Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results’ implications and SD’s modelling usefulness to guide decision making in complex health settings are discussed.

Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.

Policy makers need decision tools to determine when to use physical distancing interventions to maximize the control of COVID-19 while minimizing the economic and social costs of these interventions. We describe a pragmatic decision tool to characterize adaptive policies that combine real-time surveillance data with clear decision rules to guide when to trigger, continue, or stop physical distancing interventions during the current pandemic. In model-based experiments, we find that adaptive policies characterized by our proposed approach prevent more deaths and require a shorter overall duration of physical distancing than alternative physical distancing policies. Our proposed approach can readily be extended to more complex models and interventions.

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity.