Abstract
Various epidemics have arisen in rural locations through human-animal interaction, such as the H1N1 outbreak of 2009. Through collaboration with local government officials, we have surveyed a rural county and its communities and collected a dataset characterizing the rural population. From the respondents’ answers, we build a social (face-to-face) contact network. With this network, we explore the potential spread of epidemics through a Susceptible-Latent-Infected-Recovered (SLIR) disease model. We simulate an exact model of a stochastic SLIR Poisson process with disease parameters representing a typical influenza-like illness. We test vaccine distribution strategies under limited resources. We examine global and location-based distribution strategies, as a way to reach critical individuals in the rural setting. We demonstrate that locations can be identified through contact metrics for use in vaccination strategies to control contagious diseases.
Highlights
The spread of infectious diseases can be contained by human response using different approaches
In particular we study the impact of limited resource vaccination campaigns, using an exact model of a stochastic SLIR Poisson process
Our contributions are twofold: we construct and analyze a data-based rural contact network and we provide a thorough analysis and comparison of mitigation strategies in a rural region
Summary
The spread of infectious diseases can be contained by human response using different approaches. A vast literature exists on efficient vaccination strategies, given the need for efficient strategies to distribute vaccines that can often be insufficient for the entire population. Some of these strategies assume that human contact networks are well represented by scale free networks. The strategy of acquaintance immunization proposes the immunization of random acquaintances of random individuals [3] Another local strategy proposes to vaccinate highly connected acquaintances of randomly selected people; based on the properties of scale free networks, with this approach the probability of targeting the highly connected individuals in the contact network increases with respect to the simple random selection [4]. Using a decision-making framework for vaccine distribution policies based on a geographical and demographical data in USA, the authors of [11] find that distributing vaccines first to counties where the latest epidemic waves are expected is the most efficient policy
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