” data in the BDI during the second peak period (April 2023 to June 2023).