A Model to Estimate the Effect of International Traffic on Malaria Cases: The Case of Japan from 1999 to 2021

Aiming to identify the potentially reduced malaria cases by stagnation of international traffic after the COVID-19 pandemic, a longitudinal analysis of malaria cases as well as entries of Japanese and foreigners was conducted using data from 5 April 1999 to 30 September 2021 in Japan. Multivariable risk ratios were calculated with the Poison regression model as a predictive model of malaria cases by the number of entries for Japanese and foreigners. A generalized regression model was used to examine an association of time trend with entries for Japanese and foreigners using data before 2019, to estimate the potentially reduced number of entries after 2020. The potentially reduced number of malaria cases was estimated by the potentially reduced number of entries for Japanese and foreigners after 2020 using a multivariable Poison regression model. The multivariable risk ratio (95% confidence intervals) of malaria case numbers per 100,000 persons increment of entries per day was 3.41 (1.50–7.77) for Japanese and 1.47 (0.92–2.35) for foreigners. During 2020, a potential reduction of 28 (95% confidence limit: 22–34) malaria cases was estimated, which accounted for 58% (52–63%) of malaria cases in Japan. These finding suggest that the stagnation of international traffic during the COVID-19 pandemic reduced the number of malaria cases in Japan. This model may be helpful for countries without indigenous malaria to predict future trends of imported malaria cases.

Exploring the impact of balconies on cooling energy demand in an arid climate zone

This paper explores the impact of balconies on the energy demand required for cooling in the arid climate zone of the city of Adrar, in Algeria. For the purpose to assess several situations of the balconies, we have chosen a parametric method based on a campaign of thermal simulations. The open and eliminated balcony type were selected and characterized by four parameters: the balcony to room ratio, the orientation, the window type, and the balcony position. A set of 100 simulations was selected randomly based on the Monte-Carlo probability technique. The final sample was corrected based on Cook’s distance which gave 85 simulations as a final sample size. A generalized regression model was performed to identify the impact of each parameter. The accuracy of the model is above 97% and the sensitivity analysis shows that the most important factor is the balcony to room ratio which could reduce the energy demand up to 26% followed by the window type (24%), the orientation (8%) and the balcony position (5%). This conclusion stresses the idea of considering the balcony as a passive solution to reduce the cooling energy demand.

Use of Depth Measure for Multivariate Functional Data in Disease Prediction: An Application to Electrocardiograph Signals

In this paper we develop statistical methods to compare two independent samples of multivariate functional data that differ in terms of covariance operators. In particular we generalize the concept of depth measure to this kind of data, exploiting the role of the covariance operators in weighting the components that define the depth. Two simulation studies are carried out to validate the robustness of the proposed methods and to test their effectiveness in some settings of interest. We present an application to Electrocardiographic (ECG) signals aimed at comparing physiological subjects and patients affected by Left Bundle Branch Block. The proposed depth measures computed on data are then used to perform a nonparametric comparison test among these two populations. They are also introduced into a generalized regression model aimed at classifying the ECG signals.