Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.

Background: It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy".

Methods: We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method.

Optimized sinusoidal patterns for high-performance computational ghost imaging.

Computational ghost imaging (CGI) can reconstruct scene images by two-order correlation between sampling patterns and detected intensities from a bucket detector. By increasing the sampling rates (SRs), imaging quality of CGI can be improved, but it will result in an increasing imaging time. Herein, in order to achieve high-quality CGI under an insufficient SR, we propose two types of novel sampling methods for CGI, to the best of our knowledge, cyclic sinusoidal-pattern-based CGI (CSP-CGI) and half-cyclic sinusoidal-pattern-based CGI (HCSP-CGI), in which CSP-CGI is realized by optimizing the ordered sinusoidal patterns through "cyclic sampling patterns," and HCSP-CGI just uses half of the sinusoidal pattern types of CSP-CGI.