Treating intrusive memories after trauma in healthcare workers: a Bayesian adaptive randomised trial developing an imagery-competing task intervention (preprint)

ABSTRACT Intensive care unit (ICU) staff continue to face recurrent work-related traumatic events throughout the COVID-19 pandemic. Intrusive memories (IMs) of such traumatic events comprise sensory image-based memories. Harnessing research on preventing IMs with a novel behavioural intervention on the day of trauma, here we take critical next steps in developing this approach as a treatment for ICU staff who are already experiencing IMs days, weeks, or months post-trauma. To address the urgent need to develop novel mental health interventions, we used Bayesian statistical approaches to optimise a brief imagery-competing task intervention to reduce the number of IMs. We evaluated a digitised version of the intervention for remote, scalable delivery. We conducted a two-arm, parallel-group, randomised, adaptive Bayesian optimisation trial. Eligible participants worked clinically in a UK NHS ICU during the pandemic, experienced at least one work-related traumatic event, and at least three IMs in the week prior to recruitment. Participants were randomised to receive immediate or delayed (after four weeks) access to the intervention. Primary outcome was the number of IMs of trauma during week 4, controlling for baseline week. Analyses were conducted on an intention-to-treat basis as a between-group comparison. Prior to final analysis, sequential Bayesian analyses were conducted ( n =20,23,29,37,41,45) to inform early stopping of the trial prior to the planned maximum recruitment ( n =150). Final analysis ( n =75) showed strong evidence for a positive treatment effect (Bayes factor, BF=1.25 × 10 6 ): the immediate arm reported fewer IMs (median=1, IQR=0-3) than the delayed arm (median=10, IQR=6-16.5). With further digital enhancements, the intervention ( n =28) also showed a positive treatment effect (BF=7.31). Sequential Bayesian analyses provided evidence for reducing IMs of work-related trauma for healthcare workers. This methodology also allowed us to rule out negative effects early, reduced the planned maximum sample size, and allowed evaluation of enhancements. Trial Registration NCT02044809 ( www.clinicaltrials.gov ).

Optimal time to return to normality: parallel use of COVID-19 vaccines and circuit breakers (preprint)

By January 2020, the COVID-19 illness has caused over two million deaths. Countries have restricted disease spread through non-pharmaceutical interventions (e.g., social distancing). More severe 'lockdowns' have also been required. Although lockdowns keep people safer from the virus, they substantially disrupt economies and individual well-being. Fortunately, vaccines are becoming available. Yet, vaccination programs may take several months to implement, requiring further time for individuals to develop immunity following inoculation. To prevent health services being overwhelmed it may be necessary to implement further lockdowns in conjunction with vaccination. Here, we investigate optimal approaches for vaccination under varying lockdown lengths and/or severities to prevent COVID-19-related deaths exceeding critical thresholds. We find increases in vaccination rate cause a disproportionately larger decrease in lockdowns: with vaccination, severe lockdowns can reduce infections by up to 89%. Notably, we include demographics, modelling three groups: vulnerable, front-line workers, and non-vulnerable. We investigate the sequence of vaccination. One counter-intuitive finding is that even though the vulnerable group is high risk, demographically, this is a small group (per person, vaccination occurs more slowly) so vaccinating this group first achieves limited gains in overall disease control. Better disease control occurs by vaccinating the non-vulnerable group with longer and/or more severe lockdowns

Mechanistic modelling of coronavirus infections and the impact of confined neighbourhoods on a short time scale (preprint)

Background: To mitigate the spread of the COVID-19 coronavirus, some countries have adopted more stringent non-pharmaceutical interventions in contrast to those widely used (for e.g. the state of Kuwait). In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies such as the total confinement of highly populated areas has also been implemented.Methods: In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host may be infected if in close spatial proximity of the infectious host. Our focus is only on the short-time scale prior to the infectious period, so that no further transmission is assumed.Results: We find that the level of infection depends heavily on the population dynamics, and increases in the case of slow population diffusion, but remains stable for a high or superdiffusive population. Also, we find that the confinement of homogeneous or overcrowded sub-populations has minimal impact in the short term.Conclusion: Our results indicate that on a short time scale, confinement restrictions or complete lock down of whole residential areas may not be effective. Finally, we discuss the possible implications of our findings for total confinement in the context of the current situation in Kuwait.

Mechanistic Modelling of Coronavirus Infections and the Impact of Confined Neighbourhoods on a Short Time Scale (preprint)

To mitigate the spread of the COVID-19 coronavirus, some countries have enforced more stringent non-pharmaceutical interventions in contrast to those widely adopted (for e.g. the state of Kuwait). In addition to standard practices such as enforcing curfews, social distancing, and closure of non-essential service industries, other non-conventional policies such as the total confinement of highly populated areas has also been implemented. In this paper, we model the movement of a host population using a mechanistic approach based on random walks, which are either diffusive or super-diffusive. Infections are realised through a contact process, whereby a susceptible host may be infected if in close spatial proximity of the infectious host. Our focus is only on the short-time scale prior to the infectious period, so that no further transmission is assumed. We find that the level of infection depends heavily on the population dynamics, and increases in the case of slow population diffusion, but remains stable for a high or super-diffusive population. Also, we find that the confinement of homogeneous or overcrowded sub-populations has minimal impact in the short term. Finally, we discuss the possible implications of our findings for total confinement in the context of the current situation in Kuwait.