COVID-19 Contagion Risk Estimation Model for Indoor Environments

Risk assessment of infection of COVID-19 contacts based on scenario simulation

We constructed a rapid infection risk assessment model for contacts of COVID-19. The improved Wells-Riley model was used to estimate the probability of infection for contacts of COVID-19 in the same place and evaluate their risk grades. We used COVID-19 outbreaks that were documented to validate the accuracy of the model. We analyzed the relationship between controllable factors and infection probability and constructed common scenarios to analyze the infection risk of contacts in different scenarios. The model showed the robustness of the fitting (mean relative error = 5.89%, mean absolute error = 2.03%, root mean squared error = 2.03%, R2 = 0.991). We found that improving ventilation from poorly ventilated to naturally ventilated and wearing masks can reduce the probability of infection by about two times. Contacts in places of light activity, loud talking or singing, and heavy exercise, oral breathing (e.g., gyms, KTV, choirs) were at higher risk of infection. The model constructed in this study can quickly and accurately assess the infection risk grades of COVID-19 contacts. Simply opening doors and windows for ventilation can significantly reduce the risk of infection in certain places. The places of light activity, loud talking or singing, and heavy exercise, oral breathing, should pay more attention to prevent and control transmission of the epidemic.

Positive Diagnosis of COVID-19 in an Integrated Teaching and Healthcare Service and Its Associated Factors

Developing and implementing an epidemiological surveillance plan was necessary during the COVID-19 pandemic to ensure safe dental practice. This was due to the high risk faced by this occupational group during the COVID-19 pandemic. This study aimed to determine the factors associated with COVID-19 diagnosis in a Peruvian dental school's integrated teaching and care service. A cross-sectional study was conducted with a population made up of the records of students, teachers, and administrative personnel in a COVID-19 epidemiological surveillance plan of a dental school during the years 2021 to 2022. The year 2022 was positively associated with a positive diagnosis of COVID-19 (aPR: 1.51; 95% CI: 1.10-2.07; p = 0.010) and not having had contact with a patient with COVID-19 was negatively associated with being diagnosed with that disease (aPR: 0.20; 95% CI: 0.14-0.27; p < 0.001). In conclusion, 2022 was positively associated with having a positive COVID-19 diagnosis. In addition, not having had contact with a COVID-19 patient was negatively associated with the disease diagnosis and with the development of moderate to severe COVID-19.

A Spatial Kinetic Model of Crowd Evacuation Dynamics with Infectious Disease Contagion

This paper proposes a kinetic theory approach coupling together the modeling of crowd evacuation from a bounded domain with exit doors and infectious disease contagion. The spatial movement of individuals in the crowd is modeled by a proper description of the interactions with people in the crowd and the environment, including walls and exits. At the same time, interactions among healthy and infectious individuals may generate disease spreading if exposure time is long enough. Immunization of the population and individual awareness to contagion is considered as well. Interactions are modeled by tools of game theory, that let us propose the so-called tables of games that are introduced in the general kinetic equations. The proposed model is qualitatively studied and, through a series of case studies, we explore different scenarios related to crowding and gathering formation within indoor venues under the spread of a respiratory infectious disease, obtaining insights on specific policies to reduce contagion that may be implemented.