Impact of vaccination and non-pharmaceutical interventions on SARS-CoV-2 dynamics in Switzerland

Integrating Human Mobility Models with Epidemic Modeling: A Framework for Generating Synthetic Temporal Contact Networks

High-resolution temporal contact networks are useful ingredients for realistic epidemic simulations. Existing solutions typically rely either on empirical studies that capture fine-grained interactions via Bluetooth or wearable sensors in confined settings or on large-scale simulation frameworks that model entire populations using generalized assumptions. However, for most realistic modeling of epidemic spread and the evaluation of countermeasures, there is a critical need for highly resolved, temporal contact networks that encompass multiple venues without sacrificing the intricate dynamics of real-world contacts. This paper presents an integrated approach for generating such networks by coupling Bayesian-optimized human mobility models (HuMMs) with a state-of-the-art epidemic simulation framework. Our primary contributions are twofold: First, we embed empirically calibrated HuMMs into an epidemic simulation environment to create a parameterizable, adaptive engine for producing synthetic, high-resolution, population-wide temporal contact network data. Second, we demonstrate through empirical evaluations that our generated networks exhibit realistic interaction structures and infection dynamics. In particular, our experiments reveal that while variations in population size do not affect the underlying network properties-a crucial feature for scalability-altering location capacities naturally influences local connectivity and epidemic outcomes. Additionally, sub-graph analyses confirm that different venue types display distinct network characteristics consistent with their real-world contact patterns. Overall, this integrated framework provides a scalable and empirically grounded method for epidemic simulation, offering a powerful tool for generating and simulating contact networks.

Epihiper-A high performance computational modeling framework to support epidemic science

This paper describes Epihiper, a state-of-the-art, high performance computational modeling framework for epidemic science. The Epihiper modeling framework supports custom disease models, and can simulate epidemics over dynamic, large-scale networks while supporting modulation of the epidemic evolution through a set of user-programmable interventions. The nodes and edges of the social-contact network have customizable sets of static and dynamic attributes which allow the user to specify intervention target sets at a very fine-grained level; these also permit the network to be updated in response to nonpharmaceutical interventions, such as school closures. The execution of interventions is governed by trigger conditions, which are Boolean expressions formed using any of Epihiper's primitives (e.g. the current time, transmissibility) and user-defined sets (e.g. people with work activities). Rich expressiveness, extensibility, and high-performance computing responsiveness were central design goals to ensure that the framework could effectively target realistic scenarios at the scale and detail required to support the large computational designs needed by state and federal public health policymakers in their efforts to plan and respond in the event of epidemics. The modeling framework has been used to support the CDC Scenario Modeling Hub for COVID-19 response, and was a part of a hybrid high-performance cloud system that was nominated as a finalist for the 2021 ACM Gordon Bell Special Prize for high performance computing-based COVID-19 Research.

Role of heterogeneity: National scale data-driven agent-based modeling for the US COVID-19 Scenario Modeling Hub

UVA-EpiHiper is a national scale agent-based model to support the US COVID-19 Scenario Modeling Hub (SMH). UVA-EpiHiper uses a detailed representation of the underlying social contact network along with data measured during the course of the pandemic to initialize and calibrate the model. In this paper, we study the role of heterogeneity on model complexity and resulting epidemic dynamics using UVA-EpiHiper. We discuss various sources of heterogeneity that we encounter in the use of UVA-EpiHiper to support modeling and analysis of epidemic dynamics under various scenarios. We also discuss how this affects model complexity and computational complexity of the corresponding simulations. Using round 13 of the SMH as an example, we discuss how UVA-EpiHiper was initialized and calibrated. We then discuss how the detailed output produced by UVA-EpiHiper can be analyzed to obtain interesting insights. We find that despite the complexity in the model, the software, and the computation incurred to an agent-based model in scenario modeling, it is capable of capturing various heterogeneities of real-world systems, especially those in networks and behaviors, and enables analyzing heterogeneities in epidemiological outcomes between different demographic, geographic, and social cohorts. In applying UVA-EpiHiper to round 13 scenario modeling, we find that disease outcomes are different between and within states, and between demographic groups, which can be attributed to heterogeneities in population demographics, network structures, and initial immunity.

Intervention effect of targeted workplace closures may be approximated by single-layered networks in an individual-based model of COVID-19 control

Individual-based models of infectious disease dynamics commonly use network structures to represent human interactions. Network structures can vary in complexity, from single-layered with homogeneous mixing to multi-layered with clustering and layer-specific contact weights. Here we assessed policy-relevant consequences of network choice by simulating different network structures within an established individual-based model of SARS-CoV-2 dynamics. We determined the clustering coefficient of each network structure and compared this to several epidemiological outcomes, such as cumulative and peak infections. High-clustered networks estimate fewer cumulative infections and peak infections than less-clustered networks when transmission probabilities are equal. However, by altering transmission probabilities, we find that high-clustered networks can essentially recover the dynamics of low-clustered networks. We further assessed the effect of workplace closures as a layer-targeted intervention on epidemiological outcomes and found in this scenario a single-layered network provides a sufficient approximation of intervention effect relative to a multi-layered network when layer-specific contact weightings are equal. Overall, network structure choice within models should consider the knowledge of contact weights in different environments and pathogen mode of transmission to avoid over- or under-estimating disease burden and impact of interventions.

Efficacy thresholds and target populations for antiviral COVID-19 treatments to save lives and costs: a modelling study

Background

In 2023 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared endemic, yet hospital admissions have persisted and risen within populations at high and moderate risk of developing severe disease, which include those of older age, and those with co-morbidities. Antiviral treatments, currently only available for high-risk individuals, play an important role in preventing severe disease and hospitalisation within this subpopulation. Here, we further explore the public health and economic benefits of extending target populations for treatment, and assess efficacy thresholds for a treatment strategy to be cost-saving.

Methods

We adapted an individual-based transmission model of SARS-CoV-2, OpenCOVID, which was calibrated and validated to 2020-2023 Swiss, European, and Northern Hemisphere epidemiological data. We used the model to estimate hospitalisations and overall costs for preventatively treating three risk groups for a full range of treatment efficacies and coverages with, besides vaccination and hospital treatments, no other interventions in place. We further calculated efficacy thresholds for strategies to be cost-saving. A global sensitivity analysis was conducted to test the sensitivity of all outcomes for a wide range of treatment properties, emerging variant properties, and vaccination coverages.

Findings

In a high vaccination coverage setting, we found that a high efficacy antiviral treatment given to all those at high-risk could reduce hospitalisations by up to 40%. When expanding treatment coverage to also include all those at moderate-risk, an additional 50% of hospitalisations could be averted. Targeting both high-risk and moderate-risk groups was found to be cost-saving for a treatment efficacy greater than ∼40%. This threshold was found to be robust regardless of vaccination coverage and emerging variant properties, but highly sensitive to treatment costs.

Interpretation

For a sufficiently efficacious antiviral treatment, expanding the target population to include both high-risk and moderate-risk groups should be considered. Equitable treatment costs are found crucial in achieving the best possible public health and health economic outcomes.

Funding

Botnar Research Centre for Child Health (DZX2165 to MAP), the Swiss National Science Foundation Professorship of MAP (P00P3_203450) and Swiss National Science Foundation NFP 78 Covid-19 2020 (4079P0_198428 to MAP).

The relationship between compartment models and their stochastic counterparts: A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models (CMs) and stochastic models, including stochastic CMs and agent-based models, are widely utilized in epidemic modeling. However, the relationship between CMs and their corresponding stochastic models is not well understood. The present study aimed to address this gap by conducting a comparative study using the susceptible, exposed, infectious, and recovered (SEIR) model and its extended CMs from the coronavirus disease 2019 modeling literature. We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations. Based on this equivalence, we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment. The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics. However, it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs. Additionally, we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents. This model offered a balance between computational efficiency and accuracy. The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling. Furthermore, the results had implications for the development of hybrid models that integrated the strengths of both frameworks. Overall, the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

A case for ongoing structural support to maximise infectious disease modelling efficiency for future public health emergencies: A modelling perspective

This short communication reflects upon the challenges and recommendations of multiple COVID-19 modelling and data analytic groups that provided quantitative evidence to support health policy discussions in Switzerland and Germany during the SARS-CoV-2 pandemic. Capacity strengthening outside infectious disease emergencies will be required to enable an environment for a timely, efficient, and data-driven response to support decisions during any future infectious disease emergency. This will require 1) a critical mass of trained experts who continuously advance state-of-the-art methodological tools, 2) the establishment of structural liaisons amongst scientists and decision-makers, and 3) the foundation and management of data-sharing frameworks.

COVID-19 outbreak control strategies and their impact on the provision of essential health services in Ghana: An exploratory-sequential study

BACKGROUND

The COVID-19 pandemic has led to substantial interruptions in critical health services, with 90% of countries reporting interruptions in routine vaccinations, maternal health care and chronic disease management. The use of non-pharmaceutical interventions (NPIs) such as lockdowns and self-isolation had implications on the provision of essential health services (EHS). We investigated exemplary COVID-19 outbreak control strategies and explored the extent to which the adoption of these NPIs affected the provision of EHS including immunization coverage and facility-based deliveries. Finally, we document core health system strategies and practices adopted to maintain EHS during the early phase of the pandemic.

METHODS

This study used an explanatory sequential study design. First, we utilized data from routine health management information systems to quantify the impact of the pandemic on the provision of EHS using interrupted time series models. Second, we explored exemplary strategies and health system initiatives that were adopted to prevent the spread of COVID-19 infections while maintaining the provision of EHS using in-depth interviews with key informants including policymakers and healthcare providers.

RESULTS

The COVID-19 pandemic and the interventions that were implemented disrupted the provision of EHS. In the first month of the COVID-19 pandemic, Oral Polio and pentavalent vaccination coverage reduced by 15.2% [95% CI = -22.61, -7.87, p<0.001] and 12.4% [95% CI = 17.68, -7.13; p<0.001] respectively. The exemplary strategies adopted in maintaining the provision of EHS while also responding to the spread of infections include the development of new policy guidelines that were disseminated with modified service delivery models, new treatment and prevention guidelines, the use of telemedicine and medical drones to provide EHS and facilitate rapid testing of suspected cases.

CONCLUSION

The implementation of different NPIs during the peak phase of the pandemic disrupted the provision of EHS. However, the Ministry of Health leveraged the resilient health system and deployed efficient, all-inclusive, and integrated infectious disease management and infection prevention control strategies to maintain the provision of EHS while responding to the spread of infections.

Explicit modeling of antibody levels for infectious disease simulations in the context of SARS-CoV-2

Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These antibody levels are dynamic: due to waning, antibody levels will drop over time. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to support the planning of public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. The model was developed in response to the complexity of different immunization sequences and types and is based on neutralization titer studies. This approach allows complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.

To vaccinate or to isolate? Establishing which intervention leads to measurable mortality reduction during the COVID-19 Delta wave in Poland

Background

During the Delta variant COVID-19 wave in Poland there were serious regional differences in vaccination rates and discrepancies in the enforcement of pandemic preventive measures, which allowed us to assess the relative effectiveness of the policies implemented.

Methods

Creating a model that would predict mortality based on vaccination rates among the most vulnerable groups and the timing of the wave peak enabled us to calculate to what extent flattening the curve reduced mortality. Subsequently, a model was created to assess which preventive measures delayed the peak of infection waves. Combining those two models allowed us to estimate the relative effectiveness of those measures.

Results

Flattening the infection curve worked: according to our model, each week of postponing the peak of the wave reduced excess deaths by 1.79%. Saving a single life during the Delta wave required one of the following: either the vaccination of 57 high-risk people, or 1,258 low-risk people to build herd immunity, or the isolation of 334 infected individuals for a cumulative period of 10.1 years, or finally quarantining 782 contacts for a cumulative period of 19.3 years.

Conclusions

Except for the most disciplined societies, vaccination of high-risk individuals followed by vaccinating low-risk groups should have been the top priority instead of relying on isolation and quarantine measures which can incur disproportionately higher social costs. Our study demonstrates that even in a country with uniform policies, implementation outcomes varied, highlighting the importance of fine-tuning policies to regional specificity.

Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China

BACKGROUND

Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources.

METHODS

Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context.

FINDINGS

Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing.

INTERPRETATION

DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.

Pathways to Immunity: Patterns of Excess Death Across the United States and Within Closed Religious Communities

Public health officials promoted COVID-19 vaccines to limit burdens placed on the U.S. healthcare system and end the pandemic. People in some closed religious communities refused to vaccinate and likely acquired temporary immunity through infection. This paper compares the death rates in Amish, Old Order Mennonites, and conservative Mennonite groups to a rate estimated for the U.S. population. Approximately two-thirds of the U.S. population was immunized against COVID-19, while few in the Amish/Mennonite community were. We find divergent patterns. Once vaccines became available, excess deaths declined in the general population and remained elevated among Amish and Mennonites. Vaccination campaigns must consider and value the cultural beliefs of closed religious communities to be effective.

High potential of technology to face new respiratory viruses: mechanical ventilation devices for effective healthcare to next pandemic emergencies

Some countries in the presence of unforeseen Coronavirus Disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have experienced lower total deaths, though higher numbers of COVID-19 related infections. Results here suggest that one of the explanations is the critical role of ventilator technology in clinical health environment to cope with the initial stage of COVID-19 pandemic crisis. Statistical evidence shows that a large number of ventilators or breathing devices in countries (26.76 units per 100,000 inhabitants) is associated with a fatality rate of 1.44% (December 2020), whereas a higher fatality rate given by 2.46% is in nations with lower numbers of ventilator devices (10.38 average units per 100,000 people). These findings suggest that a large number of medical ventilators in clinical setting has a high potential for more efficient healthcare and improves the effective preparedness of crisis management to cope with new respiratory pandemic diseases in society. Hence, a forward-thinking and technology-oriented strategy in healthcare sector, based on investments in high-tech ventilator devices and other new medical technologies, can help clinicians deliver effective care and reduce negative effects of present and future respiratory infectious diseases, in particular when new drugs and appropriate treatments are missing in clinical environment to face unknown respiratory viral agents .

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

The added effect of non-pharmaceutical interventions and lifestyle behaviors on vaccine effectiveness against severe COVID-19 in Chile: a matched case-double control study

Background

All World Health Organization approved vaccines have demonstrated relatively high protection against moderate to severe COVID-19. Prospective vaccine effectiveness (VE) designs with first-hand data and population-based controls are nevertheless rare. Neighborhood compared to hospitalized controls, may differ in non-pharmaceutical interventions (NPI) compliance, which may influence VE results in real-world settings. We aimed to determine VE against COVID-19 intensive-care-unit (ICU) admission using hospital and community-matched controls in a prospective design.

Methods

We conducted a multicenter, observational study of matched cases and controls (1:3) in adults ≧18 from May to July 2021. For each case, a hospital control and two community controls were matched by age, gender, and hospital admission date or neighborhood of residence. Conditional logistic regression models were built, including interaction terms between NPIs, lifestyle behaviors, and vaccination status; the model’s β coefficients represent the added effect these terms had on COVID-19 VE.

Results

Cases and controls differed in several factors including education level, obesity prevalence, and behaviors such as compliance with routine vaccinations, use of facemasks, and routine handwashing. VE was 98·2% for full primary vaccination and 85·6% for partial vaccination when compared to community controls. VE tended to be higher when compared to community versus hospital controls, but the difference was not significant. A significant added effect to vaccination in reducing COVID-19 ICU admission was regular facemask use and VE was higher among individuals non-compliant with the national vaccine program, nor routine medical controls during the prior year.

Conclusion

VE against COVID-19 ICU admission in this stringent prospective case-double control study reached 98% two weeks after full primary vaccination, confirming the high effectiveness provided by earlier studies. Face mask use and hand washing were independent protective factors, the former adding additional benefit to VE. VE was significantly higher in subjects with increased risk behaviors.

Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats

This study reveals that a vast vaccination campaign is a necessary but not sufficient public policy to reduce the negative impact of Coronavirus Disease 2019 (COVID-19) pandemic crisis because manifold factors guide the spread of this new infectious disease and related mortality in society. Statistical evidence here, based on a worldwide sample of countries, shows a positive correlation between people fully vaccinated and COVID-19 mortality (r = + 0.65, p-value < 0.01). Multivariate regression, controlling income per capita, confirms this finding. Results suggest that the increasing share of people vaccinated against COVID-19 seems to be a necessary but not sufficient health policy to reduce mortality of COVID-19. The findings here can be explained with the role of Peltzman effect, new variants, environmental and socioeconomic factors that affect the diffusion and negative impact of COVID-19 pandemic in society. This study extends the knowledge in this research field to design effective public policies of crisis management for facing next pandemic threats.

Leveraging artificial intelligence to optimize COVID-19 robust spread and vaccination roll-out strategies in Southern Africa

The outbreak of coronavirus in the year 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted widespread illness, death, and extended economic devastation worldwide. In response, numerous countries, including Botswana and South Africa, instituted various clinical public health (CPH) strategies to mitigate and control the disease. However, the emergence of variants of concern (VOC), vaccine hesitancy, morbidity, inadequate and inequitable vaccine supply, and ineffective vaccine roll-out strategies caused continuous disruption of essential services. Based on Botswana and South Africa hospitalization and mortality data, we studied the impact of age and gender on disease severity. Comparative analysis was performed between the two countries to establish a vaccination strategy that could complement the existing CPH strategies. To optimize the vaccination roll-out strategy, artificial intelligence was used to identify the population groups in need of insufficient vaccines. We found that COVID-19 was associated with several comorbidities. However, hypertension and diabetes were more severe and common in both countries. The elderly population aged ≥60 years had 70% of major COVID-19 comorbidities; thus, they should be prioritized for vaccination. Moreover, we found that the Botswana and South Africa populations had similar COVID-19 mortality rates. Hence, our findings should be extended to the rest of Southern African countries since the population in this region have similar demographic and disease characteristics.

COVID-19 Vaccine Booster Strategies in Light of Emerging Viral Variants: Frequency, Timing, and Target Groups

Background

Vaccinations have reduced severe burden of COVID-19 and allowed for lifting of non-pharmaceutical interventions. However, with immunity waning alongside emergence of more transmissible variants of concern, vaccination strategies must be examined.

Methods

Here we apply a SARS-CoV-2 transmission model to identify preferred frequency, timing, and target groups for vaccine boosters to reduce public health burden and health systems risk. We estimated new infections and hospital admissions averted over 2 years through annual or biannual boosting of those eligible (those who received doses one and two) who are (1) most vulnerable (60+ or living with comorbidities) or (2) those 5+, at universal (98% of eligible) or lower coverage (85% of those 50+ or with comorbidities and 50% of 5–49 year olds) representing moderate vaccine fatigue and/or hesitancy. We simulated three emerging variant scenarios: (1) no new variants; (2) 25% more infectious and immune-evading Omicron-level severity variants emerge annually and become dominant; (3) emerge biannually. We further explored the impact of varying seasonality, variant immune-evading capacity, infectivity, severity, timing, and vaccine infection blocking assumptions.

Results

To reduce COVID-19-related hospitalisations over the next 2 years, boosters should be provided for all those eligible annually 3–4 months ahead of peak winter whether or not new variants of concern emerge. Only boosting those most vulnerable is unlikely to ensure reduced stress on health systems. Moreover, boosting all eligible better protects those most vulnerable than only boosting the vulnerable group. Conversely, while this strategy may not ensure reduced stress on health systems, as an indication of cost-effectiveness, per booster dose more hospitalisations could be averted through annual boosting of those most vulnerable versus all eligible, since those most vulnerable are more likely to seek hospital care once infected, whereas increasing to biannual boosting showed diminishing returns. Results were robust when key model parameters were varied. However, we found that the more frequently variants emerge, the less the effect boosters will have, regardless of whether administered annually or biannually.

Conclusions

Delivering well-timed annual COVID-19 vaccine boosters to all those eligible, prioritising those most vulnerable, can reduce infections and hospital admissions. Findings provide model-based evidence for decision-makers to plan for administering COVID-19 boosters ahead of winter 2022–2023 to help mitigate the health burden and health system stress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40121-022-00683-z.

Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden

Background

SARS-CoV-2 variants of concern, such as Omicron (B.1.1.529), continue to emerge. Assessing the impact of their potential viral properties on the probability of future transmission dominance and public health burden is fundamental in guiding ongoing COVID-19 control strategies.

Methods

With an individual-based transmission model, OpenCOVID, we simulated three viral properties; infectivity, severity, and immune-evading ability, all relative to the Delta variant, to identify thresholds for Omicron's or any emerging VOC's potential future dominance, impact on public health, and risk to health systems. We further identify for which combinations of viral properties current interventions would be sufficient to control transmission.

Results

We show that, with first-generation SARS-CoV-2 vaccines and limited physical distancing in place, a VOC's potential future dominance is primarily driven by its infectivity, which does not always lead to an increased public health burden. However, we also show that highly immune-evading variants that become dominant, even in the case of reduced variant severity, would likely require alternative measures to avoid strain on health systems, such as strengthened physical distancing measures, novel treatments, and second-generation vaccines. Expanded vaccination, that includes a booster dose for adults and child vaccination strategies, is projected to have the biggest public health benefit for a highly infective, highly severe VOC with low immune-evading capacity.

Conclusions

These findings provide quantitative guidance to decision-makers at a critical time while Omicron's properties are being assessed and preparedness for emerging VOCs is eminent. We emphasise the importance of both genomic and population epidemiological surveillance.

Modeling robust COVID-19 intensive care unit occupancy thresholds for imposing mitigation to prevent exceeding capacities

In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model using daily COVID-19 ICU and hospital census data between March and August 2020. We projected various possible ICU occupancy trajectories from September 2020 to May 2021 with two possible levels of transmission increase and uncertainty in core model parameters. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

Impact of Infective Immigrants on COVID-19 Dynamics

The COVID-19 epidemic is an unprecedented and major social and economic challenge worldwide due to the various restrictions. Inflow of infective immigrants have not been given prominence in several mathematical and epidemiological models. To investigate the impact of imported infection on the number of deaths, cumulative infected and cumulative asymptomatic, we formulate a mathematical model with infective immigrants and considering vaccination. The basic reproduction number of the special case of the model without immigration of infective people is derived. We varied two key factors that affect the transmission of COVID-19, namely the immigration and vaccination rates. In addition, we considered two different SARS-CoV-2 transmissibilities in order to account for new more contagious variants such as Omicron. Numerical simulations using initial conditions approximating the situation in the US when the vaccination program was starting show that increasing the vaccination rate significantly improves the outcomes regarding the number of deaths, cumulative infected and cumulative asymptomatic. Other factors are the natural recovery rates of infected and asymptomatic individuals, the waning rate of the vaccine and the vaccination rate. When the immigration rate is increased significantly, the number of deaths, cumulative infected and cumulative asymptomatic increase. Consequently, accounting for the level of inflow of infective immigrants may help health policy/decision-makers to formulate policies for public health prevention programs, especially with respect to the implementation of the stringent preventive lock down measure.