Infection vulnerability stratification risk modelling of COVID-19 data: a deterministic SEIR epidemic model analysis

Abstract

Basic Susceptible-Exposed-Infectious-Removed (SEIR) models of COVID-19 dynamics tend to be excessively pessimistic due to high basic reproduction values, which result in overestimations of cases of infection and death. We propose an extended SEIR model and daily data of COVID-19 cases in the U.S. and the seven largest European countries to forecast possible pandemic dynamics by investigating the effects of infection vulnerability stratification and measures on preventing the spread of infection. We assume that (i) the number of cases would be underestimated at the beginning of a new virus pandemic due to the lack of effective diagnostic methods and (ii) people more susceptible to infection are more likely to become infected; whereas during the later stages, the chances of infection among others will be reduced, thereby potentially leading to pandemic cessation. Based on infection vulnerability stratification, we demonstrate effects brought by the fraction of infected persons in the population at the start of pandemic deceleration on the cumulative fraction of the infected population. We interestingly show that moderate and long-lasting preventive measures are more effective than more rigid measures, which tend to be eventually loosened or abandoned due to economic losses, delay the peak of infection and fail to reduce the total number of cases. Our calculations relate the pandemic's second wave to high seasonal fluctuations and a low vulnerability stratification coefficient. Our characterisation of basic reproduction dynamics indicates that second wave of the pandemic is likely to first occur in Germany, Spain, France, and Italy, and a second wave is also possible in the U.K. and the U.S. Our findings show that even if the total elimination of the virus is impossible, the total number of infected people can be reduced during the deceleration stage.

COVID-19 vaccine hesitancy: a social media analysis using deep learning

“…In the operations management context, this approach can serve as an efficient resource for disease surveillance, especially as regards communication during disease outbreaks (Anparasan & Lejeune, 2019;Kumar et al, 2021aKumar et al, , 2021b. It can also provide another way of reducing the impact of the pandemic on production systems by using digital technologies (Dubey et al, 2019a(Dubey et al, , 2019bDuHadway et al, 2019;Fast et al, 2018;Griffith et al, 2019;Gupta et al, 2021;Singh et al, 2019;Wamba et al, 2019).…”

“…This highly disruptive pandemic (Henceforth, COVID-19) has caused major upheavals in virtually all industries across the globe, with severe implications in sectors such as manufacturing. Here, this global health disaster has particularly hit production networks, and the demand and supply chains underpinning manufacturing operations (Alam et al, 2021;Kapoor et al, 2021;Kumar et al, 2021aKumar et al, , 2021bPujawan & Bah, 2021;Xiong et al, 2021). This pandemic has unfolded quickly to become a humanitarian crisis threatening billions of individuals globally, and demanding swift disaster relief efforts and humanitarian operations (Anparasan & Lejeune, 2019;Queiroz et al, 2020).…”

“…Mishra & Singh, 2018;Moorhead et al, 2013). Social media analytics in particular can serve as a resource for efficient disease surveillance, and the diffusion of preventative actions to slow the spread of disease outbreaks (Anparasan & Lejeune, 2019;Kumar et al, 2021aKumar et al, , 2021b. For instance, Kumar et al (2021aKumar et al ( , 2021b developed a dynamic transmission model to investigate the impact of social media (Twitter) on the number of infections and deaths due to influenza and COVID-19.…”

“…Social media analytics in particular can serve as a resource for efficient disease surveillance, and the diffusion of preventative actions to slow the spread of disease outbreaks (Anparasan & Lejeune, 2019;Kumar et al, 2021aKumar et al, , 2021b. For instance, Kumar et al (2021aKumar et al ( , 2021b developed a dynamic transmission model to investigate the impact of social media (Twitter) on the number of infections and deaths due to influenza and COVID-19. Their findings indicate that social media is an integral part of the humanitarian logistics for pandemics emergency preparedness and contributes to the literature by informing best practices in the response to similar disasters.…”

A probabilistic epidemiological model for infectious diseases: The case of COVID‐19 at global‐level

“…In 2021, some new deterministic and stochastic models have been proposed. Regarding the deterministic ones (Carcione et al., 2020 ; Kumar et al., 2021 ; Lawal & Vincent, 2021 ; Mandal et al., 2021 ; Piccirillo, 2021 ; Varghese et al., 2021 ), the main difference, when compared to past studies (Choi & Ki, 2020 ; Ferguson, Laydon et al., 2020 ; Ferguson, Walker et al., 2020 ; Khan & Atangana, 2020 ; T. M. Chen et al., 2020 ; Yang et al., 2021 ), is the compartmental susceptible–exposed–infective–recovered (SEIR) approach (for more details see Bartlett, 1957 ). Besides being deterministic, these are not age‐structured, as we propose here.…”