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

Arvanitis, Athanasios; Furxhi, Irini; Tasioulis, Thomas; Karatzas, Konstantinos

doi:10.31181/jdaic1001202201f

Cited by 7 publications

(4 citation statements)

References 53 publications

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“…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.…”

Section: Discussionmentioning

confidence: 68%

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

Conesa,

López de Rioja,

Gullón

et al. 2024

Front. Public Health

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IntroductionWe use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.MethodsWe develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.ResultsWe find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.DiscussionOur results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

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Section: Discussionmentioning

confidence: 68%

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

Conesa,

López de Rioja,

Gullón

et al. 2024

Front. Public Health

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“…This procedure involved comparing the results obtained with several different methods [33][34][35][36]. If the results of this method differ from the results of other methods, the question arises whether these results are valid [37][38][39]. The validation was performed with six other methods, namely: fuzzy MABAC (multi-attributive border approximation area comparison) method, fuzzy MARCOS (measurement of alternatives and ranking according to compromise solution), fuzzy WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), fuzzy ARAS (a new additive ratio assessment) and fuzzy TOPSIS (a technique for order preference by similarity to ideal solution).…”

Section: Resultsmentioning

confidence: 99%

Application of Fuzzy TRUST CRADIS Method for Selection of Sustainable Suppliers in Agribusiness

et al. 2023

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This study deals with the selection of a sustainable supplier on the example of the agribusiness company Mamex from Bosnia and Herzegovina. The main problem of this research is the selection of a sustainable supplier as a part of the sustainable strategy of the Mamex company. One of the prerequisites is that suppliers must present sustainability principles in business by having an appropriate certificate. The results of the selection of sustainable suppliers are completed using a new hybrid fuzzy approach with the methods IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy TRUST (multi-normalization multi-distance assessment) CRADIS (compromise ranking of alternatives from distance to ideal solution) methods. The innovative approach is reflected in the use of a combination of these methods, especially by combining the TRUST and CRADIS methods into one method. The IMF SWARA method shows that the most important main criterion is the economic criterion, while the least important is the social criterion. By applying the fuzzy TRUST CRADIS method, it is found that out of the observed six suppliers, the second supplier has the best indicators. These results are confirmed by other fuzzy methods: MABAC (multi-attributive border approximation area comparison), WASPAS (weighted aggregated sum product assessment), fuzzy SAW (simple additive weighting), MARCOS (measurement of alternatives and ranking according to compromise solution), ARAS (a new additive ratio assessment), and TOPSIS (technique for order preference by similarity to an ideal solution). This research shows that applying more normalization when ranking alternatives reduces the influence of individual normalizations, and this approach should be used in future research.

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“…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.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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

Tešić¹,

Božanić²,

Puška³

et al. 2023

Rep. Mech. Eng.

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This study presents the MCDM model created for the selection of a dump truck for the needs of the army engineering units, based primarily on the truck’s construction features and purchasing and maintenance costs. In this study was used the Methodology of Additive Weights (LMAW) in Fuzzy surrounding for determination of weight coefficients of criteria, while for the selection of the optimal alternative (for a dump truck) it was used the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified by interval grey numbers. Input data for this methodology were obtained by engaging experts. Finally, the analysis was made of the sensitivity of output results of the proposed MCDM methodology to the change of weight coefficients of criteria, as well as the comparison of the obtained results with the results of other methodologies. In the conclusion, the proposed model showed stability but it was sensitive to weight coefficients change which should be taken into account by defining the same by experts.

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

Cited by 7 publications

References 53 publications

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

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

Application of Fuzzy TRUST CRADIS Method for Selection of Sustainable Suppliers in Agribusiness

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

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