Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.

Multiple pandemic waves vs multi-period/multi-phasic epidemics: Global shape of the COVID-19 pandemic

The overall course of the COVID-19 pandemic in Western countries has been characterised by complex sequences of phases. In the period before the arrival of vaccines, these phases were mainly due to the alternation between the strenghtening/lifting of social distancing measures, with the aim to balance the protection of health and that of the society as a whole. After the arrival of vaccines, this multi-phasic character was further emphasised by the complicated deployment of vaccination campaigns and the onset of virus' variants. To cope with this multi-phasic character, we propose a theoretical approach to the modeling of overall pandemic courses, that we term multi-period/multi-phasic, based on a specific definition of phase. This allows a unified and parsimonious representation of complex epidemic courses even when vaccination and virus' variants are considered, through sequences of weak ergodic renewal equations that become fully ergodic when appropriate conditions are met. Specific hypotheses on epidemiological and intervention parameters allow reduction to simple models. The framework suggest a simple, theory driven, approach to data explanation that allows an accurate reproduction of the overall course of the COVID-19 epidemic in Italy since its beginning (February 2020) up to omicron onset, confirming the validity of the concept.

Country-specific determinants for COVID-19 case fatality rate and response strategies from a global perspective: an interpretable machine learning framework

BACKGROUND

There are significant geographic inequities in COVID-19 case fatality rates (CFRs), and comprehensive understanding its country-level determinants in a global perspective is necessary. This study aims to quantify the country-specific risk of COVID-19 CFR and propose tailored response strategies, including vaccination strategies, in 156 countries.

METHODS

Cross-temporal and cross-country variations in COVID-19 CFR was identified using extreme gradient boosting (XGBoost) including 35 factors from seven dimensions in 156 countries from 28 January, 2020 to 31 January, 2022. SHapley Additive exPlanations (SHAP) was used to further clarify the clustering of countries by the key factors driving CFR and the effect of concurrent risk factors for each country. Increases in vaccination rates was simulated to illustrate the reduction of CFR in different classes of countries.

FINDINGS

Overall COVID-19 CFRs varied across countries from 28 Jan 2020 to 31 Jan 31 2022, ranging from 68 to 6373 per 100,000 population. During the COVID-19 pandemic, the determinants of CFRs first changed from health conditions to universal health coverage, and then to a multifactorial mixed effect dominated by vaccination. In the Omicron period, countries were divided into five classes according to risk determinants. Low vaccination-driven class (70 countries) mainly distributed in sub-Saharan Africa and Latin America, and include the majority of low-income countries (95.7%) with many concurrent risk factors. Aging-driven class (26 countries) mainly distributed in high-income European countries. High disease burden-driven class (32 countries) mainly distributed in Asia and North America. Low GDP-driven class (14 countries) are scattered across continents. Simulating a 5% increase in vaccination rate resulted in CFR reductions of 31.2% and 15.0% for the low vaccination-driven class and the high disease burden-driven class, respectively, with greater CFR reductions for countries with high overall risk (SHAP value > 0.1), but only 3.1% for the ageing-driven class.

CONCLUSIONS

Evidence from this study suggests that geographic inequities in COVID-19 CFR is jointly determined by key and concurrent risks, and achieving a decreasing COVID-19 CFR requires more than increasing vaccination coverage, but rather targeted intervention strategies based on country-specific risks.

The effectiveness of booster vaccination of inactivated COVID-19 vaccines against susceptibility, infectiousness, and transmission of omicron BA.2 variant: a retrospective cohort study in Shenzhen, China

Little studies evaluated the effectiveness of booster vaccination of inactivated COVID-19 vaccines against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission). Therefore, we conducted a retrospective cohort study to evaluate the effectiveness of booster vaccination of inactivated COVID-19 vaccines against susceptibility, infectiousness, and transmission in Shenzhen during an Omicron BA.2 outbreak period from 1 February to 21 April 2022. The eligible individuals were classified as four sub-cohorts according to the inactivated COVID-19 vaccination status of both the close contacts and their index cases: group 2-2, fully vaccinated close contacts seeded by fully vaccinated index cases (reference group); group 2-3, booster-vaccinated close contacts seeded by fully vaccinated index cases; group 3-2, fully vaccinated close contacts seeded by booster-vaccinated index cases; and group 3-3, booster-vaccinated close contacts seeded by booster-vaccinated index cases. Univariate and multivariate logistic regression analyses were applied to estimate the effectiveness of booster vaccination. The sample sizes of groups 2-2, 2-3, 3-2, and 3-3 were 846, 1,115, 1,210, and 2,417, respectively. We found that booster vaccination had an effectiveness against infectiousness of 44.9% (95% CI: 19.7%, 62.2%) for the adults ≥ 18 years, 62.2% (95% CI: 32.0%, 78.9%) for the female close contacts, and 60.8% (95% CI: 38.5%, 75.1%) for the non-household close contacts. Moreover, booster vaccination had an effectiveness against transmission of 29.0% (95% CI: 3.2%, 47.9%) for the adults ≥ 18 years, 38.9% (95% CI: 3.3%, 61.3%) for the female close contacts, and 45.8% (95% CI: 22.1%, 62.3%) for the non-household close contacts. However, booster vaccination against susceptibility did not provide any protective effect. In summary, this study confirm that booster vaccination of the inactivated COVID-19 vaccines provides low level of protection and moderate level of protection against Omicron BA.2 transmission and infectiousness, respectively. However, booster vaccination does not provide any protection against Omicron BA.2 susceptibility.

Cost-effectiveness analysis of surgical masks, N95 masks compared to wearing no mask for the prevention of COVID-19 among health care workers: Evidence from the public health care setting in India

BACKGROUND

Nonpharmacological interventions, such as personal protective equipment for example, surgical masks and respirators, and maintenance of hand hygiene along with COVID-19 vaccines have been recommended to reduce viral transmission in the community and health care settings. There is evidence from the literature that surgical and N95 masks may reduce the initial degree of exposure to the virus. A limited research that has studied the cost-effective analysis of surgical masks and N95 masks among health care workers in the prevention of COVID-19 in India. The objective of this study was to estimate the cost-effectiveness of N95 and surgical mask compared to wearing no mask in public hospital settings for preventing COVID-19 infection among Health care workers (HCWs) from the health care provider's perspective.

METHODS

A deterministic baseline model, without any mask use, based on Eikenberry et al was used to form the foundation for parameter estimation and to estimate transmission rates among HCWs. Information on mask efficacy, including the overall filtering efficiency of a mask and clinical efficiency, in terms of either inward efficiency(ei) or outward efficiency(e0), was obtained from published literature. Hospitalized HCWs were assumed to be in one of the disease states i.e., mild, moderate, severe, or critical. A total of 10,000 HCWs was considered as representative of the size of a tertiary care institution HCW population. The utility values for the mild, moderate and severe model health states were sourced from the primary data collection on quality-of-life of HCWs COVID-19 survivors. The utility scores for mild, moderate, and severe COVID-19 conditions were 0.88, 0.738 and 0.58, respectively. The cost of treatment for mild sickness (6,500 INR per day), moderate sickness (10,000 INR per day), severe (require ICU facility without ventilation, 15,000 INR per day), and critical (require ICU facility with ventilation per day, 18,000 INR) per day as per government and private COVID-19 treatment costs and capping were considered. One way sensitivity analyses were performed to identify the model inputs which had the largest impact on model results.

RESULTS

The use of N95 masks compared to using no mask is cost-saving of $1,454,632 (INR 0.106 billion) per 10,000 HCWs in a year. The use of N95 masks compared to using surgical masks is cost-saving of $63,919 (INR 0.005 billion) per 10,000 HCWs in a year. the use of surgical masks compared to using no mask is cost-saving of $1,390,713 (INR 0.102 billion) per 10,000 HCWs in a year. The uncertainty analysis showed that considering fixed transmission rate (1.7), adoption of mask efficiency as 20%, 50% and 80% reduces the cumulative relative mortality to 41%, 79% and 94% respectively. On considering ei = e0 (99%) for N95 and surgical mask with ei = e0 (90%) the cumulative relative mortality was reduced by 97% and the use of N95 masks compared to using surgical masks is cost-saving of $24,361 (INR 0.002 billion) per 10,000 HCWs in a year.

DISCUSSION

Both considered interventions were dominant compared to no mask based on the model estimates. N95 masks were also dominant compared to surgical masks.

Revealing Decision-Making Strategies of Americans in Taking COVID-19 Vaccination

Efficient coverage for newly developed vaccines requires knowing which groups of individuals will accept the vaccine immediately and which will take longer to accept or never accept. Of those who may eventually accept the vaccine, there are two main types: success-based learners, basing their decisions on others' satisfaction, and myopic rationalists, attending to their own immediate perceived benefit. We used COVID-19 vaccination data to fit a mechanistic model capturing the distinct effects of the two types on the vaccination progress. We proved the identifiability of the population proportions of each type and estimated that 47 % of Americans behaved as myopic rationalists with a high variation across the jurisdictions, from 31 % in Mississippi to 76 % in Vermont. The proportion was correlated with the vaccination coverage, proportion of votes in favor of Democrats in 2020 presidential election, and education score.

Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation

BACKGROUND

Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied.

METHODS AND FINDINGS

We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation.

CONCLUSIONS

Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.

Worsening of health disparities across COVID-19 pandemic stages in Korea

OBJECTIVES

With the end of the coronavirus disease 2019 (COVID-19) pandemic, the health outcomes of this disease in Korea must be examined. We aimed to investigate health outcomes and disparities linked to socioeconomic status during the COVID-19 pandemic in Korea and to identify risk factors for hospitalization and mortality.

METHODS

This nationwide retrospective study incorporated an analysis of individuals with and without COVID-19 in Korea between January 1, 2020 and December 31, 2022. The study period was divided into 4 stages. Prevalence, hospitalization, mortality, and case-fatality rates were calculated per 100,000 population. Multivariate logistic regression was performed to identify risk factors for COVID-19 hospitalization and mortality.

RESULTS

Overall, the incidence rate was 40,601 per 100,000 population, the mortality rate was 105 per 100,000 population, and the case-fatality rate was 259 per 100,000 cases. A total of 12,577,367 new cases (24.5%) were recorded in stage 3 and 8,979,635 cases (17.5%) in stage 4. Medical Aid recipients displayed the lowest 3-year cumulative incidence rate (32,737 per 100,000) but the highest hospitalization (5,663 cases per 100,000), mortality (498 per 100,000), and case-fatality (1,521 per 100,000) rates. Male sex, older age, lower economic status, non-metropolitan area of residence, high Charlson comorbidity index, and disability were associated with higher risk of hospitalization and death. Vaccination was found to reduce mortality risk.

CONCLUSIONS

As the pandemic progressed, surges were observed in incidence, hospitalization, and mortality, exacerbating disparities associated with economic status and disability. Nevertheless, Korea has maintained a low case-fatality rate across all economic groups.

The impact of inaccurate assumptions about antibody test accuracy on the parametrisation and results of infectious disease models of epidemics

The parametrisation of infectious disease models is often done based on epidemiological studies that use diagnostic and serology tests to establish disease prevalence or seroprevalence in the population being modelled. During outbreaks of an emerging infectious disease, tests are often used, both for disease control and epidemiological studies, before studies evaluating their accuracy in the population have concluded, with assumptions made about accuracy parameters like sensitivity and specificity. In this simulation study, we simulated such an outbreak, based on the case study of COVID-19, and found that inaccurate parametrisation of infectious disease models due to assumptions about antibody test accuracy in a seroprevalence study can cause modelling results that inform public health decisions to be inaccurate; for example, in our simulation setup, assuming that antibody test specificity was 0.99 instead of 0.90 when it was in fact 0.90 led to an average relative difference of 0.78 in model-projected peak hospitalisations, even when test sensitivity and all other parameters were accurately characterised. We therefore suggest that methods to speed up test evaluation studies are vitally important in the public health response to an emerging outbreak.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States

Introduction

Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics.

Methods

Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic.

Results

We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022.

Conclusion

Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Analysis of the yearly transition function in measles disease modeling

Globally, there were an estimated 9.8 million measles cases and 207 500 measles deaths in 2019. As the effort to eliminate measles around the world continues, modeling remains a valuable tool for public health decision-makers and program implementers. This study presents a novel approach to the use of a yearly transition function that formulates mathematically the vaccine schedules for different age groups while accounting for the effects of the age of vaccination, the timing of vaccination, and disease seasonality on the yearly number of measles cases in a country. The methodology presented adds to an existing modeling framework and expands its analysis, making its utilization more adjustable for the user and contributing to its conceptual clarity. This article also adjusts for the temporal interaction between vaccination and exposure to disease, applying adjustments to estimated yearly counts of cases and the number of vaccines administered that increase population immunity. These new model features provide the ability to forecast and compare the effects of different vaccination timing scenarios and seasonality of transmission on the expected disease incidence. Although the work presented is applied to the example of measles, it has potential relevance to modeling other vaccine-preventable diseases.

Equilibrium points and their stability of COVID-19 in US

This study aims to develop an advanced mathematic model and investigate when and how will the COVID-19 in the US be evolved to endemic. We employed a nonlinear ordinary differential equations-based model to simulate COVID-19 transmission dynamics, factoring in vaccination efforts. Multi-stability analysis was performed on daily new infection data from January 12, 2021 to December 12, 2022 across 50 states in the US. Key indices such as eigenvalues and the basic reproduction number were utilized to evaluate stability and investigate how the pandemic COVD-19 will evolve to endemic in the US. The transmissional, recovery, vaccination rates, vaccination effectiveness, eigenvalues and reproduction numbers ([Formula: see text] and [Formula: see text]) in the endemic equilibrium point were estimated. The stability attractor regions for these parameters were identified and ranked. Our multi-stability analysis revealed that while the endemic equilibrium points in the 50 states remain unstable, there is a significant trend towards stable endemicity in the US. The study's stability analysis, coupled with observed epidemiological waves in the US, suggested that the COVID-19 pandemic may not conclude with the virus's eradication. Nevertheless, the virus is gradually becoming endemic. Effectively strategizing vaccine distribution is pivotal for this transition.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

An Epidemic Model with Infection Age and Vaccination Age Structure

A model of epidemic dynamics is developed that incorporates continuous variables for infection age and vaccination age. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. This property is shown to be of major importance in the severity of the epidemic, when the infectious period of an infected individual precedes the symptomatic period. The model also analyzes the efficacy of vaccination in terms of vaccination age. The immunity to infection of vaccinated individuals varies with vaccination age and is also of major significance in the severity of the epidemic. Application of the model to the 2003 SARS epidemic in Taiwan and the COVID-19 epidemic in New York provides insights into the dynamics of these diseases. It is shown that the SARS outbreak was effectively contained due to the complete overlap of infectious and symptomatic periods, allowing for the timely isolation of affected individuals. In contrast, the pre-symptomatic spread of COVID-19 in New York led to a rapid, uncontrolled epidemic. These findings underscore the critical importance of the pre-symptomatic infectious period and the vaccination strategies in influencing the dynamics of an epidemic.

Differentiating behavioral impact with or without vaccination certification under mass vaccination and non-pharmaceutical interventions on mitigating COVID-19

As COVID-19 vaccines became widely available worldwide, many countries implemented vaccination certification, also known as a "green pass", to promote and expedite vaccination on containing virus spread from the latter half of 2021. This policy allowed those vaccinated to have more freedom in public activities compared to more constraints on the unvaccinated in addition to existing non-pharmaceutical interventions (NPIs). Accordingly, the vaccination certification also induced heterogeneous behaviors of unvaccinated and vaccinated groups. This makes it essential yet challenging to model the behavioral impact of vaccination certification on the two groups and the transmission dynamics of COVID-19 within and between the groups. Very limited quantitative work is available for addressing these purposes. Here we propose an extended epidemiological model SEIQRD[Formula: see text] to effectively distinguish the behavioral impact of vaccination certification on unvaccinated and vaccinated groups through incorporating two contrastive transmission chains. SEIQRD[Formula: see text] also quantifies the impact of the green pass policy. With the resurgence of COVID-19 in Greece, Austria, and Israel in 2021, our simulation results indicate that their implementation of vaccination certification brought about more than a 14-fold decrease in the total number of infections and deaths as compared to a scenario with no such a policy. Additionally, a green pass policy may offer a reasonable practical solution to strike the balance between public health and individual's freedom during the pandemic.

A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic

OBJECTIVE

Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model.

DESIGN

The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro.

RESULTS

Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours.

CONCLUSION

Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics.

Supplemental data for this article is available online at.

Detection of COVID-19 epidemic outbreak using machine learning

Background

The coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.

Objective

In this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.

Methods

We developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.

Results

ML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.

Conclusion

The study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

Role of booster vaccines and hybrid immunity against severe COVID-19 outcomes during BA.5 omicron predominance in Thailand

Owing to both vaccine- and infection-induced immunity, the COVID-19 seroprevalence is ~90% in most countries. It is important to examine the protective role of booster vaccines and hybrid immunity in the COVID-endemic state. Utilizing a hospital information system for COVID-19, we conducted a cohort study by linking laboratory-confirmed COVID-19 case data to the national immunization records during the BA.5 omicron predominant period (1 August-31 December 2022) in Chiang Mai, Thailand. Out of 63,009 adults with COVID-19 included in the study, there were 125 (0.2%) severe COVID outcomes and 6.4% had a previous omicron infection. Protection against severe COVID-19 was highest among those with at least one booster vaccine (63%; aHR 0.37 [95%CI 0.19-0.73]) as compared to those without prior vaccination or natural infection. Hybrid immunity offered better protection (35%; aHR 0.65 [95%CI 0.09-4.73) than primary vaccine series alone or previous infection alone. Evaluating risk by age group, those aged 70 years or more had nearly 40 times (aHR 39.58 [95%CI 18.92-82.79]) the risk of severe-COVID-19 as compared to the 18-39-year age group. While booster vaccines remain the most effective way of protecting against severe COVID-19, particularly in the elderly, hybrid immunity may offer additional benefit.

Effects of vaccination on the two-strain transmission dynamics of COVID-19: Dougherty County, Georgia, USA, as a case study

The emergence of multiple strains of SARS-COV-2 has made it complicated to predict and control the COVID-19 pandemic. Although some vaccines have been effective in reducing the severity of the disease, these vaccines are designed for a specific strain of the virus and are usually less effective for other strains. In addition, the waning of vaccine-induced immunity, reinfection of recovered people, and incomplete vaccination are challenging to the vaccination program. In this study, we developed a detailed model to describe the multi-strain transmission dynamics of COVID-19 under vaccination. We implemented our model to examine the impact of inter-strain transmission competition under vaccination on the critical outbreak indicators: hospitalized cases, undiagnosed cases, basic reproduction numbers, and the overtake-time by a new strain to the existing strain. In particular, our results on the dependence of the overtake-time on vaccination rates, progression-to-infectious rate, and relative transmission rates provide helpful information for managing a pandemic with circulating two strains. Furthermore, our results suggest that a reduction in the relative transmission rates and a decrease in vaccination dropout rates or an increase in vaccination rates help keep the reproduction number of both strains below unity and keep the number of hospitalized cases and undiagnosed cases at their lowest levels. Moreover, our analysis shows that the second and booster-dose vaccinations are useful for further reducing the reproduction number.

The effectiveness of vaccination, testing, and lockdown strategies against COVID-19

The ability of various policy activities to reduce the reproduction rate of the COVID-19 disease is widely discussed. Using a stringency index that comprises a variety of lockdown levels, such as school and workplace closures, we analyze the effectiveness of government restrictions. At the same time, we investigate the capacity of a range of lockdown measures to lower the reproduction rate by considering vaccination rates and testing strategies. By including all three components in an SIR (Susceptible, Infected, Recovery) model, we show that a general and comprehensive test strategy is instrumental in reducing the spread of COVID-19. The empirical study demonstrates that testing and isolation represent a highly effective and preferable approach towards overcoming the pandemic, in particular until vaccination rates have risen to the point of herd immunity.

Effectiveness of inactivated COVID-19 vaccines against SARS-CoV-2 Omicron subvariant BF.7 among outpatients in Beijing, China

OBJECTIVE

To evaluate the effectiveness of inactivated vaccines against SARS-CoV-2 Omicron subvariant BF.7.

METHODS

Information was extracted from outpatients diagnosed with COVID-19 between December 19, 2022 and January 5, 2023 at a single center. Univariate and multivariate logistic regression were performed and three adjusted models were conducted. Vaccine effectiveness (VE) was defined as (1 - OR) × 100 %.

RESULTS

Our study comprised a total of 752 outpatients. After adjusting for factors with a P-value < 0.10 in univariable logistic regression, the VE of booster vaccine was 65.4 % (95 % CI6.1-87.3 %, P = 0.037) in comparison with unvaccinated group. Results of the other two adjusted models were similar, which were 66.3 % (95 % CI: 9.0-87.6 %, P = 0.032) and 64.8 % (95 % CI: 3.6-87.1 %, P = 0.042), respectively. Stratified analysis based on underlying diseases indicated that inactivated vaccines did not provide any protection to patients without underlying diseases. In the population with underlying diseases, the VE of booster vaccination was 68.2 % (95 % CI: 8.4-88.9 %, P = 0.034) after adjustment. However, full vaccination did not demonstrate any protection in all models.

CONCLUSION

There was an effectiveness of three-dose inactivated vaccines against Omicron subvariant BF.7. Our findings supported the importance of booster vaccination.

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

This paper conducts a systematic statistical analysis of the characteristics of the geographical empirical distributions for the numbers of both cumulative and daily confirmed COVID-19 cases and deaths at county, city, and state levels over a time span from January 2020 to June 2022. The mathematical heavy-tailed distributions can be used for fitting the empirical distributions observed in different temporal stages and geographical scales. The estimations of the shape parameter of the tail distributions using the Generalized Pareto Distribution also support the observations of the heavy-tailed distributions. According to the characteristics of the heavy-tailed distributions, the evolution course of the geographical empirical distributions can be divided into three distinct phases, namely the power-law phase, the lognormal phase I, and the lognormal phase II. These three phases could serve as an indicator of the severity degree of the COVID-19 pandemic within an area. The empirical results suggest important intrinsic dynamics of a human infectious virus spread in the human interconnected physical complex network. The findings extend previous empirical studies and could provide more strict constraints for current mathematical and physical modeling studies, such as the SIR model and its variants based on the theory of complex networks.

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution.

Behavioral Changes Associated With COVID-19 Vaccination: Cross-National Online Survey

BACKGROUND

During the initial phases of the vaccination campaign worldwide, nonpharmaceutical interventions (NPIs) remained pivotal in the fight against the COVID-19 pandemic. In this context, it is important to understand how the arrival of vaccines affected the adoption of NPIs. Indeed, some individuals might have seen the start of mass vaccination campaigns as the end of the emergency and, as a result, relaxed their COVID-safe behaviors, facilitating the spread of the virus in a delicate epidemic phase such as the initial rollout.

OBJECTIVE

The aim of this study was to collect information about the possible relaxation of protective behaviors following key events of the vaccination campaign in four countries and to analyze possible associations of these behavioral tendencies with the sociodemographic characteristics of participants.

METHODS

We developed an online survey named "COVID-19 Prevention and Behavior Survey" that was conducted between November 26 and December 22, 2021. Participants were recruited using targeted ads on Facebook in four different countries: Brazil, Italy, South Africa, and the United Kingdom. We measured the onset of relaxation of protective measures in response to key events of the vaccination campaign, namely personal vaccination and vaccination of the most vulnerable population. Through calculation of odds ratios (ORs) and regression analysis, we assessed the strength of association between compliance with NPIs and sociodemographic characteristics of participants.

RESULTS

We received 2263 questionnaires from the four countries. Participants reported the most significant changes in social activities such as going to a restaurant or the cinema and visiting relatives and friends. This is in good agreement with validated psychological models of health-related behavioral change such as the Health Belief Model, according to which activities with higher costs and perceived barriers (eg, social activities) are more prone to early relaxation. Multivariate analysis using a generalized linear model showed that the two main determinants of the drop of social NPIs were (1) having previously tested positive for COVID-19 (after the second vaccine dose: OR 2.46, 95% CI 1.73-3.49) and (2) living with people at risk (after the second vaccine dose: OR 1.57, 95% CI 1.22-2.03).

CONCLUSIONS

This work shows that particular caution has to be taken during vaccination campaigns. Indeed, people might relax their safe behaviors regardless of the dynamics of the epidemic. For this reason, it is crucial to maintain high compliance with NPIs to avoid hindering the beneficial effects of the vaccine.

Measuring unequal distribution of pandemic severity across census years, variants of concern and interventions

BACKGROUND

The COVID-19 pandemic stressed public health systems worldwide due to emergence of several highly transmissible variants of concern. Diverse and complex intervention policies deployed over the last years have shown varied effectiveness in controlling the pandemic. However, a systematic analysis and modelling of the combined effects of different viral lineages and complex intervention policies remains a challenge due to the lack of suitable measures of pandemic inequality and nonlinear effects.

METHODS

Using large-scale agent-based modelling and a high-resolution computational simulation matching census-based demographics of Australia, we carried out a systematic comparative analysis of several COVID-19 pandemic scenarios. The scenarios covered two most recent Australian census years (2016 and 2021), three variants of concern (ancestral, Delta and Omicron), and five representative intervention policies. We introduced pandemic Lorenz curves measuring an unequal distribution of the pandemic severity across local areas. We also quantified pandemic biomodality, distinguishing between urban and regional waves, and measured bifurcations in the effectiveness of interventions.

RESULTS

We quantified nonlinear effects of population heterogeneity on the pandemic severity, highlighting that (i) the population growth amplifies pandemic peaks, (ii) the changes in population size amplify the peak incidence more than the changes in density, and (iii) the pandemic severity is distributed unequally across local areas. We also examined and delineated the effects of urbanisation on the incidence bimodality, distinguishing between urban and regional pandemic waves. Finally, we quantified and examined the impact of school closures, complemented by partial interventions, and identified the conditions when inclusion of school closures may decisively control the transmission.

CONCLUSIONS

Public health response to long-lasting pandemics must be frequently reviewed and adapted to demographic changes. To control recurrent waves, mass-vaccination rollouts need to be complemented by partial NPIs. Healthcare and vaccination resources need to be prioritised towards the localities and regions with high population growth and/or high density.

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research

BACKGROUND

Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.

METHODS

Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated.

RESULTS

Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation.

CONCLUSIONS

The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

Effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 Omicron BA.2 infection in Guangdong, China: a cohort study

The effectiveness of COVID-19 vaccines wanes over time and the emergence of the SARS-CoV-2 Omicron variant led to the accelerated expansion of efforts for booster vaccination. However, the effect and contribution of booster vaccination with inactivated COVID-19 vaccines remain to be evaluated. We conducted a retrospective close contacts cohort study to analyze the epidemiological characteristics and Omicron infection risk, and to evaluate the effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 infection, symptomatic COVID-19, and COVID-19 pneumonia during the outbreaks of Omicron BA.2 infection from 1 February to 31 July 2022 in Guangdong, China. A total of 46,547 close contacts were identified while 6.3% contracted Omicron BA.2 infection, 1.8% were asymptomatic infection, 4.1% developed mild COVID-19, and 0.3% had COVID-19 pneumonia. We found that females and individuals aged 0-17 or ≥ 60 years old were more prone to SARS-CoV-2 infection. The vaccinated individuals showed lower infection risk when compared with the unvaccinated people. The effectiveness of booster vaccination with inactivated COVID-19 vaccines against SARS-CoV-2 infection and symptomatic COVID-19 was 28.6% (95% CI: 11.6%, 35.0%) and 39.6% (95% CI: 30.0, 47.9) among adults aged ≥ 18 years old, respectively when compared with full vaccination. Booster vaccination provided a moderate level of protection against SARS-CoV-2 infection (VE: 49.9%, 95% CI: 22.3%-67.7%) and symptomatic COVID-19 (VE: 62.6%, 95% CI: 36.2%-78.0%) among adults aged ≥ 60 years old. Moreover, the effectiveness of booster vaccination was 52.2% (95% CI: 21.3%, 70.9%) and 83.8% (95% CI: 28.1%, 96.3%) against COVID-19 pneumonia in adults aged ≥ 18 and ≥ 60 years old, respectively. The reduction of absolute risk rate of COVID-19 pneumonia in the booster vaccination group was 0·96% (95% CI: 0.33%, 1.11%), and the number needed to vaccinate to prevent one case of COVID-19 pneumonia was 104 (95% CI: 91, 303) in adults aged ≥ 60 years old. In summary, booster vaccination with inactivated COVID-19 vaccines provides a low level of protection against infection and symptomatic in adults of 18-59 years old, and a moderate level of protection in older adults of more than 60 years old, but a high level of protection against COVID-19 pneumonia in older adults.

Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.

Effective vaccination strategies to control COVID-19 in Korea: a modeling study

OBJECTIVES

In Korea, as immunity levels of the coronavirus disease 2019 (COVID-19) in the population acquired through previous infections and vaccinations have decreased, booster vaccinations have emerged as a necessary measure to control new outbreaks. The objective of this study was to identify the most suitable vaccination strategy for controlling the surge in COVID-19 cases.

METHODS

A mathematical model was developed to concurrently evaluate the immunity levels induced by vaccines and infections. This model was then employed to investigate the potential for future resurgence and the possibility of control through the use of vaccines and antivirals.

RESULTS

As of May 11, 2023, if the current epidemic trend persists without further vaccination efforts, a peak in resurgence is anticipated to occur around mid-October of the same year. Under the most favorable circumstances, the peak number of severely hospitalized patients could be reduced by 43% (n=480) compared to the scenario without vaccine intervention (n=849). Depending on outbreak trends and vaccination strategies, the best timing for vaccination in terms of minimizing this peak varies from May 2023 to August 2023.

CONCLUSIONS

Our findings suggest that if the epidemic persist, the best timing for administering vaccinations would need to be earlier than currently outlined in the Korean plan. It is imperative to continue monitoring outbreak trends, as this is key to determining the best vaccination timing in order to manage potential future surges.

Synergistic effects of vaccination and virus testing on the transmission of an infectious disease

Under the background that asymptomatic virus carriers have infectivity for an infectious disease, we establish a difference equations model with vaccination and virus testing in this paper. Assuming that the vaccine is 100% effective for susceptible people but cannot stop the infectivity of asymptomatic virus carriers, we study how to combine vaccination and virus testing at the beginning of an epidemic to effectively block the spread of infectious disease in different population sizes. By considering the daily processing capacity of the vaccine and daily proportion of testing, the corresponding numerical simulation results are obtained. It is shown that when vaccine availability and virus testing capacity are insufficient, a reasonable combination of the above two measures can slow down or even block the spread of infectious disease. Single virus testing or vaccination can also block the spread of infectious disease, but this requires a lot of manpower, material and financial resources. When the daily proportion of virus testing is fixed, the ratio of the minimum daily processing capacity of vaccines used to block the spread of infectious disease to the corresponding population size is rather stable. It demonstrates that effective protective measures of the same infectious disease in countries and regions with different population sizes can be used as a reference. These results also provide a certain reference for decision makers on how to coordinate vaccines and virus testing resources to curb the spread of such an infectious disease in a certain population size.

Vaccination, human mobility, and COVID-19 health outcomes: Empirical comparison before and during the outbreak of SARS-Cov-2 B.1.1.529 (Omicron) variant

The B.1.1.529 (Omicron) variant surge has raised concerns about the effectiveness of vaccines and the impact of imprudent reopening. Leveraging over two years of county-level COVID-19 data in the US, this study aims to investigate relationships among vaccination, human mobility, and COVID-19 health outcomes (assessed via case rate and case-fatality rate), controlling for socioeconomic, demographic, racial/ethnic, and partisan factors. A set of cross-sectional models was first fitted to empirically compare disparities in COVID-19 health outcomes before and during the Omicron surge. Then, time-varying mediation analyses were employed to delineate how the effects of vaccine and mobility on COVID-19 health outcomes vary over time. Results showed that vaccine effectiveness against case rate lost significance during the Omicron surge, while its effectiveness against case-fatality rate remained significant throughout the pandemic. We also documented salient structural inequalities in COVID-19-related outcomes, with disadvantaged populations consistently bearing a larger brunt of case and death tolls, regardless of high vaccination rates. Last, findings revealed that mobility presented a significantly positive relationship with case rates during each wave of variant outbreak. Mobility substantially mediated the direct effect from vaccination to case rate, leading to a 10.276 % (95 % CI: 6.257, 14.294) decrease in vaccine effectiveness on average. Altogether, our study implies that sole reliance on vaccination to halt COVID-19 needs to be re-examined. Well-resourced and coordinated efforts to enhance vaccine effectiveness, mitigate health disparity and selectively loosen non-pharmaceutical interventions are essential to bringing the pandemic to an end.

Clinical characteristics and factors for serious outcomes among outpatients infected with the Omicron subvariant BF.7

To evaluate clinical characteristics and identify risk factors associated with severe outcomes in outpatients infected with the Omicron subvariant BF.7, data were collected from outpatients diagnosed with Corona Virus Disease 2019 from December 19, 2022 to January 5, 2023. Clinical characteristics were analyzed using descriptive statistics. Univariate and multivariate logistic regression analyses were conducted to identify factors associated with serious outcomes. Variables with a p < 0.10 in the univariate analysis were included in the multivariate model. Our study analyzed 770 patients, of whom 380 (49.4%) were male, with a median age of 59. The most common symptoms reported were cough (71.2%), fever (64.7%), and sore throat (37.7%). Fever lasted an average of 5.93 ± 3.37 days for the general population and 10.64 ± 7.12 days for impaired-immunity patients. Most cases were mild (68.7%), followed by moderate (27.1%). Severe cases accounted for 2.2%, with 0.5% critically ill. Serious outcomes occurred in 4.2% of cases, with 11 deaths during follow-up. Underlying-diseases patients had a higher rate of serious outcomes. Factors associated with serious outcomes included receiving a three-dose vaccination (odds ratio [OR] = 0.324, 95% confidence interval [CI]: 0.113-0.932, p = 0.037), male gender (OR = 2.890, 95% CI: 1.107-7.548, p = 0.030), age (OR = 1.060, 95% CI: 1.024-1.097, p = 0.001), and chest tightness or dyspnea at the time of visit (OR = 4.861, 95% CI: 2.054-11.507, p < 0.001). Our study found that cough, fever, and sore throat were the most common symptoms reported by patients. Receiving a three-dose vaccination was protective, while male gender, age, and chest tightness or dyspnea were identified as risk factors for serious outcomes.

Kalman-based compartmental estimation for covid-19 pandemic using advanced epidemic model

The practicality of administrative measures for covid-19 prevention is crucially based on quantitative information on impacts of various covid-19 transmission influencing elements, including social distancing, contact tracing, medical facilities, vaccine inoculation, etc. A scientific approach of obtaining such quantitative information is based on epidemic models of S I R family. The fundamental S I R model consists of S-susceptible, I-infected, and R-recovered from infected compartmental populations. To obtain the desired quantitative information, these compartmental populations are estimated for varying metaphoric parametric values of various transmission influencing elements, as mentioned above. This paper introduces a new model, named S E I R R P V model, which, in addition to the S and I populations, consists of the E-exposed, R e -recovered from exposed, R-recovered from infected, P-passed away, and V-vaccinated populations. Availing of this additional information, the proposed S E I R R P V model helps in further strengthening the practicality of the administrative measures. The proposed S E I R R P V model is nonlinear and stochastic, requiring a nonlinear estimator to obtain the compartmental populations. This paper uses cubature Kalman filter (CKF) for the nonlinear estimation, which is known for providing an appreciably good accuracy at a fairly small computational demand. The proposed S E I R R P V model, for the first time, stochastically considers the exposed, infected, and vaccinated populations in a single model. The paper also analyzes the non-negativity, epidemic equilibrium, uniqueness, boundary condition, reproduction rate, sensitivity, and local and global stability in disease-free and endemic conditions for the proposed S E I R R P V model. Finally, the performance of the proposed S E I R R P V model is validated for real-data of covid-19 outbreak.

Modeling the dynamics of COVID-19 in the presence of Delta and Omicron variants with vaccination and non-pharmaceutical interventions

Since its inception in December 2019, many safe and effective vaccines have been invented and approved for use against COVID-19 along with various non-pharmaceutical interventions. But the emergence of numerous SARS-CoV-2 variants has put the effectiveness of these vaccines, and other intervention measures under threat. So it is important to understand the dynamics of COVID-19 in the presence of its variants of concern (VOC) in controlling the spread of the disease. To address these situations and to find a way out of this problem, a new mathematical model consisting of a system of non-linear differential equations considering the original COVID-19 strain with its two variants of concern (Delta and Omicron) has been proposed and formulated in this paper. We then analyzed the proposed model to study the transmission dynamics of this multi-strain model and to investigate the consequences of the emergence of multiple new SARS-CoV-2 variants which are more transmissible than the previous ones. The control reproduction number, an important threshold parameter, is then calculated using the next-generation matrix method. Further, we presented the discussion about the stability of the model equilibrium. It is shown that the disease-free equilibrium (DFE) of the model is locally asymptotic stable when the control reproduction is less than unity. It is also shown that the model has a unique endemic equilibrium (EEP) which is locally asymptotic stable when the control reproduction number is greater than unity. Using the Center Manifold theory it is shown that the model also exhibits the backward bifurcation phenomenon when the control reproduction number is less than unity. Again without considering the re-infection of the recovered individuals, it is proved that the disease-free equilibrium is globally asymptotically stable when the reproduction threshold is less than unity. Finally, numerical simulations are performed to verify the analytic results and to show the impact of multiple new SARS-CoV-2 variants in the population which are more contagious than the previous variants. Global uncertainty and sensitivity analysis has been done to identify which parameters have a greater impact on disease dynamics and control disease transmission. Numerical simulation suggests that the emergence of new variants of concern increases COVID-19 infection and related deaths. It also reveals that a combination of non-pharmaceutical interventions with vaccination programs of new more effective vaccines should be continued to control the disease outbreak. This study also suggests that more doses of vaccine should provide to combat new and deadly variants like Delta and Omicron.

The dynamics of SARS-CoV-2 infection in unvaccinated and vaccinated populations in Mumbai, India, between 28 December 2020 and 30 August 2021

The emergence and evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants that could compromise vaccine efficacy (VE) with re-infections in immunized individuals have necessitated continuous surveillance of VE. Here, the occurrence and dynamics of SARS-CoV-2 infections in the context of vaccination during the second wave of infection in Mumbai were evaluated. RT-PCR cycle threshold (Ct) values of the open reading frame (ORF)/envelope (E)/nucleocapsid (N) genes obtained from a total of 42415 samples, comprising unvaccinated (96.88%) and vaccinated cases (3.12%) were analyzed between December 28, 2020, and August 30, 2021. A lower incidence of SARS-CoV-2 infection in fully vaccinated cases (5.07%) compared to partially vaccinated cases (6.5%) and unvaccinated cases (13.453%) was recorded. VE was significant after the first dose of vaccination (ORF gene p-value = 0.003429, and E/N gene p-value = 0.000866). Furthermore, VE was observed to be significant when the post-immunization (first dose) period was stratified to within 30 days (ORF gene p-value = 0.0094 and E/N gene p-value = 0.0023) and to 60 days following the second dose of vaccination (ORF gene p-value = 0.0238). Also, significantly higher efficacy was observed within individuals receiving two doses compared to a single dose (ORF gene p-value = 0.0132 and E/N gene p-value = 0.0387). The emergence of breakthrough infections was also evident (odds ratio= 0.34; 95% confidence interval= 0.27-0.43). Interestingly, viral loads trended towards being higher in some groups of partially vaccinated individuals compared to completely vaccinated and unvaccinated populations. Finally, our results delineated a significantly higher incidence of SARS-CoV-2 acquisition in males, asymptomatic individuals, individuals with comorbidities, and those who were unvaccinated.

Evolutionary implications of SARS-CoV-2 vaccination for the future design of vaccination strategies

Once the first SARS-CoV-2 vaccine became available, mass vaccination was the main pillar of the public health response to the COVID-19 pandemic. It was very effective in reducing hospitalizations and deaths. Here, we discuss the possibility that mass vaccination might accelerate SARS-CoV-2 evolution in antibody-binding regions compared to natural infection at the population level. Using the evidence of strong genetic variation in antibody-binding regions and taking advantage of the similarity between the envelope proteins of SARS-CoV-2 and influenza, we assume that immune selection pressure acting on these regions of the two viruses is similar. We discuss the consequences of this assumption for SARS-CoV-2 evolution in light of mathematical models developed previously for influenza. We further outline the implications of this phenomenon, if our assumptions are confirmed, for the future design of SARS-CoV-2 vaccination strategies.

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

Introduction

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

Methods

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

Results

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

Conclusions

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

Stability and numerical analysis via non-standard finite difference scheme of a nonlinear classical and fractional order model

In this paper, we develop a new mathematical model for an in-depth understanding of COVID-19 (Omicron variant). The mathematical study of an omicron variant of the corona virus is discussed. In this new Omicron model, we used idea of dividing infected compartment further into more classes i.e asymptomatic, symptomatic and Omicron infected compartment. Model is asymptotically locally stable whenever R0<1 and when R0≤1 at disease free equilibrium the system is globally asymptotically stable. Local stability is investigated with Jacobian matrix and with Lyapunov function global stability is analyzed. Moreover basic reduction number is calculated through next generation matrix and numerical analysis will be used to verify the model with real data. We consider also the this model under fractional order derivative. We use Grunwald-Letnikov concept to establish a numerical scheme. We use nonstandard finite difference (NSFD) scheme to simulate the results. Graphical presentations are given corresponding to classical and fractional order derivative. According to our graphical results for the model with numerical parameters, the population's risk of infection can be reduced by adhering to the WHO's suggestions, which include keeping social distances, wearing facemasks, washing one's hands, avoiding crowds, etc.

The effect of mixed vaccination rollout strategy: A modelling study

Vaccines have measurable efficacy obtained first from vaccine trials. However, vaccine efficacy (VE) is not a static measure and long-term population studies are needed to evaluate its performance and impact. COVID-19 vaccines have been developed in record time and the currently licensed vaccines are extremely effective against severe disease with higher VE after the full immunization schedule. To assess the impact of the initial phase of the COVID-19 vaccination rollout programmes, we used an extended Susceptible - Hospitalized - Asymptomatic/mild - Recovered (SHAR) model. Vaccination models were proposed to evaluate different vaccine types: vaccine type 1 which protects against severe disease only but fails to block disease transmission, and vaccine type 2 which protects against both severe disease and infection. VE was assumed as reported by the vaccine trials incorporating the difference in efficacy between one and two doses of vaccine administration. We described the performance of the vaccine in reducing hospitalizations during a momentary scenario in the Basque Country, Spain. With a population in a mixed vaccination setting, our results have shown that reductions in hospitalized COVID-19 cases were observed five months after the vaccination rollout started, from May to June 2021. Specifically in June, a good agreement between modelling simulation and empirical data was well pronounced.

Individual-Community Misalignment in Partisan Identity Predicts Distancing From Norms During the COVID-19 Pandemic

This study investigated whether misalignment between an individual and their community in partisan identity predicted psychological and behavioral distancing from local COVID-19 norms. A nationally representative sample of Republicans and Democrats provided longitudinal data in April (N = 3,492) and June 2020 (N = 2,649). Democrats in Republican communities reported especially heightened better-than-average estimates, perceiving themselves as more adherent to and approving of non-pharmaceutical interventions (NPI; e.g., mask wearing) than their community. Democrats'better-than-average estimates reflected high approval and behavior in Republican communities and substantial norm underestimation. Republicans in Democratic communities did not evidence worse-than-average estimates. In longitudinal models, injunctive norms only predicted NPI behavior when individual and community partisan identity were aligned. The strong personal approval-behavior association did not depend on misalignment; there were no effects of descriptive norms. Normative messages may have limited efficacy for a sizable subpopulation in politically polarized contexts, such as the COVID-19 pandemic.

Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R² estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.

A Methodological Framework for Assessing the Benefit of SARS-CoV-2 Vaccination following Previous Infection: Case Study of Five- to Eleven-Year-Olds

Vaccination rates against SARS-CoV-2 in children aged five to eleven years remain low in many countries. The current benefit of vaccination in this age group has been questioned given that the large majority of children have now experienced at least one SARS-CoV-2 infection. However, protection from infection, vaccination or both wanes over time. National decisions on offering vaccines to this age group have tended to be made without considering time since infection. There is an urgent need to evaluate the additional benefits of vaccination in previously infected children and under what circumstances those benefits accrue. We present a novel methodological framework for estimating the potential benefits of COVID-19 vaccination in previously infected children aged five to eleven, accounting for waning. We apply this framework to the UK context and for two adverse outcomes: hospitalisation related to SARS-CoV-2 infection and Long Covid. We show that the most important drivers of benefit are: the degree of protection provided by previous infection; the protection provided by vaccination; the time since previous infection; and future attack rates. Vaccination can be very beneficial for previously infected children if future attack rates are high and several months have elapsed since the previous major wave in this group. Benefits are generally larger for Long Covid than hospitalisation, because Long Covid is both more common than hospitalisation and previous infection offers less protection against it. Our framework provides a structure for policy makers to explore the additional benefit of vaccination across a range of adverse outcomes and different parameter assumptions. It can be easily updated as new evidence emerges.

Sensitivity of endemic behaviour of COVID-19 under a multi-dose vaccination regime, to various biological parameters and control variables

For an infectious disease such as COVID-19, we present a new four-stage vaccination model (unvaccinated, dose 1 + 2, booster, repeated boosters), which examines the impact of vaccination coverage, vaccination rate, generation interval, control reproduction number, vaccine efficacies and rates of waning immunity upon the dynamics of infection. We derive a single equation that allows computation of equilibrium prevalence and incidence of infection, given knowledge about these parameters and variable values. Based upon a 20-compartment model, we develop a numerical simulation of the associated differential equations. The model is not a forecasting or even predictive one, given the uncertainty about several biological parameter values. Rather, it is intended to aid a qualitative understanding of how equilibrium levels of infection may be impacted upon, by the parameters of the system. We examine one-at-a-time sensitivity analysis around a base case scenario. The key finding which should be of interest to policymakers is that while factors such as improved vaccine efficacy, increased vaccination rates, lower waning rates and more stringent non-pharmaceutical interventions might be thought to improve equilibrium levels of infection, this might only be done to good effect if vaccination coverage on a recurrent basis is sufficiently high.

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

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

The Incidence and Severity of COVID-19 Infection Post Vaccination in Saudi Arabia

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Presently, there is ongoing continuous research for more therapeutic options with a wide variety of vaccine availability. However, many people have worried about the vaccine's side effects. Hence, the current study was conducted to determine the prevalence of vaccinated individuals, side effects, and the rate of infectivity post vaccination including the three doses of vaccinations. Methods A cross-sectional questionnaire-based survey was conducted using Google Forms (Google, Inc., Mountain View, CA). Five hundred forty-three individuals participated and reported their status of COVID-19 infection, vaccination, and side effects. All the participants from Saudi Arabia received all the vaccine shots including the booster dose. Results Most of the Saudi nationals were fully vaccinated, and most received Pfizer vaccines for their first and second shots. Pain at the injection site was reported as the most common adverse effect followed by fever, headache, fatigue, and joint pain. Conclusion From the findings, it is concluded that most of the population of Saudi Arabia was vaccinated effectively. Pain at the injection site is identified as the primary adverse effect of vaccination. Most of the population is vaccinated with the Pfizer vaccine. Long-term side effect monitoring is recommended with large population studies to confirm the status of vaccines and adverse effects.

Determination of significant immunological timescales from mRNA-LNP-based vaccines in humans

A compartment model for an in-host liquid nanoparticle delivered mRNA vaccine is presented. Through non-dimensionalisation, five timescales are identified that dictate the lifetime of the vaccine in-host: decay of interferon gamma, antibody priming, autocatalytic growth, antibody peak and decay, and interleukin cessation. Through asymptotic analysis we are able to obtain semi-analytical solutions in each of the time regimes which allows us to predict maximal concentrations and better understand parameter dependence in the model. We compare our model to 22 data sets for the BNT162b2 and mRNA-1273 mRNA vaccines demonstrating good agreement. Using our analysis, we estimate the values for each of the five timescales in each data set and predict maximal concentrations of plasma B-cells, antibody, and interleukin. Through our comparison, we do not observe any discernible differences between vaccine candidates and sex. However, we do identify an age dependence, specifically that vaccine activation takes longer and that peak antibody occurs sooner in patients aged 55 and greater.

Are COVID-19 patients given adequate instructions about toothbrush hygiene? A cross-sectional study

OBJECTIVE

The aim of this study was to evaluate toothbrush hygienic practices, whether subjects with a positive COVID-19 test received instructions about toothbrush hygiene, and to determine if carriers of SARS CoV-2 were assigned separate bathrooms during home isolation.

METHODS

Data were collected by an anonymous online questionnaire. Variables of interest included toothbrush hygiene practices, receiving instructions on toothbrush handling if tested positive for COVID-19, and being assigned separate bathrooms during home isolation.

RESULTS

From November 2020 through April 2021; 755 responded (472 [62.5%] females, 269 [35.6%] males, 14 [1.85%] did not specify their gender). 14 (4.1%) of 341 respondents who reported a positive result of a COVID-19 test received instructions about how to maintain their toothbrush during home isolation. The majority of subjects (74.4%) reported the use of water to wet their toothbrush before brushing, this practice was significantly more common among young subjects (p < 0.001). 58.6% wash all parts of the toothbrush after use while 38.8% wash the head of the toothbrush, and 1.6% place the toothbrush in an antiseptic. 53% used separate bathrooms during isolation, this was significantly associated with age group (p = 0.006) and higher monthly income (p = 0.02).

CONCLUSIONS

The majority of participants with a positive result of the COVID-19 test were not given explicit instructions about toothbrush handling. Less than half reported good toothbrush hygienic practices. Higher monthly income was significantly associated with using a separate bathroom during home isolation. Providing explicit instructions about toothbrush hygiene is recommended to reduce the spread of contagious diseases such as COVID-19.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BACKGROUND

COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.

METHODS

We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.

RESULTS

Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.

CONCLUSIONS

This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.