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Epidemiology

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Full-Text Articles in Physical Sciences and Mathematics

Seasonal Forcing In Stochastic Epidemiology Models, Lora Billings, Eric Forgoston Nov 2017

Seasonal Forcing In Stochastic Epidemiology Models, Lora Billings, Eric Forgoston

Lora Billings

The goal of this paper is to motivate the need and lay the foundation for the analysis of stochastic epidemiological models with seasonal forcing.We consider stochastic SIS and SIR epidemic models, where the internal noise is due to the random interactions of individuals in the population. We provide an overview of the general theoretic framework that allows one to understand noise-induced rare events, such as spontaneous disease extinction. Although there are many paths to extinction, there is one path termed the optimal path that is probabilistically most likely to occur. By extending the theory, we have identified the quasi-stationary solutions …

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Seasonal Forcing In Stochastic Epidemiology Models, Lora Billings, Eric Forgoston Nov 2017

Seasonal Forcing In Stochastic Epidemiology Models, Lora Billings, Eric Forgoston

Eric Forgoston

The goal of this paper is to motivate the need and lay the foundation for the analysis of stochastic epidemiological models with seasonal forcing.We consider stochastic SIS and SIR epidemic models, where the internal noise is due to the random interactions of individuals in the population. We provide an overview of the general theoretic framework that allows one to understand noise-induced rare events, such as spontaneous disease extinction. Although there are many paths to extinction, there is one path termed the optimal path that is probabilistically most likely to occur. By extending the theory, we have identified the quasi-stationary solutions …

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Estimating The Effect Of A Community-Based Intervention With Two Communities, Mark Van Der Laan, Maya Petersen, Wenjing Zheng May 2013

Estimating The Effect Of A Community-Based Intervention With Two Communities, Mark Van Der Laan, Maya Petersen, Wenjing Zheng

Wenjing Zheng

Due to the need to evaluate the effectiveness of community-based programs in practice, there is substantial interest in methods to estimate the causal effects of community-level treatments or exposures on individual level outcomes. The challenge one is confronted with is that different communities have different environmental factors affecting the individual outcomes, and all individuals in a community share the same environment and intervention. In practice, data are often available from only a small number of communities, making it difficult if not impossible to adjust for these environmental confounders. In this paper we consider an extreme version of this dilemma, in …

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