Comparing human and model-based forecasts of COVID-19 in Germany and Poland

1AbstractForecasts based on epidemiological modelling have played an important role in shaping public policy throughout the COVID-19 pandemic. This modelling combines knowledge about infectious disease dynamics with the subjective opinion of the researcher who develops and refines the model and often also adjusts model outputs. Developing a forecast model is difficult, resource- and time-consuming. It is therefore worth asking what modelling is able to add beyond the subjective opinion of the researcher alone. To investigate this, we analysed different real-time forecasts of cases of and deaths from COVID-19 in Germany and Poland over a 1-4 week horizon submitted to the German and Polish Forecast Hub. We compared crowd forecasts elicited from researchers and volunteers, against a) forecasts from two semi-mechanistic models based on common epidemiological assumptions and b) the ensemble of all other models submitted to the Forecast Hub. We found crowd forecasts, despite being overconfident, to outperform all other methods across all forecast horizons when forecasting cases (weighted interval score relative to the Hub ensemble 2 weeks ahead: 0.89). Forecasts based on computational models performed comparably better when predicting deaths (rel. WIS 1.26), suggesting that epidemiological modelling and human judgement can complement each other in important ways.

Forecasting the COVID-19 Pandemic: Lessons learned and future directions

The Coronavirus Disease 2019 (COVID-19) has demonstrated that accurate forecasts of infection and mortality rates are essential for informing healthcare resource allocation, designing countermeasures, implementing public health policies, and increasing public awareness. However, there exists a multitude of modeling methodologies, and their relative performances in accurately forecasting pandemic dynamics are not currently comprehensively understood. In this paper, we introduce the non-mechanistic MIT-LCP forecasting model, and assess and compare its performance to various mechanistic and non-mechanistic models that have been proposed for forecasting COVID-19 dynamics. We performed a comprehensive experimental evaluation which covered the time period of November 2020 to April 2021, in order to determine the relative performances of MIT-LCP and seven other forecasting models from the United States Centers for Disease Control and Prevention (CDC) Forecast Hub. Our results show that there exist forecasting scenarios well-suited to both mechanistic and non-mechanistic models, with mechanistic models being particularly performant for forecasts that are further in the future when recent data may not be as informative, and non-mechanistic models being more effective with shorter prediction horizons when recent representative data is available. Improving our understanding of which forecasting approaches are more reliable, and in which forecasting scenarios, can assist effective pandemic preparation and management.

Mechanistic Computational Models of Epithelial Cell Transporters-the Adorned Heroes of Pharmacokinetics

Epithelial membrane transporter kinetics portray an irrefutable role in solute transport in and out of cells. Mechanistic models are used to investigate the transport of solutes at the organ, tissue, cell or membrane scale. Here, we review the recent advancements in using computational models to investigate epithelial transport kinetics on the cell membrane. Various methods have been employed to develop transport phenomena models of solute flux across the epithelial cell membrane. Interestingly, we noted that many models used lumped parameters, such as the Michaelis-Menten kinetics, to simplify the transporter-mediated reaction term. Unfortunately, this assumption neglects transporter numbers or the fact that transport across the membrane may be affected by external cues. In contrast, more recent mechanistic transporter kinetics models account for the transporter number. By creating models closer to reality researchers can investigate the downstream effects of physical or chemical disturbances on the system. Evidently, there is a need to increase the complexity of mechanistic models investigating the solute flux across a membrane to gain more knowledge of transporter-solute interactions by assigning individual parameter values to the transporter kinetics and capturing their dependence on each other. This change results in better pharmacokinetic predictions in larger scale platforms. More reliable and efficient model predictions can be made by creating mechanistic computational models coupled with dedicated in vitro experiments. It is also vital to foster collaborative efforts among transporter kinetics researchers in the modeling, material science and biological fields.

Clamping of DNA shuts the condensin neck gate

Condensin is a Structural Maintenance of Chromosomes (SMC) complex needed for the compaction of DNA into chromatids during mitosis. Lengthwise DNA compaction by condensin is facilitated by ATPase-driven loop extrusion, a process that is believed to be the fundamental activity of most, if not all SMC complexes. In order to obtain molecular insights, we obtained cryo-EM structures of yeast condensin in the presence of a slowly-hydrolysable ATP analogue and linear, as well as circular DNAs. The DNAs were shown to be clamped between the engaged heterodimeric SMC ATPase heads and the Ycs4 subunit, in a manner similar to previously reported DNA-bound SMC complex structures. Ycg1, the other non-SMC subunit was only flexibly bound to the complex, while also binding DNA tightly, and often remaining at a distance from the head module. In the clamped state, the DNA is encircled, or topologically entrapped, by the kleisin Brn1 and the two engaged head domains of Smc2 and Smc4, and this tripartite ring is closed at all interfaces, including at the neck of Smc2. We show that the neck gate opens upon head engagement in the absence of DNA, but it remains shut when DNA is present. Our work demonstrates that condensin and other SMC complexes go through similar conformations of the head modules during their ATPase cycle. In contrast, the behaviour of the Ycg1 subunit in the condensin complex might indicate differences in the implementation of the extrusion reactions and our findings will constrain further mechanistic models of loop extrusion by SMC complexes.

A review of the different boiler efficiency calculation and modeling methodologies

A review of the different mathematical methodologies for calculating energy efficiency in boilers was carried out in this work, considering both the methods included in standards and the proposals and applications published in research works. The classification was delimited in analytical methods, mechanistic modeling, and empirical modeling; moreover, the main equations for each of the methodologies are presented, which allows building a compilation that is expected to be useful for a first approach to the subject. It is displayed that those mechanistic models are used to evaluate subsystems or specific cases that require a high level of detail, while analytical models are used to make a first approximation to the systems described, and empirical models stand out in terms of their use at the industrial level if there is access to a starting database to adjust them.

The Interpersonal Neuroscience of Social Learning

The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our article examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors and, in particular, social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC and how studying BtBC can inform our understanding of such behaviors. We then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, and we suggest several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and we propose a multiperson framework for understanding how interpersonal transmission of information between individual brains shapes social learning.

117 Nutrition Modelling: What Can the Pet Field Learn (or Steal) from Recent Directions in Other Species?

Nutrition modelling has been the cornerstone of feed formulation and diet optimization in animal production systems for decades. Since the 1970s and 1980s, mechanistic models of nutrient digestion, absorption, metabolism, growth and milk/egg production have been developed and implemented to (1) amass our cumulative biological knowledge and develop theories of regulation, (2) identify knowledge gaps, and (3) propose means to manipulate nutrient dynamics in the animal. At the nutrient and metabolite level, many commonalities exist and parallels found between species. In fact, several second generation models originate from other species or research fields, and many current/existing models may be advanced by examination and consideration of models developed in other species. Many such mathematical models are implemented in practice as ‘decision support systems’ or ‘opportunity analysis tools’, in order to examine a variety of (feeding or management) scenarios for their potential outcomes, with the goal of providing targeted nutrition, improving performance, reducing cost and minimizing environmental impact. More recently, partnering artificial intelligence/machine learning modelling methodologies with newly available big data streams has ushered in a new era of possibilities for data extraction and modelling in animal systems. The niche for this type of modelling in animal production appears to be (1) pattern recognition (e.g. disease detection, activity) and (2) strong predictive/forecasting abilities (e.g. bodyweight, milk, egg production). There also appears strong potential for these two seemingly divergent modelling approaches to be integrated – for example, in precision feeding systems, or in utilizing the abundance of sensor data to better drive or develop causal-pathway based mechanistic models. This talk will broadly review trends and advances in agriculture animal species modelling, and suggest what may be borrowed, stolen or serve as inspiration to advance nutrition models in companion species.