ABSTRACT
We develop a six-compartment differential equation model for the transmission of COVID-19 by dividing the human population into susceptible, vaccinated, exposed, infectious, confirmed, and recovered. We then use the basic reproduction number R0, derived using the next generation matrix, to determine when the disease will die out and when it will stay in the community. This is done by showing that when R0< 1, then the disease-free equilibrium solution is globally asymptotically stable;and when R0> 1, the endemic equilibrium is globally asymptotically stable. Finally, we use numerical solutions to confirm the results of our stability analysis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Since the start of the pandemic in early 2020, there have been numerous studies related to the design and use of disease models to aid in understanding the transmission dynamics of COVID-19. Output of these models provide pertinent input to policies regarding restricting or relaxing movements of a population. Perhaps the most widely used class of models for COVID-19 disease transmission is the compartmental model. It is a population model that assumes homogeneous mixing, which means that each individual has the same likelihood of contact with the rest of the population. Inspite of this limitation, the approach has been effective in forecasting the number of cases based on simulated scenarios. With the shift from nationwide lockdowns to granular lockdown as well as gradual opening of limited face to face classes, there is a need to consider other models that assume heterogeneity as reflected in individual behaviors and spatial containment strategies in smaller spaces such as buildings. In this study, we use the COVID-19 Modeling Kit (COMOKIT, 2020) as a basis for the inclusion of individual and spatial components in the analysis. Specifically, we derive a version of COMOKIT specific to university setting. The model is an agent-based, spatially explicit model with the inclusion of individual epidemiological and behavior parameters to show evidence of which behavioral and non-pharmaceutical interventions lead to reduced transmission over a given period of time. The simulation environment is set up to accommodate the a) minimum number of persons required in a closed environment including classrooms, offices, study spaces, laboratories, cafeteria, prayer room and bookstore, b) parameters on viral load per building or office, and c) percentage of undetected positive cases going on campus. The model incorporates the following interventions: a) compliance to health protocol, in particular compliance to wearing masks, b) vaccine coverage, that is, the percentage distribution of single dose, two doses and booster, c) distribution of individuals into batches for alternating schedules. For mask compliance, as expected, results showed that 100% compliance resulted to lowest number of cases after 120 days, followed by 75% compliance and highest number of cases for 50% compliance. For vaccine coverage, results showed that booster shots play a significant role in lowering the number of cases. Specifically, those who are fully vaccinated (2 doses) and 100% boosted produce the lowest number of cases, followed by the 50% of the population fully vaccinated and have had their booster shots. Intervals of no onsite work or class in between weeks that have onsite classes produce the lowest number of cases. The best scenario is combining the three interventions with 100% compliance to mask wearing, 100% fully vaccinated with booster, and having two batches or groups with interval of no onsite classes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Mathematical models of the COVID-19 pandemic have been utilized in a variety of settings as a core component of national public health responses. Often based on systems of ordinary differential equations, compartmental models are commonly used to understand and forecast outbreak trajectories. In view of the primarily applied nature of COVID-19 models, theoretical analysis can provide a global and long-term perspective of key model properties, and relevant insights about the infection dynamics they represent. This work formulates and undertakes such an investigation for a compartmental model of COVID-19, which includes the effect of interventions. More specifically, this paper analyzes the characteristics of the solutions of a compartmental model by establishing the existence and stability of the equilibrium points based on the value of the basic reproductive number R0. Our results provide insights on the possible policies that can be implemented to address the health crisis. © 2021 American Institute of Physics Inc.. All rights reserved.
ABSTRACT
During the Covid-19 pandemic, science plays an important role in understanding and intervening in local outbreaks. Conducting scientific operations for pandemic response also takes on unique human and social dimensions. Reflexivity is often a key to understanding the perspectives that scientists take in applying theory to practice. In this essay we share that reflexivity by detailing personal reflections as scientists of FASSSTER, an integrated platform providing scientific intelligence to the Philippine government for monitoring and responding to the Covid-19 outbreak in the country.