Effect evaluation of non-pharmaceutical interventions taken in China to contain the COVID-19 epidemic based on the susceptible-exposed-infected-recovered model

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study

BACKGROUND

This study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005-2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China.

METHODS

Descriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025.

RESULTS

Between 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3-7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing's urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015-2019) and the dynamic zero-COVID period (2020-2022), respectively, compared to the pre-upsurge period (2005-2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases).

CONCLUSIONS

Children aged 3-7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

Sewage network operational risks based on InfoWorks ICM with nodal flow diurnal patterns under NPIs for COVID-19

Non-Pharmaceutical Interventions (NPIs) have been widely employed globally over the past three years to control the rapid spread of coronavirus disease 2019 (COVID-19). These measures have imposed restrictions on urban residents' activities and significantly influenced sewage discharge characteristics within sewage network, particularly in densely populated cities in China. This study focused on the nodal flow diurnal patterns and sewage network operational risks before and after epidemic lockdown in Beijing from March to May in 2022. Nodal flow diurnal patterns on weekdays and weekends before and after NPIs were analyzed using measured data through statistical and mathematical methods. A sewage network model was established to simulate and analyze the operational risks based on InfoWorks ICM before and after epidemic lockdown. The main conclusions were as follows: (1) In predominantly residential areas, the total wastewater volume increased by approximately 28.76 % to 33.52 % after the implementation of strict NPIs. The morning and midday "M" peaks on normalized weekdays transformed into "N" peaks, and the morning peak time was delayed by 0.5 to 1 hour after the lockdown; (2) Following NPIs, More than 90 % of manholes' average water levels rose to varying degrees, approximately 50 % of pipe lengths exhibited a full flow state; (3) When the lockdown was in place during a hot summer day, sewage overflow phenomena were observed in 4.6 % and 9.6 % of manholes, respectively, with per capita daily drainage equivalent reaching 40-50 %. These findings hold significant implications for the proactive planning and operational management of water industry infrastructure during major emergencies.

How can infectious medical waste be forecasted and transported during the COVID-19 pandemic? A hybrid two-stage method

The COVID-19 pandemic has caused an unforeseen collapse of infectious medical waste (IMW) and an abrupt smite of the conveying chain. Hospitals and related treatment centers face great challenges during the pandemic because mismanagement may lead to more severe life threats and enlarge environmental pollution. Opportune forecasting and transportation route optimization, therefore, are crucial to coping with social stress meritoriously. All related hospitals and medical waste treatment centers (MWTCs) should make decisions in perspective to reduce the economic pressure and infection risk immensely. This study proposes a hybrid dynamic method, as follows: first to forecast confirmed cases via infectious disease modeling and analyze the association between IMW outflows and cases; next to construct a model through time-varying factors and the lagging factor to predict the waste quantity; and then to optimize the transportation network route from hospitals to MWTCs. For demonstration intentions, the established methodology is employed to an illustrative example. Based on the obtained results, in finding the process of decision making, cost becomes the common concern of decision-makers. Actually, the infection risk among publics has to be considered simultaneously. Therefore, realizing early warning and safe waste management has an immensely positive effect on epidemic stabilization and lifetime health.

The clinical and virological features of two children's coinfections with human adenovirus type 7 and human coronavirus-229E virus

Objective

Human adenovirus (HAdV) coinfection with other respiratory viruses is common, but adenovirus infection combined with human coronavirus-229E (HCoV-229E) is very rare.

Study design and setting

Clinical manifestations, laboratory examinations, and disease severity were compared between three groups: one coinfected with HAdV-Ad7 and HCoV-229E, one infected only with adenovirus (mono-adenovirus), and one infected only with HCoV-229E (mono-HCoV-229E).

Results

From July to August 2019, there were 24 hospitalized children: two were coinfected with HAdV-Ad7 and HCoV-229E, and 21 were infected with a single adenovirus infection. Finally, one 14-year-old boy presented with a high fever, but tested negative for HAdV-Ad7 and HCoV-229E. Additionally, three adult asymptotic cases with HCoV-229E were screened. No significant difference in age was found in the coinfection and mono-adenovirus groups (11 vs. 8 years, p = 0.332). Both groups had the same incubation period (2.5 vs. 3 days, p = 0.8302), fever duration (2.5 vs. 2.9 days, p = 0.5062), and length of hospital stay (7 vs. 6.76 days, p = 0.640). No obvious differences were found in viral loads between the coinfection and mono-adenovirus groups (25.4 vs. 23.7, p = 0.570), or in the coinfection and mono-HCoV-229E groups (32.9 vs. 30.06, p = 0.067). All cases recovered and were discharged from the hospital.

Conclusion

HAdV-Ad7 and HCoV-229E coinfection in healthy children may not increase the clinical severity or prolong the clinical course. The specific interaction mechanism between the viruses requires further study.

Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China

The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function β(t) to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in β(t) and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.

Extended Warranty Strategy and Its Environment Impact of Remanufactured Supply Chain

To reduce environmental pollution, the government has issued relevant laws and regulations, and more and more enterprises engage in remanufacturing and recycling used products. Trade old for new and trade old for remanufactured have become marketing means to promote product recycling. The extended warranty service is used to promote the recycling of waste products. To design an optimal extended warranty service strategy and analyze its environment impact in a remanufactured supply chain, game theory is used to model the competitive relationship between a manufacturer and an E-commerce platform. Considering whether the E-commerce platform provides extended warranty service, four models are constructed, and the extended warranty service strategy and its environment impact can be analyzed. The results show that, when the level of substitutability between remanufactured and new products meets a certain rage, new or remanufactured products with extended warranty service strategy can increase the demand for new or remanufactured products, respectively. In the four models, the changing trends of manufacturer's profit, E-commerce platform's profit and supply chain's profit, consumer surplus, environmental impact and social welfare are the same, but only the thresholds are different. From the perspectives of supply chain member, supply chain system, consumer, environment and society, the new and remanufactured products with extended warranty service strategy is the best choice.