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
Contact-tracing is part and parcel of interventions in reducing the rate of transmission of the disease. After identifying the rate of transmission, the next important step is to determine the spread of the disease using network analysis. Using manual or digital methods, active tracing requires testing of suspect cases to identify and isolate positive cases from identified close contacts. Passive tracing allows citizens to report symptoms to designated local health authorities. Most contact tracing efforts implemented by the local government units to stem the transmission of COVID-19 follow standard manual contract tracing procedures. However, with the rapid increase in the speed of the outbreak, traditional contact tracing approaches are not sufficient to contain the spread of the disease. FASSSTrace is designed to make contact tracing more efficient by developing a contact tracing model for digital platforms. Specifically, the platform provides a social network model of confirmed (C) and suspect (S) cases and visualizes the transmission dynamics with the inclusion of suspect cases. The method allows for the construction of a model reflecting the contact network of the confirmed cases as recorded in the official disease surveillance tool which produces a contact network determining superspreaders from key individuals and locations. he contacts network uses a two-mode network incorporating geographical locations as nodes to bridge the unlinked confirmed cases. Analysis involves micro-level network measures to determine key individuals and locations and macro-level network measures to study the patterns in transmission dynamics of the disease. © 2021 ACM.