Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community. ARTICLE HISTORY

Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, whilst keeping the significant delivery cost under control. To that end, the firm can try to influence customers when they are booking their delivery time slot so as to steer them towards choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for which time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared to current industry practice.

This study develops a standardised checklist approach to improve the reporting of discrete-event simulation, system dynamics and agent-based simulation models within the field of Operational Research and Management Science. Incomplete or ambiguous reporting means that many simulation studies are not reproducible, leaving other modellers with an incomplete picture of what has been done and unable to judge the reliability of the results. Crucially, unclear reporting makes it difficult to reproduce or reuse findings. In this paper, we review the evidence on the quality of model reporting and consolidate previous work. We derive general good practice principles and three 20-item checklists aimed at Strengthening The Reporting of Empirical Simulation Studies (STRESS): STRESS-DES, STRESS-ABS and STRESS-SD for discrete-event simulation, agentbased simulation and system dynamics, respectively. Given the variety of simulation projects, we provide usage and troubleshooting advice to cover a wide range of situations.

The interaction between polymer brushes and mesoscopic particles is investigated both theoretically and experimentally. We present an analytical mean-field theory for a polymer brush (a layer of long polymer chains end-grafted to a substrate) with varying excluded volume interactions between monomer units. This system mimics the reversible adsorption of mesoscopic particles, such as surfactant micelles or proteins, on the grafted chains. The equilibrium structural properties of the brush (the brush thickness and overall degree of complexation) as well as the number of adsorbed particles per unit area, ⌫, are analysed as functions of the affinity between particle and chain, grafting density and excluded volume interactions. In our model ⌫ is found to have a maximum as a function of . Experimentally the adsorption of BSA on a hydrophobic substrate with grafted PEO chains is measured with reflectometry. In the case of short grafted chains the adsorbed amount of BSA, ⌫, decreases continuously with increasing , which agrees with previous results and model calculations in the literature. In the case of long PEO chains, however, ⌫ is found to have a maximum as a function of . Qualitatively the experimental dependence of ⌫ on is found to agree with the results of our mean-field model. PEO chains show no affinity for BSA in the bulk, whereas in a grafted conformation an effective attraction is found. Some comments are made on the nature of this affinity, which is not yet fully understood.

As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.