Global stability analysis for a generalized delayed SIR model with vaccination and treatment.

In this work, we investigate the stability of an SIR epidemic model with a generalized nonlinear incidence rate and distributed delay. The model also includes vaccination term and general treatment function, which are the two principal control measurements to reduce the disease burden. Using the Lyapunov functions, we show that the disease-free equilibrium state is globally asymptotically stable if R 0 ≤ 1 , where R 0 is the basic reproduction number. On the other hand, the disease-endemic equilibrium is globally asymptotically stable when R 0 > 1 . For a specific type of treatment and incidence functions, our analysis shows the success of the vaccination strategy, as well as the treatment depends on the initial size of the susceptible population. Moreover, we discuss, numerically, the behavior of the basic reproduction number with respect to vaccination and treatment parameters.

Android and ODK based data collection framework to aid in epidemiological analysis.

Periodic collection of field data, analysis and interpretation of data are key to a good healthcare service. This data is used by the subsequent decision makers to recognize preventive measures, provide timely support to the affected and to help measure the effects of their interventions. While the resources required for good disease surveillance and proactive healthcare are available more readily in developed countries, the lack of these in developing countries may compromise the quality of service provided. This combined with the critical nature of some diseases makes this an essential issue to be addressed. Taking advantage of the rapid growth of cell phone usage and related infrastructure in developed as well as developing countries, several systems have been established to address the gaps in data collection. Android, being an open sourced platform, has gained considerable popularity in this aspect. Open data kit is one such tool developed to aid in data collection. The aim of this paper is to present a prototype framework built using few such existing tools and technologies to address data collection for seasonal influenza, commonly referred to as the flu.