Modeling the probability distribution of the bacterial burst size via a game-theoretic approach

2018 ◽  
Vol 16 (04) ◽  
pp. 1850012
Author(s):  
Seyed Amir Malekpour ◽  
Parsa Pakzad ◽  
Mohammad-Hadi Foroughmand-Araabi ◽  
Sama Goliaei ◽  
Ruzbeh Tusserkani ◽  
...  

Based on previous studies, empirical distribution of the bacterial burst size varies even in a population of isogenic bacteria. Since bacteriophage progenies increase linearly with time, it is the lysis time variation that results in the bacterial burst size variations.Here, the burst size variation is computationally modeled by considering the lysis time decisions as a game. Each player in the game is a bacteriophage that has initially infected and lysed its host bacterium. Also, the payoff of each burst size strategy is the average number of bacteria that are solely infected by the bacteriophage progenies after lysis. For calculating the payoffs, a new version of ball and bin model with time dependent occupation probabilities (TDOP) is proposed.We show that Nash equilibrium occurs for a range of mixed burst size strategies that are chosen and played by bacteriophages, stochastically. Moreover, it is concluded that the burst size variations arise from choosing mixed lysis strategies by each player. By choosing the lysis time and also the burst size stochastically, the released bacteriophage progenies infect a portion of host bacteria in environment and avoid extinction. The probability distribution of the mixed burst size strategies is also identified.

1982 ◽  
Vol 55 (3) ◽  
pp. 367 ◽  
Author(s):  
Carl Alan Batlin ◽  
Susan Hinko

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Maya Diamant ◽  
Shoham Baruch ◽  
Eias Kassem ◽  
Khitam Muhsen ◽  
Dov Samet ◽  
...  

AbstractThe overuse of antibiotics is exacerbating the antibiotic resistance crisis. Since this problem is a classic common-goods dilemma, it naturally lends itself to a game-theoretic analysis. Hence, we designed a model wherein physicians weigh whether antibiotics should be prescribed, given that antibiotic usage depletes its future effectiveness. The physicians’ decisions rely on the probability of a bacterial infection before definitive laboratory results are available. We show that the physicians’ equilibrium decision rule of antibiotic prescription is not socially optimal. However, we prove that discretizing the information provided to physicians can mitigate the gap between their equilibrium decisions and the social optimum of antibiotic prescription. Despite this problem’s complexity, the effectiveness of the discretization solely depends on the type of information available to the physician to determine the nature of infection. This is demonstrated on theoretic distributions and a clinical dataset. Our results provide a game-theory based guide for optimal output of current and future decision support systems of antibiotic prescription.


2021 ◽  
pp. 127407
Author(s):  
Yuhan Bai ◽  
Kai Fan ◽  
Kuan Zhang ◽  
Xiaochun Cheng ◽  
Hui Li ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document