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J Transp Health ; 22: 101087, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1240476

ABSTRACT

Introduction: It is common knowledge that mobility refers to a distinct vector for pathogens, but the importance of prevention and the infusion of public health practices within transportation systems is not manifest. Replication studies of this effect are important because transportation remains veiled in modern societies, since its demand is not direct, but derived. Methods: Variables mirroring transportation and logistics' systems intensity (trade data, the logistics performance index, and investment in transportation) are cross-tabulated with epidemiological data from the recent coronavirus pandemic. As the samples of the data pertain to a dependent commonality, the statistical hypothesis test applicable is McNemar's test. In addition, the statistical power of the test(s) is calculated as a marker of methodological validity and reliability. To further strengthen the analytical methodology, a plethora of descriptive statistics have been calculated and multiple correspondence analysis (MCA) has been conducted. Results: This work confirms that the domain of transportation bears a strong association with not only mortality of a disease, but its recovery rates as well. All crosstabs provide statistically significant results and the statistical power calculated is very high, signifying the appropriateness of the methodology and the very low probability of Type II error. The MCA results are significant, as well. Conclusions: The impact, or even the presence of transportation is veiled, as transportation comprises of derived demand dynamics. As such, its activities and even the prerequisites for its efficient operations many times go unnoticed. This work replicates a known effect, that mobility exacerbates the presence of a pathogen. The significance of this research lies on the fact that distinct indicators that reflect transportation and logistics are (though a robust calculatory methodology) statistically associated with epidemiological data.

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