From Outbreak to Near Disappearance: How Did Non-pharmaceutical Interventions Against COVID-19 Affect the Transmission of Influenza Virus?

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

The study investigates the significance of employing advanced systemic models in community health management, with a focus on COVID-19 as a respiratory virus. Through the development of a system dynamics model integrating an uncertain SEIAR model, our research addresses the critical issue of parameter uncertainty using Ensemble Kalman Filter (EnKF) and Metropolis-Hastings (MH) algorithms. We present a case study using real COVID-19 outbreaks in Iran, offering insights into effective outbreak control scenarios and considering the global impact of respiratory viruses. The research yields distinctive results, showcasing variable mortality rates (40,500 to 436,500) across scenarios in Iran. Model accuracy is rigorously evaluated using the Normalized Root-Mean-Square Deviation (NRMSD) for new cases, deaths, and recoveries (0.2 %, 1.2 %, and 0.6 % respectively). The outcomes not only contribute to the existing body of knowledge but also offer practical implications for healthcare policies, economic considerations, and sensitivity assessments related to respiratory diseases. This study stands out from others in its approach to modeling and addressing uncertainty within a system dynamics framework. The integration of EnKF and MH algorithms provides a nuanced understanding of parameter uncertainty, adding a layer of sophistication to the analysis. The application of the model to real-world COVID-19 outbreaks in Iran further enhances the study's relevance and applicability. In conclusion, the research introduces an uncertain SEIAR system dynamics model with unique contributions to policymaking, economic considerations, and sensitivity assessments for respiratory diseases. The outcomes and insights derived from the study not only advance our understanding of disease dynamics but also provide actionable information for effective public health management.

The short and long-term impact of nonpharmaceutical interventions on the prevalence of varicella in Xi'an during the COVID-19 pandemic

Varicella is a highly prevalent infectious disease with a similar transmission pathway to coronavirus disease 2019 (COVID-19). In the context of the COVID-19 pandemic, anti-COVID-19 nonpharmaceutical interventions (NPIs) have been implemented to prevent the spread of the infection. This study aims to analyze varicella's epidemiological characteristics and further investigate the effect of anti-COVID-19 NPIs on varicella in Xi'an, northwestern China. Based on the varicella surveillance data, search engine indices, meteorological factors from 2011 to 2021 in Xi'an, and different levels of emergency response to COVID-19 during the pandemic, we applied Bayesian Structural Time Series models and interrupted time series analysis to predict the counterfactual incidence of varicella and quantify the impact of varying NPIs intensities on varicella. From 2011 to 2021, varicella incidence increased, especially in 2019, with a high incidence of 111.69/100 000. However, there was a sharp decrease of 43.18% in 2020 compared with 2019, and the peak of varicella incidence in 2020 was lower than in previous years from the 21st to the 25th week. In 2021, the seasonality of varicella incidence gradually returned to a seasonal pattern in 2011-2019. The results suggest that anti-COVID-19 NPIs effectively reduce the incidence of varicella, and the reduction has spatiotemporal heterogeneity.

Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis

Objective

Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China.

Methods

Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods.

Results

From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447).

Conclusions

The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.

Spatiotemporal impact of non-pharmaceutical interventions against COVID-19 on the incidence of infectious diarrhea in Xi'an, China

Background

Non-pharmaceutical interventions (NPIs) against COVID-19 may prevent the spread of other infectious diseases. Our purpose was to assess the effects of NPIs against COVID-19 on infectious diarrhea in Xi'an, China.

Methods

Based on the surveillance data of infectious diarrhea, and the different periods of emergence responses for COVID-19 in Xi'an from 2011 to 2021, we applied Bayesian structural time series model and interrupted time series model to evaluate the effects of NPIs against COVID-19 on the epidemiological characteristics and the causative pathogens of infectious diarrhea.

Findings

A total of 102,051 cases of infectious diarrhea were reported in Xi'an from 2011 to 2021. The Bayesian structural time series model results demonstrated that the cases of infectious diarrhea during the emergency response period was 40.38% lower than predicted, corresponding to 3,211 fewer cases, during the COVID-19 epidemic period of 2020-2021. The reduction exhibited significant variations in the demography, temporal and geographical distribution. The decline in incidence was especially evident in children under 5-years-old, with decreases of 34.09% in 2020 and 33.99% in 2021, relative to the 2017-2019 average. Meanwhile, the incidence decreased more significantly in industrial areas.

Interpretation

NPIs against COVID-19 were associated with short- and long-term reductions in the incidence of infectious diarrhea, and this effect exhibited significant variations in epidemiological characteristics.