COVID-19 SIR with Public Health Interventions (1.0.0)
            This is an extension of the  basic Suceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in two spaces, one a treatment, and one a control. Through the modeling options, one can explore how changing assumptions about the number of susceptible people, starting number of infected people, the disease’s infection probability, and average duration impacts the outcome. In addition, this version allows users to explore how public health interventions like social distancing, masking, and isolation can affect the number of people infected. The model shows that the interactions of agents, and the interventions can drastically affect the results of the model.
We used the model in our course about COVID-19: https://www.csats.psu.edu/science-of-covid19
             
            Release Notes
            This is the second model in our COVID-19 course.
            Associated Publications
            The Science of COVID-19—CSATS: Center for Science and the Schools | Penn State. (2020.). Retrieved September 28, 2021, from https://www.csats.psu.edu/science-of-covid19
         
    
    
        
        
            
        
        COVID-19 SIR with Public Health Interventions 1.0.0
        
            
                Submitted by
                
                    Kit Martin
                
            
            
                
                    Published Sep 28, 2021
                
            
            
                Last modified Sep 28, 2021
            
         
        
        
            
                This is an extension of the  basic Suceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in two spaces, one a treatment, and one a control. Through the modeling options, one can explore how changing assumptions about the number of susceptible people, starting number of infected people, the disease’s infection probability, and average duration impacts the outcome. In addition, this version allows users to explore how public health interventions like social distancing, masking, and isolation can affect the number of people infected. The model shows that the interactions of agents, and the interventions can drastically affect the results of the model.
We used the model in our course about COVID-19: https://www.csats.psu.edu/science-of-covid19
             
            
                
                
                
            
            
            Release Notes
            
                
This is the second model in our COVID-19 course.