The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

A multivariate-based variable selection framework for clustering traffic conflicts in a brazilian freeway.

More than one million people die or suffer non-fatal injuries annually due to road accidents around the world. Understanding the causes that give rise to different types of conflict events, as well as their characteristics, can help researchers and traffic authorities to draw up strategies aimed at mitigating collision risks. This paper proposes a framework for grouping traffic conflicts relying on similar profiles and factors that contribute to conflict occurrence using self-organizing maps (SOM). In order to improve the quality of the formed groups, we developed a novel variable importance index relying on the outputs of the nonlinear principal component analysis (NLPCA) that intends to identify the most informative variables for grouping collision events. Such index guides a backward variable selection procedure in which less relevant variables are removed one-by-one; after each removal, the clustering quality is assessed via the Davies-Bouldin (DB) index. The proposed framework was applied to a real-time dataset collected from a Brazilian highway aimed at allocating traffic conflicts into groups presenting similar profiles. The selected variables suggest that lower average speeds, which are typically verified during congestion events, contribute to conflict occurrence. Higher variability on speed (denoted by high standard deviation, and speed's coefficient of variation levels on that variable), which are also perceived in the assessed freeway near to congestion periods, also contribute to conflicts.

A multivariate-based conflict prediction model for a Brazilian freeway.

Real-time collision risk prediction models relying on traffic data can be useful in dynamic management systems seeking at improving traffic safety. Models have been proposed to predict crash occurrence and collision risk in order to proactively improve safety. This paper presents a multivariate-based framework for selecting variables for a conflict prediction model on the Brazilian BR-290/RS freeway. The Bhattacharyya Distance (BD) and Principal Component Analysis (PCA) are applied to a dataset comprised of variables that potentially help to explain occurrence of traffic conflicts; the parameters yielded by such multivariate techniques give rise to a variable importance index that guides variables removal for later selection. Next, the selected variables are inserted into a Linear Discriminant Analysis (LDA) model to estimate conflict occurrence. A matched control-case technique is applied using traffic data processed from surveillance cameras at a segment of a Brazilian freeway. Results indicate that the variables that significantly impacted on the model are associated to total flow, difference between standard deviation of lanes' occupancy, and the speed's coefficient of variation. The model allowed to asses a characteristic behavior of major Brazilian's freeways, by identifying the Brazilian typical heterogeneity of traffic pattern among lanes, which leads to aggressive maneuvers. Results also indicate that the developed LDA-PCA model outperforms the LDA-BD model. The LDA-PCA model yields average 76% classification accuracy, and average 87% sensitivity (which measures the rate of conflicts correctly predicted).