In this paper we present a deterministic discrete-time networked SEIR model that includes a number of transportation networks, and present assumptions under which it is well defined. We analyze the limiting behavior of the model and present necessary and sufficient conditions for estimating the spreading parameters from data. We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.

This work examines the discrete-time networked SIR (susceptible-infected-recovered) epidemic model, where the infection and recovery parameters may be time-varying. We provide a sufficient condition for the SIR model to converge to the set of healthy states exponentially. We propose a stochastic framework to estimate the system states from observed testing data and provide an analytic expression for the error of the estimation algorithm. Employing the estimated and the true system states, we provide two novel eradication strategies that guarantee at least exponential convergence to the set of healthy states. We illustrate the results via simulations over northern Indiana, USA.

Two studies were conducted to determine the effectiveness of digital multimedia modules as training tools for animal care workers. Employees at a commercial feedlot (n = 17) and a commercial dairy (n = 10) were asked to independently complete a 10-question quiz prior to and following viewing of training modules. Module topics in the feedlot were proper handling of non-ambulatory animals and humane methods of euthanasia; modules were administered to the workers, as a group, in either English (n = 7) or Spanish (N = 10), depending on previously indicated worker preference. Modules addressing dairy cattle health practices and dairy cattle handling were presented to the dairy care workers who had a preference for learning in either English (n = 7) or Spanish (n = 3). For feedlot workers, post-test scores were improved by 28% after viewing the modules compared to pre-test scores (74% vs. 58%; P < 0.01), across language and topic. There were no interactions (P > 0.30) between language, topic, and between-test variation, indicating that the modules were equally effective at information Bilingual, Digital, Audio-Visual Training Modules Improve Technical Knowledge… Vol. 5, Issue 7 (2010) 2 delivery to both audiences in both languages. For the dairy workers, test scores improved by 27% from pre-viewing to post-viewing (73% vs. 92%; P < 0.01); there was an interaction between the effect of module and language preference (P < 0.01) indicating that although scores increased for both of the topic areas for the English-speaking workers, only the score for the animal health topic increased for the Spanish-speaking workers. Regardless of nationality, level of formal education, topic, or preferred language, digital media are effective at improving knowledge transfer to animal care professionals.

No abstract

No abstract