Temporal dynamic characteristics of human monkeypox epidemic in 2022 around the world under the COVID-19 pandemic background.

Background: The reemergence of the monkeypox epidemic has aroused great concern internationally. Concurrently, the COVID-19 epidemic is still ongoing. It is essential to understand the temporal dynamics of the monkeypox epidemic in 2022 and its relationship with the dynamics of the COVID-19 epidemic. In this study, we aimed to explore the temporal dynamic characteristics of the human monkeypox epidemic in 2022 and its relationship with those of the COVID-19 epidemic. Methods: We used publicly available data of cumulative monkeypox cases and COVID-19 in 2022 and COVID-19 at the beginning of 2020 for model validation and further analyses. The time series data were fitted with a descriptive model using the sigmoid function. Two important indices (logistic growth rate and semi-saturation period) could be obtained from the model to evaluate the temporal characteristics of the epidemic. Results: As for the monkeypox epidemic, the growth rate of infection and semi-saturation period showed a negative correlation (r = 0.47, p = 0.034). The growth rate also showed a significant relationship with the locations of the country in which it occurs [latitude (r = -0.45, p = 0.038)]. The development of the monkeypox epidemic did not show significant correlation compared with the that of COVID-19 in 2020 and 2022. When comparing the COVID-19 epidemic with that of monkeypox, a significantly longer semi-saturation period was observed for monkeypox, while a significant larger growth rate was found in COVID-19 in 2020. Conclusions: This novel study investigates the temporal dynamics of the human monkeypox epidemic and its relationship with the ongoing COVID-19 epidemic, which could provide more appropriate guidance for local governments to plan and implement further fit-for-purpose epidemic prevention policies.

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

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.

Enlightenment on oscillatory properties of 23 class B notifiable infectious diseases in the mainland of China from 2004 to 2020.

A variety of infectious diseases occur in mainland China every year. Cyclic oscillation is a widespread attribute of most viral human infections. Understanding the outbreak cycle of infectious diseases can be conducive for public health management and disease surveillance. In this study, we collected time-series data for 23 class B notifiable infectious diseases from 2004 to 2020 using public datasets from the National Health Commission of China. Oscillatory properties were explored using power spectrum analysis. We found that the 23 class B diseases from the dataset have obvious oscillatory patterns (seasonal or sporadic), which could be divided into three categories according to their oscillatory power in different frequencies each year. These diseases were found to have different preferred outbreak months and infection selectivity. Diseases that break out in autumn and winter are more selective. Furthermore, we calculated the oscillation power and the average number of infected cases of all 23 diseases in the first eight years (2004 to 2012) and the next eight years (2012 to 2020) since the update of the surveillance system. A strong positive correlation was found between the change of oscillation power and the change in the number of infected cases, which was consistent with the simulation results using a conceptual hybrid model. The establishment of reliable and effective analytical methods contributes to a better understanding of infectious diseases' oscillation cycle characteristics. Our research has certain guiding significance for the effective prevention and control of class B infectious diseases.

Mechanisms of recurrent outbreak of COVID-19: a model-based study.

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.