Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease

Changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS): a two-year prospective cohort study in Israel

Background

Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS).

Methods

We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS.

Findings

The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34-45] hours for influenza, 53 [95% CI: 49-58] hours for COVID-19, and 38 [95% CI: 32-46] hours for GAS, with 73 [95% CI: 67-78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18-27] hours for COVID-19, and 62 [95% CI: 54-68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8-72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4-66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1-64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing.

Interpretation

These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions.

Funding

This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.

From the Public Health Perspective: a Scalable Model for Improving Epidemiological Testing Efficacy in Low- and Middle-Income Areas

The globe is an organically linked whole, and in the pandemic era, COVID-19 has brought heavy public safety threats and economic costs to humanity as almost all countries began to pay more attention to taking steps to minimize the risk of harm to society from sudden-onset diseases. It is worth noting that in some low- and middle-income areas, where the environment for epidemic detection is complex, the causative and comorbid factors are numerous, and where public health resources are scarce. It is often more difficult than in other areas to obtain timely and effective detection and control in the event of widespread virus transmission, which, in turn, is a constant threat to local and global public health security. Pandemics are preventable through effective disease surveillance systems, with nonpharmacological interventions (NPIs) as the mainstay of the control system, effectively controlling the spread of epidemics and preventing larger outbreaks. However, current state-of-the-art NPIs are not applicable in low- and middle-income areas and tend to be decentralized and costly. Based on a 3-year case study of SARS-CoV-2 preventive detection in low-income areas in south-central China, we explored a strategic model for enhancing disease detection efficacy in low- and middle-income areas. For the first time, we propose an integrated and comprehensive approach that covers structural, social, and personal strategies to optimize the epidemic surveillance system in low- and middle-income areas. This model can improve the local epidemic detection efficiency, ensure the health care needs of more people, reduce the public health costs in low- and middle-income areas in a coordinated manner, and ensure and strengthen local public health security sustainably.

Lessons should be learned: Why did we not learn from the Spanish flu?

COVID-19 has become a global pandemic that has affected millions of people worldwide. The disease is caused by the novel coronavirus that was first reported in Wuhan, China, in December 2019. The virus is highly contagious and can spread from person to person through respiratory droplets when an infected person coughs, sneezes, talks, or breathes. The symptoms of COVID-19 include fever, cough, and shortness of breath, and in severe cases, it can lead to respiratory failure, pneumonia, and death. The Spanish flu, caused by the H1N1 influenza virus, and the COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 are two of the most significant global health crises in history. While these two pandemics occurred almost a century apart and are caused by different types of viruses, there are notable similarities in their impact, transmission, and public health responses. Here are some key similarities between the Spanish flu and SARS-CoV-2. The Spanish flu pandemic of 1918-1919 stands as one of the deadliest pandemics in human history, claiming the lives of an estimated 50 million people worldwide. Its impact reverberated across continents, leaving behind a legacy of devastation and lessons that, unfortunately, seem to have been forgotten or ignored over time. Despite the advancements in science, medicine, and public health in the intervening century, humanity found itself facing a strikingly similar situation with the outbreak of the COVID-19 pandemic. Additionally, amidst the search for effective measures to combat COVID-19, novel approaches such as iodine complexes, such as Iodine-V has emerged as potential interventions, reflecting the ongoing quest for innovative solutions to mitigate the impact of pandemics. This raises the poignant question: why did we not learn from the Spanish flu?

Adaptive Time-Location Sampling for COMPASS: A SARS-CoV-2 Prevalence Study in Fifteen Diverse Communities in the United States

BACKGROUND

COVID-19 has placed a disproportionate burden on underserved racial and ethnic groups, community members working in essential industries, those living in areas of high population density, and those reliant on in-person services such as transportation. The goal of this study was to estimate the cross-sectional prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 sociodemographically diverse communities in the United States and to develop a statistical design that could be rigorously implemented amidst unpredictable stay-at-home COVID-19 guidelines.

METHODS

We used time-location sampling with complex sampling involving stratification, clustering of units, and unequal probabilities of selection to recruit individuals from selected communities. We safely conducted informed consent, specimen collection, and face-to-face interviews outside of public venues immediately following recruitment.

RESULTS

We developed an innovative sampling design that adapted to constraints such as closure of venues, changing infection hotspots, and uncertain policies. We updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. We created site-specific survey weights that adjusted sampling probabilities for nonresponse and calibrated to county-level margins on age and sex at birth.

CONCLUSIONS

Although the study itself was specific to COVID-19, the strategies presented in this article could serve as a case study that can be adapted for performing population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

Compliance with COVID-19 Physical Distancing Mandates in Oman: The Role of Health Literacy and Internal Health Locus of Control

BACKGROUND

Research indicates that the effectiveness of coronavirus disease 2019 (COVID-19) physical distancing mandates is influenced by several individual factors, including health literacy; internal health locus of control (IHLOC), the belief that physical distancing can reduce COVID-19 risk; social norms; self-efficacy; and perceptions of the benefits and barriers associated with distancing. However, further investigation is needed to understand the links between these factors and compliance intentions.

OBJECTIVE

This study investigates the mechanism linking these factors with the intentions to comply with physical distancing mandates.

METHODS

A total of 759 participants (Mean age = 29.13, standard deviation [SD] = 8.33; 68.5% women) were surveyed online from September 2020 to October 2020. Data were analyzed using ANOVA (analysis of variance) and structural equation modeling.

KEY RESULTS

Health literacy was associated with more perceived benefits (β = .175, p = .001), greater self-efficacy (β = .193, p < .001), and less perceived barriers (β = -.391, p < .001). IHLOC was significantly associated with greater perceived benefits (β = .156, p = .007) and self-efficacy (β = .294, p < .001). Family descriptive norms were significantly associated with fewer perceived barriers (β = -.276, p < .001), while injunctive norms were associated with more perceived benefits (β = .202, p = .001) and higher self-efficacy (β = .299, p < .001). Intentions to adhere to physical distancing mandates were significantly associated with past compliance (β = .427, p < .001) and perceived barriers (β = -.205, p < .001) and benefits (β = .295, p < .001). Post-hoc mediation analyses revealed several small yet significant indirect effects, highlighting the complex pathways shaping adherence intentions.

CONCLUSIONS

This study identifies how health literacy, IHLOC, social norms, perceived benefits and barriers, and self-efficacy intricately shape intentions to comply with physical distancing mandates. These findings offer valuable implications for public health policy and interventions. [HLRP: Health Literacy Research and Practice. 2024;8(2):e69-e78.].

Evaluating the Effectiveness of Public Health Measures During Infectious Disease Outbreaks: A Systematic Review

Over the previous three decades, the incidence of infectious disease outbreaks has considerably increased and the trend is expected to increase further. Public health measures are essential for controlling and preventing emerging outbreaks of infectious illnesses. This study is aimed at evaluating the effectiveness of public health measures during infectious disease outbreaks by summarizing the outcomes from the available evidence in the literature. A systematic review was carried out through a detailed search strategy using specific keywords applied across different electronic databases, including the Science Direct, PubMed, and EMBASE databases. Studies published between 2015 and 2024 were included with a focus on cohorts, clinical trials, longitudinal studies, case-control, and quasi-experimental studies. Low-quality studies and those published before 2015 along with incorrect findings or measures were excluded. A standardized form was used for data extraction. The quality of included studies and the risk of bias were assessed through relevant techniques. The obtained data was narrative synthesized and findings were organized systematically. The reviewed studies revealed that public health measures are considerably effective against infectious disease outbreaks. The success of various measures such as social isolation, confinement measures, and public education on hygiene against different outbreaks of respiratory infectious diseases has been well-established in the literature. Moreover, the timing of intervention application plays a vital role in their success. The implementation in the early phase of an outbreak is highly effective, as it protects more people from infection and controls the overall burden of the disease. The systematic review provided valuable insights into the efficiency of public health measures in monitoring outbreaks of infectious illnesses. The main findings suggest that appropriate public health interventions are effective in controlling the incidence of contagious disease outbreaks. Ongoing research strives to investigate measures that are most effective from the perspective of public health against various transmittable diseases to prevent future outbreaks.

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.

Covid19Vaxplorer: A free, online, user-friendly COVID-19 vaccine allocation comparison tool

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison. We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison. We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023, 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters.

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.

Assessing the Impact of Non-Pharmaceutical Interventions on Consumer Mobility Patterns and COVID-19 Transmission in the US

The initial outbreak of COVID-19 during late December 2019 and the subsequent global pandemic markedly changed consumer mobility patterns worldwide, largely in response to government-ordered non-pharmaceutical interventions (NPIs). In this study, we investigate these changes as they relate to the initial spread of COVID-19 within two states-Massachusetts and Michigan. Specifically, we use linear and generalized linear mixed-effects models to quantify the relationship between four NPIs and individuals' point-of-sale (POS) credit card transactions, as well as the relationship between subsequent changes in POS transactions and county-level COVID-19 case growth rates. Our analysis reveals a significant negative association between NPIs and daily POS transactions, particularly a dose-response relationship, in which stringent workplace closures, stay-at-home requirements, and gathering restrictions were all associated with decreased POS transactions. We also uncover a significant positive association between 12-day lagged changes in POS transactions compared to pre-pandemic baselines and county-level COVID-19 case growth rates. Overall, our study supports previous findings that early NPIs reduced human mobility and COVID-19 transmission in the US, providing policymakers with quantitative evidence concerning the effectiveness of NPIs.

Social Distancing in the COVID-19 Pandemic: Associated Factors, Health Outcomes, and Implications

Social distancing has reemerged as a public health measure for containing the spread of COVID-19. This integrative review aims to analyze the historical use of social distancing, the current application during COVID-19, individual factors that affect social distancing practices, and consequential health outcomes. We analyzed relevant literature from searches conducted on Scopus, PubMed, and PsycINFO. We found that resources, culture, age, gender, and personality are associated with the degree to which people practice social distancing. Furthermore, social distancing changes our lifestyles and behavior and results in multifaceted health outcomes, including decreased physical activity and sunlight exposure, increased weight gain, and impaired sleep quality. On the positive side, social distancing has been linked to reduced crime rates and environmental damage, as well as better social and family ties. Future interventions may be utilized to increase adherence to social distancing practices and to mitigate the negative health effects of social distancing.

An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic

The COVID-19 pandemic has remarkably heightened concerns regarding the prediction of communicable disease spread. This study introduces an innovative agent-based modeling approach. In this model, the quantification of human-to-human transmission aligns with the dynamic variations in the viral load within an individual, termed "within-host" and adheres to the susceptible-infected-recovered (SIR) process, referred to as "between-host." Variations in the viral load over time affect the infectivity between individual agents. This model diverges from the traditional SIR model, which employs a constant transmission probability, by incorporating a dynamic, time-dependent transmission probability influenced by the viral load in a host agent. The proposed model retains the time-integrated transmission probability characteristic of the conventional SIR model. As observed in this model, the overall epidemic size remains consistent with the predictions of the standard SIR model. Nonetheless, compared to predictions based on the classical SIR process, notable differences existed in the peak number of the infected individuals and the timing of this peak. These nontrivial differences are induced by the direct correlation between the time-evolving transmission probability and the viral load within a host agent. The developed model can inform targeted intervention strategies and public health policies by providing detailed insights into disease spread dynamics, crucial for effectively managing epidemics.

Covid19Vaxplorer: a free, online, user-friendly COVID-19 Vaccine Allocation Comparison Tool

Background

There are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in ad-hoc policies. We developed Covid19Vaxplorer (https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison.

Methods

We developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool, Covid19Vaxplorer that was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status). Covid19Vaxplorer connects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison.

Results

We provide two usage examples of Covid19Vaxplorer for vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters. Covid19Vaxplorer allows users in 183 regions in the world to compare several vaccination strategies simultaneously, adjusting parameters to their local epidemics, infrastructure and logistics. Covid19Vaxplorer is an online, free, user-friendly tool that facilitates evidence-based decision making for vaccine distribution.

Sociodemographic Variables Can Guide Prioritized Testing Strategies for Epidemic Control in Resource-Limited Contexts

BACKGROUND

Targeted surveillance allows public health authorities to implement testing and isolation strategies when diagnostic resources are limited, and can be implemented via the consideration of social network topologies. However, it remains unclear how to implement such surveillance and control when network data are unavailable.

METHODS

We evaluated the ability of sociodemographic proxies of degree centrality to guide prioritized testing of infected individuals compared to known degree centrality. Proxies were estimated via readily available sociodemographic variables (age, gender, marital status, educational attainment, household size). We simulated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics via a susceptible-exposed-infected-recovered individual-based model on 2 contact networks from rural Madagascar to test applicability of these findings to low-resource contexts.

RESULTS

Targeted testing using sociodemographic proxies performed similarly to targeted testing using known degree centralities. At low testing capacity, using proxies reduced infection burden by 22%-33% while using 20% fewer tests, compared to random testing. By comparison, using known degree centrality reduced the infection burden by 31%-44% while using 26%-29% fewer tests.

CONCLUSIONS

We demonstrate that incorporating social network information into epidemic control strategies is an effective countermeasure to low testing capacity and can be implemented via sociodemographic proxies when social network data are unavailable.

Perceived Effectiveness of COVID-19 Preventive Practices and Behavioral Intention: Survey of a Representative Adult Sample in the United States

BACKGROUND

Using existing models of behavioral health promotion, specifically the Extended Parallel Process Model, previous research has identified factors that may impact engagement in preventive health behaviors during the COVID-19 pandemic such as perceived threat, perceived susceptibility to the threat, perceived severity, and perceived efficacy.

OBJECTIVE

This study aims to examine the role of perceived effectiveness of COVID-19 preventive behaviors, perceived susceptibility, perceived threat, and perceived severity of COVID-19 in participants' intentions to engage in Centers for Disease Control (CDC)-recommended individual health behaviors in the first year of the pandemic.

METHODS

In October 2020, a representative sample of 506 US adults completed a web-based survey through the RAND American Life Panel.

RESULTS

The study primarily found that participants who perceived that CDC-recommended health practices were effective had stronger intentions to engage in those practices. The second strongest correlate was participants' perceived severity of COVID-19 across the United States. Perceived effectiveness of recommended practices accounted for the largest variance in behavioral intention. However, analysis of individual behaviors indicated a mismatch in the behaviors perceived to be the most effective (avoiding sick people and mask-wearing) and those participants indicated intention to engage in (throwing away used tissues, avoiding sick people, and coughing into their elbows) in the next 30 days.

CONCLUSIONS

The authors recommend tailoring public health messaging to address the perceived threat of COVID-19 and self-efficacy. Thus, health promotion efforts should emphasize the effectiveness of CDC-recommended practices while highlighting the pandemic's severity. Additionally, rebuilding trust in public health messaging and messengers is necessary to increase perceived self-efficacy. As the COVID-19 pandemic continues, health messaging must continue to promote and build trust in CDC-recommended health practices and educate regarding the efficacy of vaccination and other preventive behaviors.

Validation of remote assessment of preschool children's anthropometrics and motor skills

Introduction

Remotely delivered treatment and research procedures were rapidly adopted in response to the COVID-19 pandemic. However, it is unclear if these measures are valid. The purpose of this study was to compare the validity of anthropometry and motor skill proficiency measurements collected in a remote-setting to in-person setting among a sample of children ages 3-4 years.

Methods

Child anthropometry and motor skill performance were measured in-person by trained assessors and by parents at home with remote supervision via videoconference by trained assessors. The following measures from the National Institutes of Health Toolbox were collected: anthropometry (height and weight), manual dexterity/manipulation (9-hole pegboard), motor coordination and agility (supine timed up and go), lower body strength (standing long jump), and postural stability (one-leg standing balance). Differences in expert and parent-based measurements were assessed using Bland-Altman plots, paired samples t-tests, and Pearson correlations.

Results

A total of n = 14 children completed the assessments. No significant differences were observed between measurement locations for weight and motor skills (p > .05). Remote measurement of height (M = 101.1 cm, SD = 5.40) was significantly greater than in-person measurements (M = 98.2 cm, SD = 5.16); p < .0001.

Discussion

Remote measurements of motor skills and weight are valid assessments for researchers and clinicians to utilize in young children. Remote assessment with guidance offers comparable and valid estimates as in-person assessment, potentially offering a solution to resource-constricted barriers in research and access to care. There is an opportunity for researchers to fine-tune remote height and individual-level assessment strategies.

Direct indicators of social distancing effectiveness in COVID-19 outbreak stages: a correlational analysis of case contacts and population mobility in Korea

OBJECTIVES

The effectiveness of social distancing during the coronavirus disease 2019 (COVID-19) pandemic has been evaluated using the magnitude of changes in population mobility. This study aimed to investigate a direct indicator-namely, the number of close contacts per patient with confirmed COVID-19.

METHODS

From week 7, 2020 to week 43, 2021, population movement changes were calculated from the data of two Korean telecommunication companies and Google in accordance with social distancing stringency levels. Data on confirmed cases and their close contacts among residents of Gyeonggi Province, Korea were combined at each stage. Pearson correlation analysis was conducted to compare the movement data with the change in the number of contacts for each confirmed case calculated by stratification according to age group. The reference value of the population movement data was set using the value before mid-February 2020, considering each data's characteristics.

RESULTS

In the age group of 18 or younger, the number of close contacts per confirmed case decreased or increased when the stringency level was strengthened or relaxed, respectively. In adults, the correlation was relatively low, with no correlation between the change in the number of close contacts per confirmed case and the change in population movement after the commencement of vaccination for adults.

CONCLUSIONS

The effectiveness of governmental social distancing policies against COVID-19 can be evaluated using the number of close contacts per confirmed case as a direct indicator, especially for each age group. Such an analysis can facilitate policy changes for specific groups.

Hazardous Drinking Mediates the Relation Between Externalizing Personality and Reduced Adherence to COVID-19 Public Health Guidelines in University Students

Using a multigroup path analysis, we examined if hazardous alcohol use mediated the relations between elevated externalizing personality traits (i.e., impulsivity or sensation seeking) and reduced adherence to COVID-19 public health guidelines. We hypothesized that those high in externalizing personality traits would demonstrate less adherence to public health guidelines and that hazardous alcohol use would mediate this relationship. First- and second-year undergraduates (N = 1232; ages 18-25) from five Canadian universities participated in a cross-sectional survey between January to April 2021. Individuals with higher levels of impulsive or sensation seeking personality traits demonstrated poorer adherence to COVID-19 public health guidelines and these relations were mediated by hazardous alcohol use. Results suggest that hazardous drinking is an important target for students high in impulsivity and sensation seeking to increase their adherence to public health guidelines and thereby help control viral spread.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Metrics of Mobility: Assessing the Impact of COVID-19 on Travel Behavior

The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis-initial post-disruption mobility index, changepoint on restoration of a "new normal," and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes.

ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves

We present the software ModInterv as an informatics tool to monitor, in an automated and user-friendly manner, the evolution and trend of COVID-19 epidemic curves, both for cases and deaths. The ModInterv software uses parametric generalized growth models, together with LOWESS regression analysis, to fit epidemic curves with multiple waves of infections for countries around the world as well as for states and cities in Brazil and the USA. The software automatically accesses publicly available COVID-19 databases maintained by the Johns Hopkins University (for countries as well as states and cities in the USA) and the Federal University of Viçosa (for states and cities in Brazil). The richness of the implemented models lies in the possibility of quantitatively and reliably detecting the distinct acceleration regimes of the disease. We describe the backend structure of software as well as its practical use. The software helps the user not only to understand the current stage of the epidemic in a chosen location but also to make short term predictions as to how the curves may evolve. The app is freely available on the internet (http://fisica.ufpr.br/modinterv), thus making a sophisticated mathematical analysis of epidemic data readily accessible to any interested user.

Social Disparities and Social Distancing During the Covid Pandemic

According to Putnam (2000) and Bourdieu (1986), social disparities may result in the formation of narrow social bonds that exacerbate existing social cleavages and impede collective action. Motivated by this insight, we examine the relationship between social disparities and social distancing during the pre-vaccine Covid pandemic in the US. Using a panel of weekly, county-level observations, we find that income, educational and racial disparities are associated with a statistically significant decrease in the social distancing. This result is robust to controls for a wide variety of socioeconomic variables, the Covid infection rate, and a measure of social capital.

Changes in utilization of online pediatric educational resources during the coronavirus disease 2019 pandemic

The COVID-19 global pandemic disrupted healthcare, society, and medical education. Use of online video educational content increased at the onset of the COVID-19 pandemic, across two platforms. This demonstrates the potential of online videos to provide timely information in a scalable fashion, quickly meeting clinical information needs.

A dynamic Bayesian network-based emergency decision-making framework highlighting emergency propagations: Illustrated using the Fukushima nuclear accidents and the Covid-19 pandemic

When facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. However, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. In the work reported here, we develop a framework based upon dynamic Bayesian networks in order to simulate emergency scenarios and support corresponding decisions. In this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. We illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. By incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. Scenarios developed from two public emergencies: the 2011 Fukushima nuclear power plant accidents and the Covid-19 pandemic, are utilized to illustrate the framework in this paper. Capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real-world situations. Our analysis results reveal many similarities between these two seemingly distinct events. This indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. Valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically.

Exploring the lived experiences of women with children during COVID-19: Maternal stress and coping mechanisms

This paper presents the results of a qualitative study of mothers' lived experiences during the COVID-19 lockdown in the United States. An analysis of open-ended interviews with 44 mothers who had children ages zero-to-five identified two main themes: (1) increased stress among mothers; and (2) resilience through the use of coping mechanisms. The findings indicate that the COVID-19 pandemic has led to higher stress among mothers due to issues of work-family life balance, family and children's needs, decision-making about getting sick, concerns for children's development, and lack of clarity from government officials. Mothers described using a variety of problem-focused and emotion-focused methods to cope with this stress. The lived experiences of mothers during the pandemic highlights the need for innovations in childcare modalities, paid leave policies to relieve stress, and strengthening whole family processes and resilience through the use of coping mechanisms.

Examining socioeconomic disparities in changes in adolescent mental health before and during different phases of the coronavirus disease 2019 pandemic

This study examined socioeconomic disparities in changes in adolescent mental health between fall 2019 (pre-COVID-19), spring 2020 (initial coronavirus disease 2019 (COVID-19) phase), and fall 2020 (prevailing COVID-19 phase). Using data from 1429 adolescents (M_age  = 17.9) from tertiary vocational schools in the Netherlands with n = 386 participating in all three waves, linear and latent basis growth curve models were assessed and multigroup analyses conducted. Results showed a small but significant decrease in life satisfaction and small but significant increases in emotional problems, peer relationship problems, conduct problems, and hyperactivity-inattention problems. For emotional problems and peer relationship problems, increases between pre-COVID-19 and the initial COVID-19 phase were more pronounced than increases between the initial and prevailing COVID-19 phase. In contrast, linear decreases were found for life satisfaction and linear increases for conduct problems and hyperactivity-inattention problems over the course of the study. Mental health patterns were largely comparable for adolescents from families with varying socioeconomic status.

Unintended health benefits of adopting preventive behaviors during a virus outbreak

This study investigates whether changes in risk perception play a critical role in improving of preventive behaviors and health outcomes by examining the 2009 H1N1 influenza (or swine flu) pandemic in Korea. We employ a difference-in-differences estimation strategy by comparing the differential effects of the H1N1 outbreak on the confirmed cases of diseases which can be prevented by preventive behaviors (e.g., intestinal infections) and the cases of diseases which cannot (e.g., injuries). Using unique administrative data from South Korea's National Health Insurance Service (NHIS), we find that the exogenous increase in health risk reduced the incidence of intestinal infections compared to the injuries during the H1N1 influenza outbreak. The reduction was the most substantial among children under five years of age, with a 25.4% decline in cases of intestinal infections relative to injuries. Our findings are robust across various alternative specifications. We provide suggestive evidence that active adoption of preventive behaviors is one of the channels underlying the unexpected decrease in diarrhea cases. The effects, however, faded away shortly after the end of the pandemic and did not last in the long run.

Trends in gastrointestinal disease hospitalizations and outcomes during the first year of the coronavirus pandemic

BACKGROUND

The impact of the coronavirus on hospitalizations for gastrointestinal (GI) disease, particularly at a population level is understudied.

AIM

To investigate trends in hospitalizations, inpatient endoscopy resource utilization, and outcomes during the first year of the coronavirus pandemic and subsequent lockdowns.

METHODS

Using the California State Inpatient Database for 2018-2020, we explored year-to-year and 2020 month-to-month trends in hospitalizations, length of stay, and inpatient mortality (all-cause & viral pneumonia-specific) for common inpatient GI diagnoses including acute pancreatitis, diverticulitis, cholelithiasis, non-infectious gastroenteritis, upper and lower GI bleeding (LGIB), Clostridium difficile, viral gastroenteritis, inflammatory bowel disease, and acute cholangitis.

RESULTS

Disease-specific hospitalizations decreased for all included conditions except nonvariceal upper GI bleeding (NVUGIB), LGIB, and ulcerative colitis (UC) (ptrend < 0.0001). All-cause inpatient mortality was higher in 2020 vs 2019, for acute pancreatitis (P = 0.029), diverticulitis (P = 0.04), NVUGIB (P = 0.003), and Crohn’s disease (P = 0.004). In 2020, hospitalization rates were lowest in April, November, and December. There was no significant corresponding increase in inpatient mortality except in UC (ptrend = 0.048). Viral pneumonia and viral pneumonia complicated by respiratory failure increased (P < 0.001) among GI hospitalizations. Endoscopy utilization within 24 h of admission was unchanged for GI emergencies except NVUGIB (P < 0.001).

CONCLUSION

Our findings suggest that hospitalization rates for common GI conditions significantly declined in California during the COVID pandemic, particularly in April, November and December 2020. All-cause mortality was significantly higher among acute pancreatitis, diverticulitis, NVUGIB, and Crohn’s disease hospitalizations. Emergency endoscopy rates were mostly comparable between 2020 and 2019.

Predictors of patients' acceptance of video consultation in general practice during the coronavirus disease 2019 pandemic applying the unified theory of acceptance and use of technology model

Background

The coronavirus disease 2019 pandemic has led to an increase in remote consultations in health care. This study aimed to assess the acceptance of video consultation as an alternative to face-to-face in-office visits in general practice (GP) and to investigate its drivers and barriers.

Methods

A cross-sectional study was conducted in Germany during the coronavirus disease 2019 pandemic from December 2020 to April 2021. Participants were recruited among patients in 16 GP surgeries. Assessed were sociodemographic and medical data as well as information and communications technology related data. Acceptance of video consultation and its predictors were determined using a modified questionnaire based on a short version of the renowned unified theory of acceptance and use of technology model.

Results

In total, 371 participants were included in the data analysis. Acceptance of video consultation was moderate. A hierarchical regression revealed acceptance was significantly predicted by the PHQ-2, taking no regular medication, computer proficiency, knowledge about digital health care solutions, no prior use of video consultation, and the unified theory of acceptance and use of technology predictors performance expectancy, effort expectancy, and social influence. The extended unified theory of acceptance and use of technology model explained significantly more variance than the restricted unified theory of acceptance and use of technology model in acceptance of video consultation.

Conclusions

In this study computer proficiency, existing knowledge about digital health care solutions and depressive symptoms functioned as drivers to acceptance, no prior use of video consultation could be identified as a potential barrier. Patients with regular medication have been particularly receptive to video consultation. The study confirmed the validity of the unified theory of acceptance and use of technology model in determining acceptance of video consultation. Considering that there is growing demand and acceptance for different approaches to engage with health care providers, additional steps should be taken to establish video consultation as a genuine alternative.

ADAPTIVE TIME LOCATION SAMPLING FOR COMPASS, A SARS-COV-2 PREVALENCE STUDY IN FIFTEEN DIVERSE COMMUNITIES IN THE UNITED STATES

The COVPN 5002 (COMPASS) study aimed to estimate the prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 socio-demographically diverse communities in the United States. To protect against potential challenges in implementing traditional sampling strategies, time-location sampling (TLS) using complex sampling involving stratification, clustering of units, and unequal probabilities of selection was used to recruit individuals from neighborhoods in selected communities. The innovative design adapted to constraints such as closure of venues; changing infection hotspots; and uncertain policies. Recruitment of children and the elderly raised additional challenges in sample selection and implementation. To address these challenges, the TLS design adaptively updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. Although the study itself was specific to COVID-19, the strategies presented in this paper could serve as a case study that can be adapted for performing rigorous population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

A Trendline and Predictive Analysis of the First-Wave COVID-19 Infections in Malta

Following the first COVID-19 infected cases, Malta rapidly imposed strict lockdown measures, including restrictions on international travel, together with national social distancing measures, such as prohibition of public gatherings and closure of workplaces. The study aimed to elucidate the effect of the intervention and relaxation of the social distancing measures upon the infection rate by means of a trendline analysis of the daily case data. In addition, the study derived a predictive model by fitting historical data of the SARS-CoV-2 positive cases within a two-parameter Weibull distribution, whilst incorporating swab-testing rates, to forecast the infection rate at minute computational expense. The trendline analysis portrayed the wave of infection to fit within a tri-phasic pattern, where the primary phase was imposed with social measure interventions. Following the relaxation of public measures, the two latter phases transpired, where the two peaks resolved without further escalation of national measures. The derived forecasting model attained accurate predictions of the daily infected cases, attaining a high goodness-of-fit, utilising uncensored government-official infection-rate and swabbing-rate data within the first COVID-19 wave in Malta.

Adoption of measures to mitigate the impact of COVID-19: In search of a Hofstedian explanation for patterns among individual countries and country clusters

We consider multiple safety measures in relation to the COVID-19 virus and look at their adoption levels for a variety of 15 individual countries, based on data from Yougov.co.uk. Subsequently, we establish correlation coefficients between measure-specific uptake levels and Hofstede dimension scores for all countries considered. We notably find that Power Distance Index (PDI) and Individualism (IDV) have a considerable explanatory power. In addition, we carried out a Principal Components Analysis (PCA) and a cluster analysis to see whether the behavioural patterns across countries can be grouped, and which Hofstede dimensions correlate strongest with the two main components that follow from the PCA. The PCA provides further confirmation of PDI and IDV being the most important explanatory factors for the uptake of measures across countries. The cluster analysis, in turn, reveals four broad groups, which only partly coincide with the way that the mental image clustering scheme by Wursten (2019) allots countries into its respective clusters. Hence, this provides a basis to suggest that data-driven exercises like the ones from our paper can serve to adjust Wursten's intuitive scheme.

Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe

This paper provides estimates of COVID-19 transmission rates and explains their evolution for selected European countries since the start of the pandemic taking account of changes in voluntary and government mandated social distancing, incentives to comply, vaccination and the emergence of new variants. Evidence based on panel data modeling indicates that the diversity of outcomes that we document may have resulted from the nonlinear interaction of mandated and voluntary social distancing and the economic incentives that governments provided to support isolation. The importance of these factors declined over time, with vaccine uptake driving heterogeneity in country experiences in 2021. Our approach also allows us to identify the basic reproduction number, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_{0}$$\end{document}R0, which is precisely estimated around 5, which is much larger than the values in the range of 2.4–3.9 assumed in the extant literature.

Improving the efficiency and biosafety of respiratory syncytial virus identification using a nucleic acid extraction-free reagent

Respiratory syncytial virus (RSV) is the most important virus that causes lower respiratory tract disease in children; efficient viral identification is an important component of disease prevention and treatment. Here, we developed and evaluated a ready-to-use (RTU) nucleic acid extraction-free direct reagent for identification of RSV (RTU-Direct test) in clinical samples. The limit of detection (LOD) of the RSV RTU-Direct test was consistent with the LOD of the standard test using extracted nucleic acids. The virus inactivation ability of RTU-Direct reagent was confirmed by viral infectivity assays involving RTU-Direct-treated samples containing RSV and human coronavirus OC43. RSV RNA stability was significantly better in RTU-Direct reagent than in conventional virus transport medium (VTM) at room temperature and 4°C (p < 0.05). The clinical performance of the RTU-Direct test was evaluated using 155 respiratory specimens from patients with suspected RSV infection. Positive agreement between the RTU-Direct test and the VTM standard test was 100% (42/42); negative agreement was 99.1% (112/113), and the kappa statistic was 0.968 (p < 0.001). The distributions of Ct values did not significantly differ between the RTU-Direct test and the standard test (p > 0.05). Overall, the RTU-Direct reagent can improve the efficiency and biosafety of RSV detection, while reducing the cost of detection.

Effect of flight connectivity on the introduction and evolution of the COVID-19 outbreak in Canadian provinces and territories

BACKGROUND

The COVID-19 pandemic has challenged health services and governments in Canada and around the world. Our research aims to evaluate the effect of domestic and international air travel patterns on the COVID-19 pandemic in Canadian provinces and territories.

METHODS

Air travel data were obtained through licensed access to the 'BlueDot Intelligence Platform', BlueDot Inc. Daily provincial and territorial COVID-19 cases for Canada and global figures, including mortality, cases recovered and population data were downloaded from public datasets. The effects of domestic and international air travel and passenger volume on the number of local and non-local infected people in each Canadian province and territory were evaluated with a semi-Markov model. Provinces and territories are grouped into large (>100 000 confirmed COVID-19 cases and >1 000 000 inhabitants) and small jurisdictions (≤100 000 confirmed COVID-19 cases and ≤1 000 000 inhabitants).

RESULTS

Our results show a clear decline in passenger volumes from March 2020 due to public health policies, interventions and other measures taken to limit or control the spread of COVID-19. As the measures were eased, some provinces and territories saw small increases in passenger volumes, although travel remained below pre-pandemic levels. During the early phase of disease introduction, the burden of illness is determined by the connectivity of jurisdictions. In provinces with a larger population and greater connectivity, the burden of illness is driven by case importation, although local transmission rapidly replaces imported cases as the most important driver of increasing new infections. In smaller jurisdictions, a steep increase in cases is seen after importation, leading to outbreaks within the community.

CONCLUSIONS

Historical travel volumes, combined with data on an emerging infection, are useful to understand the behaviour of an infectious agent in regions of Canada with different connectivity and population size. Historical travel information is important for public health planning and pandemic resource allocation.

Health Consequences of the COVID-19 Pandemic among Health-Care Workers: A Comparison between Groups Involved and Not Involved in COVID-19 Care

Health consequences have been reported among health-care workers (HCWs) exposed to COVID-19. Sweden chose to manage the pandemic with a lower and more equal long-lasting work strain and shorter periods of recovery than in other countries. Few studies have examined the health consequences among HCWs working in such conditions. This study compared the health consequences after the first wave of the COVID-19 pandemic between HCWs involved in the care of COVID-19 patients and other HCWs and between occupational groups working in COVID-19 care. Multinomial logistic regression and univariate general linear models were used to identify differences. The levels of depression, emotional and physical fatigue, sleep quality, and general health were measured 6 months after the onset of the pandemic in 3495 HCW employed in Sweden. HCWs directly involved in COVID-19 care reported significantly poorer sleep quality and higher scores on emotional and physical exhaustion than those not involved in such care. Health consequences did not differ significantly between different occupational groups involved in COVID-19 care except for specialist nurses/midwives. HCWs more frequently involved in COVID-19 care reported higher levels of emotional and physical fatigue and poorer sleep but less severe than those reported in more severely affected countries.

Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework

BACKGROUND

Universities are vulnerable to infectious disease outbreaks, making them ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures. Here, we analyze multimodal COVID-19-associated data collected during the 2020-2021 academic year at Colorado Mesa University and introduce a SARS-CoV-2 surveillance and response framework.

METHODS

We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and WiFi-based co-location data) alongside pathogen surveillance data (wastewater and diagnostic testing, and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy. We applied relative risk, multiple linear regression, and social network assortativity to identify attributes or behaviors associated with contracting SARS-CoV-2. To characterize SARS-CoV-2 transmission, we used viral sequencing, phylogenomic tools, and functional assays.

FINDINGS

Athletes, particularly those on high-contact teams, had the highest risk of testing positive. On average, individuals who tested positive had more contacts and longer interaction durations than individuals who never tested positive. The distribution of contacts per individual was overdispersed, although not as overdispersed as the distribution of phylogenomic descendants. Corroboration via technical replicates was essential for identification of wastewater mutations.

CONCLUSIONS

Based on our findings, we formulate a framework that combines tools into an integrated disease surveillance program that can be implemented in other congregate settings with limited resources.

FUNDING

This work was supported by the National Science Foundation, the Hertz Foundation, the National Institutes of Health, the Centers for Disease Control and Prevention, the Massachusetts Consortium on Pathogen Readiness, the Howard Hughes Medical Institute, the Flu Lab, and the Audacious Project.

Is self-reported depression, HIV status, COVID-19 health risk profile and SARS-CoV-2 exposure associated with difficulty in adhering to COVID-19 prevention measures among residents in West Africa?

BACKGROUND

The aim of this study was to determine whether self-reported depression, coronavirus disease of 2019 (COVID-19) health risk profile, HIV status, and SARS-CoV-2 exposure were associated with the use of COVID-19 prevention measures.

METHODS

This survey collected data electronically between June 29 and December 31, 2020 from a convenient sample of 5050 adults 18 years and above living in 12 West African countries. The dependent variables were: social distancing, working remotely, difficulty obtaining face masks and difficulty washing hands often. The independent variables were self-reported depression, having a health risk for COVID-19 (high, moderate and little/no risk), living with HIV and COVID-19 status (SARS-CoV-2 positive tests, having COVID-19 symptoms but not getting tested, having a close friend who tested positive for SARS-CoV-2 and knowing someone who died from COVID-19). Four binary logistic regression models were developed to model the associations between the dependent and independent variables, adjusting for socio-demographic variables (age, gender, educational status, employment status and living status).

RESULTS

There were 2412 (47.8%) male participants and the mean (standard deviation) age was 36.94 (11.47) years. Respondents who reported depression had higher odds of working remotely (AOR: 1.341), and having difficulty obtaining face masks (AOR: 1.923;) and washing hands often (AOR: 1.263). People living with HIV had significantly lower odds of having difficulty washing hands often (AOR: 0.483). Respondents with moderate health risk for COVID-19 had significantly higher odds of social distancing (AOR: 1.144) and those with high health risk had difficulty obtaining face masks (AOR: 1.910). Respondents who had a close friend who tested positive for SARS-CoV-2 (AOR: 1.132) and knew someone who died of COVID-19 (AOR: 1.094) had significantly higher odds of social distancing. Those who tested positive for SARS-CoV-2 had significantly lower odds of social distancing (AOR: 0.629) and working remotely (AOR: 0.713). Those who had symptoms of COVID-19 but did not get tested had significantly lower odds of social distancing (AOR: 0.783) but significantly higher odds of working remotely (AOR: 1.277).

CONCLUSIONS

The study signifies a disparity in the access to and use of COVID-19 preventative measures that is allied to the health and COVID-19 status of residents in West Africa. Present findings point to risk compensation behaviours in explaining this outcome.

Invited Commentary: Mechanistic and Biologically Based Models in Epidemiology-A Powerful Underutilized Tool

Mechanistic and biologically based mathematical models of chronic and behavioral disease processes aim to capture the main mechanistic or biological features of the disease development and to connect these with epidemiologic outcomes. These approaches have a long history in epidemiologic research and are complementary to traditional epidemiologic or statistical approaches to investigate the role of risk factor exposures on disease risk. Simonetto et al. (Am J Epidemiol. 2022;191(10):1766-1775) present a mechanistic, process-oriented model to investigate the role of smoking, hypertension, and dyslipidemia in the development of atherosclerotic lesions and their progression to myocardial infarction. Their approach builds on and brings to cardiovascular disease the ideas and perspectives of earlier mechanistic and biologically based models for the epidemiology of cancer and other chronic diseases, providing important insights into the mechanisms and epidemiology of smoking related myocardial infarction. We argue that although mechanistic modeling approaches have demonstrated their value and place in epidemiology, they are highly underutilized. We call for efforts to grow mechanistic and biologically based modeling research, expertise, and awareness in epidemiology, including the development of training and collaboration opportunities to attract more students and researchers from science, technology, engineering, and medical field into the epidemiology field.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin-destination (O-D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

The role of precautions: Organising a medical conference during COVID-19 pandemic-Lessons from IADVL MIDDERMACON 2021

Background

The use of virtual platforms for clinical meetings has become the default approach during this pandemic era. Organising an offline conference during a pandemic is a challenge and is possible if the participating crowd is vaccinated and is willing to follow appropriate pandemic protocols.

Objective

To determine the feasibility of conducting a conference among mostly vaccinated delegates using standard precautionary protocols.

Methods

This study was conducted at IADVL MIDDERMACON 2021, held in Mangalore, India, in late October 2021, during the phase of decline of the Delta variant of SARS-CoV-2. The study population included all conference attendees, including support staff. Details were collected about their vaccination status, comorbidities, and mode of travel to the conference venue. An reverse-transcription polymerase chain reaction (RT-PCR) test was done randomly among the attendees for COVID-19 infection. A post-conference assessment and RT-PCR tests were done at the end of 2 weeks to assess the occurrence of infections among study participants.

Results

A total of 1744 people were present at the venue, of which 576 (33.03%) participated in the study. The percentage of fully vaccinated was 88.88% (512/576). The majority had taken the vaccine Covishield (manufactured by AstraZeneca), that is, 85.06% (490/576). Infection post the conference was reported in 0.195% (1/576).

Conclusions

Holding large gatherings like medical conferences pose a challenge during a pandemic. However, to increase the benefits of the conference, it is advisable to hold them offline with vaccinated delegates, follow the advice of the conference organising committee, and practise safe precautionary measures.

Elucidating the role of environmental management of forests, air quality, solid waste and wastewater on the dissemination of SARS-CoV-2

The increasing frequency of zoonotic diseases is amongst several catastrophic repercussions of inadequate environmental management. Emergence, prevalence, and lethality of zoonotic diseases is intrinsically linked to environmental management which are currently at a destructive level globally. The effects of these links are complicated and interdependent, creating an urgent need of elucidating the role of environmental mismanagement to improve our resilience to future pandemics. This review focused on the pertinent role of forests, outdoor air, indoor air, solid waste and wastewater management in COVID-19 dissemination to analyze the opportunities prevailing to control infectious diseases considering relevant data from previous disease outbreaks. Global forest management is currently detrimental and hotspots of forest fragmentation have demonstrated to result in zoonotic disease emergences. Deforestation is reported to increase susceptibility to COVID-19 due to wildfire induced pollution and loss of forest ecosystem services. Detection of SARS-CoV-2 like viruses in multiple animal species also point to the impacts of biodiversity loss and forest fragmentation in relation to COVID-19. Available literature on air quality and COVID-19 have provided insights into the potential of air pollutants acting as plausible virus carrier and aggravating immune responses and expression of ACE2 receptors. SARS-CoV-2 is detected in outdoor air, indoor air, solid waste, wastewater and shown to prevail on solid surfaces and aerosols for prolonged hours. Furthermore, lack of protection measures and safe disposal options in waste management are evoking concerns especially in underdeveloped countries due to high infectivity of SARS-CoV-2. Inadequate legal framework and non-adherence to environmental regulations were observed to aggravate the postulated risks and vulnerability to future waves of pandemics. Our understanding underlines the urgent need to reinforce the fragile status of global environmental management systems through the development of strict legislative frameworks and enforcement by providing institutional, financial and technical supports.

Intimate Partner Violence Experiences During COVID-19 Among Male Couples

In addition to the growing morbidity and mortality related to the 2019 novel coronavirus (SAR-CoV-2) pandemic, social distancing measures during the pandemic may result in increased intimate partner violence (IPV). However, it is yet unknown if gay, bisexual, and other men who have sex with men (GBMSM)'s IPV risk has increased during this time. This article describes and analyzes IPV experiences during the COVID-19 pandemic in a sample of coupled-GBMSM in the United States. We hypothesized that pandemic-driven stressors would be associated with increased IPV prevalence and severity. A sample of 214 coupled men living in the US who had previously participated in HIV-related couple studies was surveyed in July-September 2020. Respondents reported demographic, sexual and substance use behaviors, and relationship characteristics. Surveys also collected data on pandemic-related life-changes (employment, substance use, COVID-19 illness). IPV victimization and perpetration were measured with the Gay and Bisexual Men Intimate Partner Violence scale and measured individually experienced or perpetrated violence, sexual, emotional, monitoring, or controlling behaviors, and if a given behavior was new and/or had changed in frequency during the pandemic. Reported prevalence and pandemic-related changes in victimization and perpetration were described. New or more frequent IPV victimization was modeled against employment, substance use changes, COVID-19 illness, and outside sexual partners (modified by a couple's sexual agreement). IPV perpetration prevalence was 15.17%, 34.44% of which was new or more frequent. Victimization prevalence was 14.95%, of which 46.88% was new or more frequent. After adjustment, outside sexual partners were associated with IPV among those with nonmonogamous sexual agreements; each outside sexual partner increased the odds of new or more frequent victimization by 70% (OR = 1.70; 95% CI [1.16, 2.51]). Given this study's documented rise in IPV among a sample of coupled men, additional research into IPV predictors, interventions, and support strategies in GBMSM populations are warranted.

Clinical Outcomes after Immunotherapies in Cancer Setting during COVID-19 Era: A Systematic Review and Meta-Regression

Background: This study aims to describe COVID-19–related clinical outcomes after immunotherapies (ICIs) for cancer patients. Methods: In this meta-analysis, we searched databases to collect data that addressed outcomes after immunotherapies (ICIs) during the COVID-19 pandemic. The primary endpoint was COVID-19–related mortality. Secondary endpoints included COVID-related hospital readmission, emergency room (ER) visits, opportunistic infections, respiratory complications, need for ventilation, and thrombo-embolic events. Pooled event rates (PERs) were calculated and a meta-regression analysis was performed. Results: A total of 262 studies were identified. Twenty-two studies with a total of forty-four patients were eligible. The PER of COVID-19–related mortality was 39.73%, while PERs of COVID-19–related ER visits, COVID-19–related pulmonary complications, and COVID-19–related ventilator needs were 40.75%, 40.41%, and 34.92%, respectively. The PER of opportunistic infections was 34.92%. The PERs of the use of antivirals, antibiotics, steroids, prophylactic anticoagulants, and convalescent plasma were 62.12%, 57.12%, 51.36%, 41.90%, and 26.48%, respectively. There was a trend toward an association between previous respiratory diseases and COVID-19–related mortality. Conclusion: The rates of COVID-19–related mortality, ER visits, pulmonary complications, need for a ventilator, and opportunistic infections are still high after ICIs during the COVID-19 pandemic. There was a trend toward an association between previous respiratory diseases and COVID-19–related mortality.

Knowledge overconfidence is associated with anti-consensus views on controversial scientific issues

Public attitudes that are in opposition to scientific consensus can be disastrous and include rejection of vaccines and opposition to climate change mitigation policies. Five studies examine the interrelationships between opposition to expert consensus on controversial scientific issues, how much people actually know about these issues, and how much they think they know. Across seven critical issues that enjoy substantial scientific consensus, as well as attitudes toward COVID-19 vaccines and mitigation measures like mask wearing and social distancing, results indicate that those with the highest levels of opposition have the lowest levels of objective knowledge but the highest levels of subjective knowledge. Implications for scientists, policymakers, and science communicators are discussed.

Dynamics of SIR model with heterogeneous response to intervention policy

In classical epidemic theory, behavior is assumed to be stationary. In recent years, epidemic models have been extended to include behaviors that transition in response to the current state of the epidemic. However, it is widely known that human behavior can exhibit strong history-dependence as a consequence of learned experiences. This history-dependence is similar to hysteresis phenomena that have been well-studied in control theory. To illustrate the importance of history-dependence for epidemic theory, we study dynamics of a variant of the SIRS model where individuals exhibit lazy-switch responses to prevalence dynamics, based on the Preisach hysteresis operator. The resulting model can possess a continuum of endemic equilibrium states characterized by different proportions of susceptible, infected and recovered populations. We consider how the limit point of the epidemic trajectory and the infection peak along this trajectory depend on the degree of heterogeneity of the response. Our approach supports the argument that public health responses during the emergence of a new disease can have fundamental long-term consequences for subsequent management efforts.

Generalized Susceptible-Exposed-Infectious-Recovered model and its contributing factors for analysing the death and recovery rates of the COVID-19 pandemic

COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. The spread and the death rates of the virus displayed dramatic differences among countries globally, showing that there are several factors affecting its spread and mortality. By utilizing the cumulative number of cases from John Hopkins University, the recovery rate, death rate, and the number of active, recovered, and death cases were simulated to analyse the trends and patterns within the chosen countries. 10 countries from 3 different case severity categories (high cases, medium cases, and low cases) and 5 continents (Asia, North America, South America, Europe, and Oceania) were studied. A generalized SEIR model which considers control measures such as isolation, and preventive measures such as vaccination is applied in this study. This model is able to capture not only the dynamics between the states, but also the time evolution of the states by using the fourth-order-Runge-Kutta process. This study found no significant patterns in the countries under the same case severity category, suggesting that there are other factors contributing to the pattern in these countries. One of the factors influencing the pattern in each country is the population's age. COVID-19 related deaths were found to be notably higher among older people, indicating that countries comprising of a larger proportion of older age groups have an increased risk of experiencing higher death rates. Tighter governmental control measures led to fewer infections and eventually reduced the number of death cases, while increasing the recovery rate, and early implementations were found to be far more effective in controlling the spread of the virus and produced better outcomes.

Impact of the COVID-19 Pandemic on Individuals With Low Vision: Life Goes On

This qualitative case study explored the impact of lockdown and social distancing measures used during COVID-19 on the lives of individuals with low vision. A purposive sample of 6 participants with low vision was recruited. Data were collected using in-depth semistructured interviews consisting of 10 open-ended questions. A thematic analysis was conducted to understand the unified subjective experiences of the participants. The analysis revealed 4 themes: (1) Changes, a lot of them; (2) Emotional roller coaster; (3) Life goes on; and (4) Technology as the catalyst. Participants demonstrated ongoing adaptation to meet the changing demands imposed by the pandemic.