The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model

Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study

INTRODUCTION

Understanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies.

METHODS

This modeling study analyzed data from 2017 to 2022, in Beijing, China. Weekly influenza positive rate and influenza-like rate were incorporated to quantify the community-level influenza activities. The effective reproduction number and influenza attack rate were estimated using a branching process model and a transmission dynamics model, respectively. The impact of PHSMs was quantified through log-linear regression and counterfactual simulations under varying PHSM scenarios.

RESULTS

The transmissibility of influenza decreased by 68.41% (95%CI: 52.43, 78.80) in 2020, 67.07% (95%CI: 50.80, 77.89) in 2021 and 79.08% (95%CI: 63.18, 88.06) in 2022, and the attack rate dropped by 93.47% (95%CI: 85.86, 95.78), 95.37% (95%CI: 94.30, 96.89) and 71.61% (95%CI: 42.96, 81.24) over the same period, primarily due to the PHSMs. The simulation shows that strict PHSMs effectively suppressed the current flu epidemic effectively. When susceptible individuals drop to 50%, a relaxed strategy results in a smaller rebound in the next flu season, with epidemic sizes increasing to 1.18 (1.10, 1.30), 1.41 (1.20, 1.54), and 1.54 (1.35, 1.55) for relaxed, moderate, and strict measures, respectively.

CONCLUSIONS

Our study confirms the suppressive effect of coronavirus disease 2019 PHSMs on influenza transmission in Beijing. However, the relaxation of these measures' triggers resurgence, emphasizing the need for adaptive control strategies tailored to the population susceptibility and epidemic dynamics.

Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023

Introduction

Infectious diseases pose a significant global health and economic burden, underscoring the critical need for precise predictive models. The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems.

Methods

Baidu search engine data on the keyword "fever" were extracted from 255 cities in China from November 2022 to January 2023. Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days.

Results

The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days, minimizing false starts. Peak detection was optimized using a 7-day moving average, balancing stability and precision.

Discussion

The use of internet search data, such as the Baidu index, significantly improves the timeliness and accuracy of disease surveillance models. This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.

The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.