Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong

Impact of infectious density-induced additional screening and treatment saturation on COVID-19: Modeling and cost-effective optimal control

This study introduces a novel SI2HR model, where "I2" denotes two infectious classes representing asymptomatic and symptomatic infections, aiming to investigate and analyze the cost-effective optimal control measures for managing COVID-19. The model incorporates a novel concept of infectious density-induced additional screening (IDIAS) and accounts for treatment saturation. Furthermore, the model considers the possibility of reinfection and the loss of immunity in individuals who have previously recovered. To validate and calibrate the proposed model, real data from November-December 2022 in Hong Kong are utilized. The estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical simulations. An analysis of the model reveals that delays in screening, treatment, and quarantine contribute to an increase in the basic reproduction number R0, indicating a tendency towards endemicity. In particular, from the elasticity of R0, we deduce that normalized sensitivity indices of baseline screening rate (θ), quarantine rates (γ, αs), and treatment rate (α) are negative, which shows that delaying any of these may cause huge surge in R0, ultimately increases the disease burden. Further, by the contour plots, we note the two-parameter behavior of the infectives (both symptomatic and asymptomatic). Expanding upon the model analysis, an optimal control problem (OCP) is formulated, incorporating three control measures: precautionary interventions, boosted IDIAS, and boosted treatment. The Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the OCP. The numerical simulations highlight that enhanced screening and treatment, coupled with preventive interventions, can effectively contribute to sustainable disease control. However, the cost-effectiveness analysis (CEA) conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other strategies. The CEA results provide valuable insights into identifying specific strategies based on their cost-efficacy ranking, which can be implemented to maximize impact while minimizing costs. Overall, this research offers significant insights for policymakers and healthcare professionals, providing a framework to optimize control efforts for COVID-19 or similar epidemics in the future.

Relative Role of Age Groups and Indoor Environments in Influenza Transmission Under Different Urbanization Rates in China

Exploring the relative role of different indoor environments in respiratory infections transmission remains unclear, which is crucial for developing targeted nonpharmaceutical interventions. In this study, a total of 2,583,441 influenza-like illness cases tested from 2010 to 2017 in China were identified. An agent-based model was built and calibrated with the surveillance data, to assess the roles of 3 age groups (children <19 years, younger adults 19-60 years, older adults >60 years) and 4 types of indoor environments (home, schools, workplaces, and community areas) in influenza transmission by province with varying urbanization rates. When the urbanization rates increased from 35% to 90%, the proportion of children aged <19 years among influenza cases decreased from 76% to 45%. Additionally, we estimated that infections originating from children decreased from 95.1% (95% confidence interval (CI): 92.7, 97.5) to 59.3% (95% CI: 49.8, 68.7). Influenza transmission in schools decreased from 80.4% (95% CI: 76.5, 84.3) to 36.6% (95% CI: 20.6, 52.5), while transmission in the community increased from 2.4% (95% CI: 1.9, 2.8) to 45.4% (95% CI: 35.9, 54.8). With increasing urbanization rates, community areas and younger adults contributed more to infection transmission. These findings could help the development of targeted public health policies. This article is part of a Special Collection on Environmental Epidemiology. This article is part of a Special Collection on Environmental Epidemiology.

A review on sterilization methods of environmental decontamination to prevent the coronavirus SARS-CoV-2 (COVID-19 virus): A new challenge towards eco-friendly solutions

In recent years, the COVID-19 pandemic is currently wreaking havoc on the planet. SARS-CoV-2, the Severe Acute Respiratory Syndrome Coronavirus, is the current term for this outbreak. Reports about this novel coronavirus have been presented since the pandemic's breakout, and they have demonstrated that it transmits rapidly from person to person, primarily by droplets in the air. Findings have illustrated that SARS-CoV-2 can survive on surfaces from hours to days. Therefore, it is essential to find practical solutions to reduce the virus's impact on human health and the environment. This work evaluated common sterilization methods that can decontaminate the environment and items. The goal is that healthcare facilities, disease prevention organizations, and local communities can overcome the new challenge of finding eco-friendly solutions. Further, a foundation of information encompassing various sterilization procedures and highlighting their limits to choose the most appropriate method to stop disease-causing viruses in the new context has been presented. The findings of this crucial investigation contribute to gaining insight into the comprehensive sterilization approaches against the coronavirus for human health protection and sustainable environmental development.

Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia

Introduction

Epidemiological modeling is widely used to offer insights into the COVID-19 pandemic situation in Asia. We reviewed published computational (mathematical/simulation) models conducted in Asia that assessed impacts of pharmacological and non-pharmacological interventions against COVID-19 and their implications for vaccination strategy.

Methods

A search of the PubMed database for peer-reviewed, published, and accessible articles in English was performed up to November 2022 to capture studies in Asian populations based on computational modeling of outcomes in the COVID-19 pandemic. Extracted data included model type (mechanistic compartmental/agent-based, statistical, both), intervention type (pharmacological, non-pharmacological), and procedures for parameterizing age. Findings are summarized with descriptive statistics and discussed in terms of the evolving COVID-19 situation.

Results

The literature search identified 378 results, of which 59 met criteria for data extraction. China, Japan, and South Korea accounted for approximately half of studies, with fewer from South and South-East Asia. Mechanistic models were most common, either compartmental (61.0%), agent-based (1.7%), or combination (18.6%) models. Statistical modeling was applied less frequently (11.9%). Pharmacological interventions were examined in 59.3% of studies, and most considered vaccination, except one study of an antiviral treatment. Non-pharmacological interventions were also considered in 84.7% of studies. Infection, hospitalization, and mortality were outcomes in 91.5%, 30.5%, and 30.5% of studies, respectively. Approximately a third of studies accounted for age, including 10 that also examined mortality. Four of these studies emphasized benefits in terms of mortality from prioritizing older adults for vaccination under conditions of a limited supply; however, one study noted potential benefits to infection rates from early vaccination of younger adults. Few studies (5.1%) considered the impact of vaccination among children.

Conclusion

Early in the COVID-19 pandemic, non-pharmacological interventions helped to mitigate the health burden of COVID-19; however, modeling indicates that high population coverage of effective vaccines will complement and reduce reliance on such interventions. Thus, increasing and maintaining immunity levels in populations through regular booster shots, particularly among at-risk and vulnerable groups, including older adults, might help to protect public health. Future modeling efforts should consider new vaccines and alternative therapies alongside an evolving virus in populations with varied vaccination histories.

Close contact behaviors of university and school students in 10 indoor environments

Close contact routes, including short-range airborne and large-droplet routes, play an important role in the transmission of SARS-CoV-2 in indoor environments. However, the exposure risk of such routes is difficult to quantify due to the lack of data on the close contact behavior of individuals. In this study, a digital wearable device, based on semi-supervised learning, was developed to automatically record human close contact behavior. We collected 337,056 s of indoor close contact of school and university students from 194.5 h of depth video recordings in 10 types of indoor environments. The correlation between aerosol exposure and close contact behaviors was then evaluated. Individuals in restaurants had the highest close contact ratio (64%), as well as the highest probability of face-to-face pattern (78%) during close contact. Accordingly, university students showed greater exposure potential in dormitories than school students in homes, however, a lower exposure was observed in classrooms and postgraduate student offices in comparison with school students in classrooms. In addition, restaurants had the highest aerosol exposure volume for both short-range inhalation and direct deposition on the facial mucosa. Thus, the classroom was established as the primary indoor environment where school students are exposed to aerosols.

Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Using simulation modelling and systems science to help contain COVID-19: A systematic review

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.

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.

Optimization of COVID-19 prevention and control with low building energy consumption

COVID-19 is a global threat. Non-pharmaceutical interventions were commonly adopted for COVID-19 prevention and control. However, during stable periods of the pandemic, energy would be inevitably wasted if all interventions were implemented. The study aims to reduce the building energy consumption when meet the demands of epidemic prevention and control under the stable period of COVID-19. Based on the improved Wells-Riley model considering dynamic quanta generation and pulmonary ventilation rate, we established the infection risk - equivalent fresh air volume - energy consumption model to analyze the infection risk and building energy consumption during different seasons and optimized the urban building energy consumption according to the spatio-temporal population distribution. Shopping centers and restaurants contributed the most in urban energy consumption, and if they are closed during the pandemic, the total infection risk would be reduced by 25%-40% and 15%-25% respectively and the urban energy consumption would be reduced by 30%-40% and 13%-20% respectively. If people wore masks in all public indoor environments (exclude restaurants and KTV), the infection risk could be reduced by 60%-70% and the energy consumption could be reduced by 20%-60%. Gyms pose the highest risk for COVID-19 transmission. If the energy consumption kept the same with the current value, after the optimization, infection risk in winter, summer and the transition season could be reduced by 65%, 53% and 60%, respectively. After the optimization, under the condition of R _t  < 1, the energy consumption in winter, summer, and the transition season could be reduced by 72%, 64%, and 68% respectively.

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures.

Optimization of energy efficiency and COVID-19 pandemic control in different indoor environments

The COVID-19 pandemic has led to considerable morbidity and mortality, and consumed enormous resources (e.g. energy) to control and prevent the disease. It is crucial to balance infection risk and energy consumption when reducing the spread of infection. In this study, a quantitative human, behavior-based, infection risk-energy consumption model for different indoor environments was developed. An optimal balance point for each indoor environment can be obtained using the anti-problem method. For this study we selected Wangjing Block, one of the most densely populated places in Beijing, as an example. Under the current ventilation standard (30 m³/h/person), prevention and control of the COVID-19 pandemic would be insufficient because the basic reproduction number (R₀ ) for students, workers and elders are greater than 1. The optimal required fresh air ventilation rates in most indoor environments are near or below 60 m³/h/person, after considering the combined effects of multiple mitigation measures. In residences, sports buildings and restaurants, the demand for fresh air ventilation rate is relatively high. After our global optimization of infection risk control (R₀  ≤ 1), energy consumption can be reduced by 13.7% and 45.1% on weekdays and weekends, respectively, in contrast to a strategy of strict control (R₀  = 1 for each indoor environment).

Human Close Contact Behavior-Based Interventions for COVID-19 Transmission

COVID-19 has threatened human lives. Countries have implemented various interventions such as vaccination, mask-wearing, body temperature screening, and isolation. However, the effectiveness of single and combined interventions has not yet been accurately analyzed. In this study, an improved SEIR model considering both real human indoor close contact behaviors and susceptibility to COVID-19 was established. Taking Hong Kong as an example, a quantitative assessment of the relationship between the efficiency of single and combined interventions and implementation time and intensity was carried out. The results showed that the infection risk (one-hour close contact with an infected person) of COVID-19 of students, workers, and non-workers/non-students was 3.1%, 8.7%, and 13.6%, respectively. Workplace closures were more effective among built environment interventions. If mask-wearing was mandatorily required in schools, workplaces, supermarkets, shopping centers, and public transport, COVID-19 could not be totally restricted. Workers should be prioritized for vaccination, followed by non-workers/non-students and students. Among all interventions, reducing close contact rate and increasing vaccination rate were better interventions. There was no COVID-19 outbreak (basic reproduction number R0 = 1) if the close contact reduction rate was 59.9% or the vaccination rate reached 89.5%. The results may provide scientific support for COVID-19 prevention and control.

The associations between air pollutant exposure and neutralizing antibody titers of an inactivated SARS-CoV-2 vaccine

Air pollution is a critical risk factor for the prevalence of COVID-19. However, few studies have focused on whether air pollution affects the efficacy of the SARS-CoV-2 vaccine. To better guide the knowledge surrounding this vaccination, we conducted a cross-section study to identify the relationships between air pollutant exposure and plasma neutralizing antibody (NAb) titers of an inactivated SARS-CoV-2 vaccine (Vero cell, CoronaVac, SINOVΛC, China). We recruited 239 healthcare workers aged 21-50 years who worked at Suining Central Hospital. Of these, 207 were included in this study, depending on vaccination date. The data regarding air pollutants were collected to calculate individual daily exposure dose (DED). The geometric mean of all six pollutant DEDs was applied to estimate the combined toxic effects (DED_complex). Then, the participants were divided into two groups based on the mean value of DED_complex. The median plasma NAb titer was 12.81 AU/mL, with 85.99% vaccine efficacy in healthcare workers against SARS-CoV-2. In exposure group, observations included lower plasma NAb titers (median: 11.13 AU/mL vs. 14.56 AU/mL), more peripheral counts of white blood cells and monocytes (mean: 6.71 × 10⁹/L vs. 6.29 × 10⁹/L and 0.49 × 10⁹/L vs. 0.40 × 10⁹/L, respectively), and a higher peripheral monocyte ratio (7.38% vs. 6.50%) as compared to the reference group. In addition, elevated air pollutant DEDs were associated with decreased plasma NAb titers. To our knowledge, this study is the first to report the relationship between air pollutant exposure and plasma NAb titers of the SARS-CoV-2 vaccine. This suggests that long-term exposure to air pollutants may inhibit plasma NAb expression by inducing chronic inflammation. Therefore, to achieve early herd immunity and hopefully curb the COVID-19 epidemic, vaccinations should be administered promptly to those eligible, and environmental factors should be considered as well.

COVID-19 Vaccination Did Not Change the Personal Protective Behaviors of Healthcare Workers in China

Personal protective behaviors of healthcare workers (HCWs) and dynamic changes in them are known to play a major role in the hospital transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, 1,499 HCWs in Chinese hospitals completed an online survey about their knowledge on SARS-CoV-2 transmission and their personal protective behaviors before and after coronavirus disease 2019 (COVID-19) vaccination. Of all the respondents, 89% were vaccinated at the time of the survey and 96% believed that the vaccine was effective or highly effective. Further, 88% of the vaccinated HCWs expressed that they would get revaccinated if the vaccination failed. Compared with HCWs with a lower education level, those with a higher education level had less fear of being infected with SARS-CoV-2 and reported a lower negative impact of the pandemic on how they treated patients. Physicians and nurses were willing to believe that short-range airborne and long-range fomite are possible transmission routes. HCWs with a higher education level had a better knowledge of COVID-19 but worse personal protective behaviors. The fact that HCWs with a longer work experience had worse personal protective behaviors showed that HCWs gradually relax their personal protective behaviors over time. Moreover, vaccination reduced the negative effects of the COVID-19 pandemic on how the HCWs treated patients. Importantly, the survey revealed that after vaccination, HCWs in China did not relax their personal protective behaviors, and it may bring a low potential risk for following waves of variant virus (e.g., delta).

Weakening personal protective behavior by Chinese university students after COVID-19 vaccination

Personal protective behaviors and their dynamic change are known to play a major role in the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal pathogen of the coronavirus disease 2019 (COVID-19) pandemic. In this study, a total of 3229 students in Chinese universities completed an online survey about their knowledge on transmission and personal protective behavior before and after COVID-19 vaccination. Of the respondents, 87.6% had been vaccinated. Most students believed that the large droplet (97.0%) and short-range airborne (89.3%) routes were the two most likely SARS-CoV-2 transmission routes, whereas only 24.1% considered long-range airborne transmission to be possible. Students who would be expected to possess better knowledge about virus transmission (e.g., students of medicine) reported better personal protective behaviors. Female students reported relatively better personal hygiene practices than male students, so did the confident students than their diffident peers. Students washed their hands on average of 5.76 times per day during the pandemic. Students at universities in southern regions washed their hands more frequently but paid less attention to indoor ventilation than did their northern counterparts. Interestingly, students who are fear of being infected had the bad personal hygiene. University students wore 22% less masks in public indoor environments after vaccination. Chinese university students weakened their personal protective behavior after vaccination and it may increase the potential risk of infection in the new waves of variant virus (e.g. delta).

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

Spatial analysis of COVID-19 vaccination research is increasing in recent literature due to the availability of COVID-19 vaccination data that usually contain location components. However, to our knowledge, no previous study has provided a comprehensive review of this research area. Therefore, in this scoping review, we examined the breadth of spatial and spatiotemporal vaccination studies to summarize previous findings, highlight research gaps, and provide guidelines for future research. We performed this review according to the five-stage methodological framework developed by Arksey and O'Malley. We screened all articles published in PubMed/MEDLINE, Scopus, and Web of Science databases, as of 21 September 2021, that had employed at least one form of spatial analysis of COVID-19 vaccination. In total, 36 articles met the inclusion criteria and were organized into four main themes: disease surveillance (n = 35); risk analysis (n = 14); health access (n = 16); and community health profiling (n = 2). Our findings suggested that most studies utilized preliminary spatial analysis techniques, such as disease mapping, which might not lead to robust inferences. Moreover, few studies addressed data quality, modifiable areal unit problems, and spatial dependence, highlighting the need for more sophisticated spatial and spatiotemporal analysis techniques.