Prediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data

Abstract

This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces

“…Our results point in the direction that mobility is either directly causal or highly directly correlated with other measures that directly affect the propagation of the disease via mixing when the population is highly susceptible. Other papers that use mobility data to make short-term predictions of the effective reproduction number (Rt) ( 40 ) have shown similar results. From data in Poland, Turkey, and South Korea ( 41 ) it has been shown that while the stringency index was associated with mobility data of the same day, mobility changes were associated with the number of cases 1 month later.…”

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

“…Stability of the results of the proposed methodology must be checked in one of the following ways (Durmić et al, 2020;Biswas, 2020;Jovčić et al, 2020;Gorcun et al, 2021;Badi & Abdulshahed, 2021;Arvanitis et al, 2021;Biswas et al, 2022;Tešić et al, 2022;Stević, et al, 2022;Đukić et al, 2022;Chakraborty et al, 2022). Results consistency checking is done by analyzing sensitivity with Grey MARCOS method to the weight coefficients change by using 26 different scenarios as shown in the Figure 3.…”