Projecting social contact matrices in 152 countries using contact surveys and demographic data

Assessing Age-Specific Vaccination Strategies and Post-vaccination Reopening Policies for COVID-19 Control Using SEIR Modeling Approach

As the availability of COVID-19 vaccines, it is badly needed to develop vaccination guidelines to prioritize the vaccination delivery in order to effectively stop COVID-19 epidemic and minimize the loss. We evaluated the effect of age-specific vaccination strategies on the number of infections and deaths using an SEIR model, considering the age structure and social contact patterns for different age groups for each of different countries. In general, the vaccination priority should be given to those younger people who are active in social contacts to minimize the number of infections, while the vaccination priority should be given to the elderly to minimize the number of deaths. But this principle may not always apply when the interaction of age structure and age-specific social contact patterns is complicated. Partially reopening schools, workplaces or households, the vaccination priority may need to be adjusted accordingly. Prematurely reopening social contacts could initiate a new outbreak or even a new pandemic out of control if the vaccination rate and the detection rate are not high enough. Our result suggests that it requires at least nine months of vaccination (with a high vaccination rate > 0.1%) for Italy and India before fully reopening social contacts in order to avoid a new pandemic.

The effectiveness of COVID-19 testing and contact tracing in a US city

Although testing, contact tracing, and case isolation programs can mitigate COVID-19 transmission and allow the relaxation of social distancing measures, few countries worldwide have succeeded in scaling such efforts to levels that suppress spread. The efficacy of test-trace-isolate likely depends on the speed and extent of follow-up and the prevalence of SARS-CoV-2 in the community. Here, we use a granular model of COVID-19 transmission to estimate the public health impacts of test-trace-isolate programs across a range of programmatic and epidemiological scenarios, based on testing and contact tracing data collected on a university campus and surrounding community in Austin, TX, between October 1, 2020, and January 1, 2021. The median time between specimen collection from a symptomatic case and quarantine of a traced contact was 2 days (interquartile range [IQR]: 2 to 3) on campus and 5 days (IQR: 3 to 8) in the community. Assuming a reproduction number of 1.2, we found that detection of 40% of all symptomatic cases followed by isolation is expected to avert 39% (IQR: 30% to 45%) of COVID-19 cases. Contact tracing is expected to increase the cases averted to 53% (IQR: 42% to 58%) or 40% (32% to 47%), assuming the 2- and 5-day delays estimated on campus and in the community, respectively. In a tracing-accelerated scenario, in which 75% of contacts are notified the day after specimen collection, cases averted increase to 68% (IQR: 55% to 72%). An accelerated contact tracing program leveraging rapid testing and electronic reporting of test results can significantly curtail local COVID-19 transmission.

Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world

BACKGROUND

The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic.

METHODS

In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region's SARS-COV-2 transmission dynamic.

RESULTS

We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI's, over and above the ones identified in i) and ii).

CONCLUSION

In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI's) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV.

A generalized epidemiological model with dynamic and asymptomatic population

In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (Susceptible, Infected and pre-symptomatic, Infected and Symptomatic but Not Tested, Tested Positive, Recorded Recovered, Unrecorded Recovered, and Expired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.

Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study

Background. Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, be optimized to maximize economic benefits while achieving substantial reductions in disease. Methods. Here, we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay (WTP) for health improvement. Results. We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the WTP per quality adjusted life year loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusion. It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

Integrating economic and health evidence to inform Covid-19 policy in low- and middle- income countries

Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy; highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries; where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.

COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: What could have happened?

This study illustrates what may have happened, in terms of coronavirus disease 2019 (COVID-19) infections, hospitalizations and deaths in Canada, had public health measures not been used to control the COVID-19 epidemic, and had restrictions been lifted with low levels of vaccination, or no vaccination, of the Canadian population. The timeline of the epidemic in Canada, and the public health interventions used to control the epidemic, are reviewed. Comparisons against outcomes in other countries and counterfactual modelling illustrate the relative success of control of the epidemic in Canada. Together, these observations show that without the use of restrictive measures and without high levels of vaccination, Canada could have experienced substantially higher numbers of infections and hospitalizations and almost a million deaths.

Optimal policies for mitigating pandemic costs: a minimal model

There have been a number of pharmaceutical and non-pharmaceutical interventions associated with COVID-19 over the past two years. Of the various non-pharmaceutical interventions that were proposed and implemented to control the spread of the COVID-19 pandemic partial and complete lockdowns were used repeatedly in an attempt to minimize the costs associated with mortality, economic losses and social factors, while being subject to constraints such as finite hospital capacity. Here, we use a minimal model to understand the costs and benefits of these strategies that mitigate pandemic costs subject to constraints, we adopt the language of optimal control theory. This allows us to determine top-down policies for the nature and dynamics of social contact rates given an age-structured model for the dynamics of the disease. Depending on the relative weights allocated to mortality and socioeconomic losses, we see that the optimal strategies range from long-term social-distancing only for the most vulnerable, to partial lockdown to ensure not over-running hospitals, to alternating-shifts with significant reduction in mortality and/or socioeconomic losses. Crucially, commonly used strategies that involve long periods of broad lockdown are almost never optimal, as they are highly unstable to reopening {and entail high socioeconomic costs}. Using parameter estimates from data available for Germany and the USA early in the pandemic, we quantify these policies and use sensitivity analysis in the relevant model parameters and initial conditions to determine the range of robustness of our policies. Finally we also discuss how bottom-up behavioral changes affect the dynamics of the pandemic and show they can work in tandem with top-down control policies to mitigate pandemic costs even more effectively.

The hiatus of the handshake

Human contact has been altered in ways that may affect endemic infections for years to come.

Learning transmission dynamics modelling of COVID-19 using comomodels

The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignettes.

Optimizing Spatio-Temporal Allocation of the COVID-19 Vaccine Under Different Epidemiological Landscapes

An efficient and safe vaccine is expected to allow people to return to normal life as soon as possible. However, vaccines for new diseases are likely to be in short supply during the initial deployment due to narrow production capacity and logistics. There is an urgent need to optimize the allocation of limited vaccines to improve the population effectiveness of vaccination. Existing studies mostly address a single epidemiological landscape. The robustness of the effectiveness of other proposed strategies is difficult to guarantee under other landscapes. In this study, a novel vaccination allocation model based on spatio-temporal heterogeneity of epidemiological landscapes is proposed. This model was combined with optimization algorithms to determine the near-optimal spatio-temporal allocation for vaccines with different effectiveness and coverage. We fully simulated the epidemiological landscapes during vaccination, and then minimized objective functions independently under various epidemiological landscapes and degrees of viral transmission. We find that if all subregions are in the middle or late stages of the pandemic, the difference between the effectiveness of the near-optimal and pro-rata strategies is very small in most cases. In contrast, under other epidemiological landscapes, when minimizing deaths, the optimizer tends to allocate the remaining doses to sub-regions with relatively higher risk and expected coverage after covering the elderly. While to minimize symptomatic infections, allocating vaccines first to the higher-risk sub-regions is near-optimal. This means that the pro-rata allocation is a good option when the subregions are all in the middle to late stages of the pandemic. Moreover, we suggest that if all subregions are in the period of rapid virus transmission, vaccines should be administered to older adults in all subregions simultaneously, while when the epidemiological dynamics of the subregions are significantly different, priority can be given to older adults in subregions that are still in the early stages of the pandemic. After covering the elderly in the region, high-risk sub-regions can be prioritized.

Delays, Masks, the Elderly, and Schools: First Covid-19 Wave in the Czech Republic

Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.

Patterns of human social contact and mask wearing in high-risk groups in China

Background

The pandemic of coronavirus disease 2019 (COVID-19) has changed human behavior in areas such as contact patterns and mask-wearing frequency. Exploring human–human contact patterns and mask-wearing habits in high-risk groups is an essential step in fully understanding the transmission of respiratory infection-based diseases. This study had aims to quantify local human–human (H–H) contacts in high-risk groups in representative provinces of China and to explore the occupation-specific assortativity and heterogeneity of social contacts.

Methods

Delivery workers, medical workers, preschoolers, and students from Qinghai, Shanghai, and Zhejiang were recruited to complete an online questionnaire that queried general information, logged contacts, and assessed the willingness to wear a mask in different settings. The “group contact” was defined as contact with a group at least 20 individuals. The numbers of contacts across different characteristics were assessed and age-specific contact matrices were established. A generalized additive mixed model was used to analyze the associations between the number of individual contacts and several characteristics. The factors influencing the frequency of mask wearing were evaluated with a logistic regression model.

Results

A total of 611,287 contacts were reported by 15,635 participants. The frequency of daily individual contacts averaged 3.14 (95% confidence interval: 3.13–3.15) people per day, while that of group contacts was 37.90 (95% CI: 37.20–38.70). Skin-to-skin contact and long-duration contact were more likely to occur at home or among family members. Contact matrices of students were the most assortative (all contacts q-index = 0.899, 95% CI: 0.894–0.904). Participants with larger household sizes reported having more contacts. Higher household income per capita was significantly associated with a greater number of contacts among preschoolers (P_{50,000–99,999} = 0.033) and students (P_{10,000–29,999} = 0.017). In each of the public places, the frequency of mask wearing was highest for delivery workers. For preschoolers and students with more contacts, the proportion of those who reported always wearing masks was lower (P < 0.05) in schools/workplaces and public transportation than preschoolers and students with fewer contacts.

Conclusions

Contact screening efforts should be concentrated in the home, school, and workplace after an outbreak of an epidemic, as more than 75% of all contacts, on average, will be found in such places. Efforts should be made to improve the mask-wearing rate and age-specific health promotion measures aimed at reducing transmission for the younger demographic. Age-stratified and occupation-specific social contact research in high-risk groups could help inform policy-making decisions during the post-relaxation period of the COVID-19 pandemic.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00988-8.

Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model

The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the "0-20 first" strategy was the most effective way to reduce confirmed cases. In addition, "21-30 first" and "31-40 first" strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to "0-20" groups always had a favorable effect, and the prioritizing vaccine allocation among the "60+" age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.

SARS-CoV-2 Risk Quantification Model and Validation Based on Large-Scale Dutch Test Events

In response to the outbreak of SARS-CoV-2, many governments decided in 2020 to impose lockdowns on societies. Although the package of measures that constitute such lockdowns differs between countries, it is a general rule that contact between people, especially in large groups of people, is avoided or prohibited. The main reasoning behind these measures is to prevent healthcare systems from becoming overloaded. As of 2021 vaccines against SARS-CoV-2 are available, but these do not guarantee 100% risk reduction and it will take a while for the world to reach a sufficient immune status. This raises the question of whether and under which conditions events like theater shows, conferences, professional sports events, concerts, and festivals can be organized. The current paper presents a COVID-19 risk quantification method for (large-scale) events. This method can be applied to events to define an alternative package of measures replacing generic social distancing.

Optimal Social Distancing Policy for COVID-19 Control in Korea: A Model-Based Analysis

BACKGROUND

Since March 2020, when coronavirus disease 2019 (COVID-19) was declared a pandemic, many countries have applied unprecedented restrictive measures to contain the spread of the virus. This study aimed to explore the optimal social distancing policy for COVID-19 control in South Korea to safely reopen the society.

METHODS

We developed an age-specific, deterministic compartment epidemic model to examine the COVID-19 control decision-making process, including the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 July 2021 and 30 December 2022. The model consists of the natural history of COVID-19, testing performance, vaccinations, and social distancing enforcement measures to detect and control SARS-CoV-2. We modelled potential intervention scenarios with three distinct components: 1) social distancing duration and level; 2) testing intensity; and 3) vaccination uptake rate. The primary and secondary outcomes were COVID-19 incidence and prevalence of severe patients requiring intensive care unit (ICU) care.

RESULTS

Four (or more) months of social distancing (that can reduce 40-60% transmission) may mitigate epidemic resurgence and ICU demand in the future and keep the cases below the capacity limit if the testing intensity and vaccination rate remain constant or increase by 20% (with respect to the current level). In contrast, two months of strict social distancing enforcement may also successfully mitigate future epidemic surge and ICU demand as long as testing intensity and vaccination rates are increased by 20%.

CONCLUSION

In South Korea, given the relatively high vaccination coverage and low incidence, four or more months of social distancing enforcement can effectively mitigate epidemic resurgence after lifting the social distancing measures. In addition, increasing the testing intensity and vaccination rate may help reduce necessary social distancing levels and duration to prevent a future epidemic resurgence and mitigate social and economic damage.

Potential reduction in transmission of COVID-19 by digital contact tracing systems: a modelling study

BACKGROUND

Digital tools are being developed to support contact tracing as part of the global effort to control the spread of COVID-19. These include smartphone apps, Bluetooth-based proximity detection, location tracking and automatic exposure notification features. Evidence on the effectiveness of alternative approaches to digital contact tracing is so far limited.

METHODS

We use an age-structured branching process model of the transmission of COVID-19 in different settings to estimate the potential of manual contact tracing and digital tracing systems to help control the epidemic. We investigate the effect of the uptake rate and proportion of contacts recorded by the digital system on key model outputs: the effective reproduction number, the mean outbreak size after 30 days and the probability of elimination.

RESULTS

Effective manual contact tracing can reduce the effective reproduction number from 2.4 to around 1.5. The addition of a digital tracing system with a high uptake rate over 75% could further reduce the effective reproduction number to around 1.1. Fully automated digital tracing without manual contact tracing is predicted to be much less effective.

CONCLUSIONS

For digital tracing systems to make a significant contribution to the control of COVID-19, they need be designed in close conjunction with public health agencies to support and complement manual contact tracing by trained professionals.

Disease-economy trade-offs under alternative epidemic control strategies

Public policy and academic debates regarding pandemic control strategies note disease-economy trade-offs, often prioritizing one outcome over the other. Using a calibrated, coupled epi-economic model of individual behavior embedded within the broader economy during a novel epidemic, we show that targeted isolation strategies can avert up to 91% of economic losses relative to voluntary isolation strategies. Unlike widely-used blanket lockdowns, economic savings of targeted isolation do not impose additional disease burdens, avoiding disease-economy trade-offs. Targeted isolation achieves this by addressing the fundamental coordination failure between infectious and susceptible individuals that drives the recession. Importantly, we show testing and compliance frictions can erode some of the gains from targeted isolation, but improving test quality unlocks the majority of the benefits of targeted isolation.

Booster Vaccination Strategies for “Living With COVID-19”

Although the primary and secondary vaccination rates in Korea account for over 75% of the total population, confirmed cases of COVID-19 are dramatically increasing due to immune waning and the Omicron variant. Therefore, it is urgent to evaluate the effectiveness of booster vaccination strategies for living with COVID-19. In this work, we have developed an age-specific mathematical model with eight age groups and included age-specific comorbidities to evaluate the effectiveness of age-specific vaccination prioritization strategies to minimize morbidity and mortality. Furthermore, we have investigated the impacts of age-specific vaccination strategies for different vaccine supplies and non-pharmaceutical intervention levels during two periods: (1) when vaccine supply was insufficient and (2) after the emergence of the omicron variant. During the first period, the best option was to vaccinate the 30–49 year age group and the group with comorbidities to minimize morbidity and mortality, respectively. However, a booster vaccination should prioritize the 30–49 year age group to promote both minimal morbidity and mortality. Critical factors, such as vaccination speed, vaccine efficacy, and non-pharmaceutical interventions (NPIs), should be considered for effective vaccination prioritization as well. Primary, secondary vaccinations, and a booster shot vaccinations require different age prioritization strategies under different vaccination rates, vaccine efficacies, and NPI levels.

Modeling the COVID-19 pandemic: a primer and overview of mathematical epidemiology

Since the start of the still ongoing COVID-19 pandemic, there have been many modeling efforts to assess several issues of importance to public health. In this work, we review the theory behind some important mathematical models that have been used to answer questions raised by the development of the pandemic. We start revisiting the basic properties of simple Kermack-McKendrick type models. Then, we discuss extensions of such models and important epidemiological quantities applied to investigate the role of heterogeneity in disease transmission e.g. mixing functions and superspreading events, the impact of non-pharmaceutical interventions in the control of the pandemic, vaccine deployment, herd-immunity, viral evolution and the possibility of vaccine escape. From the perspective of mathematical epidemiology, we highlight the important properties, findings, and, of course, deficiencies, that all these models have.

An assessment of the potential impact of the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand

New Zealand delayed the introduction of the Omicron variant of SARS-CoV-2 into the community by the continued use of strict border controls through to January 2022. This allowed time for vaccination rates to increase and the roll out of third doses of the vaccine (boosters) to begin. It also meant more data on the characteristics of Omicron became available prior to the first cases of community transmission. Here we present a mathematical model of an Omicron epidemic, incorporating the effects of the booster roll out and waning of vaccine-induced immunity, and based on estimates of vaccine effectiveness and disease severity from international data. The model considers differing levels of immunity against infection, severe illness and death, and ignores waning of infection-induced immunity. This model was used to provide an assessment of the potential impact of an Omicron wave in the New Zealand population, which helped inform government preparedness and response. At the time the modelling was carried out, the date of introduction of Omicron into the New Zealand community was unknown. We therefore simulated outbreaks with different start dates, as well as investigating different levels of booster uptake. We found that an outbreak starting on 1 February or 1 March led to a lower health burden than an outbreak starting on 1 January because of increased booster coverage, particularly in older age groups. We also found that outbreaks starting later in the year led to worse health outcomes than an outbreak starting on 1 March. This is because waning immunity in older groups started to outweigh the increased protection from higher booster coverage in younger groups. For an outbreak starting on 1 February and with high booster uptake, the number of occupied hospital beds in the model peaked between 800 and 3,300 depending on assumed transmission rates. We conclude that combining an accelerated booster programme with public health measures to flatten the curve are key to avoid overwhelming the healthcare system.

A country-specific model of COVID-19 vaccination coverage needed for herd immunity in adult only or population wide vaccination programme

We present a country specific method to calculate the COVID-19 vaccination coverage needed for herd immunity by considering age structure, age group-specific contact patterns, relative infectivity and susceptibility of children to adults, vaccination effectiveness and seroprevalence prior to vaccination. We find that across all six countries, vaccination of adults age 60 and above has little impact on R_eff and this is could be due to the smaller number of contacts between this age group and the rest of the population according to the contact matrices used. If R₀ is above 6, herd immunity by vaccine alone is unattainable for most countries either if vaccination is only available for adults or that vaccine effectiveness is lower at 65% against symptomatic infections. In this situation, additional control measures, booster shots, if they improve protection against infection, or the extension of vaccination to children, are required. For a highly transmissible variant with R₀ up to 8, herd immunity is possible for all countries and for all four scenarios of varying relative infectivity and susceptibility of children compared to adults, if vaccine effectiveness is very high at 95% against symptomatic infections and that high vaccination coverage is achieved for both adults and children. In addition, we show that the effective reproduction number will vary between countries even if the same proportion of the population is vaccinated, depending on the demographics, the contact rates and the previous pre-vaccination seroprevalence in the country. This therefore means that care must be taken in extrapolating population level impacts of certain vaccine coverages from one country to another.

Estimating the effect of non-pharmaceutical interventions on US SARS-CoV-2 infections in the first year of the pandemic

SARS-CoV-2 emerged in late 2019 as a zoonotic infection of humans, and proceeded to cause a worldwide pandemic of historic magnitude. Here, we use a simple epidemiological model and consider the full range of initial estimates from published studies for infection and recovery rates, seasonality, changes in mobility, the effectiveness of masks and the fraction of people wearing them. Monte Carlo simulations are used to simulate the progression of possible pandemics and we show a match for the real progression of the pandemic during 2020 with an R 2 of 0.91. The results show that the combination of masks and changes in mobility avoided approximately 248.3 million (σ = 31.2 million) infections in the US before vaccinations became available.

Modeling waning and boosting of COVID-19 in Canada with vaccination

SARS-CoV-2, the causative agent of COVID-19, has caused devastating health and economic impacts around the globe since its appearance in late 2019. The advent of effective vaccines leads to open questions on how best to vaccinate the population. To address such questions, we developed a model of COVID-19 infection by age that includes the waning and boosting of immunity against SARS-CoV-2 in the context of infection and vaccination. The model also accounts for changes to infectivity of the virus, such as public health mitigation protocols over time, increases in the transmissibility of variants of concern, changes in compliance to mask wearing and social distancing, and changes in testing rates. The model is employed to study public health mitigation and vaccination of the COVID-19 epidemic in Canada, including different vaccination programs (rollout by age), and delays between doses in a two-dose vaccine. We find that the decision to delay the second dose of vaccine is appropriate in the Canadian context. We also find that the benefits of a COVID-19 vaccination program in terms of reductions in infections is increased if vaccination of 15-19 year olds are included in the vaccine rollout.

Addressing the socioeconomic divide in computational modeling for infectious diseases

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Evaluation of different types of face masks to limit the spread of SARS-CoV-2: a modeling study

We expanded a published mathematical model of SARS-CoV-2 transmission with complex, age-structured transmission and with laboratory-derived source and wearer protection efficacy estimates for a variety of face masks to estimate their impact on COVID-19 incidence and related mortality in the United States. The model was also improved to allow realistic age-structured transmission with a pre-specified R0 of transmission, and to include more compartments and parameters, e.g. for groups such as detected and undetected asymptomatic infectious cases who mask up at different rates. When masks are used at typically-observed population rates of 80% for those ≥ 65 years and 60% for those < 65 years, face masks are associated with 69% (cloth) to 78% (medical procedure mask) reductions in cumulative COVID-19 infections and 82% (cloth) to 87% (medical procedure mask) reductions in related deaths over a 6-month timeline in the model, assuming a basic reproductive number of 2.5. If cloth or medical procedure masks' source control and wearer protection efficacies are boosted about 30% each to 84% and 60% by cloth over medical procedure masking, fitters, or braces, the COVID-19 basic reproductive number of 2.5 could be reduced to an effective reproductive number ≤ 1.0, and from 6.0 to 2.3 for a variant of concern similar to delta (B.1.617.2). For variants of concern similar to omicron (B.1.1.529) or the sub-lineage BA.2, modeled reductions in effective reproduction number due to similar high quality, high prevalence mask wearing is more modest (to 3.9 and 5.0 from an R0 = 10.0 and 13.0, respectively). None-the-less, the ratio of incident risk for masked vs. non-masked populations still shows a benefit of wearing masks even with the higher R0 variants.

Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: evidence from Bayesian model averaging

The COVID-19 pandemic resulted in great discrepancies in both infection and mortality rates between countries. Besides the biological and epidemiological factors, a multitude of social and economic criteria also influenced the extent to which these discrepancies appeared. Consequently, there is an active debate regarding the critical socio-economic and health factors that correlate with the infection and mortality rates outcome of the pandemic. Here, we leverage Bayesian model averaging techniques and country level data to investigate whether 28 variables, which describe a diverse set of health and socio-economic characteristics, correlate with the final number of infections and deaths during the first wave of the coronavirus pandemic. We show that only a few variables are able to robustly correlate with these outcomes. To understand the relationship between the potential correlates in explaining the infection and death rates, we create a Jointness Space. Using this space, we conclude that the extent to which each variable is able to provide a credible explanation for the COVID-19 infections/mortality outcome varies between countries because of their heterogeneous features.

The interconnection between independent reactive control policies drives the stringency of local containment

The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic.

Social distancing and supply disruptions in a pandemic

We integrate an epidemiological model, augmented with contact and mobility analyses, with a two-sector macroeconomic model, to assess the economic costs of labor supply disruptions in a pandemic. The model is designed to capture key characteristics of the U.S. input-output tables with a core sector that produces intermediate inputs not easily replaceable by the other sectors, possibly subject to minimum-scale requirements. Using epidemiological and mobility data to inform our exercises, we show that the reduction in labor services due to the observed social distancing (spontaneous and mandatory) could explain up to 6-8 percentage points of the roughly 12% U.S. GDP contraction in the second quarter of 2020. We show that public measures designed to protect workers in core industries and occupations with tasks that cannot be performed from home, can flatten the epidemiological curve at reduced economic costs-and contain vulnerabilities to supply disruptions, namely a new surge of infections. Using state-level data for the United States, we provide econometric evidence that spontaneous social distancing was no less costly than mandated social distancing.

The Impacts of Gradually Terminating Nonpharmaceutical Interventions for SARS-CoV-2: A Mathematical Modelling Analysis

With the expansion of vaccination programs, the policy of terminating nonpharmaceutical interventions for preventing the SARS-CoV-2 pandemic should become more flexible. The current study investigated the clinical and economic outcomes of intervention policies combining nonpharmaceutical interventions and vaccination programs for dealing with the SARS-CoV-2 pandemic. An agent-based transmission model was adopted that describes how a SARS-CoV-2 virus spreads in the populations of China. The model inputs were derived from the literature and expert opinion. The following intervention policies were simulated: no intervention, strict nonpharmaceutical interventions, and nonpharmaceutical interventions for workplace, community, school and home gradually terminated by combining vaccination programs for specified age groups (vaccination age in years: 20–60, 20–70, 20–80, ≥20, ≥10 and whole population). Cumulative infections and deaths in one calendar year, costs and quality-adjusted life years (QALYs) were measured. When the vaccination program was taken up in at least the ≥20 years age group in all populations, nonpharmaceutical interventions for workplace and community settings could be gradually terminated because the cumulative number of infections was < 100 per 100,000 persons. Further ending nonpharmaceutical interventions in school and home settings could not meet the target even when the vaccination program had been taken up in all populations. When cumulative deaths were used as the endpoint, nonpharmaceutical interventions in workplace, community and school settings could be gradually terminated. Vaccine efficacy and coverage have substantial impacts. Terminating nonpharmaceutical interventions in workplace settings could produce the lowest cost when vaccination programs are taken up at least in the ≥10 years age group; this method dominates most intervention strategies due to its lower costs and higher QALYs. According to our findings, nonpharmaceutical interventions might be gradually terminated in Chinese settings.

Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be 40%-60% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest.

Evaluating Strategies For Tuberculosis to Achieve the Goals of WHO in China: A Seasonal Age-Structured Model Study

Although great progress has been made in the prevention and mitigation of TB in the past 20 years, China is still the third largest contributor to the global burden of new TB cases, accounting for 833,000 new cases in 2019. Improved mitigation strategies, such as vaccines, diagnostics, and treatment, are needed to meet goals of WHO. Given the huge variability in the prevalence of TB across age-groups in China, the vaccination, diagnostic techniques, and treatment for different age-groups may have different effects. Moreover, the statistics data of TB cases show significant seasonal fluctuations in China. In view of the above facts, we propose a non-autonomous differential equation model with age structure and seasonal transmission rate. We derive the basic reproduction number, [Formula: see text], and prove that the unique disease-free periodic solution, [Formula: see text] is globally asymptotically stable when [Formula: see text], while the disease is uniformly persistent and at least one positive periodic solution exists when [Formula: see text]. We estimate that the basic reproduction number [Formula: see text] ([Formula: see text]), which means that TB is uniformly persistent. Our results demonstrate that vaccinating susceptible individuals whose ages are over 65 and between 20 and 24 is much more effective in reducing the prevalence of TB, and each of the improved vaccination strategy, diagnostic strategy, and treatment strategy leads to substantial reductions in the prevalence of TB per 100,000 individuals compared with current approaches, and the combination of the three strategies is more effective. Scenario A (i.e., coverage rate [Formula: see text], diagnosis rate [Formula: see text], relapse rate [Formula: see text]) is the best and can reduce the prevalence of TB per 100,000 individuals by [Formula: see text] and [Formula: see text] in 2035 and 2050, respectively. Although the improved strategies will significantly reduce the incidence rate of TB, it is challenging to achieve the goal of WHO in 2050. Our findings can provide guidance for public health authorities in projecting effective mitigation strategies of TB.

Updating age-specific contact structures to match evolving demography in a dynamic mathematical model of tuberculosis vaccination

We investigated the effects of updating age-specific social contact matrices to match evolving demography on vaccine impact estimates. We used a dynamic transmission model of tuberculosis in India as a case study. We modelled four incremental methods to update contact matrices over time, where each method incorporated its predecessor: fixed contact matrix (M0), preserved contact reciprocity (M1), preserved contact assortativity (M2), and preserved average contacts per individual (M3). We updated the contact matrices of a deterministic compartmental model of tuberculosis transmission, calibrated to epidemiologic data between 2000 and 2019 derived from India. We additionally calibrated the M0, M2, and M3 models to the 2050 TB incidence rate projected by the calibrated M1 model. We stratified age into three groups, children (<15y), adults (≥15y, <65y), and the elderly (≥65y), using World Population Prospects demographic data, between which we applied POLYMOD-derived social contact matrices. We simulated an M72-AS01_E-like tuberculosis vaccine delivered from 2027 and estimated the per cent TB incidence rate reduction (IRR) in 2050 under each update method. We found that vaccine impact estimates in all age groups remained relatively stable between the M0–M3 models, irrespective of vaccine-targeting by age group. The maximum difference in impact, observed following adult-targeted vaccination, was 7% in the elderly, in whom we observed IRRs of 19% (uncertainty range 13–32), 20% (UR 13–31), 22% (UR 14–37), and 26% (UR 18–38) following M0, M1, M2 and M3 updates, respectively. We found that model-based TB vaccine impact estimates were relatively insensitive to demography-matched contact matrix updates in an India-like demographic and epidemiologic scenario. Current model-based TB vaccine impact estimates may be reasonably robust to the lack of contact matrix updates, but further research is needed to confirm and generalise this finding.

Mathematical models are increasingly used to predict the impact of new and existing tools, e.g., vaccines, that aim to control the transmission of infectious diseases. Within these models, investigators often assume that individuals contact each other according to specific patterns, particularly between and within different age groups. These patterns are typically derived from surveys of social contact or other models and reflect the particular age composition of their source population. However, when models are set over long time scales, e.g., decades, population age composition is likely to change. Despite this reality, few models update their contact patterns to match changing age composition. Furthermore, none have assessed whether their final estimates of disease-control intervention impact are affected by updating contact patterns. We measured whether different techniques to update social contact patterns to match evolving demography produce different vaccine impact estimates, using a mathematical model of tuberculosis set in an India-like scenario between 2025–2050. We found that vaccine impact was stable across a range of different update methods. Thus, existing model-based vaccine impact estimates may be stable to a lack of these updates, but further work is required to confirm these findings.

Prioritizing COVID-19 vaccination efforts and dose allocation within Madagascar

Background

While mass COVID-19 vaccination programs are underway in high-income countries, limited availability of doses has resulted in few vaccines administered in low and middle income countries (LMICs). The COVID-19 Vaccines Global Access (COVAX) is a WHO-led initiative to promote vaccine access equity to LMICs and is providing many of the doses available in these settings. However, initial doses are limited and countries, such as Madagascar, need to develop prioritization schemes to maximize the benefits of vaccination with very limited supplies. There is some consensus that dose deployment should initially target health care workers, and those who are more vulnerable including older individuals. However, questions of geographic deployment remain, in particular associated with limits around vaccine access and delivery capacity in underserved communities, for example in rural areas that may also include substantial proportions of the population.

Methods

To address these questions, we developed a mathematical model of SARS-CoV-2 transmission dynamics and simulated various vaccination allocation strategies for Madagascar. Simulated strategies were based on a number of possible geographical prioritization schemes, testing sensitivity to initial susceptibility in the population, and evaluating the potential of tests for previous infection.

Results

Using cumulative deaths due to COVID-19 as the main outcome of interest, our results indicate that distributing the number of vaccine doses according to the number of elderly living in the region or according to the population size results in a greater reduction of mortality compared to distributing doses based on the reported number of cases and deaths. The benefits of vaccination strategies are diminished if the burden (and thus accumulated immunity) has been greatest in the most populous regions, but the overall strategy ranking remains comparable. If rapid tests for prior immunity may be swiftly and effectively delivered, there is potential for considerable gain in mortality averted, but considering delivery limitations modulates this.

Conclusion

At a subnational scale, our results support the strategy adopted by the COVAX initiative at a global scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13150-8.

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

BACKGROUND

Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests.

METHODS

We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario.

RESULTS

We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics.

CONCLUSIONS

Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Optimizing social and economic activity while containing SARS-CoV-2 transmission using DAEDALUS

To study the trade-off between economic, social and health outcomes in the management of a pandemic, DAEDALUS integrates a dynamic epidemiological model of SARS-CoV-2 transmission with a multi-sector economic model, reflecting sectoral heterogeneity in transmission and complex supply chains. The model identifies mitigation strategies that optimize economic production while constraining infections so that hospital capacity is not exceeded but allowing essential services, including much of the education sector, to remain active. The model differentiates closures by economic sector, keeping those sectors open that contribute little to transmission but much to economic output and those that produce essential services as intermediate or final consumption products. In an illustrative application to 63 sectors in the United Kingdom, the model achieves an economic gain of between £161 billion (24%) and £193 billion (29%) compared to a blanket lockdown of non-essential activities over six months. Although it has been designed for SARS-CoV-2, DAEDALUS is sufficiently flexible to be applicable to pandemics with different epidemiological characteristics.

Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria

The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves.

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

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

Projecting COVID-19 Mortality as States Relax Nonpharmacologic Interventions

Question

What is the expected trend in COVID-19 mortality if US states were to lift nonpharmacologic interventions (NPIs) at different times over the remainder of 2022?

Findings

In this simulation modeling study, lifting NPIs was likely to result in rebounding epidemics regardless of the delay in lifting. The degree of population-level immunity was associated with the size of the rebounding peak in incident deaths.

Meaning

This simulation study found no path to the end of the COVID-19 pandemic that avoided difficult trade-offs between prolonged NPIs and increased COVID-19 mortality following their removal.

Importance

A key question for policy makers and the public is what to expect from the COVID-19 pandemic going forward as states lift nonpharmacologic interventions (NPIs), such as indoor mask mandates, to prevent COVID-19 transmission.

Objective

To project COVID-19 deaths between March 1, 2022, and December 31, 2022, in each of the 50 US states, District of Columbia, and Puerto Rico assuming different dates of lifting of mask mandates and NPIs.

Design, Setting, and Participants

This simulation modeling study used the COVID-19 Policy Simulator compartmental model to project COVID-19 deaths from March 1, 2022, to December 31, 2022, using simulated populations in the 50 US states, District of Columbia, and Puerto Rico. Projected current epidemiologic trends for each state until December 31, 2022, assuming the current pace of vaccination is maintained into the future and modeling different dates of lifting NPIs.

Exposures

Date of lifting statewide NPI mandates as March 1, April 1, May 1, June 1, or July 1, 2022.

Main Outcomes and Measures

Projected COVID-19 incident deaths from March to December 2022.

Results

With the high transmissibility of current circulating SARS-CoV-2 variants, the simulated lifting of NPIs in March 2022 was associated with resurgences of COVID-19 deaths in nearly every state. In comparison, delaying by even 1 month to lift NPIs in April 2022 was estimated to mitigate the amplitude of the surge. For most states, however, no amount of delay was estimated to be sufficient to prevent a surge in deaths completely. The primary factor associated with recurrent epidemics in the simulation was the assumed high effective reproduction number of unmitigated viral transmission. With a lower level of transmissibility similar to those of the ancestral strains, the model estimated that most states could remove NPIs in March 2022 and likely not see recurrent surges.

Conclusions and Relevance

This simulation study estimated that the SARS-CoV-2 virus would likely continue to take a major toll in the US, even as cases continued to decrease. Because of the high transmissibility of the recent Delta and Omicron variants, premature lifting of NPIs could pose a substantial threat of rebounding surges in morbidity and mortality. At the same time, continued delay in lifting NPIs may not prevent future surges.

Nationally Representative Social Contact Patterns among U.S. adults, August 2020-April 2021

The response to the COVID-19 pandemic in the U.S prompted abrupt and dramatic changes to social contact patterns. Monitoring changing social behavior is essential to provide reliable input data for mechanistic models of infectious disease, which have been increasingly used to support public health policy to mitigate the impacts of the pandemic. While some studies have reported on changing contact patterns throughout the pandemic., few have reported on differences in contact patterns among key demographic groups and none have reported nationally representative estimates. We conducted a national probability survey of US households and collected information on social contact patterns during two time periods: August-December 2020 (before widespread vaccine availability) and March-April 2021 (during national vaccine rollout). Overall, contact rates in Spring 2021 were similar to those in Fall 2020, with most contacts reported at work. Persons identifying as non-White, non-Black, non-Asian, and non-Hispanic reported high numbers of contacts relative to other racial and ethnic groups. Contact rates were highest in those reporting occupations in retail, hospitality and food service, and transportation. Those testing positive for SARS-CoV-2 antibodies reported a higher number of daily contacts than those who were seronegative. Our findings provide evidence for differences in social behavior among demographic groups, highlighting the profound disparities that have become the hallmark of the COVID-19 pandemic.

Combating COVID-19 crisis and predicting the second wave in Europe: an Age-structured modeling

We employ an age-structured susceptible-infected-quarantined-recovered model to simulate the progression of COVID-19 in France, Spain, and Germany. In the absence of a vaccine or conventional treatment, non-pharmaceutical interventions become more valuable, so our model takes into account the efficacy of official social distancing and lockdown measures. Using data from February to July 2020, we make useful predictions for the upcoming months, and further simulate the effect of lifting the lockdown at a later stage. A control model is also proposed and conditions for optimality are also obtained using optimal control theory. Motivated by the recent surge in cases in France and Spain, we also examine the possibility of a second wave of the pandemic. We conclude that further measures need to be taken in these two countries, while Germany is on its way to mitigating the disease.

Evaluating COVID-19 Booster Vaccination Strategies in a Partially Vaccinated Population: A Modeling Study

BACKGROUND

Several countries are implementing COVID-19 booster vaccination campaigns. The objective of this study was to model the impact of different primary and booster vaccination strategies.

METHODS

We used a compartmental model fitted to hospital admission data in France to analyze the impact of primary and booster vaccination strategies on morbidity and mortality, assuming waning of immunity and various levels of virus transmissibility during winter.

RESULTS

Strategies prioritizing primary vaccinations were systematically more effective than strategies prioritizing boosters. Regarding booster strategies targeting different age groups, their effectiveness varied with immunity and virus transmissibility levels. If the waning of immunity affects all adults, people aged 30 to 49 years should be boosted in priority, even for low transmissibility levels.

CONCLUSIONS

Increasing the primary vaccination coverage should remain a priority. If a plateau has been reached, boosting the immunity of younger adults could be the most effective strategy, especially if SARS-CoV-2 transmissibility is high.

Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys

The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over [Formula: see text] of the Hungarian population to simultaneously record contact matrices through a longitudinal online and sequence of representative phone surveys. To correct non-representative biases characterising the online data, by using census data and the representative samples we develop a reconstruction method to provide a scalable, cheap, and flexible way to dynamically obtain closer-to-representative contact matrices. Our results demonstrate that although some conventional socio-demographic characters correlate significantly with the change of contact numbers, the strongest predictors can be collected only via surveys techniques and combined with census data for the best reconstruction performance. We demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and to inform epidemic models with crucial data.

Estimates of the basic reproduction number for rubella using seroprevalence data and indicator-based approaches

The basic reproduction number (R0) of an infection determines the impact of its control. For many endemic infections, R0 is often estimated from appropriate country-specific seroprevalence data. Studies sometimes pool estimates from the same region for settings lacking seroprevalence data, but the reliability of this approach is unclear. Plausibly, indicator-based approaches could predict R0 for such settings. We calculated R0 for rubella for 98 settings and correlated its value against 66 demographic, economic, education, housing and health-related indicators. We also trained a random forest regression algorithm using these indicators as the input and R0 as the output. We used the mean-square error to compare the performances of the random forest, simple linear regression and a regional averaging method in predicting R0 using 4-fold cross validation. R0 was <5, 5–10 and >10 for 81, 14 and 3 settings respectively, with no apparent regional differences and in the limited available data, it was usually lower for rural than urban areas. R0 was most correlated with educational attainment, and household indicators for the Pearson and Spearman correlation coefficients respectively and with poverty-related indicators followed by the crude death rate considering the Maximum Information Coefficient, although the correlation for each was relatively weak (Pearson correlation coefficient: 0.4, 95%CI: (0.24,0.48) for educational attainment). A random forest did not perform better in predicting R0 than simple linear regression, depending on the subsets of training indicators and studies, and neither out-performed a regional averaging approach. R0 for rubella is typically low and using indicators to estimate its value is not straightforward. A regional averaging approach may provide as reliable an estimate of R0 for settings lacking seroprevalence data as one based on indicators. The findings may be relevant for other infections and studies estimating the disease burden and the impact of interventions for settings lacking seroprevalence data.

Effect of vaccination, border testing, and quarantine requirements on the risk of COVID-19 in New Zealand: A modelling study

We couple a simple model of quarantine and testing strategies for international travellers with a model for transmission of SARS-CoV-2 in a partly vaccinated population. We use this model to estimate the risk of an infectious traveller causing a community outbreak under various border control strategies and different levels of vaccine coverage in the population. Results are calculated from N = 100,000 independent realisations of the stochastic model. We find that strategies that rely on home isolation are significantly higher risk than the current mandatory 14-day stay in government-managed isolation. Nevertheless, combinations of testing and home isolation can still reduce the risk of a community outbreak to around one outbreak per 100 infected travellers. We also find that, under some circumstances, using daily lateral flow tests or a combination of lateral flow tests and polymerase chain reaction (PCR) tests can reduce risk to a comparable or lower level than using PCR tests alone. Combined with controls on the number of travellers from countries with high prevalence of COVID-19, our results allow different options for managing the risk of COVID-19 at the border to be compared. This can be used to inform strategies for relaxing border controls in a phased way, while limiting the risk of community outbreaks as vaccine coverage increases.

Challenges for modelling interventions for future pandemics

Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.

Estimating COVID-19-Related Infections, Deaths, and Hospitalizations in Iran Under Different Physical Distancing and Isolation Scenarios

BACKGROUND

Iran is one of the first few countries that was hit hard with the coronavirus disease 2019 (COVID-19) pandemic. We aimed to estimate the total number of COVID-19 related infections, deaths, and hospitalizations in Iran under different physical distancing and isolation scenarios.

METHODS

We developed a susceptible-exposed-infected/infectious-recovered/removed (SEIR) model, parameterized to the COVID-19 pandemic in Iran. We used the model to quantify the magnitude of the outbreak in Iran and assess the effectiveness of isolation and physical distancing under five different scenarios (A: 0% isolation, through E: 40% isolation of all infected cases). We used Monte-Carlo simulation to calculate the 95% uncertainty intervals (UIs).

RESULTS

Under scenario A, we estimated 5 196 000 (UI 1 753 000-10 220 000) infections to happen till mid-June with 966 000 (UI 467 800-1 702 000) hospitalizations and 111 000 (UI 53 400-200 000) deaths. Successful implantation of scenario E would reduce the number of infections by 90% (ie, 550 000) and change the epidemic peak from 66 000 on June 9, to 9400 on March 1, 2020. Scenario E also reduces the hospitalizations by 92% (ie, 74 500), and deaths by 93% (ie, 7800).

CONCLUSION

With no approved vaccination or therapy available, we found physical distancing and isolation that include public awareness and case-finding and isolation of 40% of infected people could reduce the burden of COVID-19 in Iran by 90% by mid-June.

Optimal COVID-19 lockdown strategies in an age-structured SEIR model of Northern Ireland

An age-structured SEIR model simulates the propagation of COVID-19 in the population of Northern Ireland. It is used to identify optimal timings of short-term lockdowns that enable long-term pandemic exit strategies by clearing the threshold for herd immunity or achieving time for vaccine development with minimal excess deaths.

Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study

BACKGROUND

During the Coronavirus Disease 2019 (COVID-19) pandemic, the United Kingdom government imposed public health policies in England to reduce social contacts in hopes of curbing virus transmission. We conducted a repeated cross-sectional study to measure contact patterns weekly from March 2020 to March 2021 to estimate the impact of these policies, covering 3 national lockdowns interspersed by periods of less restrictive policies.

METHODS AND FINDINGS

The repeated cross-sectional survey data were collected using online surveys of representative samples of the UK population by age and gender. Survey participants were recruited by the online market research company Ipsos MORI through internet-based banner and social media ads and email campaigns. The participant data used for this analysis are restricted to those who reported living in England. We calculated the mean daily contacts reported using a (clustered) bootstrap and fitted a censored negative binomial model to estimate age-stratified contact matrices and estimate proportional changes to the basic reproduction number under controlled conditions using the change in contacts as a scaling factor. To put the findings in perspective, we discuss contact rates recorded throughout the year in terms of previously recorded rates from the POLYMOD study social contact study. The survey recorded 101,350 observations from 19,914 participants who reported 466,710 contacts over 53 weeks. We observed changes in social contact patterns in England over time and by participants' age, personal risk factors, and perception of risk. The mean reported contacts for adults 18 to 59 years old ranged between 2.39 (95% confidence interval [CI] 2.20 to 2.60) contacts and 4.93 (95% CI 4.65 to 5.19) contacts during the study period. The mean contacts for school-age children (5 to 17 years old) ranged from 3.07 (95% CI 2.89 to 3.27) to 15.11 (95% CI 13.87 to 16.41). This demonstrates a sustained decrease in social contacts compared to a mean of 11.08 (95% CI 10.54 to 11.57) contacts per participant in all age groups combined as measured by the POLYMOD social contact study in 2005 to 2006. Contacts measured during periods of lockdowns were lower than in periods of eased social restrictions. The use of face coverings outside the home has remained high since the government mandated use in some settings in July 2020. The main limitations of this analysis are the potential for selection bias, as participants are recruited through internet-based campaigns, and recall bias, in which participants may under- or overreport the number of contacts they have made.

CONCLUSIONS

In this study, we observed that recorded contacts reduced dramatically compared to prepandemic levels (as measured in the POLYMOD study), with changes in reported contacts correlated with government interventions throughout the pandemic. Despite easing of restrictions in the summer of 2020, the mean number of reported contacts only returned to about half of that observed prepandemic at its highest recorded level. The CoMix survey provides a unique repeated cross-sectional data set for a full year in England, from the first day of the first lockdown, for use in statistical analyses and mathematical modelling of COVID-19 and other diseases.