An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost

Lives Versus Livelihoods: The Epidemiological, Social, And Economic Impact Of COVID-19 In Latin America And The Caribbean

During the COVID-19 pandemic, Latin American and Caribbean countries implemented stringent public health and social measures that disrupted economic and social activities. This study used an integrated model to evaluate the epidemiological, economic, and social trade-offs in Argentina, Brazil, Jamaica, and Mexico throughout 2021. Argentina and Mexico displayed a higher gross domestic product (GDP) loss and lower deaths per million compared with Brazil. The magnitude of the trade-offs differed across countries. Reducing GDP loss at the margin by 1 percent would have increased daily deaths by 0.5 per million in Argentina but only 0.3 per million in Brazil. We observed an increase in poverty rates related to the stringency of public health and social measures but no significant income-loss differences by sex. Our results indicate that the economic impact of COVID-19 was uneven across countries as a result of different pandemic trajectories, public health and social measures, and vaccination uptake, as well as socioeconomic differences and fiscal responses. Policy makers need to be informed about the trade-offs to make strategic decisions to save lives and livelihoods.

Estimating time-dependent contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland

Mathematical modelling plays a key role in understanding and predicting the epidemiological dynamics of infectious diseases. We construct a flexible discrete-time model that incorporates multiple viral strains with different transmissibilities to estimate the changing patterns of human contact that generates new infections. Using a Bayesian approach, we fit the model to longitudinal data on hospitalisation with COVID-19 from the Republic of Ireland and Northern Ireland during the first year of the pandemic. We describe the estimated change in human contact in the context of government-mandated non-pharmaceutical interventions in the two jurisdictions on the island of Ireland. We take advantage of the fitted model to conduct counterfactual analyses exploring the impact of lockdown timing and introducing a novel, more transmissible variant. We found substantial differences in human contact between the two jurisdictions during periods of varied restriction easing and December holidays. Our counterfactual analyses reveal that implementing lockdowns earlier would have decreased subsequent hospitalisation substantially in most, but not all cases, and that an introduction of a more transmissible variant - without necessarily being more severe - can cause a large impact on the health care burden.

Cost-effectiveness analysis of COVID-19 variants effects in an age-structured model

This study analyzes the impact of COVID-19 variants on cost-effectiveness across age groups, considering vaccination efforts and nonpharmaceutical interventions in Republic of Korea. We aim to assess the costs needed to reduce COVID-19 cases and deaths using age-structured model. The proposed age-structured model analyzes COVID-19 transmission dynamics, evaluates vaccination effectiveness, and assesses the impact of the Delta and Omicron variants. The model is fitted using data from the Republic of Korea between February 2021 and November 2022. The cost-effectiveness of interventions, medical costs, and the cost of death for different age groups are evaluated through analysis. The impact of different variants on cases and deaths is also analyzed, with the Omicron variant increasing transmission rates and decreasing case-fatality rates compared to the Delta variant. The cost of interventions and deaths is higher for older age groups during both outbreaks, with the Omicron outbreak resulting in a higher overall cost due to increased medical costs and interventions. This analysis shows that the daily cost per person for both the Delta and Omicron variants falls within a similar range of approximately $10-$35. This highlights the importance of conducting cost-effect analyses when evaluating the impact of COVID-19 variants.

Understanding the role of mask-wearing during COVID-19 on the island of Ireland

Non-pharmaceutical interventions have played a key role in managing the COVID-19 pandemic, but it is challenging to estimate their impacts on disease spread and outcomes. On the island of Ireland, population mobility restrictions were imposed during the first wave, but mask-wearing was not mandated until about six months into the pandemic. We use data on mask-wearing, mobility, and season, over the first year of the pandemic to predict independently the weekly infectious contact estimated by an epidemiological model. Using our models, we make counterfactual predictions of infectious contact, and ensuing hospitalizations, under a hypothetical intervention where 90% of the population wore masks from the beginning of community spread until the dates of the mask mandates. Over periods including the first wave of the pandemic, there were 1601 hospitalizations with COVID-19 in Northern Ireland and 1521 in the Republic of Ireland. Under the counterfactual mask-wearing scenario, we estimate 512 (95% CI 400, 730) and 344 (95% CI 266, 526) hospitalizations in the respective jurisdictions during the same periods. This could be partly due to other factors that were also changing over time.

Age-specific transmission dynamics under suppression control measures during SARS-CoV-2 Omicron BA.2 epidemic

BACKGROUND

From March to June 2022, an Omicron BA.2 epidemic occurred in Shanghai. We aimed to better understand the transmission dynamics and identify age-specific transmission characteristics for the epidemic.

METHODS

Data on COVID-19 cases were collected from the Shanghai Municipal Health Commission during the period from 20th February to 1st June. The effective reproductive number (R_t) and transmission distance between cases were calculated. An age-structured SEIR model with social contact patterns was developed to reconstruct the transmission dynamics and evaluate age-specific transmission characteristics. Least square method was used to calibrate the model. Basic reproduction number (R₀) was estimated with next generation matrix.

RESULTS

R₀ of Omicron variant was 7.9 (95% CI: 7.4 to 8.4). With strict interventions, R_t had dropped quickly from 3.6 (95% CI: 2.7 to 4.7) on 4th March to below 1 on 18th April. The mean transmission distance of the Omicron epidemic in Shanghai was 13.4 km (95% CI: 11.1 to 15.8 km), which was threefold longer compared with that of epidemic caused by the wild-type virus in Wuhan, China. The model estimated that there would have been a total 870,845 (95% CI: 815,400 to 926,289) cases for the epidemic from 20th February to 15th June, and 27.7% (95% CI: 24.4% to 30.9%) cases would have been unascertained. People aged 50-59 years had the highest transmission risk 0.216 (95% CI: 0.210 to 0.222), and the highest secondary attack rate (47.62%, 95% CI: 38.71% to 56.53%).

CONCLUSIONS

The Omicron variant spread more quickly and widely than other variants and resulted in about one third cases unascertained for the recent outbreak in Shanghai. Prioritizing isolation and screening of people aged 40-59 might suppress the epidemic more effectively. Routine surveillance among people aged 40-59 years could also provide insight into the stage of the epidemic and the timely detection of new variants.

TRIAL REGISTRATION

We did not involve clinical trial.

Impacts of observation frequency on proximity contact data and modeled transmission dynamics

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Teaching lessons learnt by civil-engineering teachers from the COVID-19 pandemic at the University of Burgos, Spain

The COVID-19 lockdown in Spain caused abrupt changes for students following the Bachelor's Degree in Civil Engineering at the University of Burgos when face-to-face classes switched to online teaching. The recovery of face-to-face teaching after lockdown meant that classes were taught with obligatory social distancing and the use of masks. Teachers were therefore unable to interact with students closely, to perceive their facial expressions during class, or to conduct group work. The changes to civil-engineering teaching linked to the COVID-19 pandemic and the lessons that civil-engineering teachers learnt from the new teaching scenarios are studied in this paper. The reflections of teachers throughout all three stages of the pandemic (pre-pandemic and lockdown, during lockdown, and post-lockdown), and the qualitative and mixed analysis of their responses to a survey of open-ended questions contributed to the identification of six major lessons: (1) asking questions and using real-time quiz tools enliven classes and help to determine which concepts to emphasize for proper student understanding; (2) autonomous student learning can be promoted through the provision of supplementary documentation and the digitalization of solutions to classroom exercises; (3) virtual site visits and real visual examples interspersed with explanations bring concepts closer to their real applications; (4) the delivery of projects in the form of audio-recorded presentations enable their distribution, so that other students can also learn from them as well as the students who created them; (5) online videoconferences, adapted to the concepts that are addressed, facilitate fast and flexible communication with students; and (6) online continuous-assessment exams can promote better student learning patterns and final-exam preparation. Nevertheless, these six lessons were drawn from the experience of teachers at a small Spanish university where the period of solely online teaching during the COVID-19 pandemic lasted only four months. Thus, it would be interesting to analyze the experience of civil-engineering teachers at larger universities and universities that had longer periods of solely online teaching. A study of the level of implementation of the six aspects when the pandemic is declared over might also be worthwhile.

Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates

Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driven scenario models. The model was developed as a policy advice tool for New Zealand's 2021 outbreak of the Delta variant of SARS-CoV-2 and includes the effects of age structure, non-pharmaceutical interventions, and the ongoing vaccine rollout occurring during the time period studied. We use an approximate Bayesian computation approach to infer the time-varying transmission coefficient from real-time data on reported cases. We then compare projections of the model with future, out-of-sample data. We find that this approach produces a good fit with in-sample data and reasonable forward projections given the inherent limitations of predicting epidemic dynamics during periods of rapidly changing policy and behaviour. Results from the model helped inform the New Zealand Government's policy response throughout the outbreak.

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.