Effectiveness of non-pharmaceutical interventions on COVID-19 transmission in 190 countries from 23 January to 13 April 2020

Machine learning models to predict the COVID-19 reproduction rate: combining non-pharmaceutical interventions with sociodemographic and cultural characteristics

Since the beginning of the COVID-19 pandemic, countries worldwide have implemented a set of Non-Pharmaceutical Interventions (NPIs) to prevent the dissemination of the pandemic. Few studies applied machine learning models to compare the use of NPIs, socioeconomic and demographic characteristics, and cultural dimensions in predicting the reproduction rate Rt. We adopted the CRISP-DM methodology using as data sources the "Our World in Data COVID-19," the "Oxford COVID-19 Government Response Tracker" and the Hofstede Insights data. We analyzed the impact that Hofstede's cultural dimensions, the implementation of various degrees of restriction of NPIs and the sociodemographic variables may have in the reproduction rate by applying machine learning models to understand whether cultural characteristics are useful information to improve reproduction rate predictions. We included data from 101 countries to train several machine learning models to compare the results between the models with and without Hofstede's cultural dimensions. Our results show the use of cultural dimensions helps to improve the models, and that the ones that obtained a better prediction of the Rt were the ensemble models, especially the Random Forest.

The great divide between employees: Clustering employee "well-being" during a pandemic

The Covid-19 pandemic is a textbook case of significant situational stress induced by various disruptions beyond mere health concerns, such as social isolation and financial constraints. For the workforce, it is essential to anticipate how these disruptions may undermine employees' resilience, to avoid a negative spiral where poor well-being lowers productivity, reduces economic prospects, and continues to increase worker stress. We measure multiple forms of stress and worries as drivers of well-being-health, economic, social, and psychological-encountered by the workforce during the acute period of the Covid-19 pandemic. The study analyzed data from 2,780 employees across five European countries: France, Germany, Italy, Spain, and Sweden. Overall Concern Score: The overall concern score was 56.8% across four domains: health, economic, social, and psychological. Stressors can be synthesized into five typical groups associated with a variety of mediating factors such as institutional trust, lifestyle, and worker education. The implication is that workers' well-being is heterogeneous and that human resource practices may need at least a segmented approach to well-being if they wish to create an environment of a resilient and productive workforce.

Social distancing and mental health two years into a global pandemic: a sequential exploratory mixed-methods investigation in Hong Kong

INTRODUCTION

The COVID-19 pandemic has led to the implementation of social distancing measures on an unprecedented scale, but their impacts on the mental health of the general public, especially in places with strict and prolonged restrictions, are not well understood. This study explored the Hong Kong public's views on social distancing measures that were implemented approximately two years into the pandemic and examined how these perceptions influence their stay-at-home motivation and mental health based on Protection Motivation Theory (PMT).

DESIGN

A two-phase sequential exploratory mixed-methods study was conducted.

SETTING

Data were collected as part of a larger study of a universal online public health campaign in Hong Kong.

PARTICIPANTS

First, qualitative interviews with 26 participants from diverse backgrounds explored their views on COVID-19 and social distancing measures. Subsequently, a quantitative online survey of 1025 participants from the general population examined the relationship between PMT constructs, stay-at-home motivation, and mental health.

OUTCOME MEASURES

Mental health and PMT constructs, including protective motivation, perceived severity, perceived vulnerability, self-efficacy, response cost, and response efficacy.

RESULTS

The qualitative results identified three themes: 'the inescapable and unpredictable nature of COVID-19', 'stay-at-home impacts all facets of life', and 'the realities and challenges of social distancing during a pandemic'. Subsequently, quantitative findings showed that most PMT constructs, such as perceived severity (ß=0.21, 95% CI=0.15-0.27), perceived vulnerability (ß=0.24, 95% CI=0.18-0.30), self-efficacy (ß=0.27, 95% CI=0.20-0.34) and response efficacy (ß=0.09, 95% CI=0.02-0.15), were associated with stay-at-home motivation (all p<0.01), except for response cost. Higher perceived severity (ß=1.42-1.74), perceived vulnerability (ß=0.93-1.36), and response cost (ß=1.29-1.64) were associated with poorer mental health.

CONCLUSION

This study examined the public perceptions and experiences of strict and prolonged social distancing measures two years into a global pandemic. The findings highlighted the significance of using the PMT model to understand the factors influencing stay-at-home intentions and their mental health during the pandemic. The findings provide valuable insights to guide the development and implementation of future public health interventions.

Productivity Losses due to Health Problems Arising from COVID-19 Pandemic: A Systematic Review of Population-Level Studies Worldwide

AIM

To systematically review the evidence on productivity losses due to health problems arising from the COVID-19 pandemic based on evidence from population-level studies.

METHODS

Following PRISMA statement, we conducted a systematic review using Medline, Embase, Scopus, Web of Science, EconLit, WHO COVID-19 Research and EuropePMC databases and a grey literature search. We included population-level studies using secondary data and qualitatively assessed eligible studies. For a quantitative cross-study comparison, we calculated losses in 2020 international dollars and as a share of gross domestic product. PROSPERO registration number: CRD42023478059.

RESULTS

Thirty-eight studies were eligible for review, most of which reported losses in high-income countries and the European region. COVID-19 was a focus of 33 studies while 3 studies investigated losses from both long COVID and excess mortality. The Human Capital Approach dominated (30 studies) and no study used the Friction Cost Approach. Most studies (84%) reported on premature mortality losses and a quarter provided estimates of losses due to absenteeism. Of the 33 studies eligible for quantitative comparison, we found that the productivity losses ranged from 0 to 2.1% of gross domestic product; the greatest losses were in the high-income countries and for those aged 40-59 years; and losses among men contributed to around 3/4 of the total burden.

CONCLUSION

The available evidence on the topic is limited, particularly considering the methodological approaches used. Thus, more research is needed to reach a more comprehensive understanding of economy-level productivity losses resulting from the recent COVID-19 pandemic.

Secondary SARS-CoV-2 transmission by type of exposure setting among university students

Objective: We aimed to investigate the association between exposure settings and secondary SARS-CoV-2 transmission among university students. Participants: Students diagnosed with COVID-19 (N = 139) and randomly selected controls (N = 262) identified between April 4-December 5, 2021. Methods: This was a 1:2 case-control study. Exposure setting was categorized as academic/occupational, household, social/athletics, and multiple settings. Transmission was assessed by record of positive SARS-CoV-2 test among contacts within 14 days after most recent exposure. Results: Compared to exposure in the academic/occupational setting, all other settings had significantly higher odds of secondary SARS-CoV-2 transmission, adjusting for contact vaccination status, index case vaccination status, and contact sex (p-values ≤ 0.05). In the adjusted model, contact sex was found to be significantly associated with SARS-CoV-2 transmission. Conclusion: Among university students, academic/occupational settings had the lowest odds of SARS-CoV-2 transmission given safety measures in place. Future studies should analyze SARS-CoV-2 genomic sequence data to verify sources of infection.

Correlates of psychological distress among undergraduate women engaged in remote learning through a New York City college during the COVID-19 pandemic

OBJECTIVE

The study's objective is to explore psychological distress (PD) among remote learners during COVID-19.

PARTICIPANTS

Female undergraduates matriculated at an NYC college in Winter 2020.

METHODS

Using the Kessler-6 scale, we defined PD as no/low (LPD), mild/moderate (MPD), and severe (SPD) and assessed if residing in/near NYC modified associations.

RESULTS

PD was common (MPD: 34.1%, SPD: 38.9%). Students identifying as Other/Multiracial had lower MPD odds (aOR = 0.39 [0.17-0.88]). SPD was associated with identifying as White (aOR = 2.02 [1.02-3.99]), unbalanced meals (aOR = 2.59 [1.06-6.30]), violence experience (aOR = 1.77 [1.06-2.94]), no social support (aOR = 3.24 [1.37-7.64]), and loneliness (aOR = 2.52 [1.29-4.95]). Among students in/near NYC, moderate/high drug use (aOR = 2.76 [1.15-6.61]), no social support (aOR = 3.62 [1.10-1.19]), and loneliness (aOR = 2.92 [1.11-7.63]) were SPD correlates.

CONCLUSIONS

PD was high and associated with food insecurity, violence experience, no social support, and loneliness. Living in/near NYC modified drug use, loneliness, and social support associations. Mental health initiatives should address modifiable risk factors to ameliorate pandemic-associated PD.

The Effectiveness of Face Mask Mandates on COVID-19 Transmission in Utah

OBJECTIVE

Throughout the COVID-19 pandemic, the effectiveness of face mask mandates was intensely debated. The objective of this study was to describe how face mask mandates at the state, county, and local levels differed in their effectiveness in reducing the number of COVID-19 cases in the jurisdiction where the mandate was implemented and throughout Utah.

METHODS

We used publicly available data from the Utah Department of Health and Human Services. We calculated the effectiveness of face mask mandates (EFm) in each local health district after 3 key mandates: the enactment of face mask mandates in Salt Lake and Summit counties (SLSC) on June 28, 2020, and June 27, 2020, respectively; the enactment of a statewide face mask mandate on November 9, 2020; and the lifting of the statewide face mask mandate on April 10, 2021.

RESULTS

Most counties in Utah had a reduction in the growth rate of COVID-19 cases after enactment of face mask mandates. We found an average 11.9% reduction in EFm after the introduction of the SLSC face mask mandates, with 8 of 13 local health districts experiencing a reduction, and an average 12.7% reduction after introduction of the statewide mandate, with 12 of 13 local health districts experiencing a reduction. After mandates were lifted, many counties had an increase in EFm.

CONCLUSION

Face mask mandates were an effective way to reduce transmission of COVID-19 in local jurisdictions and in neighboring jurisdictions in Utah. Our evidence supports the use of face mask mandates as a way to prevent disease transmission and be better equipped to respond to future pandemics.

Respiratory syncytial virus disease burden in children and adults from Latin America: a systematic review and meta-analysis

Background

Respiratory Syncytial Virus (RSV) is a common cause of lower respiratory tract infections (LRTI) and hospitalization worldwide. The impact of RSV in Latin America and the Caribbean (LAC) including expensive treatment options, such as palivizumab, have been extensively discussed. However, publications on the impact of RSV disease burden in the region are scarce. This systematic review aimed to determine the incidence and prevalence of RSV in LAC by age and RSV subtype.

Methods

We conducted a systematic review following Cochrane methods to evaluate the disease burden of RSV in LAC countries. We searched studies from January 2012 to January 2023 in literature databases and grey literature without language restrictions. We included guidelines, observational, economic, and surveillance studies from LAC countries. Pairs of reviewers independently selected, and extracted data from included studies. The risk of bias was assessed using the Study Quality Assessment Tools (NHLBI) and AGREE-II. We performed proportion meta-analyses using methods to stabilize the variance. The protocol was registered in PROSPERO (CRD42023393731).

Results

We included 156 studies, mainly from Brazil (25%), Colombia (14.5%), and Argentina (13.8%), as well as four clinical practice guidelines. Most studies were cross-sectional (76.9%) and were classified as low risk of bias (52.6%). The majority included inpatients (85.6%), pediatric (73.7%), and normal-risk patients (67.1%). The highest pooled prevalence was estimated in patients <1 year old (58%), with type A and B prevalence of 52 and 34%, respectively. The RSV-LRTI incidence was 15/100 symptomatic infants aged <2 years old, and the ICU admission was 42%. The RSV-LRTI lethality was 0.6, 3% in patients aged <2 and 0-5 years old, respectively, and 23% among >65 years old high-risk patients. The identified guidelines lack methodological rigor and have limitations in their applicability. The seasonality was more evident in South America than in Central America and The Caribbean, with a clear gap during the pandemic.

Conclusion

This is the most exhaustive and updated body of evidence describing a significant burden of RSV in LAC, particularly at the extremes of life, and its seasonality patterns. Our findings could contribute could contribute facilitating effective prevention and treatment strategies for this significant public health problem.

Systematic review registration

PROSPERO CRD UK (registration number: CRD42023393731).

Mathematical assessment of the roles of age heterogeneity and vaccination on the dynamics and control of SARS-CoV-2

The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained during Wave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic.

A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations

Comparing COVID-19 response strategies across nations is a key step in preparing for future pandemics. Conventional comparisons, which rank individual non-pharmaceutical intervention (NPI) effects, are limited by: (i) a focus on epidemiological outcomes; (ii) NPIs typically being applied as packages of interventions; and (iii) different political, economic and social conditions among nations. Here, we develop a coupled epidemiological-behavioural-macroeconomic model that can transfer NPI effects from a reference nation to a focal nation. This approach quantifies epidemiological, behavioural and economic outcomes while accounting for both packaged NPIs and differing conditions among nations. As a first proof of concept, we take Germany as our focal nation during Spring 2020, and New Zealand and Switzerland as reference nations with contrasting NPI strategies. Our results suggest that, while New Zealand's more aggressive strategy would have yielded modest epidemiological gains in Germany, it would have resulted in substantially higher economic costs while dramatically reducing social contacts. In contrast, Switzerland's more lenient strategy would have prolonged the first wave in Germany, but would also have increased relative costs. More generally, these findings indicate that our approach can provide novel, multifaceted insights on the efficacy of pandemic response strategies, and therefore merits further exploration and development.

Use of the PRECEDE-PROCEED Model in Piloting Vaccine Promotion and Infection Self-Protection: Intervention Development and Effectiveness Examination

OBJECTIVES

This study aimed to tailor and pilot a health education program using the PRECEDE-PROCEED model to promote vaccination and enhance self-protective behaviors against COVID-19 in Hong Kong populations.

STUDY DESIGN

Quasi-experimental study.

METHODS

Phases 1-4 of the PRECEDE-PROCEED model were used to identify the needs for COVID-19 prevention. Strategies to address predisposing, reinforcing, and enabling factors in the PRECEDE-PROCEED model were developed, and an intervention package was generated thereafter. A pre-post experimental study was conducted among 50 participants to preliminarily assess the effects of the intervention based on Phases 5 and 8 of the PRECEDE-PROCEED model.

RESULTS

The 3-month intervention package contained 16 health education videos, 36 health tips, individual consultations, regular reminders of vaccination, incentive of anti-epidemic packages, and vaccine booking services. By the third month, 33 participants took a new dose of COVID-19 vaccine, and 5 participants withdrew. The vaccination rate for new dose achieved 73.3% (95% CI: 58.06-85.40%). Compared with the Hong Kong population in the same period, our study demonstrated higher increase in vaccination rate (9.97 vs. 1.36 doses per 1000 person-days). The percentage of early testing in personal and family level increased to 86.7% and 84.4%, respectively (both p < 0.05). For correct mask wearing and hand washing, the scores increased from a baseline score of 9.1 ± 1.6 and 4.9 ± 1.3 to 9.5 ± 1.0 and 5.3 ± 1.2, respectively (both p < 0.05).

CONCLUSIONS

The application of the PRECEDE-PROCEED model effectively facilitated the stepwise development, implementation, and evaluation of a health education program for improving vaccination rates and fostering self-protective behaviors against infections.

COVID-19 prevention and control effect of non-pharmaceutical interventions-fuzzy-sets qualitative comparative analysis based on 69 countries in the world

Introduction

Coronavirus disease 2019 occurred unexpectedly in late December 2019, it was difficult to immediately develop an effective vaccine or propose targeted medical interventions in the early stages of the outbreak. At this point, non-pharmaceutical interventions (NPIs) are essential components of the public health response to COVID-19. How to combine different NPIs in the early stages of an outbreak to control the spread of epidemics and ensure that the policy combination does not incur high socio-economic costs became the focus of this study.

Methods

We mainly used the fuzzy set qualitative comparative analysis to assess the impact of different combinations of NPIs on the effectiveness of control in the COVID-19 pandemic early stage, using open datasets containing case numbers, country populations and policy responses.

Results

We showed that the configuration of high morbidity results includes one, which is the combination of non-strict face covering, social isolation and travel restrictions. The configuration of non-high morbidity results includes three, one is strict mask wearing measures, which alone constitute sufficient conditions for interpreting the results; the second is strict testing and contact tracing, social isolation; the third is strict testing and contact tracing, travel restriction. The results of the robustness test showed that the number, components and consistency of the configurations have not changed after changing the minimum case frequency, which proved that the analysis results are reliable.

Conclusion

In the early stages of the epidemic, the causes of high morbidity are not symmetrical with the causes of non-high morbidity. Strict face covering is the most basic measure required to prevent and control epidemics, and the combination of non-strict face covering and containment is the most important factor leading to poor prevention and control, and the combination of strict containment and proactive pursuit is the way to achieve superior prevention and control, timely and proactive containment strategies have better prevention and control, and should mobilize the public to cooperate.

A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics

By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.

Viral interference between severe acute respiratory syndrome coronavirus 2 and influenza A viruses

Some respiratory viruses can cause a viral interference through the activation of the interferon (IFN) pathway that reduces the replication of another virus. Epidemiological studies of coinfections between SARS-CoV-2 and other respiratory viruses have been hampered by non-pharmacological measures applied to mitigate the spread of SARS-CoV-2 during the COVID-19 pandemic. With the ease of these interventions, SARS-CoV-2 and influenza A viruses can now co-circulate. It is thus of prime importance to characterize their interactions. In this work, we investigated viral interference effects between an Omicron variant and a contemporary influenza A/H3N2 strain, in comparison with an ancestral SARS-CoV-2 strain and the 2009 pandemic influenza A/H1N1 virus. We infected nasal human airway epitheliums with SARS-CoV-2 and influenza, either simultaneously or 24 h apart. Viral load was measured by RT-qPCR and IFN-α/β/λ1/λ2 proteins were quantified by immunoassay. Expression of four interferon-stimulated genes (ISGs; OAS1/IFITM3/ISG15/MxA) was also measured by RT-droplet digital PCR. Additionally, susceptibility of each virus to IFN-α/β/λ2 recombinant proteins was determined. Our results showed that influenza A, and especially A/H3N2, interfered with both SARS-CoV-2 viruses, but that SARS-CoV-2 did not significantly interfere with A/H3N2 or A/H1N1. Consistently with these results, influenza, and particularly the A/H3N2 strain, caused a higher production of IFN proteins and expression of ISGs than SARS-CoV-2. SARS-CoV-2 induced a marginal IFN production and reduced the IFN response during coinfections with influenza. All viruses were susceptible to exogenous IFNs, with the ancestral SARS-CoV-2 and Omicron being less susceptible to type I and type III IFNs, respectively. Thus, influenza A causes a viral interference towards SARS-CoV-2 most likely through an IFN response. The opposite is not necessarily true, and a concurrent infection with both viruses leads to a lower IFN response. Taken together, these results help us to understand how SARS-CoV-2 interacts with another major respiratory pathogen.

Factors influencing individual vaccine preferences for COVID-19 in the Sunyani Municipality, Ghana: An observational study using discrete choice experiment analysis

Background and Aims

There has been hesitancy among people with regard to accepting vaccines, especially that of coronavirus disease-2019 (COVID-19). This hesitancy is aggravated by the different vaccine alternatives available and what one considers before choosing a particular vaccine. The aim of this article was to investigate some driving factors that can influence an individual's COVID-19 vaccine preference in the presence of other alternatives, using some specific vaccine characteristics.

Methods

Discrete choice questionnaire was designed using the attributes and their corresponding levels to collect data on participants' preference for a COVID vaccine over a period of 12 Weeks in Sunyani, Ghana, with the help of an observational study design. A total of 150 participants receiving Covid-19 vaccines at the University of Energy and Natural Resources Hospital were systematically selected and interviewed. Factors considered included: Efficacy of the vaccine, credibility of the manufacturing company, side effects of the vaccine, and availability of the vaccine. Data was analyzed using the conditional probit of the discrete choice experiment (DCE).

Results

Results from the study using the conditional probit of the discrete choice experiment (DCE) showed efficacy, side effects, and availability as significant attributes for preference. However, there was no preference with respect to the credibility of the manufacturing company. In addition, vaccine availability was not a dis-utility in comparison to the alternatives that are readily available. This disutility was however higher among males than females.

Conclusion

From the study, most respondents preferred a COVID-19 vaccine that is highly efficacious or a vaccine with milder side effect or a vaccine that may not necessarily be readily available. It was also observed that dis-utility is higher among males when it comes to vaccine not being readily available than females as the odds of a female choosing a vaccine that is readily available is much higher compared to their males counterparts.

Persistence and Variation of the Indirect Effects of COVID-19 Restrictions on the Spectrum of Notifiable Infectious Diseases in China: Analysis of National Surveillance Among Children and Adolescents From 2018 to 2021

BACKGROUND

Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown.

OBJECTIVE

This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels.

METHODS

The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference.

RESULTS

A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6% during 2020 but rebounded by 56.9% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95% CI 0.53-0.87), but rebounded in 2021.

CONCLUSIONS

The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections.

Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Factors Influencing Compliance With Social Distancing as a Nonpharmaceutical Intervention Before Vaccine Availability During the COVID-19 Pandemic: A Cross-Sectional Study in South Korea

This study aimed to identify factors influencing compliance with social distancing, a key nonpharmaceutical intervention during the early stages of the coronavirus disease (COVID-19) pandemic. The study population comprised 182 758 Koreans who participated in the 2020 Community Health Survey. Personal characteristics were classified into sociodemographic, health behavioral, and psychosocial factors, and factors associated with social distancing compliance were identified. Health behaviors and psychosocial factors were highly related to compliance with social distancing. Approximately 13% of smokers were less likely to practice physical distancing and 50% of high-risk drinkers were less likely to limit going out or attending gatherings and events. Higher concern about COVID-19 and a more positive perception of the government's response policy were associated with a higher compliance with social distancing. Strategic public health policies considering the characteristics of the public are needed to enhance compliance with nonpharmaceutical interventions during disease outbreaks lacking effective treatments and vaccines.

Morocco's population contact matrices: A crowd dynamics-based approach using aggregated literature data

Estimation of contact patterns is often based on questionnaires and time-use data. The results obtained using these methods have been used extensively over the years and recently to predict the spread of the COVID-19 pandemic. They have also been used to test the effectiveness of non-pharmaceutical measures such as social distance. The latter is integrated into epidemiological models by multiplying contact matrices by control functions. We present a novel method that allows the integration of social distancing and other scenarios such as panic. Our method is based on a modified social force model. The model is calibrated using data relating to the movements of individuals and their interactions such as desired walking velocities and interpersonal distances as well as demographic data. We used the framework to assess contact patterns in different social contexts in Morocco. The estimated matrices are extremely assortative and exhibit patterns similar to those observed in other studies including the POLYMOD project. Our findings suggest social distancing would reduce the numbers of contacts by 95%. Further, we estimated the effect of panic on contact patterns, which indicated an increase in the number of contacts of 11%. This approach could be an alternative to questionnaire-based methods in the study of non-pharmaceutical measures and other specific scenarios such as rush hours. It also provides a substitute for estimating children's contact patterns which are typically assessed through parental proxy reporting in surveys.

Modeling the impact of distancing measures on infectious disease spread: a case study of COVID-19 in the Moroccan population

This paper explores the impact of various distancing measures on the spread of infectious diseases, focusing on the spread of COVID-19 in the Moroccan population as a case study. Contact matrices, generated through a social force model, capture population interactions within distinct activity locations and age groups. These matrices, tailored for each distancing scenario, have been incorporated into an SEIR model. The study models the region as a network of interconnected activity locations, enabling flexible analysis of the effects of different distancing measures within social contexts and between age groups. Additionally, the method assesses the influence of measures targeting potential superspreaders (i.e., agents with a very high contact rate) and explores the impact of inter-activity location flows, providing insights beyond scalar contact rates or survey-based contact matrices. The results suggest that implementing intra-activity location distancing measures significantly reduces in the number of infected individuals relative to the act of imposing restrictions on individuals with a high contact rate in each activity location. The combination of both measures proves more advantageous. On a regional scale, characterized as a network of interconnected activity locations, restrictions on the movement of individuals with high contact rates was found to result in a $ 2 \% $ reduction, while intra-activity location-based distancing measures was found to achieve a $ 44 \% $ reduction. The combination of these two measures yielded a $ 48\% $ reduction.

Impacts of Vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 Variants Alpha and Delta on Coronavirus Disease 2019 Transmission Dynamics in Four Metropolitan Areas of the United States

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x  to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

In-hospital real-time prediction of COVID-19 severity regardless of disease phase using electronic health records

Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.

The interplay among individuals' distress, daily activities, and perceptions of COVID-19 and neighborhood cohesion: A study using network analysis

The reduction of social interactions through non-pharmaceutical interventions (NPIs) has been shown to effectively curb COVID-19 transmission. However, these control measures were often accompanied by changes in people's daily routines and constraints on their activity space, which could lead to mental distress (i.e., anxiety and depression). This study examined the interplay among individuals' anxiety, depression, daily activities, and perceptions of COVID-19 and neighborhood cohesion. Taking Hong Kong as an example, an online survey (N = 376) was conducted to collect data from participants between March 14 to May 11, 2022. The data include respondents' self-reported anxiety and depressive symptoms, daily activities (e.g., smartphone use), perceptions of COVID-19 (e.g., the possibility of infecting COVID-19), and perceptions of neighborhood cohesion. Using network analysis, we found that excessive smartphone use, life disturbance by COVID-19, and a community with people getting along well with each other were significant factors associated with participants' anxiety and depression. Using critical path analysis, we observed that NPIs reduced human mobility, led to delayed bedtime, and increased smartphone use, which were associated with participants' mental distress. We also found that NPIs and COVID-19 were associated with people's perceptions of infection and the severity of COVID-19 and human mobility flexibility, which may further lead to mental distress. Our results also demonstrated that people with high education levels were vulnerable. These results provided important insights for designing appropriate interventions without generating deleterious impacts on people's mental health in the future.

Assessment of Factories on Adherence to COVID-19 Standard Operating Procedures: A Case Study of Wakiso, Mukono, Buikwe, and Jinja Districts, Uganda

Background

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Uganda confirmed the first case of COVID-19 on 21st March, 2020, which led to the first total lockdown in the country. During the lockdown, some factories remained operational; hence, there is a need for a study aimed at assessing the level of adherence to COVID-19 standard operating procedures (SOPs) in factories as a mitigator for the pandemic.

Methods

A cross-sectional study to assess compliance of factories to COVID-19 SOPs was conducted in Wakiso, Mukono, Buikwe, and Jinja districts during the month of September, 2021. This involved visitation of factories and collection of data using the KoboCollect tool by interviewing general managers as well as human resource managers of the factories. A total of 39 factories were included in the study and were categorized into four major groups; food and beverages (15), plastics (5), construction (8), and others (11). Data analysis was done using STATA version 14.2.

Results

Overall adherence to COVID-19 SOPs by the factories was 64.1% (95% CI = 49.1-79.1). Communication and training of employees (79.5%; 95% CI = 66.8-92.2), wearing personal protective equipment (PPE), and respiratory protection (79.5%; 95% CI = 66.8-92.2) as well as enhanced cleaning and disinfection of surfaces (74.4%; 95% CI = 60.6-88.1) were the most implemented SOPs in the factories. Implementation of the SOPs was the highest in Mukono district (88.9%; 95% CI = 68.4-100) and the least in Wakiso district (40.0%; 95% CI = 9.6-70.4). The COVID-19 SOPs were followed mostly in construction material factories (87.5%; 95% CI = 64.6-100) and least in food and beverage factories (40%; 95% CI = 15.2-64.8). There was no significant difference in the adherence of COVID-19 SOPs among the districts (X2 = 5.02 and P=0.17) and factories (X2 = 7.04 and P=0.07). Although good overall adherence to SOPs noted was not dependent on location and type of factory, adherence to some SOPs such as exposure control plan, presence of signages on COVID-19, maintenance of social distance, and implementation of a health control plan varied with location of the district. Likewise, exposure control plan, signages on COVID-19, and maintenance of social distance varied significantly with the type of factory.

Conclusion

This study revealed moderately good overall adherence to COVID-19 SOPs by factories, with variations in the level of implementation of individual SOPs being observed.

Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States

To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. Estimated takeoff times are consistent with genomic surveillance data.

One-Sentence Summary

Using a compartmental model parameterized to reproduce available reports of new Coronavirus Disease 2019 (COVID-19) cases, we quantified the impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2) on regional epidemics in the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix.

The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach

Objectives

This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling.

Methods

To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0).

Results

After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %).

Conclusion

We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.

Effectiveness of Adapted COVID-19 Vaccines and Ability to Establish Herd Immunity against Omicron BA.1 and BA4-5 Variants of SARS-CoV-2

The emergence of novel SARS-CoV-2 variants has raised concerns about the ability of COVID-19 vaccination programs to establish adequate herd immunity levels in the population. This study assessed the effectiveness of adapted vaccines in preventing SARS-CoV-2 infection and the ability of the adapted vaccines to establish herd immunity against emerging Omicron variants. A systematic literature review was conducted to estimate the absolute vaccine effectiveness (aVE) in preventing SARS-CoV-2 infection using adapted vaccines targeting Omicron variants. The ability of the adapted vaccines to establish herd immunity was assessed by taking into account the following factors: aVE, Ro values of SARS-CoV-2 and the use of non-pharmacological interventions (NPIs). This study found meta-analysis-based aVEs in preventing severe disease and SARS-CoV-2 infection of 56-60% and 36-39%, respectively. Adapted vaccines could not establish herd immunity against the Omicron BA.1 and BA.4-5 variants without using non-pharmacological interventions (NPIs). The adapted vaccines could establish herd immunity only by achieving >80% vaccination coverage, using NPIs with greater effectiveness and when 20-30% of individuals were already protected against SARS-CoV-2 in the population. New adapted COVID-19 vaccines with greater effectiveness in preventing SARS-CoV-2 infection must be developed to increase herd immunity levels against emerging SARS-CoV-2 variants in the population.

The unintended consequences of inconsistent closure policies and mobility restrictions during epidemics

Background

Controlling the spread of infectious diseases-even when safe, transmission-blocking vaccines are available-may require the effective use of non-pharmaceutical interventions (NPIs), e.g., mask wearing, testing, limits on group sizes, venue closure. During the SARS-CoV-2 pandemic, many countries implemented NPIs inconsistently in space and time. This inconsistency was especially pronounced for policies in the United States of America (US) related to venue closure.

Methods

Here, we investigate the impact of inconsistent policies associated with venue closure using mathematical modeling and high-resolution human mobility, Google search, and county-level SARS-CoV-2 incidence data from the USA. Specifically, we look at high-resolution location data and perform a US-county-level analysis of nearly 8 million SARS-CoV-2 cases and 150 million location visits, including 120 million church visitors across 184,677 churches, 14 million grocery visitors across 7662 grocery stores, and 13.5 million gym visitors across 5483 gyms.

Results

Analyzing the interaction between venue closure and changing mobility using a mathematical model shows that, across a broad range of model parameters, inconsistent or partial closure can be worse in terms of disease transmission as compared to scenarios with no closures at all. Importantly, changes in mobility patterns due to epidemic control measures can lead to increase in the future number of cases. In the most severe cases, individuals traveling to neighboring jurisdictions with different closure policies can result in an outbreak that would otherwise have been contained. To motivate our mathematical models, we turn to mobility data and find that while stay-at-home orders and closures decreased contacts in most areas of the USA, some specific activities and venues saw an increase in attendance and an increase in the distance visitors traveled to attend. We support this finding using search query data, which clearly shows a shift in information seeking behavior concurrent with the changing mobility patterns.

Conclusions

While coarse-grained observations are not sufficient to validate our models, taken together, they highlight the potential unintended consequences of inconsistent epidemic control policies related to venue closure and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44263-023-00028-z.

Vaccine effectiveness of COVID-19 and rebound in the real world

We intend to evaluate the relationship between the rates of global SARS-CoV-2 vaccination and the number of COVID-19 confirmed cases, as well as the mortality rate after the declaration of a pandemic. Of the data from 191 countries at the time of data retraction, we selected 111 countries that have SARS-CoV-2 vaccination reports. We stratified countries into high-income and non-high-income countries (HIC and non-HIC) based on World Bank income-group. We used a fixed-effects model (FEM) and performed a longitudinal analysis. The number of confirmed cases decreased as the vaccination rates increased in both non-HICs (B =  - 0.027, T =  - 2.0) and HICs (B =  - 0.207, T =  - 17.5). The number of deaths decreased as the vaccination rates increased in both non-HICs (B =  - 0.151, T =  - 2.3) and HICs (B =  - 0.230, T =  - 40.9). For full vaccination, this measure had a negative association with daily confirmed cases and daily deaths in both non-HICs and HICs. In non-HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased. However in HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased in the early phase, but after a certain period, they tended to increase again. We observed a significant association between the increase in vaccination coverage in the real world and reduced daily confirmed cases and deaths. However, as the confirmed cases and deaths have rebounded in HICs, our findings indicate that COVID-19 is not completely prevented through vaccine distribution.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: "contact restriction (up to 5 people)", "strict contact restriction", "curfew", "events permitted up to 100 people", "mask requirement in shopping malls", "restaurant closure", "restaurants permitted only with test", "school closure" and "general behavioral changes". We found a considerable reduction in the instantaneous reproduction number by "general behavioral changes", "strict contact restriction", "restaurants permitted only with test", "contact restriction (up to 5 people)", "restaurant closure" and "curfew". No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

On the adoption of nonpharmaceutical interventions during the pandemic: An evolutionary game model

The adoption of behavioral nonpharmaceutical interventions (NPIs) among the public is essential for tackling the COVID-19 pandemic, yet presents challenges due to the complexity of human behaviors. A large body of literature has utilized classic game theory to investigate the population's decisions regarding the adoption of interventions, where the static solution concept such as the Nash equilibrium is studied. However, individual adoption behavior is not static, instead it is a dynamic process that involves the strategic interactions with other counterparts over time. The study of quantitatively analyzing the dynamics on precautionary behavior during an outbreak is rather scarce. This article fills the research gap by developing an evolutionary game-theoretic framework to model the dynamics of population behavior on the adoption of NPI. We construct the two-group asymmetric game, where behavioral change for each group is characterized by replicator equations. Sensitivity analyses are performed to examine the long-term stability of equilibrium points with respect to perturbation of model parameters. We found that the limiting behavior of intervention adoption in the population consists of only pure strategies in a game setting, indicating that the evolutionary outcome is that everyone either takes up the preventive measure or not. We also applied the framework to examine the mask-wearing behavior, and validated with actual data. Overall, this article provides insights into population dynamics on the adoption of intervention strategy during the outbreak, which can be beneficial for policy makers to better understand the evolutionary trajectory of population behavior.

Psychological distress is affected by fear of COVID-19 via lifestyle disruption and leisure restriction among older adults in Japan: a cross-sectional study

Introduction

Engaging in social activities is an essential component of a healthy lifestyle for community-dwelling older adults. Critically, as with past disasters, there is concern about the effects of long-term activity restrictions due to the coronavirus disease 2019 (COVID-19) pandemic on health of older adults. However, the precise associations between fear of COVID-19, lifestyle satisfaction, leisure activities, and psychological distress are unclear.

Objective

The purpose of this study was to comprehensively determine the associations between fear of COVID-19, lifestyle satisfaction, leisure engagement, and psychological distress among community-dwelling older adults in the context of the ongoing COVID-19 pandemic.

Materials and methods

A questionnaire survey administered by mail was conducted from October 1 to October 15, 2021. The questionnaire included the Fear of COVID-19 Scale, the Lifestyle Satisfaction Scale, the Leisure Activity Scale for Contemporary Older Adults, and the Kessler Psychological Distress Scale-6. Based on previous studies, we developed a hypothetical model for the association between fear of COVID-19, lifestyle satisfaction, leisure engagement, and psychological distress and performed structural equation modeling to assess the relationships between these variables.

Results

Participants included 301 Japanese citizens (23.6% male, 76.4% female), with a mean age of 76.7 ± 4.58 years. Goodness-of-fit from structural equation modeling was generally good. Analysis of standardized coefficients revealed a significant positive relationship between fear of COVID-19 and psychological distress (β = 0.33, p < 0.001) and lifestyle satisfaction and leisure activities (β = 0.35, p < 0.001). We further observed a significant negative relationship between fear of COVID-19 and lifestyle satisfaction (β = -0.23, p < 0.001) and between leisure activities and psychological distress (β = -0.33, p < 0.001).

Conclusion

Fear of COVID-19 is significantly associated with psychological distress, both directly and via its effects on lifestyle satisfaction and leisure activities. That is, not only did fear of COVID-19 directly impact psychological distress of participants, it also affected psychological distress through lifestyle disruption and leisure restriction. This results may be used to better understand how a national emergency that substantially restricts daily life, such as COVID-19 or an earthquake disaster, can affect the psychological health and wellbeing of older, community-dwelling adults.

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Contact patterns of older adults with and without frailty in the Netherlands during the COVID-19 pandemic

BACKGROUND

During the COVID-19 pandemic, social distancing measures were imposed to protect the population from exposure, especially older adults and people with frailty, who have the highest risk for severe outcomes. These restrictions greatly reduced contacts in the general population, but little was known about behaviour changes among older adults and people with frailty themselves. Our aim was to quantify how COVID-19 measures affected the contact behaviour of older adults and how this differed between older adults with and without frailty.

METHODS

In 2021, a contact survey was carried out among people aged 70 years and older in the Netherlands. A random sample of persons per age group (70-74, 75-79, 80-84, 85-89, and 90 +) and gender was invited to participate, either during a period with stringent (April 2021) or moderate (October 2021) measures. Participants provided general information on themselves, including their frailty, and they reported characteristics of all persons with whom they had face-to-face contact on a given day over the course of a full week.

RESULTS

In total, 720 community-dwelling older adults were included (overall response rate of 15%), who reported 16,505 contacts. During the survey period with moderate measures, participants without frailty had significantly more contacts outside their household than participants with frailty. Especially for females, frailty was a more informative predictor of the number of contacts than age. During the survey period with stringent measures, participants with and without frailty had significantly lower numbers of contacts compared to the survey period with moderate measures. The reduction of the number of contacts was largest for the eldest participants without frailty. As they interact mostly with adults of a similar high age who are likely frail, this reduction of the number of contacts indirectly protects older adults with frailty from SARS-CoV-2 exposure.

CONCLUSIONS

The results of this study reveal that social distancing measures during the COVID-19 pandemic differentially affected the contact patterns of older adults with and without frailty. The reduction of contacts may have led to the direct protection of older adults in general but also to the indirect protection of older adults with frailty.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

BACKGROUND

Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.

METHODS

To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).

RESULTS

A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.

CONCLUSIONS

The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

Protection against Severe Illness versus Immunity-Redefining Vaccine Effectiveness in the Aftermath of COVID-19

Anti-SARS-CoV-2 vaccines have played a pivotal role in reducing the risk of developing severe illness from COVID-19, thus helping end the COVID-19 global public health emergency after more than three years. Intriguingly, as SARS-CoV-2 variants emerged, individuals who were fully vaccinated did get infected in high numbers, and viral loads in vaccinated individuals were as high as those in the unvaccinated. However, even with high viral loads, vaccinated individuals were significantly less likely to develop severe illness; this begs the question as to whether the main effect of anti-SARS-CoV-2 vaccines is to confer protection against severe illness or immunity against infection. The answer to this question is consequential, not only to the understanding of how anti-SARS-CoV-2 vaccines work, but also to public health efforts against existing and novel pathogens. In this review, we argue that immune system sensitization-desensitization rather than sterilizing immunity may explain vaccine-mediated protection against severe COVID-19 illness even when the SARS-CoV-2 viral load is high. Through the lessons learned from COVID-19, we make the case that in the disease's aftermath, public health agencies must revisit healthcare policies, including redefining the term "vaccine effectiveness."

Effectiveness of behavioural interventions to influence COVID-19 outcomes: A scoping review

Behavioural non-pharmaceutical interventions (NPIs) (e.g., mask wearing, quarantine, restriction on gatherings, physical distancing) have been used to interrupt transmission of COVID-19 and to reduce the impacts of the pandemic. The aim of this scoping review was to document the efficacy of behavioural NPIs to positively influence COVID-19 outcomes. Following PRISMA guidelines, a systematic search was conducted of PubMed, ScienceDirect, Psych INFO, Medline, CINAHL and Scopus for studies published between January 2020 and February 2023. Seventy -seven studies were eligible to be included in the review. Majority of the studies were conducted in high-income countries, with fewer studies in low- or middle-income countries. School closure, mask wearing, and non-essential business closure and shelter-in-place orders were the most prevalent NPIs investigated. School closure and mask wearing reported high effectiveness while shelter-in-place orders reported less effectiveness. Shelter-in-place orders when used in conjunction with other measures, did not enhance effectiveness. Public event bans, physical distancing, handwashing, and travel restrictions were largely effective, while the effectiveness of gathering restrictions depended on the limitation on numbers. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, the use of behavioural NPIs in combinations was reported to yield more effective results. Moreover, behavioural NPIs were reported to be dependent on their consistent use and were difficult behaviours to maintain, highlighting the need for behavioural change. This review highlighted the effectiveness of behavioural NPIs to positively influence COVID-19 reduction outcomes. Further research to promote country- and context-specific documents that will enhance the effectiveness of behavioural NPIs.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

The Effects of Non-Pharmaceutical Interventions on COVID-19 Cases, Hospitalizations, and Mortality: A Systematic Literature Review and Meta-Analysis

Introduction

To assess the effects of various non-pharmaceutical interventions (NPI) on cases, hospitalizations, and mortality during the first wave of the COVID-19 pandemic.

Methods

To empirically investigate the impacts of different NPIs on COVID-19-related health outcomes, a systematic literature review was conducted. We studied the effects of 10 NPIs on cases, hospitalizations, and mortality across three periodic lags (2, 3, and 4 weeks-or-more following implementation). Articles measuring the impact of NPIs were sourced from three databases by May 10, 2022, and risk of bias was assessed using the Newcastle-Ottawa scale.

Results

Across the 44 papers, we found that mask wearing corresponded to decreased per capita cases across all lags (up to -2.71 per 100,000). All NPIs studied except business and bar/restaurant closures corresponded to reduced case growth rates in the two weeks following implementation, while policy stringency and travelling restrictions were most effective after four. While we did not find evidence of reduced deaths in our per capita estimates, policy stringency, masks, SIPOs, limited gatherings, school and business closures were associated with decreased mortality growth rates. Moreover, the two NPIs studied in hospitalizations (SIPOs and mask wearing) showed negative estimates.

Conclusions

When assessing the impact of NPIs, considering the duration of effectiveness following implementation has paramount significance. While some NPIs may reduce the COVID-19 impact, others can disrupt the mitigative progression of containing the virus. Policymakers should be aware of both the scale of their effectiveness and duration of impact when adopting these measures for future COVID-19 waves.

A global analysis of COVID-19 infection fatality rate and its associated factors during the Delta and Omicron variant periods: an ecological study

Background

The Omicron variant of SARS-CoV-2 is more highly infectious and transmissible than prior variants of concern. It was unclear which factors might have contributed to the alteration of COVID-19 cases and deaths during the Delta and Omicron variant periods. This study aimed to compare the COVID-19 average weekly infection fatality rate (AWIFR), investigate factors associated with COVID-19 AWIFR, and explore the factors linked to the increase in COVID-19 AWIFR between two periods of Delta and Omicron variants.

Materials and methods

An ecological study has been conducted among 110 countries over the first 12 weeks during two periods of Delta and Omicron variant dominance using open publicly available datasets. Our analysis included 102 countries in the Delta period and 107 countries in the Omicron period. Linear mixed-effects models and linear regression models were used to explore factors associated with the variation of AWIFR over Delta and Omicron periods.

Findings

During the Delta period, the lower AWIFR was witnessed in countries with better government effectiveness index [β = -0.762, 95% CI (-1.238)-(-0.287)] and higher proportion of the people fully vaccinated [β = -0.385, 95% CI (-0.629)-(-0.141)]. In contrast, a higher burden of cardiovascular diseases was positively associated with AWIFR (β = 0.517, 95% CI 0.102-0.932). Over the Omicron period, while years lived with disability (YLD) caused by metabolism disorders (β = 0.843, 95% CI 0.486-1.2), the proportion of the population aged older than 65 years (β = 0.737, 95% CI 0.237-1.238) was positively associated with poorer AWIFR, and the high proportion of the population vaccinated with a booster dose [β = -0.321, 95% CI (-0.624)-(-0.018)] was linked with the better outcome. Over two periods of Delta and Omicron, the increase in government effectiveness index was associated with a decrease in AWIFR [β = -0.438, 95% CI (-0.750)-(-0.126)]; whereas, higher death rates caused by diabetes and kidney (β = 0.472, 95% CI 0.089-0.855) and percentage of population aged older than 65 years (β = 0.407, 95% CI 0.013-0.802) were associated with a significant increase in AWIFR.

Conclusion

The COVID-19 infection fatality rates were strongly linked with the coverage of vaccination rate, effectiveness of government, and health burden related to chronic diseases. Therefore, proper policies for the improvement of vaccination coverage and support of vulnerable groups could substantially mitigate the burden of COVID-19.

The city-wide full-scale interactive application of sewage surveillance programme for assisting real-time COVID-19 pandemic control - A case study in Hong Kong

The paper discusses the implementation of Hong Kong's tailor-made sewage surveillance programme led by the Government, which has demonstrated how an efficient and well-organized sewage surveillance system can complement conventional epidemiological surveillance to facilitate the planning of intervention strategies and actions for combating COVID-19 pandemic in real-time. This included the setting up of a comprehensive sewerage network-based SARS-CoV-2 virus surveillance programme with 154 stationary sites covering 6 million people (or 80 % of the total population), and employing an intensive monitoring programme to take samples from each stationary site every 2 days. From 1 January to 22 May 2022, the daily confirmed case count started with 17 cases per day on 1 January to a maximum of 76,991 cases on 3 March and dropped to 237 cases on 22 May. During this period, a total of 270 "Restriction-Testing Declaration" (RTD) operations at high-risk residential areas were conducted based on the sewage virus testing results, where over 26,500 confirmed cases were detected with a majority being asymptomatic. In addition, Compulsory Testing Notices (CTN) were issued to residents, and the distribution of Rapid Antigen Test kits was adopted as alternatives to RTD operations in areas of moderate risk. These measures formulated a tiered and cost-effective approach to combat the disease in the local setting. Some ongoing and future enhancement efforts to improve efficacy are discussed from the perspective of wastewater-based epidemiology. Forecast models on case counts based on sewage virus testing results were also developed with R2 of 0.9669-0.9775, which estimated that up to 22 May 2022, around 2,000,000 people (~67 % higher than the total number of 1,200,000 reported to the health authority, due to various constraints or limitations) had potentially contracted the disease, which is believed to be reflecting the real situation occurring in a highly urbanized metropolis like Hong Kong.

Epidemiology of Respiratory Infections during the COVID-19 Pandemic

To face the COVID-19 outbreak, a wide range of non-pharmaceutical interventions (NPIs) aimed at limiting the spread of the virus in communities, such as mask-wearing, hand hygiene, social distancing, travel restrictions, and school closures, were introduced in most countries. Thereafter, a significant reduction of new asymptomatic and symptomatic COVID-19 cases occurred, although there were differences between countries according to the type and duration of the NPIs. In addition, the COVID-19 pandemic has been accompanied by significant variations in the global incidence of diseases due to the most common non-SARS-CoV-2 respiratory viruses and some bacteria. In this narrative review, the epidemiology of the most common non-SARS-CoV-2 respiratory infections during the COVID-19 pandemic is detailed. Moreover, factors that could have had a role in modifying the traditional circulation of respiratory pathogens are discussed. A literature analysis shows that NPIs were the most important cause of the general reduction in the incidence of influenza and respiratory syncytial virus infection in the first year of the pandemic, although the different sensitivity of each virus to NPIs, the type and duration of measures used, as well as the interference among viruses may have played a role in modulating viral circulation. Reasons for the increase in the incidences of Streptococcus pneumoniae and group A Streptococcus infections seem strictly linked to immunity debt and the role played by NPIs in reducing viral infections and limiting bacterial superimposed infections. These results highlight the importance of NPIs during pandemics, the need to monitor the circulation of infectious agents that cause diseases similar to those caused by pandemic agents, and the need to make efforts to improve coverage with available vaccines.

Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.

Built environment factors moderate pandemic fatigue in social distance during the COVID-19 pandemic: A nationwide longitudinal study in the United States

Non-pharmaceutical interventions (NPIs) remain some of the most effective measures for coping with the ever-changing coronavirus disease 2019 (COVID-19) pandemic. Pandemic fatigue, which manifests as the declined willingness to follow the recommended protective behaviors (e.g., keeping social distance policies, wearing masks), has commanded increasing attention from researchers and policymakers after the prolonged NPIs and COVID-19 worldwide. However, long-term changes in pandemic fatigue are not well understood, especially amidst the ever-changing pandemic landscape. Built environment factors have been shown to positively affect mental and physical health, but it is still unclear whether built environments can moderate pandemic fatigue. In this study, we used Google mobility data to investigate longitudinal trends of pandemic fatigue in social distance since the onset of NPIs enforcement in the United States. The results indicated that pandemic fatigue continuously worsened over nearly two years of NPIs implementation, and a sharp increase occurred after the vaccination program began. Additionally, we detected a significant moderation effect of greenspace and urbanicity levels on pandemic fatigue. People living in areas with high levels of greenness or urbanicity experienced lower levels of pandemic fatigue. These findings not only shed new light on the effects of greenness and urbanicity on COVID-19 pandemic fatigue, but also provide evidence for developing more tailored and effective strategies to cope with pandemic fatigue.

Understanding the travel challenges and gaps for older adults during the COVID-19 outbreak: Insights from the New York City area

The COVID-19 pandemic has greatly impacted lifestyles and travel patterns, revealing existing societal and transportation gaps and introducing new challenges. In the context of an aging population, this study investigated how the travel behaviors of older adults (aged 60+) in New York City were affected by COVID-19, using an online survey and analyzing younger adult (aged 18-59) data for comparative analysis. The purpose of the study is to understand the pandemic's effects on older adults' travel purpose and frequency, challenges faced during essential trips, and to identify potential policies to enhance their mobility during future crises. Descriptive analysis and Wilcoxon signed-rank tests were used to summarize the changes in employment status, trip purposes, transportation mode usage, and attitude regarding transportation systems before and during the outbreak and after the travel restrictions were lifted. A Natural Language Processing model, Gibbs Sampling Dirichlet Multinomial Mixture, was adopted to open-ended questions due to its advantage in extracting information from short text. The findings show differences between older and younger adults in telework and increased essential-purpose trips (e.g., medical visits) for older adults. The pandemic increased older adults' concern about health, safety, comfort, prices when choosing travel mode, leading to reduced transit use and walking, increased driving, and limited bike use. To reduce travel burdens and maintain older adults' employment, targeted programs improving digital skills (telework, telehealth, telemedicine) are recommended. Additionally, safe, affordable, and accessible transportation alternatives are necessary to ensure mobility and essential trips for older adults, along with facilitation of walkable communities.

Determinants of proper disposal of single-use masks: knowledge, perception, behavior, and intervention measures

Background

Although many studies testify to consumer behavior's role in the context of waste-related sustainability objectives, little research examined what people know, think, and feel about the environmental impacts of their personal protective equipment (PPE) or their behavior towards them, in general. Therefore, the present article complements existing information about the public perceptions, knowledge, and behavior of single-use masks in a context where the pandemic has put increasing pressure on waste management public services. From February to June 2020, municipal solid waste increased ten times in Romania. The study identified the factors that predicted the proper disposal of single-use masks and the measures preferred to prevent or minimize the negative impact of single-use mask waste.

Method

Data from a representative sample of 705 Romanians were collected using a structured questionnaire. The data were analyzed with SPSS and SmartPLS. The Cochran's Q test was run to determine the existence of differences between percentages of people who preferred various measures. Dunn's test with a Bonferroni correction was used to identify the exact pair of groups where the differences were located. The study utilized structural equation models (SEM) based on at least partial squares with SmartPLS software (3.2.8) to investigate causal links between constructs. The model considered that the dependent variable (environmentally friendly behavior: proper disposal of single-use masks) could be influenced by the knowledge, perception, behavior, and demographics variables.

Results

The findings indicated that knowledge of the type of material of single-use masks had a direct positive (β = 0.173) and significant effect on their proper disposal. The perception of mask waste impact has a negative and significant (β = -0.153, p < 0.001) impact on the proper disposal of single-use masks. This path coefficient illustrates that the worse the perceived impact of single-use masks on waste management activity, the higher the proper disposal of single-use masks. Gender has a positive (β = 0.115) and significant (p < 0.001) effect on the proper disposal of single-use masks.

Conclusions

It was concluded that the 5Rs waste management approach should be reconsidered for single-use mask waste. For example, "Reuse" and the classic "Recycle" have limited applications since they may lead to virus transmission and possible infection. "Reducing" the use of single-use masks could have repercussions on one's health. Summing up, the study outlined recommendations for effective interventions for the proper disposal of single-use masks from the perspective of behavioral studies.