Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

Abstract

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March, 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May, 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February, 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, D-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population. The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of R o =2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

COVID-19 Contact Tracing as an Indicator for Evaluating a Pandemic Situation: Simulation Study

“…Several expanded models to an SEIR model have been attempted, including either one or more quarantine compartments and one or more protected individuals through vaccination [ 27 ]. In addition, the concept of high-risk exposure and exposed individuals (frequently included in compartment E) did not entirely comprise the DGS definition of high-risk contact in that, in all models, an exposed individual could not return to being susceptible [ 28 ]. Furthermore, in most models, a quarantined individual could have come from being susceptible and either return to being susceptible or progress to being infected [ 29 ].…”

Aerosol transmission in passenger car cabins: Effects of ventilation configuration and driving speed

“…The wall unit was chosen to lie in 30–300 range, within the log-layer region. 49–52 Grid independence analyses were performed using three different incremental grid resolutions, where the boundary layer element size was varied from 0.004 to 0.0008 m. These showed no appreciable difference for the overall pressure, velocity and concentration fields, as well as for the averaged concentration and air-exchange rates.…”

Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

“…These models can compare several scenarios depending on the available data in order to forecast the path of the pandemic, as well as propose measures for managing it. Several such models have been reported in the literature, with some of the widely known ones being the susceptible-infected-recovered model [29][30][31], curve-fitting model [32], extended-susceptible-infected-recovered model [33], susceptibleexposed-infected-quarantined-dead-hospitalized-recovered model [27], susceptible-unascertained-cases-pre-symptomatic infectiousness-exposed-infectious-recovered model [34], susceptible-infected-diagnosed-ailing-recognized-threatened-healed-extinct model [35], and susceptible-exposed-asymptomatic-infected-hospitalized-recovered-dead due to COVID-19 infection-susceptible model [36]. Although these are all mathematical models, their complexity increases and applicability decreases as the amount of data increases, necessitating an exponential growth in computational power.…”