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Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings.

Introduction: COVID-19 related non-pharmaceutical interventions (NPIs) led to a suppression of RSV circulation in winter 2020/21 in the UK and an off-season resurgence in Summer 2021. We explore how the parameters of RSV epidemiology shape the size and dynamics of post-suppression resurgence and what we can learn about them from the resurgence patterns observed so far. Methods: We developed an age-structured dynamic transmission model of RSV and sampled the parameters governing RSV seasonality, infection susceptibility and post-infection immunity, retaining simulations fitting the UK’s pre-pandemic epidemiology by a set of global criteria consistent with likelihood calculations. From Spring 2020 to Summer 2021 we assumed a reduced contact frequency, returning to pre-pandemic levels from Spring 2021. We simulated transmission forwards until 2023 and evaluated the impact of the sampled parameters on the projected trajectories of RSV hospitalisations and compared these to the observed resurgence. Results: Simulations replicated an out-of-season resurgence of RSV in 2021. If unmitigated, paediatric RSV hospitalisation incidence in the 2021/22 season was projected to increase by 30–60% compared to pre-pandemic levels. The increase was larger if infection risk was primarily determined by immunity acquired from previous exposure rather than age-dependent factors, exceeding 90 % and 130 % in 1–2 and 2–5 year old children, respectively. Analysing the simulations replicating the observed early outbreak in 2021 in addition to pre-pandemic RSV data, we found they were characterised by weaker seasonal forcing, stronger age-dependence of infection susceptibility and higher baseline transmissibility. Conclusion: COVID-19 mitigation measures in the UK stopped RSV circulation in the 2020/21 season and generated immunity debt leading to an early off-season RSV epidemic in 2021. A stronger dependence of infection susceptibility on immunity from previous exposure increases the size of the resurgent season. The early onset of the RSV resurgence in 2021, its marginally increased size relative to previous seasons and its decline by January 2022 suggest a stronger dependence of infection susceptibility on age-related factors, as well as a weaker effect of seasonality and a higher baseline transmissibility. The pattern of resurgence has been complicated by contact levels still not back to pre-pandemic levels. Further fitting of RSV resurgence in multiple countries incorporating data on contact patterns will be needed to further narrow down these parameters and to better predict the pathogen’s future trajectory, planning for a potential expansion of new immunisation products against RSV in the coming years.

Simulation models from the early COVID-19 pandemic highlighted the urgency of applying non-pharmaceutical interventions (NPIs), but had limited empirical data. Here we use data from 2020–2021 to retrospectively model the impact of NPIs in Ontario, Canada. Our model represents age groups and census divisions in Ontario, and is parameterized with epidemiological, testing, demographic, travel, and mobility data. The model captures how individuals adopt NPIs in response to reported cases. We compare a scenario representing NPIs introduced within Ontario (closures of workplaces/schools, reopening of schools/workplaces with NPIs in place, individual-level NPI adherence) to counterfactual scenarios wherein alternative strategies (e.g. no closures, reliance on individual NPI adherence) are adopted to ascertain the extent to which NPIs reduced cases and deaths. Combined school/workplace closure and individual NPI adoption reduced the number of deaths in the best-case scenario for the case fatality rate (CFR) from 178548 [CI: 171845, 185298] to 3190 [CI: 3095, 3290] in the Spring 2020 wave. In the Fall 2020/Winter 2021 wave, the introduction of NPIs in workplaces/schools reduced the number of deaths from 20183 [CI: 19296, 21057] to 4102 [CI: 4075, 4131]. Deaths were several times higher in the worst-case CFR scenario. Each additional 9−16(resp. 285−578) individuals who adopted NPIs in the first wave prevented one additional infection (resp., death). Our results show that the adoption of NPIs prevented a public health catastrophe. A less comprehensive approach, employing only closures or individual-level NPI adherence, would have resulted in a large number of cases and deaths.

• An agent-based modelling framework GEOFIL was developed for modelling lymphatic filariasis transmission. • GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. • Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. • Mosquito biting rates were found to be a critical determinant of infection risk. In this study, a spatially-explicit agent-based modelling framework GEOFIL was developed to predict lymphatic filariasis (LF) transmission dynamics in American Samoa. GEOFIL included individual-level information on age, gender, disease status, household location, household members, workplace/school location and colleagues/schoolmates at each time step during the simulation. In American Samoa, annual mass drug administration from 2000 to 2006 successfully reduced LF prevalence dramatically. However, GEOFIL predicted continual increase in microfilaraemia prevalence in the absence of further intervention. Evidence from seroprevalence and transmission assessment surveys conducted from 2010 to 2016 indicated a resurgence of LF in American Samoa, corroborating GEOFIL's predictions. The microfilaraemia and antigenaemia prevalence in 6-7-yo children were much lower than in the overall population. Mosquito biting rates were found to be a critical determinant of infection risk. Transmission hotspots are likely to disappear with lower biting rates. GEOFIL highlights current knowledge gaps, such as data on mosquito abundance, biting rates and within-host parasite dynamics, which are important for improving the accuracy of model predictions. [ABSTRACT FROM AUTHOR]

Mathematical models of disease transmission are used to improve our understanding of patterns of infection and to identify factors influencing them. During recent public and animal health crises, such as pandemic influenza, Ebola, Zika, foot-and-mouth disease, models have made important contributions in addressing policy questions, especially through the assessment of the trajectory and scale of outbreaks, and the evaluation of control interventions. However, their mathematical formulation means that they may appear as a “black box” to those without the appropriate mathematical background. This may lead to a negative perception of their utility for guiding policy, and generate expectations, which are not in line with what these models can deliver. It is therefore important for policymakers, as well as public health and animal health professionals and researchers who collaborate with modelers and use results generated by these models for policy development or research purpose, to understand the key concepts and assumptions underlying these models. The software application epidemix ( http://shinyapps.rvc.ac.uk ) presented here aims to make mathematical models of disease transmission accessible to a wider audience of users. By developing a visual interface for a suite of eight models, users can develop an understanding of the impact of various modelling assumptions – especially mixing patterns – on the trajectory of an epidemic and the impact of control interventions, without having to directly deal with the complexity of mathematical equations and programming languages. Models are compartmental or individual-based, deterministic or stochastic, and assume homogeneous or heterogeneous-mixing patterns (with the probability of transmission depending on the underlying structure of contact networks, or the spatial distribution of hosts). This application is intended to be used by scientists teaching mathematical modelling short courses to non-specialists – including policy makers, public and animal health professionals and students – and wishing to develop hands-on practicals illustrating key concepts of disease dynamics and control. [ABSTRACT FROM AUTHOR]

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings. [Display omitted] • SARS-CoV-2 outbreak sizes during training were influenced by imported infections. • Imported infections are predictable from national and regional incidence patterns. • Increasing off-base infections reduced the impact of several control measures. • Data-driven models help optimize the mixture of controls in institutional settings. [ABSTRACT FROM AUTHOR]

COVID-19 related non-pharmaceutical interventions (NPIs) led to a suppression of RSV circulation in winter 2020/21 in the UK and an off-season resurgence in Summer 2021. We explore how the parameters of RSV epidemiology shape the size and dynamics of post-suppression resurgence and what we can learn about them from the resurgence patterns observed so far. We developed an age-structured dynamic transmission model of RSV and sampled the parameters governing RSV seasonality, infection susceptibility and post-infection immunity, retaining simulations fitting the UK's pre-pandemic epidemiology by a set of global criteria consistent with likelihood calculations. From Spring 2020 to Summer 2021 we assumed a reduced contact frequency, returning to pre-pandemic levels from Spring 2021. We simulated transmission forwards until 2023 and evaluated the impact of the sampled parameters on the projected trajectories of RSV hospitalisations and compared these to the observed resurgence. Simulations replicated an out-of-season resurgence of RSV in 2021. If unmitigated, paediatric RSV hospitalisation incidence in the 2021/22 season was projected to increase by 30–60% compared to pre-pandemic levels. The increase was larger if infection risk was primarily determined by immunity acquired from previous exposure rather than age-dependent factors, exceeding 90 % and 130 % in 1–2 and 2–5 year old children, respectively. Analysing the simulations replicating the observed early outbreak in 2021 in addition to pre-pandemic RSV data, we found they were characterised by weaker seasonal forcing, stronger age-dependence of infection susceptibility and higher baseline transmissibility. COVID-19 mitigation measures in the UK stopped RSV circulation in the 2020/21 season and generated immunity debt leading to an early off-season RSV epidemic in 2021. A stronger dependence of infection susceptibility on immunity from previous exposure increases the size of the resurgent season. The early onset of the RSV resurgence in 2021, its marginally increased size relative to previous seasons and its decline by January 2022 suggest a stronger dependence of infection susceptibility on age-related factors, as well as a weaker effect of seasonality and a higher baseline transmissibility. The pattern of resurgence has been complicated by contact levels still not back to pre-pandemic levels. Further fitting of RSV resurgence in multiple countries incorporating data on contact patterns will be needed to further narrow down these parameters and to better predict the pathogen's future trajectory, planning for a potential expansion of new immunisation products against RSV in the coming years. • Post-lockdown RSV resurgence depends on infection susceptibility primarily shaped by age or immunity from previous infections. • Resurgent seasons are larger if immunity from previous exposure dominates. • Stronger dependence on age-related factors (respiratory tract development) result in a smaller resurgent season • UK's RSV resurgence suggests stronger age-dependence, weaker seasonality & higher transmissibility than pre-COVID-19 data. • We can learn more about the epidemiology of RSV from fitting its post-suppression resurgence patterns. [ABSTRACT FROM AUTHOR]

Simulation models from the early COVID-19 pandemic highlighted the urgency of applying non-pharmaceutical interventions (NPIs), but had limited empirical data. Here we use data from 2020–2021 to retrospectively model the impact of NPIs in Ontario, Canada. Our model represents age groups and census divisions in Ontario, and is parameterized with epidemiological, testing, demographic, travel, and mobility data. The model captures how individuals adopt NPIs in response to reported cases. We compare a scenario representing NPIs introduced within Ontario (closures of workplaces/schools, reopening of schools/workplaces with NPIs in place, individual-level NPI adherence) to counterfactual scenarios wherein alternative strategies (e.g. no closures, reliance on individual NPI adherence) are adopted to ascertain the extent to which NPIs reduced cases and deaths. Combined school/workplace closure and individual NPI adoption reduced the number of deaths in the best-case scenario for the case fatality rate (CFR) from 178548 [CI: 171845, 185298] to 3190 [CI: 3095, 3290] in the Spring 2020 wave. In the Fall 2020/Winter 2021 wave, the introduction of NPIs in workplaces/schools reduced the number of deaths from 20183 [CI: 19296, 21057] to 4102 [CI: 4075, 4131]. Deaths were several times higher in the worst-case CFR scenario. Each additional 9 − 16 (resp. 285 − 578) individuals who adopted NPIs in the first wave prevented one additional infection (resp., death). Our results show that the adoption of NPIs prevented a public health catastrophe. A less comprehensive approach, employing only closures or individual-level NPI adherence, would have resulted in a large number of cases and deaths. • Non-pharmaceutical interventions substantially reduce COVID-19 cases and deaths. • Closures should accompany individual adherence to non-pharmaceutical interventions. • Impact of individual adherence to non-pharmaceutical interventions can be quantified. [ABSTRACT FROM AUTHOR]

Abstract: Bovine spongiform encephalopathy, otherwise known as mad cow disease, can spread when an individual cow consumes feed containing the infected tissues of another individual, forming a one-species feedback loop. Such feedback is the primary means of transmission for BSE during epidemic conditions. Following outbreaks in the European Union and elsewhere, many governments enacted legislation designed to limit the spread of such diseases via elimination or reduction of one-species feedback loops in agricultural systems. However, two-species feedback loops—those in which infectious material from one-species is consumed by a secondary species whose tissue is then consumed by the first species—were not universally prohibited and have not been studied before. Here we present a basic ecological disease model which examines the rôle feedback loops may play in the spread of BSE and related diseases. Our model shows that there are critical thresholds between the infection's expansion and decrease related to the lifespan of the hosts, the growth rate of the prions, and the amount of prions circulating between hosts. The ecological disease dynamics can be intrinsically oscillatory, having outbreaks as well as refractory periods which can make it appear that the disease is under control while it is still increasing. We show that non-susceptible species that have been intentionally inserted into a feedback loop to stop the spread of disease do not, strictly by themselves, guarantee its control, though they may give that appearance by increasing the refractory period of an epidemic's oscillations. We suggest ways in which age-related dynamics and cross-species coupling should be considered in continuing evaluations aimed at maintaining a safe food supply. [Copyright &y& Elsevier]