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In this work, we develop and analyze a mathematical model for the dynamics of COVID‐19 with re‐infection in order to assess the impact of prior comorbidity (specifically, diabetes mellitus ) on COVID‐19 complications. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID‐19 infection by comorbid susceptibles as well as the rate of reinfection by those who have recovered from a previous COVID‐19 infection. Simulations of the cumulative number of active cases (including those with comorbidity), at different reinfection rates, show infection peaks reducing with decreasing reinfection of those who have recovered from a previous COVID‐19 infection. In addition, optimal control and cost‐effectiveness analysis of the model reveal that the strategy that prevents COVID‐19 infection by comorbid susceptibles is the most cost‐effective of all the control strategies for the prevention of COVID‐19.

The new coronavirus disease 2019 (COVID-19) infection is a double challenge for people infected with comorbidities such as cardiovascular and cerebrovascular diseases and diabetes. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re-infection. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection by comorbid susceptibles as well as the rate of re-infection by those who have recovered from a previous COVID-19 infection. Sensitivity analysis of the model when the population of individuals co-infected with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co-infection model are the effective contact rate for COVID-19 transmission, $\beta\sst{cv}$, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, $\chi\sst{cm}$, the comorbidity development rate, $\theta\sst{cm}$, the detection rate for singly infected and co-infected individuals, $\eta_1$ and $\eta_2$, as well as the recovery rate from COVID-19 for co-infected individuals, $\varphi\sst{i2}$. Simulations of the model reveal that the cumulative confirmed cases (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases (including those co-infected with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re-infection rates for COVID-19 is 0.1 per day. It may be worse than this if the re-infection rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected individuals to 0.7 per day, then the reproduction number can be brought very low below one, and COVID-19 infection eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

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