Impact of Social Distancing Measures on Coronavirus Disease Healthcare Demand, Central Texas, USA

A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model

BACKGROUND

Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach.

METHODS

We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration.

RESULTS

Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration.

CONCLUSIONS

Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration.

HIGHLIGHTS

This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.

Communicating scientific uncertainty in the early stages of the COVID-19 pandemic: A message experiment

The World Health Organization (WHO) officially declared COVID-19 a pandemic on March 11, 2020. It was a time of significant uncertainty as experts were not yet certain whether social distancing behaviors were necessary to slow the spread of the virus. Some public communicators opted to acknowledge uncertainty based on the limited evidence, whereas others downplayed uncertainty. This situation provided researchers with an opportunity to advance theory by explicating and testing cognitive responses to message uncertainty. Immediately following the WHO declaration (March 13-19, 2020), U.S. adults (N = 1186) were randomly assigned to one of six conditions in a 2 (message uncertainty: low, high) × 3 (argument support: expert, threat, precedent) between-participants experiment. Overall, perceived uncertainty negatively mediated the impact of message uncertainty on intentions. However, participant education was a key moderator. For those with more than a high school education, uncertain messages were related to higher intentions to social distance through increased critical reflection. For those with a high school education or less, uncertain messages were related to lower intentions through decreased message credibility.

Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023

Background

Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023.

Methods

Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023.

Results

We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China.

Conclusions

The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.

Spatial distribution of COVID-19 patients in Sri Lanka

BACKGROUND

A new type of viral pneumonia, which has been named Coronavirus disease (COVID-19) began in Wuhan, China in late 2019 and has spread across the world since then. It has claimed more than 370 million confirmed cases and over 5.6 million deaths have been reported globally by the end of January 2022. This study aimed to analyze the trends, highly-nuanced patterns, and related key results relative to COVID-19 epidemiology in Sri Lanka.

METHODS

Data on COVID-19 from March 2020 to January 2022 were obtained from published databases maintained by the Epidemiology Unit of the Ministry of Health in Sri Lanka and information regarding populations in administrative districts was obtained from the Department of Census and Statistics, Sri Lanka. Descriptive spatiotemporal analysis and autocorrelations were analyzed using SPSS statistical software.

RESULTS

In Sri Lanka, the first case of COVID-19 was a Chinese national and the first local case was identified in the second week of March. As of 31st of January 2022, a total of 610,103 COVID-19 cases had been recorded in the country, and 15,420 patients had died. At the beginning, the disease was mainly concentrated in the Western province and with time, it spread to other provinces. However, very low numbers of patients were identified in the North, Eastern, North Central, and Uva provinces until April 2021. The peak of COVID-19 occurred in August and September 2021 in all provinces in Sri Lanka. Then a decreasing trend of COVID-19 cases showed after September 2021.

CONCLUSIONS

COVID-19 is an emerging public health problem in Western and Southern Sri Lanka where the population density is high. A decreasing trend of COVID-19 cases showed in all provinces after September 2021. Public awareness programs for the prevention and control of the disease in endemic regions are essential to reduce the incidence of this infection.

Respiratory pandemic preparedness learnings from the June 2020 COVID-19 outbreak at San Quentin California State Prison

PURPOSE

This study aims to characterize the June 2020 COVID-19 outbreak at San Quentin California State Prison and to describe what made San Quentin so vulnerable to uncontrolled transmission.

DESIGN/METHODOLOGY/APPROACH

Since its onset, the COVID-19 pandemic has exposed and exacerbated the profound health harms of carceral settings, such that nearly half of state prisons reported COVID-19 infection rates that were four or more times (and up to 15 times) the rate found in the state's general population. Thus, addressing the public health crises and inequities of carceral settings during a respiratory pandemic requires analyzing the myriad factors shaping them. In this study, we reported observations and findings from environmental risk assessments during visits to San Quentin California State Prison. We complemented our assessments with analyses of administrative data.

FINDINGS

For future respiratory pathogens that cannot be prevented with effective vaccines, this study argues that outbreaks will no doubt occur again without robust implementation of additional levels of preparedness - improved ventilation, air filtration, decarceration with emergency evacuation planning - alongside addressing the vulnerabilities of carceral settings themselves.

ORIGINALITY/VALUE

This study addresses two critical aspects that are insufficiently covered in the literature: how to prepare processes to safely implement emergency epidemic measures when needed, such as potential evacuation, and how to address unique challenges throughout an evolving pandemic for each carceral setting.

Compliance Trajectory and Patterns of COVID-19 Preventive Measures, Japan, 2020-2022

COVID-19 remains a global health threat. Compliance with nonpharmaceutical interventions is essential because of limited effectiveness of COVID-19 vaccines, emergence of highly contagious variants, and declining COVID-19 antibody titers over time. We evaluated compliance with 14 nonpharmaceutical intervention-related COVID-19 preventive behaviors, including mask wearing, ventilation, and surface sanitation, in a longitudinal study in Japan using 4 waves of Internet survey data obtained during 2020-2022. Compliance with most preventive behaviors increased or remained stable during the 2-year period, except for surface sanitation and going out behaviors; compliance with ventilation behavior substantially decreased in winter. Compliance patterns identified from latent class analysis showed that the number of persons in the low compliance class decreased, whereas those in the personal hygiene class increased. Our findings reflect the relaxation of mobility restriction policy in Japan, where the COVID-19 pandemic continues. Policymakers should consider behavioral changes caused by new policies to improve COVID-19 prevention strategies.

Retrospective analysis of equity-based optimization for COVID-19 vaccine allocation

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counterfactual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American-Indian or Alaska-Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Impact of the COVID-19 Pandemic on the Public Perceptions of the Roles and Functions of Community Pharmacies in South Korea: Updated Cross-Sectional Self-Reported Web-Based Survey

BACKGROUND

Community pharmacists confronted dual burdens in response to the COVID-19 pandemic by expanding the scope of pharmaceutical practices.

OBJECTIVE

This study aimed to assess the perceived roles and functions of community pharmacies during the pandemic and to explore their updated roles after the pandemic began.

METHODS

We conducted a self-reported web-based survey in October 2022. Based on Korean census data, we recruited the study participants (n=1000) through quota sampling stratified by age, sex, and region, yielding a 7.45% (1000/13,423) response rate. The questionnaires were composed of 3 sections: demographics, the roles and functions of community pharmacies during the pandemic, and the updated roles of community pharmacies during disasters. Each question in the second and third sections was rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), and each item's mean scores and SDs were reported. The study participants were categorized into 2 groups: individuals who had a family pharmacy and those who did not. A chi-square test and ordered logistic regression analyses were conducted.

RESULTS

Out of 1000 respondents, 418 (41.8%) had a history of COVID-19, and 639 (63.9%) had a family pharmacy. Assigning specific roles and functions to community pharmacies during the pandemic contributed to positive assessments. Respondents gave higher scores to community pharmacies that had responded appropriately (a mean Likert score of 3.66, SD .077 out of 5) and provided continuous pharmaceutical services (mean 3.67, SD 0.87) during the pandemic. The pandemic served as an opportunity to positively recognize the role of community pharmacies (mean 3.59, SD 0.83). In the ordered logistic model, having a family pharmacy was consistently associated with positive perceptions. Respondents perceived that community pharmacies collaborated with general practitioners and health authorities. However, community pharmacies need to function appropriately in terms of knowledge. The mean score of the 4 domains of community pharmacy functions was the highest for collaboration (mean 3.66, SD 0.83), followed by communication (mean 3.57, SD 0.87), responsiveness (mean 3.54, SD 0.87), and knowledge (mean 3.41, SD 0.91).

CONCLUSIONS

The pandemic resulted in interprofessional collaboration between community pharmacists and general practitioners. Family pharmacies could be a valuable asset to the comprehensive case management of patients. However, community pharmacists should have the expertise to build solid interprofessional collaborations and fulfill their expanded and updated roles.

Experiences of nurses on COVID-19 preventive protocols implementation in Tamale Metropolis, Ghana: A qualitative exploration

The Coronavirus Disease, 2019 (COVID-19) disrupted healthcare delivery. Health workers, particularly nurses are key members of the interdisciplinary healthcare team. They are faced with many challenges due to the pandemic. In addition to providing basic healthcare services, nurses are required to adhere to the COVID-19 recommended safety protocols. This study explored experiences of nurses on the implementation of COVID-19 preventive protocols in Tamale Metropolis, Ghana. A qualitative study was conducted among seventeen (17) nurses, comprising five (5) staff with COVID-19 infection, and twelve (12) ward managers or in-charges who did not have COVID-19 infection, using explorative design and an interview guide. The participants were purposively selected. The ward managers/in-charges and infected staff were interviewed face-to-face and by mobile phone respectively. Content analysis was conducted on the data and the results presented as themes and sub-themes. After the analysis, five (5) main themes and fourteen (14) sub-themes were identified on experiences of nurses regarding COVID-19 preventive protocols implementation. These included understanding COVID-19 transmission/spread (patients-to-staff, staff-to-staff and through fomites), communicating the preventive protocols (social media, ward meetings and administrative memoranda), and attitude of nurses on the protocol's implementation (growing apathy, discomfort in applying personal protective equipment (PPEs) and outright defiance). Nurses also experienced some challenges and inadequate support (progressive decline in supply of PPEs, infrequent supply of water and limited infrastructure), in addition to dealing with issues of protocols implementation in healthcare setting (inapplicability of social distancing in hospital setting and improvising PPEs). In conclusion, the nurses had varied experiences on COVID-19 preventive protocols implementation. The themes explored were mode of COVID-19 transmission, communication approaches, negative attitudes, inadequate logistics and inability to implement social distance. Overall, these affected the effective implementation of the protocols. Thus, health facilities should be provided with adequate logistics/supplies and trainings to enable nurses implement COVID-19 preventive protocols effectively.

Disproportionate impacts of COVID-19 in a large US city

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021 and fine-grain infection hospitalization rates, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 23.7% (95% CrI: 22.5-24.8%) infection rate and 29.4% (95% CrI: 28.0-31.0%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (11.2% [95% CrI: 10.3-12.0%] vs 25.1% [95% CrI: 23.7-26.4%]), but more likely to be hospitalized (1,965 per 100,000 vs 376 per 100,000) and have their infections reported (53% [95% CrI: 49-57%] vs 28% [95% CrI: 27-30%]). We used a mixed effect poisson regression model to estimate disparities in infection and reporting rates as a function of social vulnerability. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 60%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. Our results suggest that further public health efforts are needed to mitigate local COVID-19 disparities and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

Retrospective Analysis of Equity-Based Optimization for COVID-19 Vaccine Allocation

Marginalized racial and ethnic groups in the United States were disproportionally affected by the COVID-19 pandemic. To study these disparities, we construct an age-and-race-stratified mathematical model of SARS-CoV-2 transmission fitted to age-and-race-stratified data from 2020 in Oregon and analyze counter-factual vaccination strategies in early 2021. We consider two racial groups: non-Hispanic White persons and persons belonging to BIPOC groups (including non-Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American Indian or Alaska Native persons, and Hispanic or Latino persons). We allocate a limited amount of vaccine to minimize overall disease burden (deaths or years of life lost), inequity in disease outcomes between racial groups (measured with five different metrics), or both. We find that, when allocating small amounts of vaccine (10% coverage), there is a trade-off between minimizing disease burden and minimizing inequity. Older age groups, who are at a greater risk of severe disease and death, are prioritized when minimizing measures of disease burden, and younger BIPOC groups, who face the most inequities, are prioritized when minimizing measures of inequity. The allocation strategies that minimize combinations of measures can produce middle-ground solutions that similarly improve both disease burden and inequity, but the trade-off can only be mitigated by increasing the vaccine supply. With enough resources to vaccinate 20% of the population the trade-off lessens, and with 30% coverage, we can optimize both equity and mortality. Our goal is to provide a race-conscious framework to quantify and minimize inequity that can be used for future pandemics and other public health interventions.

Evaluating the effectiveness of lockdowns and restrictions during SARS-CoV-2 variant waves in the Canadian province of Nova Scotia

Introduction

After the initial onset of the SARS-CoV-2 pandemic, the government of Canada and provincial health authorities imposed restrictive policies to limit virus transmission and mitigate disease burden. In this study, the pandemic implications in the Canadian province of Nova Scotia (NS) were evaluated as a function of the movement of people and governmental restrictions during successive SARS-CoV-2 variant waves (i.e., Alpha through Omicron).

Methods

Publicly available data obtained from community mobility reports (Google), the Bank of Canada Stringency Index, the "COVID-19 Tracker" service, including cases, hospitalizations, deaths, and vaccines, population mobility trends, and governmental response data were used to relate the effectiveness of policies in controlling movement and containing multiple waves of SARS-CoV-2.

Results

Our results indicate that the SARS-CoV-2 pandemic inflicted low burden in NS in the initial 2 years of the pandemic. In this period, we identified reduced mobility patterns in the population. We also observed a negative correlation between public transport (-0.78), workplace (-0.69), retail and recreation (-0.68) and governmental restrictions, indicating a tight governmental control of these movement patterns. During the initial 2 years, governmental restrictions were high and the movement of people low, characterizing a 'seek-and-destroy' approach. Following this phase, the highly transmissible Omicron (B.1.1.529) variant began circulating in NS at the end of the second year, leading to increased cases, hospitalizations, and deaths. During this Omicron period, unsustainable governmental restrictions and waning public adherence led to increased population mobility, despite increased transmissibility (26.41-fold increase) and lethality (9.62-fold increase) of the novel variant.

Discussion

These findings suggest that the low initial burden caused by the SARS-CoV-2 pandemic was likely a result of enhanced restrictions to contain the movement of people and consequently, the spread of the disease. Easing public health restrictions (as measured by a decline in the BOC index) during periods of high transmissibility of circulating COVID-19 variants contributed to community spread, despite high levels of immunization in NS.

Analyzing the effect of restrictions on the COVID-19 outbreak for some US states

The ongoing pandemic disease COVID‑19 has caused worldwide social and financial disruption. As many countries are engaged in designing vaccines, the harmful second and third waves of COVID‑19 have already appeared in many countries. To investigate changes in transmission rates and the effect of social distancing in the USA, we formulate a system of ordinary differential equations using data of confirmed cases and deaths in these states: California, Texas, Florida, Georgia, Illinois, Louisiana, Michigan, and Missouri. Our models and their parameter estimations show social distancing can reduce the transmission of COVID‑19 by 60% to 90%. Thus, obeying the movement restriction rules is crucial in reducing the magnitude of the outbreak waves. This study also estimates the percentage of people who were not social distancing ranges between 10% and 18% in these states. Our analysis shows the management restrictions taken by these states do not slow the disease progression enough to contain the outbreak.

Insight into the liver dysfunction in COVID-19 patients: Molecular mechanisms and possible therapeutic strategies

As of June 2022, more than 530 million people worldwide have become ill with coronavirus disease 2019 (COVID-19). Although COVID-19 is most commonly associated with respiratory distress (severe acute respiratory syndrome), meta-analysis have indicated that liver dysfunction also occurs in patients with severe symptoms. Current studies revealed distinctive patterning in the receptors on the hepatic cells that helps in viral invasion through the expression of angiotensin-converting enzyme receptors. It has also been reported that in some patients with COVID-19, therapeutic strategies, including repurposed drugs (mitifovir, lopinavir/ritonavir, tocilizumab, etc.) triggered liver injury and cholestatic toxicity. Several proven indicators support cytokine storm-induced hepatic damage. Because there are 1.5 billion patients with chronic liver disease worldwide, it becomes imperative to critically evaluate the molecular mechanisms concerning hepatotropism of COVID-19 and identify new potential therapeutics. This review also designated a comprehensive outlook of comorbidities and the impact of lifestyle and genetics in managing patients with COVID-19.

Hybrid Work from Home Clinical Academic Environment: A One-Year Follow-Up Survey of Attitudes and Beliefs of Members of a Department of Palliative Care, Rehabilitation, and Integrative Medicine

Context: Palliative, Rehabilitation, and Integrative Medicine (PRIM) department members anonymously reported a positive experience with work from home (WFH) two months after its rapid pandemic transition in March 2020. Data are limited on the stability of such preferences and experiences over time. Objectives: The objectives of this study were to survey the attitudes and beliefs of PRIM employees toward remote work 16 months after the start of the coronavirus disease 2019 (COVID-19) pandemic since vaccines and to determine changes in perceptions of WFH. Methods: All 138 PRIM employees were invited to participate in an anonymous survey from mid-July to mid-August 2021. The 30-question survey included demographics, perceptions toward WFH, and the pandemic. Results: One hundred fifteen (83%) employees completed the survey: 29 (74%) research, 62 (83%) clinicians, and 24 (100%) administrative personnel. Most were female (76%), 30-59 years old (88%), PRIM employees before May 2020 (89%), shared office space (52%), and had received either first or second dose of the COVID-19 vaccine (88%). Overall experience (86%) and emotional response (74%) with WFH were positive and not significantly different from 2020 (p = 0.128 and 0.782, respectively). Positive experience was associated with having adequate equipment (p = 0.002), perception of productivity (p = 0.002), financial advantage (p = 0.002), and time demands caring for dependents (p = 0.038). Clinicians reported less positive response (78%, p = 0.002) and less productivity (49%, p = 0.002) with WFH and higher level of stress (54%, p = 0.026) since COVID-19. Employees continued to support WFH permanently (79%) for two or more days/week (82%). There was continued increased emotional exhaustion (71%) similar to 2020 (p = 0.868), and being asked to work partially or completely from home permanently was favored by 64% versus 97% and 96% of clinicians, research, and administrative, respectively (p = 0.002). Conclusions: Support for WFH was sustained a year later and after three pandemic waves. These findings serve as a model for future rapid work transitions and can help elucidate factors associated with stress and emotional exhaustion in a new post-COVID-19 work environment.

The Impact of the COVID-19 Pandemic on Quality Education of the Medical Young Generation

(1) Generating the need to impose social distancing to reduce the spread of the virus, the COVID-19 pandemic altered the ways in which the teaching process normally happens. The aim of our study was to determine the impact of online teaching on medical students during this period. (2) Our study included 2059 medical, dental and pharmacy students from the University of Medicine and Pharmacy "Grigore T. Popa", Iasi, Romania. We used a modified metacognition questionnaire after translation into Romanian and validation. Our questionnaire included 38 items, and it was divided into four parts. Academic results and preferences regarding the on-site or online courses, information regarding practical training, self-awareness in terms of one's feelings such as anger, boredom and anxiety and also substance use linked to online teaching, and contextualization of the relationship with colleagues, teachers, friends and family were among the most important points evaluated. A comparison was made between preclinical and clinical students. A five-item Linkert-like scale was used for rating the answers in the last three parts that evaluated the impact of the SARS-CoV-2 pandemic on the educational process. (3) Preclinical medical students, compared to preclinical dental students, obtained statistically significant improvements in their evaluation results, with fewer failed exams (p < 0.001) and with similar results being obtained by comparing dental with pharmacy students. All students obtained statistically significant improvements in their academic results during the online evaluation. A statistically significant increase in anxiety and depression with a p-value of <0.001 was registered among our students. (4) The majority found it difficult to cope with this intense period. Both teachers and students found it difficult to adjust on such short notice to the challenges posed by the new concept of online teaching and learning.

Mathematical Modeling of SARS-CoV-2 Omicron Wave under Vaccination Effects

Over the course of the COVID-19 pandemic millions of deaths and hospitalizations have been reported. Different SARS-CoV-2 variants of concern have been recognized during this pandemic and some of these variants of concern have caused uncertainty and changes in the dynamics. The Omicron variant has caused a large amount of infected cases in the US and worldwide. The average number of deaths during the Omicron wave toll increased in comparison with previous SARS-CoV-2 waves. We studied the Omicron wave by using a highly nonlinear mathematical model for the COVID-19 pandemic. The novel model includes individuals who are vaccinated and asymptomatic, which influences the dynamics of SARS-CoV-2. Moreover, the model considers the waning of the immunity and efficacy of the vaccine against the Omicron strain. This study uses the facts that the Omicron strain has a higher transmissibility than the previous circulating SARS-CoV-2 strain but is less deadly. Preliminary studies have found that Omicron has a lower case fatality rate compared to previous circulating SARS-CoV-2 strains. The simulation results show that even if the Omicron strain is less deadly it might cause more deaths, hospitalizations and infections. We provide a variety of scenarios that help to obtain insight about the Omicron wave and its consequences. The proposed mathematical model, in conjunction with the simulations, provides an explanation for a large Omicron wave under various conditions related to vaccines and transmissibility. These results provide an awareness that new SARS-CoV-2 variants can cause more deaths even if their fatality rate is lower.

Modelling the impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia

Countries around the world have implemented a series of interventions to contain the pandemic of coronavirus disease (COVID-19), and significant lessons can be drawn from the study of the full transmission dynamics of the disease caused by-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-in the Eastern, Madinah, Makkah, and Riyadh regions of Saudi Arabia, where robust non-pharmaceutical interventions effectively suppressed the local outbreak of this disease. On the basis of 333732 laboratory-confirmed cases, we used mathematical modelling to reconstruct the complete spectrum dynamics of COVID-19 in Saudi Arabia between 2 March and 25 September 2020 over 5 periods characterised by events and interventions. Our model account for asymptomatic and presymptomatic infectiousness, time-varying ascertainable infection rate, and transmission rates. Our results indicate that non-pharmaceutical interventions were effective in containing the epidemic, with reproduction numbers decreasing on average to 0.29 (0.19-0.66) in the Eastern, Madinah, Makkah, and Riyadh region. The chance of resurgence after the lifting of all interventions after 30 consecutive days with no symptomatic cases is also examined and emphasizes the danger presented by largely hidden infections while switching control strategies. These findings have major significance for evaluating methods for maintaining monitoring and interventions to eventually reduce outbreaks of COVID-19 in Saudi Arabia in the future.

Aligning staff schedules, testing, and isolation reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study

COVID-19 outbreaks in congregate settings remain a serious threat to the health of disproportionately affected populations such as people experiencing incarceration or homelessness, the elderly, and essential workers. An individual-based model accounting for individual infectiousness over time, staff work schedules, and testing and isolation schedules was developed to simulate community transmission of SARS-CoV-2 to staff in a congregate facility and subsequent transmission within the facility that could cause an outbreak. Systematic testing strategies in which staff are tested on the first day of their workweek were found to prevent up to 16% more infections than testing strategies unrelated to staff schedules. Testing staff at the beginning of their workweek, implementing timely isolation following testing, limiting test turnaround time, and increasing test frequency in high transmission scenarios can supplement additional mitigation measures to aid outbreak prevention in congregate settings.

COVID-19: Data-Driven optimal allocation of ventilator supply under uncertainty and risk

This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. We also define a new region-based sub-problem and bounds on the problem and then show their computational benefits in terms of the optimality and relaxation gaps. The computational results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Mathematical modeling to study the impact of immigration on the dynamics of the COVID-19 pandemic: A case study for Venezuela

We propose two different mathematical models to study the effect of immigration on the COVID-19 pandemic. The first model does not consider immigration, whereas the second one does. Both mathematical models consider five different subpopulations: susceptible, exposed, infected, asymptomatic carriers, and recovered. We find the basic reproduction number R₀ using the next-generation matrix method for the mathematical model without immigration. This threshold parameter is paramount because it allows us to characterize the evolution of the disease and identify what parameters substantially affect the COVID-19 pandemic outcome. We focus on the Venezuelan scenario, where immigration and emigration have been important over recent years, particularly during the pandemic. We show that the estimation of the transmission rates of the SARS-CoV-2 are affected when the immigration of infected people is considered. This has an important consequence from a public health perspective because if the basic reproduction number is less than unity, we can expect that the SARS-CoV-2 would disappear. Thus, if the basic reproduction number is slightly above one, we can predict that some mild non-pharmaceutical interventions would be enough to decrease the number of infected people. The results show that the dynamics of the spread of SARS-CoV-2 through the population must consider immigration to obtain better insight into the outcomes and create awareness in the population regarding the population flow.

Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.

Is Crisis and Emergency Risk Communication as Effective as Vaccination for Preventing Virus Diffusion? Measuring the Impacts of Failure in CERC with MERS-CoV Outbreak in South Korea

This study measured the impacts of failure in Crisis and Emergency Risk Communication (CERC) during the outbreak of a contagious Corona viral disease. The study measured the impacts by the number of individuals and hospitals exposed to the virus. The 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea was used to investigate the consequences of CERC failure, where the names of hospitals exposed to MERS-CoV were withheld from the public during the early stage of virus diffusion. Empirical data analyses and simulated model tests were conducted. The findings of analyses and tests show that an early announcement of the hospital names and publicizing the necessary preventive measures could have reduced the rate of infection by approximately 85% and the number of contaminated healthcare facilities by 39% at maximum. This level of reduction is comparable to that of vaccination and of social distancing.

Did U.S. Governments Violate Individual Human Rights? A Thomistic Response to COVID-19 Government Mandates

In response to the COVID-19 pandemic, some Americans have claimed that U.S. governments have superseded their jurisdiction and violated individuals' human rights in the use of government mandates. Many citizens and politicians have also claimed that governments are utilizing the pandemic as a smoke screen to take individual rights away from citizens to gain further power. In light of such claims, I provide a Thomistic response to argue that state and local political authorities' use of public health mandates were other-regarding in seeking to protect the common good in an unprecedented health crisis. Further, I argue that the characterization of individual rights atomized from community has led to an improper understanding of political authorities, individual rights, and our duties to our communities. Rejecting the reductive, skeptical, individualistic, and atomistic views that many Americans have engendered, I provide a Thomistic political orientation that more adequately helps us think about political authorities' and citizens' responsibilities within our political communities.

Modeling comparative cost-effectiveness of SARS-CoV-2 vaccine dose fractionation in India

Given global Coronavirus Disease 2019 (COVID-19) vaccine shortages and inequity of vaccine distributions, fractionation of vaccine doses might be an effective strategy for reducing public health and economic burden, notwithstanding the emergence of new variants of concern. In this study, we developed a multi-scale model incorporating population-level transmission and individual-level vaccination to estimate the costs of hospitalization and vaccination and the economic benefits of reducing COVID-19 deaths due to dose-fractionation strategies in India. We used large-scale survey data of the willingness to pay together with data of vaccine and hospital admission costs to build the model. We found that fractional doses of vaccines could be an economically viable vaccination strategy compared to alternatives of either full-dose vaccination or no vaccination. Dose-sparing strategies could save a large number of lives, even with the emergence of new variants with higher transmissibility.

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients' status. Internet of Things (IoT) technologies have been used for monitoring patients' health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.

An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy: a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of τ days. We also implement a random testing strategy with a threshold over the population. We found that τ=7 is a good choice for the quarantine strategy since it reduces the infected population and, conveniently, it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, and the medical facilities to deal with the additional critical patients would be relatively low.

Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration

With social networking enabling the expressions of billions of people to be posted online, sentiment analysis and massive computational power enables systematic mining of information about populations including their affective states with respect to epidemiological concerns during a pandemic. Gleaning rationale for behavioral choices, such as vaccine hesitancy, from public commentary expressed through social media channels may provide quantifiable and articulated sources of feedback that are useful for rapidly modifying or refining pandemic spread predictions, health protocols, vaccination offerings, and policy approaches. Additional potential gains of sentiment analysis may include lessening of vaccine hesitancy, reduction in civil disobedience, and most importantly, better healthcare outcomes for individuals and their communities. In this article, we highlight the evolution of select epidemiological models; conduct a critical review of models in terms of the level and depth of modeling of social media, social network factors, and sentiment analysis; and finally, partially illustrate sentiment analysis using COVID-19 Twitter data.

Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population

Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.

Quarantine and testing strategies to ameliorate transmission due to travel during the COVID-19 pandemic: a modelling study

BACKGROUND

Numerous countries have imposed strict travel restrictions during the COVID-19 pandemic, contributing to a large socioeconomic burden. The long quarantines that have been applied to contacts of cases may be excessive for travel policy.

METHODS

We developed an approach to evaluate imminent countrywide COVID-19 infections after 0-14-day quarantine and testing. We identified the minimum travel quarantine duration such that the infection rate within the destination country did not increase compared to a travel ban, defining this minimum quarantine as "sufficient."

FINDINGS

We present a generalised analytical framework and a specific case study of the epidemic situation on November 21, 2021, for application to 26 European countries. For most origin-destination country pairs, a three-day or shorter quarantine with RT-PCR or antigen testing on exit suffices. Adaptation to the European Union traffic-light risk stratification provided a simplified policy tool. Our analytical approach provides guidance for travel policy during all phases of pandemic diseases.

INTERPRETATION

For nearly half of origin-destination country pairs analysed, travel can be permitted in the absence of quarantine and testing. For the majority of pairs requiring controls, a short quarantine with testing could be as effective as a complete travel ban. The estimated travel quarantine durations are substantially shorter than those specified for traced contacts.

FUNDING

EasyJet (JPT and APG), the Elihu endowment (JPT), the Burnett and Stender families' endowment (APG), the Notsew Orm Sands Foundation (JPT and APG), the National Institutes of Health (MCF), Canadian Institutes of Health Research (SMM) and Natural Sciences and Engineering Research Council of Canada EIDM-MfPH (SMM).

Real-time pandemic surveillance using hospital admissions and mobility data

Forecasting COVID-19 healthcare demand has been hindered by poor data throughout the pandemic. We introduce a robust model for predicting COVID-19 transmission and hospitalizations based on COVID-19 hospital admissions and cell phone mobility data. This approach was developed by a municipal COVID-19 task force in Austin, TX, which includes civic leaders, public health officials, healthcare executives, and scientists. The model was incorporated into a dashboard providing daily healthcare forecasts that have raised public awareness, guided the city’s staged alert system to prevent unmanageable ICU surges, and triggered the launch of an alternative care site to accommodate hospital overflow.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

Policy and behavioral response to shock events: An agent-based model of the effectiveness and equity of policy design features

In the aftermath of shock events, policy responses tend to be crafted under significant time constraints and high levels of uncertainty. The extent to which individuals comply with different policy designs can further influence how effective the policy responses are and how equitably their impacts are distributed in the population. Tools which allow policymakers to model different crisis trajectories, policy responses, and behavioral scenarios ex ante can provide crucial timely support in the decision-making process. Set in the context of COVID-19 shelter in place policies, in this paper we present the COVID-19 Policy Evaluation (CoPE) tool, which is an agent-based modeling framework that enables researchers and policymakers to anticipate the relative impacts of policy decisions. Specifically, this framework illuminates the extent to which policy design features and behavioral responsiveness influence the efficacy and equity of policy responses to shock events. We show that while an early policy response can be highly effective, the impact of the timing is moderated by other aspects of policy design such as duration and targeting of the policy, as well as societal aspects such as trust and compliance among the population. More importantly, we show that even policies that are more effective overall can have disproportionate impacts on vulnerable populations. By disaggregating the impact of different policy design elements on different population groups, we provide an additional tool for policymakers to use in the design of targeted strategies for disproportionately affected populations.

The clinical characteristics of pediatric coronavirus disease 2019 in 2020 in Japan

BACKGROUND

The COVID-19 pandemic has affected the lives of people of all ages. Most reports on pediatric cases suggest that children experience fewer and milder symptoms than do adults. This is the first nationwide study in Japan focusing on pediatric cases reported by pediatricians, including cases with no or mild symptoms.

METHODS

We analyzed the epidemiological and clinical characteristics and transmission patterns of 840 pediatric (<16 years old) COVID-19 cases reported between February and December 2020 in Japan, using a dedicated database which was maintained voluntarily by members of the Japan Pediatric Society.

RESULTS

Almost half of the patients (47.7%) were asymptomatic, while most of the others presented mild symptoms. At the time of admission or first outpatient clinic visit, 84.0% of the cases were afebrile (<37.5°C). In total, 609 cases (72.5%) were exposed to COVID-19-positive household members. We analyzed the influence of nationwide school closures that were introduced in March 2020 on COVID-19 transmission routes among children in Japan. Transmission within households occurred most frequently, with no significant difference between the periods before and after declaring nationwide school closures (70.9% and 74.5%, respectively).

CONCLUSIONS

COVID-19 symptoms in children are less severe than those in adults. School closure appeared to have a limited effect on transmission. Controlling household transmission from adult family members is the most important measure for prevention of COVID-19 among children.

Projecting the Combined Health Care Burden of Seasonal Influenza and COVID-19 in the 2020-2021 Season

Background. In mid-2020, there was significant concern that the overlapping 2020-2021 influenza season and COVID-19 pandemic would overwhelm already stressed health care systems in the Northern Hemisphere, particularly if influenza immunization rates were low. Methods. Using a mathematical susceptible-exposed-infected-recovered (SEIR) compartmental model incorporating the age-specific viral transmission rates and disease severity of Austin, Texas, a large metropolitan region, we projected the incidence and health care burden for both COVID-19 and influenza across observed levels of SARS-CoV-2 transmission and influenza immunization rates for the 2020-2021 season. We then retrospectively compared scenario projections made in August 2020 with observed trends through June 2021. Results. Across all scenarios, we projected that the COVID-19 burden would dwarf that of influenza. In all but our lowest transmission scenarios, intensive care units were overwhelmed by COVID-19 patients, with the levels of influenza immunization having little impact on health care capacity needs. Consistent with our projections, sustained nonpharmaceutical interventions (NPIs) in Austin prevented COVID-19 from overwhelming health care systems and almost completely suppressed influenza during the 2020-2021 respiratory virus season. Limitations. The model assumed no cross-immunity between SARS-CoV-2 and influenza, which might reduce the burden or slow the transmission of 1 or both viruses. Conclusion. Before the widespread rollout of the SARS-CoV-2 vaccine, COVID-19 was projected to cause an order of magnitude more hospitalizations than seasonal influenza because of its higher transmissibility and severity. Consistent with predictions assuming strong NPIs, COVID-19 strained but did not overwhelm local health care systems in Austin, while the influenza burden was negligible. Implications. Nonspecific NPI efforts can dramatically reduce seasonal influenza burden and preserve health care capacity during respiratory virus season.

Highlights

As the COVID-19 pandemic threatened lives worldwide, the Northern Hemisphere braced for a potential "twindemic" of seasonal influenza and COVID-19.Using a validated mathematical model of influenza and SARS-CoV-2 co-circulation in a large US city, we projected the impact of COVID-19-driven nonpharmaceutical interventions combined with influenza vaccination on health care capacity during the 2020-2021 respiratory virus season.We describe analyses conducted during summer 2020 to help US cities prepare for the 2020-2021 influenza season and provide a retrospective evaluation of the initial projections.

Quarantine and testing strategies to ameliorate transmission due to travel during the COVID-19 pandemic: a modelling study

BACKGROUND

Numerous countries imposed strict travel restrictions, contributing to the large socioeconomic burden during the COVID-19 pandemic. The long quarantines that apply to contacts of cases may be excessive for travel policy.

METHODS

We developed an approach to evaluate imminent countrywide COVID-19 infections after 0-14-day quarantine and testing. We identified the minimum travel quarantine duration such that the infection rate within the destination country did not increase compared to a travel ban, defining this minimum quarantine as "sufficient."

FINDINGS

We present a generalised analytical framework and a specific case study of the epidemic situation on November 21, 2021, for application to 26 European countries. For most origin-destination country pairs, a three-day or shorter quarantine with RT-PCR or antigen testing on exit suffices. Adaptation to the European Union traffic-light risk stratification provided a simplified policy tool. Our analytical approach provides guidance for travel policy during all phases of pandemic diseases.

INTERPRETATION

For nearly half of origin-destination country pairs analysed, travel can be permitted in the absence of quarantine and testing. For the majority of pairs requiring controls, a short quarantine with testing could be as effective as a complete travel ban. The estimated travel quarantine durations are substantially shorter than those specified for traced contacts.

FUNDING

EasyJet (JPT and APG), the Elihu endowment (JPT), the Burnett and Stender families' endowment (APG), the Notsew Orm Sands Foundation (JPT and APG), the National Institutes of Health (MCF), Canadian Institutes of Health Research (SMM) and Natural Sciences and Engineering Research Council of Canada EIDM-MfPH (SMM).

No causal effect of school closures in Japan on the spread of COVID-19 in spring 2020

Among tool kits to combat the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, school closures are one of the most frequent non-pharmaceutical interventions. However, school closures bring about substantial costs, such as learning loss. To date, studies have not reached a consensus about the effectiveness of these policies at mitigating community transmission, partly because they lack rigorous causal inference. Here we assess the causal effect of school closures in Japan on reducing the spread of COVID-19 in spring 2020. By matching each municipality with open schools to a municipality with closed schools that is the most similar in terms of potential confounders, we can estimate how many cases the municipality with open schools would have had if it had closed its schools. We do not find any evidence that school closures in Japan reduced the spread of COVID-19. Our null results suggest that policies on school closures should be reexamined given the potential negative consequences for children and parents.

Direct and indirect cost of COVID-19 patients in Iran

OBJECTIVES

This study aimed to estimate the cost of COVID-19 patients and some affecting factors in Iran.

METHODS

This was a prevalence-based cost-of-illness study based on a bottom-up costing approach which was conducted from March 2020 to July 2020. Data were extracted from the hospital's Hospital Information System (HIS) and Cost-of-illness (COI) assessment checklist. Indirect costs were assessed based on the Human Capital Approach. Data were analyzed using SPSS software version 22 and Microsoft EXCEL 2016.

RESULTS

A total of 745 Covid-19 patients were included in the analysis. The mean total cost was estimated at 8813.15 (PPP, Current International $), accounting for 60% of GDP per capita. The mean direct and indirect cost was 3362.49 (PPP, Current International $) (38% of the total cost and 23% of the GDP per capita), and 5450.66 (PPP, Current International $) (62% of the total cost and 37% of the GDP per capita), respectively. The mean hospitalization cost was higher among patients who died and those who were covered by supplemental insurance. Also, the costs of disease experienced a dramatic rise with increasing age. For different scenarios in terms of outbreak rate, hospitalization rate and mortality rate, the total estimated cost of illness for Covid-19 ranged from 6263 million (PPP, Current International $) to 63,474 million (PPP, Current International $).

CONCLUSIONS

Covid-19 imposes a substantial financial burden on people, health care systems, insurance organizations and the country's economy as a whole. Since the economic burden of this disease increases dramatically by increasing disease outbreak, more attention should be paid to the development and implementation of appropriate preventive programs.

Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis

BACKGROUND

The World Health Organization and US Centers for Disease Control and Prevention recommend that both infected and susceptible people wear face masks to protect against COVID-19.

METHODS

We develop a dynamic disease model to assess the effectiveness of face masks in reducing the spread of COVID-19, during an initial outbreak and a later resurgence, as a function of mask effectiveness, coverage, intervention timing, and time horizon. We instantiate the model for the COVID-19 outbreak in New York, with sensitivity analyses on key natural history parameters.

RESULTS

During the initial epidemic outbreak, with no social distancing, only 100% coverage of masks with high effectiveness can reduce the effective reproductive number R e below 1. During a resurgence, with lowered transmission rates due to social distancing measures, masks with medium effectiveness at 80% coverage can reduce R e below 1 but cannot do so if individuals relax social distancing efforts. Full mask coverage could significantly improve outcomes during a resurgence: with social distancing, masks with at least medium effectiveness could reduce R e below 1 and avert almost all infections, even with intervention fatigue. For coverage levels below 100%, prioritizing masks that reduce the risk of an infected individual from spreading the infection rather than the risk of a susceptible individual from getting infected yields the greatest benefit.

LIMITATIONS

Data regarding COVID-19 transmission are uncertain, and empirical evidence on mask effectiveness is limited. Our analyses assume homogeneous mixing, providing an upper bound on mask effectiveness.

CONCLUSIONS

Even moderately effective face masks can play a role in reducing the spread of COVID-19, particularly with full coverage, but should be combined with social distancing measures to reduce R e below 1.[Box: see text].

Application of the health Belief Model to assess community preventive practices against COVID-19 in Saudi Arabia

Background

The novel coronavirus disease (COVID-19) has affected hundreds of thousands of people across more than 200 countries. As the pandemic continues, the health agencies, worldwide, are recommending strict preventive practices to avert its transmission at community scale. We sought to predict the behavior of the Saudi population for adopting community preventive practices during the COVID-19 pandemic.

Methods

An online questionnaire consisting of 22 items pertaining to the Health Belief Model constructs was used to measure the perceived susceptibility and perceived severity of contracting COVID-19, along with the perceived benefits and perceived barriers to follow the Ministry of Health’s recommendations. The outcome was assessed by their readiness to be compliant with the community protective measures. Data were analyzed using STATA at significance level of 0.05.

Results

A total of 900 individuals received the online survey link, of which 688 (response rate: 76.4%) respondents consented to participate in the study. The mean age of the respondents was 31.39 (SD = 8.94). Positive associations were observed between perceived susceptibility (Beta: 0.24; p value < 0.001), perceived severity (Beta: 0.16; p value < 0.001), perceived benefits (Beta: 0.41; p value < 0.001), cue to action (Beta: 2.61; p value < 0.001) and the participation in community preventive practices during the pandemic of the COVID-19.

Conclusions

Health belief model's constructs of perceived susceptibility, severity, benefits and cue to action can be adopted to help strengthen COVID-19 limiting behaviors and prevention programs which can delivered through community pharmacies in Saudi Arabia as well as around the world.

Reasons for SARS-CoV-2 infection in children and their role in the transmission of infection according to age: a case-control study

BACKGROUND

The locations where children get exposed to SARS-CoV-2 infection and their contribution in spreading the infection are still not fully understood. Aim of the article is to verify the most frequent reasons for SARS-CoV-2 infection in children and their role in the secondary transmission of the infection.

METHODS

A case-control study was performed in all SARS-CoV-2 positive children (n = 81) and an equal number of age- and sex- matched controls who were referred to the S. Camillo-Forlanini Pediatric Walk-in Center of Rome. The results of all SARS-CoV-2 nasopharyngeal swabs performed in children aged < 18 years from October 16 to December 19, 2020 were analyzed.

RESULTS

School contacts were more frequent in controls than in cases (OR 0.49; 95% CI: 0.3-0.9), while household contacts were higher in cases (OR 5.09; 95% CI: 2.2-12.0). In both cases and controls, school contacts were significantly less frequent, while on the contrary household contacts seemed to be more frequent in nursery school children compared to primary school or middle/high school children. A multivariate logistic regression showed that the probability of being positive to SARS-CoV-2 was significantly lower in children who had school contacts or who had flu symptoms compared to children who had household contacts. Results showed a 30.6% secondary attack rate for household contacts.

CONCLUSION

In our study population, the two most frequent reasons for SARS-CoV-2 infection were school and home contacts. The risk of being positive was 5 times lower in children who had school contacts than in children who had household contacts.

Measuring the impact of suspending Umrah, a global mass gathering in Saudi Arabia on the COVID-19 pandemic

Since the early days of the coronavirus (COVID-19) outbreak in Wuhan, China, Saudi Arabia started to implement several preventative measures starting with the imposition of travel restrictions to and from China. Due to the rapid spread of COVID-19, and with the first confirmed case in Saudi Arabia in March 2019, more strict measures, such as international travel restriction, and suspension or cancellation of major events, social gatherings, prayers at mosques, and sports competitions, were employed. These non-pharmaceutical interventions aim to reduce the extent of the epidemic due to the implications of international travel and mass gatherings on the increase in the number of new cases locally and globally. Since this ongoing outbreak is the first of its kind in the modern world, the impact of suspending mass gatherings on the outbreak is unknown and difficult to measure. We use a stratified SEIR epidemic model to evaluate the impact of Umrah, a global Muslim pilgrimage to Mecca, on the spread of the COVID-19 pandemic during the month of Ramadan, the peak of the Umrah season. The analyses shown in the paper provide insights into the effects of global mass gatherings such as Hajj and Umrah on the progression of the COVID-19 pandemic locally and globally.

The Effect of Social Distancing on the Early Spread of the Novel Coronavirus

This article quantifies the effect of individual social distancing on the spread of the novel coronavirus. To do so, we use data on time spent by individuals on activities that would potentially expose them to crowds from the American Time Use Survey linked with state-level data on positive tests from the COVID Tracking Project. We estimate count data specifications of observed COVID-19 infections at the state level as a function of control demographic variables, and a measure of social distance that captures the amount of time individuals across the states spend in activities that potentially expose them to crowds. Parameter estimates reveal that the number of state-level novel coronavirus infections decrease with respect to our measure of individual social distance. From a practical perspective, our parameter estimates suggest that if the typical individual in a U.S. state were to spend eight hours away from crowds completely, this would translate into approximately 240,000 fewer COVID-19 infections across the states. Our results suggest that, at least in the United States, social distancing policies are effective in slowing the spread of the novel coronavirus.

Analysis of Delayed Vaccination Regimens: A Mathematical Modeling Approach

The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.

Using Proper Mean Generation Intervals in Modeling of COVID-19

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of infection attack rate (AR) and control efficacy. We argue that it is important to use suitable epidemiological parameter values for proper estimation/prediction. Furthermore, we propose an epidemic model to assess the transmission dynamics of COVID-19 for Belgium, Israel, and the United Arab Emirates (UAE). We estimated a time-varying reproductive number [R0(t)] based on the COVID-19 deaths data and we found that Belgium has the highest AR followed by Israel and the UAE.

Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France’s ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

Selection of COVID-19 mitigation measures requires balancing health outcomes with economic impacts. Here, the authors derive a system to set triggers for increasing mitigation measures to preserve healthcare capacity, and describe how it has been used to support public health decision making in Austin, Texas.

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

BACKGROUND

Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.

OBJECTIVE

Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.

METHODS

We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.

RESULTS

Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.

CONCLUSIONS

Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

Analysis of Key Factors of a SARS-CoV-2 Vaccination Program: A Mathematical Modeling Approach

The administration of vaccines against the coronavirus disease 2019 (COVID-19) started in early December of 2020. Currently, there are only a few approved vaccines, each with different efficacies and mechanisms of action. Moreover, vaccination programs in different regions may vary due to differences in implementation, for instance, simply the availability of the vaccine. In this article, we study the impact of the pace of vaccination and the intrinsic efficacy of the vaccine on prevalence, hospitalizations, and deaths related to the SARS-CoV-2 virus. Then we study different potential scenarios regarding the burden of the COVID-19 pandemic in the near future. We construct a compartmental mathematical model and use computational methodologies to study these different scenarios. Thus, we are able to identify some key factors to reach the aims of the vaccination programs. We use some metrics related to the outcomes of the COVID-19 pandemic in order to assess the impact of the efficacy of the vaccine and the pace of the vaccine inoculation. We found that both factors have a high impact on the outcomes. However, the rate of vaccine administration has a higher impact in reducing the burden of the COVID-19 pandemic. This result shows that health institutions need to focus on increasing the vaccine inoculation pace and create awareness in the population about the importance of COVID-19 vaccines.

Projecting COVID-19 isolation bed requirements for people experiencing homelessness

As COVID-19 spreads across the United States, people experiencing homelessness (PEH) are among the most vulnerable to the virus. To mitigate transmission, municipal governments are procuring isolation facilities for PEH to utilize following possible exposure to the virus. Here we describe the framework for anticipating isolation bed demand in PEH communities that we developed to support public health planning in Austin, Texas during March 2020. Using a mathematical model of COVID-19 transmission, we projected that, under no social distancing orders, a maximum of 299 (95% Confidence Interval: 223, 321) PEH may require isolation rooms in the same week. Based on these analyses, Austin Public Health finalized a lease agreement for 205 isolation rooms on March 27th 2020. As of October 7th 2020, a maximum of 130 rooms have been used on a single day, and a total of 602 PEH have used the facility. As a general rule of thumb, we expect the peak proportion of the PEH population that will require isolation to be roughly triple the projected peak daily incidence in the city. This framework can guide the provisioning of COVID-19 isolation and post-acute care facilities for high risk communities throughout the United States.

Adherence to Social Distancing Guidelines Throughout the COVID-19 Pandemic: The Roles of Pseudoscientific Beliefs, Trust, Political Party Affiliation, and Risk Perceptions

BACKGROUND

Adherence to COVID-19 social distancing guidelines varies across individuals.

PURPOSE

This study examined the relations of pseudoscientific and just world beliefs, generalized and institutional trust, and political party affiliation to adherence to COVID-19 social distancing guidelines over three months, as well as the explanatory role of COVID-19 risk perceptions in these relations.

METHODS

A U.S. nationwide sample of 430 adults (49.8% women; mean age = 40.72) completed a prospective online study, including an initial assessment (between March 27 and April 5, 2020), a 1 month follow-up (between April 27 and May 21, 2020), and a 3 month follow-up (between June 26 and July 15, 2020). We hypothesized that greater pseudoscientific and just world beliefs, lower governmental, institutional, and dispositional trust, and Republican Party affiliation would be associated with lower initial adherence to social distancing and greater reductions in social distancing over time and that COVID-19 risk perceptions would account for significant variance in these relations.

RESULTS

Results revealed unique associations of lower governmental trust, greater COVID-19 pseudoscientific beliefs, and greater trust in the Centers for Disease Control and Prevention (CDC) to lower initial adherence to social distancing. Whereas greater COVID-19 risk perceptions and CDC trust were associated with less steep declines in social distancing over time, both Republican (vs. Democratic) Party affiliation and greater COVID-19 pseudoscientific beliefs were associated with steeper declines in social distancing over time (relations accounted for by lower COVID-19 risk perceptions).

CONCLUSIONS

Results highlight the utility of public health interventions aimed at improving scientific literacy and emphasizing bipartisan support for social distancing guidelines.