Published: 2017-01-12

Learning from an early start but late end epidemics via an incidence rate restricted bivariate distribution and data analysis

Ramalingam Shanmugam


Background: An ideal expectation of public health administrators or field medical workers is to have a late start and quick ending of any epidemic. Instead, when an epidemic starts early but ends late, it is where much can be learned from the incidences. A case in point for discussion in this article is the pattern of 2009 H1N1 epidemic.

Methods: With a parameter to portray an existing health environment as a deterrent for an epidemic like H1N1 to outbreak in any location at a week, a bivariate distribution is created and is used to analyze the data for a learning so that it helps to prevent a too long prevailing future epidemic. This new distribution is named Incidence Rate Restricted Bivariate Distribution (IRRBGD). Statistical properties of IRRBGD are derived and illustrated using 2009 H1N1 incidences in all five continental regions (Africa, Asia, Europe, Americas, and Oceanic) across on earth.

Results: The Asian continent, compared to other four continental regions, had most vulnerability for H1N1 incidences. The odds for no H1N1 to occur is lowest only in Oceanic among the four continental regions, namely Africa, Europe, Americas, and Oceanic. Since the beginning of the year 2009 with 52 weeks, the week number, Y in which the H1N1 appeared first and the number, X of weeks the H1N1 continued on in a region are consistently highly correlated in all five continental regions.   

Conclusions: From the data analyses of 2009 H1N1 incidences, no continental region is risk free with respect another round of H1N1 epidemic in future. The medical community and public healthcare administrators ought to identify the common and region specific unique deterrents of the epidemic like H1N1. The impact of such deterrents to H1N1 is captured in our model and analysis. By increasing the deterrent level, the outbreak of an epidemic like H1N1 could be delayed, according to our model and data information.



Conditional discrete distribution, Two parameter geometric distribution, Survival function, Correlation, Regression

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