Mathematical Modeling Predicts That Strict Social Distancing Measures Would Be Needed to Shorten the Duration of Waves of COVID-19 Infections in Vietnam

Abstract

Coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in 2019, has spread throughout the world and has since then been declared a pandemic. As a result, COVID-19 has caused a major threat to global public health. In this paper, we use mathematical modeling to analyze the reported data of COVID-19 cases in Vietnam and study the impact of non-pharmaceutical interventions. To achieve this, two models are used to describe the transmission dynamics of COVID-19. The first model belongs to the susceptible-exposed-infectious-recovered (SEIR) type and is used to compute the basic reproduction number. The second model adopts a multi-scale approach which explicitly integrates the movement of each individual. Numerical simulations are conducted to quantify the effects of social distancing measures on the spread of COVID-19 in urban areas of Vietnam. Both models show that the adoption of relaxed social distancing measures reduces the number of infected cases but does not shorten the duration of the epidemic waves. Whereas, more strict measures would lead to the containment of each epidemic wave in one and a half months.

Social distancing as a public-good dilemma for socio-economic cost: An evolutionary game approach

“…Social distancing is one of the impactful disease prevention practices during any airborne disease outbreak [68] , [69] , [70] . In the absence of effective vaccines and therapeutics, this strategy can impede disease transmission and thus can provide some precious time for reallocating and optimizing medical resources [71] . Recent works enlighten us about the effectiveness of social distancing amid the COVID-19 pandemic.…”

Estimating the Risk of Contracting COVID-19 in Different Settings Using a Multiscale Transmission Dynamics Model

“…Here, we model the transport and diffusion of the virus in the air following a cough or sneeze of an infectious individual. We consider that whenever an individual coughs or sneezes, they produce a concentration of the virus between 277 × 10 −6 copies/cm 3 and 36,030 copies/cm 3 [58][59][60], depending on their viral load, and within a diameter of d 0 = 1 m. Then, the concentration of viral particles is spread to the computational mesh using a radial-basis function [19][20][21]:…”

“…Indeed, a social force model was applied to quantify the impact of non-pharmaceutical interventions on indoor disease transmission [18]. We have previously used a social force model to develop a multi-scale framework that describes COVID-19 transmission dynamics [19][20][21]. This framework describes transmission through respiratory droplets and contamination by infected surfaces.…”

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

“…As we noted above, the traditional compartmental model with constant transition rates allowing to simulate only one peak in the spread of infection, so many authors use various techniques to account for changes in model parameters over time. Bouchnita et al (2021) adapted SEIR model to review the restrictions introduced by Vietnamese government to stop the first wave of COVID-19 pandemic. Authors divided the observed timespan into two periods.…”