Dynamic calibration with approximate Bayesian computation for a microsimulation of disease spread.

The global COVID-19 pandemic brought considerable public and policy attention to the field of infectious disease modelling. A major hurdle that modellers must overcome, particularly when models are used to develop policy, is quantifying the uncertainty in a model's predictions. By including the most recent available data in a model, the quality of its predictions can be improved and uncertainties reduced. This paper adapts an existing, large-scale, individual-based COVID-19 model to explore the benefits of updating the model in pseudo-real time. We use Approximate Bayesian Computation (ABC) to dynamically recalibrate the model's parameter values as new data emerge. ABC offers advantages over alternative calibration methods by providing information about the uncertainty associated with particular parameter values and the resulting COVID-19 predictions through posterior distributions. Analysing such distributions is crucial in fully understanding a model and its outputs. We find that forecasts of future disease infection rates are improved substantially by incorporating up-to-date observations and that the uncertainty in forecasts drops considerably in later simulation windows (as the model is provided with additional data). This is an important outcome because the uncertainty in model predictions is often overlooked when models are used in policy.

Sea swimming and snorkeling in tropical coastal blue spaces and mental well-being: Findings from Indonesian island communities during the COVID-19 pandemic

The COVID-19 pandemic has considerable mental health impacts. Immersive nature-based interventions, such as swimming or snorkeling, may help mitigate the global mental health crisis caused by the pandemic. To investigate this, we collected cross-sectional data from residents of coastal villages (n = 308) in Kepulauan Selayar, Indonesia. Analysis of Covariance (ANCOVA) was used with mental well-being as the outcome variable, operationalized as the Mental Component Summary (MCS) scores from the SF-12 (12-item Short Form Health Survey). After adjusting for covariates, the activity of sea swimming or snorkeling was found to be significantly associated with better mental well-being (η2 = 0.036;p < 0.01). Predictive margins analysis revealed that those who engaged in sea swimming or snorkeling for one to three days a week gained a 2.7 increase in their MCS scores, compared to those who did not. A non-linear dose-response relationship was detected: for those swimming or snorkeling more than three days per week, there was only an increase of 1.7 MCS score compared to the 0-day. Overall this study contributes to the expanding of evidence base, showing that interactions with blue spaces can be beneficial for mental health, especially in a potentially stressful time such as the current pandemic.

A dynamic microsimulation model for epidemics.

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

Area level deprivation and monthly COVID-19 cases: The impact of government policy in England.

This paper aims to understand the relationship between area level deprivation and monthly COVID-19 cases in England in response to government policy throughout 2020. The response variable is monthly reported COVID-19 cases at the Middle Super Output Area (MSOA) level by Public Health England, with Index of Multiple Deprivation (IMD), ethnicity (percentage of the population across 5 ethnicity categories) and the percentage of the population older than 70 years old and time as predictors. A GEE population-averaged panel-data model was employed to model trends in monthly COVID-19 cases with the population of each MSOA included as the exposure variable. Area level deprivation is significantly associated with COVID-19 cases from March 2020; however, this relationship is reversed in December 2020. Follow up analysis found that this reversal was maintained when controlling for the novel COVID-19 variant outbreak in the South East of England. This analysis indicates that changes in the role of deprivation and monthly reported COVID-19 over time cases may be linked to two government policies: (1) the premature easing of national restrictions in July 2020 when cases were still high in the most deprived areas in England and (2) the introduction of a regional tiered system in October predominantly in the North of England. The analysis adds to the evidence showing that deprivation is a key driver of COVID-19 outcomes and highlights the unintended negative impact of government policy.

Single-use plastic and COVID-19 in the NHS: Barriers and opportunities.

BACKGROUND: Single-use personal protective equipment (PPE) has been essential to protect healthcare workers during the COVID-19 pandemic. However, intensified use of PPE could counteract the previous efforts made by the UK NHS Trusts to reduce their plastic footprint. DESIGN AND METHODS: In this study, we conducted an in-depth case study in the Royal Cornwall Hospitals NHS Trust to investigate plastic-related issues in a typical NHS Trust before, during and after the pandemic. We first collected hospital routine data on both procurement and usage of single-use PPE (including face masks, aprons, and gowns) for the time period between April 2019 and August 2020. We then interviewed 12 hospital staff across a wide remit, from senior managers to consultants, nurses and catering staff, to gather qualitative evidence on the overall impact of COVID-19 on the Trust regarding plastic use. RESULTS: We found that although COVID-19 had increased the procurement and the use of single-use plastic substantially during the pandemic, it did not appear to have changed the focus of the hospital on implementing measures to reduce single-use plastic in the long term. We then discussed the barriers and opportunities to tackle plastic issues within the NHS in the post-COVID world, for example, a circular healthcare model. CONCLUSION: investment is needed in technologies and processes that can recycle and reuse a wider range of single-use plastics, and innovate sustainable alternatives to replace single-use consumables used in the NHS to construct a fully operational closed material loop healthcare system.

Changes in Domestic Energy and Water Usage during the UK COVID-19 Lockdown Using High-Resolution Temporal Data.

In response to the COVID-19 outbreak, the UK Government provided public health advice to stay at home from 16 March 2020, followed by instruction to stay at home (full lockdown) from 24 March 2020. We use data with high temporal resolution from utility sensors installed in 280 homes across social housing in Cornwall, UK, to test for changes in domestic electricity, gas and water usage in response to government guidance. Gas usage increased by 20% following advice to stay at home, the week before full lockdown, although no difference was seen during full lockdown itself. During full lockdown, morning electricity usage shifted to later in the day, decreasing at 6 a.m. and increasing at midday. These changes in energy were echoed in water usage, with a 17% increase and a one-hour delay in peak morning usage. Changes were consistent with people getting up later, spending more time at home and washing more during full lockdown. Evidence for these changes was also observed in later lockdowns, but not between lockdowns. Our findings suggest more compliance with an enforced stay-at-home message than with advice. We discuss implications for socioeconomically disadvantaged households given the indication of inability to achieve increased energy needs during the pandemic.