Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Co-infection Assay to Determine Yersinia pestis Competitive Fitness in Fleas

Co-infection refers to the simultaneous infection of a host by multiple pathogenic organisms. Experimental co-infection studies using a mutant and its isogenic wild type have proven to be profoundly sensitive to analysis of pathogen factor mutation-associated fitness effects in in vivo models of infectious disease. Here we discuss the use of such co-infection experiments in studying the interaction between Yersinia pestis and its flea vector to more sensitively determine the critical bacterial determinants for Y. pestis survival, adaptation, and transmission from fleas. This chapter comprises two main sections, the first detailing how to infect fleas with mutant and wild type Y. pestis strains, and secondly how to process infected fleas and specifically quantify distinct Y. pestis strain burdens per flea. The Y. pestis competitive fitness co-infection model in fleas is insightful in evaluating the consequence of a mutation which may not be obvious in single-strain flea infections where there is less selective pressure.

Validation of new transmission detector transmission factors for online dosimetry: an experimental study

Background

The demand for dose verification during treatment has risen with the increasing use of intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) in modern radiation therapy. This study aims to validate the transmission factors of a new transmission detector, the Dolphin online monitoring system (IBA Dosimetry, Schwarzenbruck, Germany), for clinical use.

Methods

The transmission factors of the Dolphin detector were evaluated using 6 MV, 6 flattening filter free (FFF), 10 MV, and 10 FFF clinical beams from a TrueBeam STx linear accelerator system. Two-dimensional (2D) dose distributions were measured through portal dosimetry with and without Dolphin to derive the transmission factors. The measurements were performed using 10 IMRT and 10 VMAT treatment plans. The transmission factors were calculated using a non-negative least squares problem solver for the 2D dose matrix. Normalized plans were generated using the derived transmission factors. Patient-specific quality assurance with normalized plans was performed using portal dosimetry and an ArcCheck detector to verify the transmission factors. The gamma passing rates were calculated for the 2%/2 mm and 1%/1 mm criteria.

Results

The transmission factors for the 6 MV, 6 FFF, 10 MV, and 10 FFF beams, were 0.878, 0.824, 0.913, and 0.883, respectively. The average dose difference between the original plan without Dolphin and the normalized plan with Dolphin was less than 1.8% for all measurements. The mean passing rates of the gamma evaluation were 98.1 ± 2.1 and 82.9 ± 12.6 for the 2%/2 mm and 1%/1 mm criteria, respectively, for portal dosimetry of the original plan. In the case of the portal dosimetry of the normalized plan, the mean passing rates of the gamma evaluation were 97.2 ± 2.8 and 79.1 ± 14.8 for the 2%/2 mm and 1%/1 mm criteria, respectively.

Conclusions

The Dolphin detector can be used for online dosimetry when valid transmission factors are applied to the clinical plan.

Determination of transmission factors in beta radiation beams

In beta emitters, in order to evaluate the absorbed dose rate at different tissue depths, it is necessary to determine the transmission factors. In this work, the transmission factors determined in beta secondary standard radiation beams are presented. For this purpose, an extrapolation chamber was used. The results obtained were considered acceptable, and they are within the uncertainties in comparison with the values provided by the source calibration certificate. The maximum differences between the results obtained in this work and those from the calibration certificate were 3.3%, 3.8% and 5.9% for ⁹⁰Sr/⁹⁰Y, ⁸⁵Kr and ¹⁴⁷Pm sources respectively.