Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates.

In sequential experiments, subjects become available for the study over a period of time, and covariates are often measured at the time of arrival. We consider the setting where the sample size is fixed but covariate values are unknown until subjects enrol. Given a model for the outcome, a sequential optimal design approach can be used to allocate treatments to minimize the variance of the estimator of the treatment effect. We extend existing optimal design methodology so it can be used within a nonmyopic framework, where treatment allocation for the current subject depends not only on the treatments and covariates of the subjects already enrolled in the study, but also the impact of possible future treatment assignments within a specified horizon. The nonmyopic approach requires recursive formulae and suffers from the curse of dimensionality. We propose a pseudo-nonmyopic approach which has a similar aim to the nonmyopic approach, but does not involve recursion and instead relies on simulating trajectories of future possible decisions. Our simulation studies show that, for the simple case of a logistic regression with a single binary covariate and a binary treatment, and a more realistic case with four binary covariates, binary treatment and treatment-covariate interactions, the nonmyopic and pseudo-nonmyopic approaches provide no competitive advantage over the myopic approach, both in terms of the size of the estimated treatment effect and also the efficiency of the designs. Results are robust to the size of the horizon used in the nonmyopic approach, and the number of simulated trajectories used in the pseudo-nonmyopic approach.

Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions.

Scientific advice to the UK government throughout the COVID-19 pandemic has been informed by ensembles of epidemiological models provided by members of the Scientific Pandemic Influenza group on Modelling. Among other applications, the model ensembles have been used to forecast daily incidence, deaths and hospitalizations. The models differ in approach (e.g. deterministic or agent-based) and in assumptions made about the disease and population. These differences capture genuine uncertainty in the understanding of disease dynamics and in the choice of simplifying assumptions underpinning the model. Although analyses of multi-model ensembles can be logistically challenging when time-frames are short, accounting for structural uncertainty can improve accuracy and reduce the risk of over-confidence in predictions. In this study, we compare the performance of various ensemble methods to combine short-term (14-day) COVID-19 forecasts within the context of the pandemic response. We address practical issues around the availability of model predictions and make some initial proposals to address the shortcomings of standard methods in this challenging situation.

Skills2Care: An innovative, cooperative learning programme for community health workers in South Africa.

BACKGROUND: Community health workers (CHWs) hold potential to support universal health coverage and better health for vulnerable communities. They are integral to the re-engineered Primary Health Care (PHC) strategy, introduced in South Africa in 2011. This study focussed on how to train CHWs in large numbers, especially in resource-limited, rural settings. Skills2Care, a method of cooperative learning for CHWS, has been pioneered in the Eastern Cape of South Africa. AIM: To determine whether Skills2Care could improve the cognitive knowledge of CHWs; to understand their response and attitude to the programme; to explore factors that enabled and inhibited learning and to consider its viability as a training method. SETTING: Research was conducted in 2019 in the Ngqeleni subdistrict of the O.R. Tambo district, in rural Eastern Cape. METHODS: A group-learning model using specifically tailored study modules in booklet format, addressing mother and baby care, was used. A facilitator promoted learning. Knowledge assessment was conducted by pre- and post-study testing using multiple choice questions. Focus group discussions and interviews explored the appropriateness and acceptability of this method, and factors enabling and inhibiting the learning. RESULTS: This method of peer group cooperative learning can significantly increase the cognitive knowledge of CHWs. Test scores indicated a significant (13%) improvement. Focus group discussions indicated that participants valued this method as it increased knowledge and boosted their confidence. CONCLUSION: This innovative approach to district-based, continuing education suggests that CHWs could be trained in large numbers without the need for additional resources.