A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Novel mixed integer optimization sparse regression approach in chemometrics.

Sparse mathematical modelling plays an increasingly important role in chemometrics due to its interpretability and prediction power. While many sparse techniques used in chemometrics rely on L1 penalization to create sparser models, Mixed Integer Optimization (MIO) achieves sparsity by imposing constraints directly in the model. In this paper, we develop an intuitive and flexible robust sparse regression framework using MIO. We use constraints and penalization to achieve sparsity and robustness respectively. We test and compare results with those obtained using other techniques generating sparser models such as LASSO and sparse PLS. We also use PLS as a baseline to compare predictive performance. We use a LIBS data set of certified reference materials (CRM) of various mineral ores to illustrate the framework using different objective functions. The MIO framework proposed improves accuracy, sparsity and robustness vs. LASSO and SPLS. MIO achieves an average R2 higher than other methods on average by at least 10.6%. Robust MIO approach also improves interpretability. It also uses 4.3 variables on average while LASSO and SPLS use 16.1 and 805.8 respectively. We also illustrate how interpretability can help build better models through examples derived from the data sets used. When adding noise to the signal, MIO achieves an R2 of 0.69 on average when all models have negative R2 values. The MIO framework proposed is versatile and could be used in other chemometrics applications.