Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study

Abstract

Background COVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic. Objective Nonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China. Methods Time series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People’s Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak. Results We found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude. Conclusions Our results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.

Oscillatory properties of class C notifiable infectious diseases in China from 2009 to 2021

“…The multi-taper method attempts to reduce the variance of spectral estimates by pre-multiplying the data with several orthogonal tapers, yield a more reliable ensemble estimate of noisy data. Power spectra of the time-series data of infected cases of each disease was calculated from 2009 to 2021 ( Figure 1 graphs on the bottom), which has been used in the earlier work of our lab ( 2 , 17 ).…”

“…The epidemic of infectious diseases has a significant impact on all aspects of people's lives, such as people's daily activities ( 1 , 2 ), education ( 3 – 6 ), diet ( 1 , 7 ), mental health ( 8 – 13 ), and even countries' economies and development ( 14 , 15 ). Three types of infectious diseases (class A, B, and C) were defined by the Chinese government using the country's infectious disease surveillance system after the epidemic of Severe Acute Respiratory Syndrome (SARS) in 2003 ( 16 ).…”

Temporal Analysis of Infectious Diseases: A Case Study on COVID-19

“…They used item response theory to identify changes in each country or region, then described inflection point characteristics on a time bar graph to locate the COVID-19 inflection point. Zhao et al [16] calculated peak-valley amplitudes, disease selectivity, preferred outbreak time, and oscillation intensity for 23 notifiable diseases in China from 2017 to 2021, finding changes before and after epidemics [16,17].…”

Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic

“…Although many published studies 9 – 13 have analyzed the impact of PHSMs on notifiable infectious diseases (NIDs), there remains a significant gap in our understanding of this relationship. Most of these studies 9 , 10 have focused primarily on the impact of PHSMs in their early stages and have tended to overlook the potential impact of PHSMs of varying durations.…”