HIV Projection Models

Abstract

The HIV forecasting disease modelling leads to clear statements of assumptions about the biological and social mechanisms, which influence diseases spread and dynamism. The model formulation process is more valuable for statistician, epidemiologists, mathematicians, and modelers, because it forces them to be precise about the relevant aspects of disease transmission, course of infectivity, recovery, treatment prognosis, and renewal of susceptibility (Newell ML et al: J AIDS 12:832–837, 1998; Wig N et al: Indian J Med Sci 60(1):3–12, 2006; Adegboye ZA et al: Pac J Sci Technol 13:238–243, 2012; Agarwala BD et al: Far East J Appl Math 6(1.) (2002):25–70, 2002; Czerwinski IA et al: Fish Res 86:120–128, 2007; Detels R, Munoz A et al: J AIDS 19(17):2009–2018, 2005; Jai Sankar R, Prabakaran K, Kannan S et al: J Mod Math Stat 4(2):53–57, 2010; Kulikov GY et al: Russ J Numer Anal Math Model 18:13–41, 2003). The statisticians need to formulate the models clearly and precisely using different clinical and biological parameters, which have been very well-understood in connection to the dynamics of HIV diseases, such as increase in CD4 count after inception of HAART, spectrum of HIV-TB co-infection, HCV and HBV co-infection, etc (Naresh R, Tripathi A, Omar S et al: Appl Math Comput 178:262–272, 2006; Newell ML et al: J AIDS 12:832–837, 1998; Simwa R, Pokhariyal GP et al: A dynamical model for stage-specific HIV incidences with application to sub-Saharan Africa. Department of Mathematics, University of Nairobi, 2003; Wig N et al: Indian J Med Sci 60(1):3–12, 2006). The complete statements of the assumptions have been crucial role for testing the geometric progression of HIV, so that the reasonableness of the parameters can be interpreted by the relevant conclusion. The matrix of the forecasting model will help physician, policymakers, young researcher, innovators, etc. A limited number of HIV forecasting modelling based-studies have been documented at global scenario. In this context, the present study aims to formulate the different forecasting models and also assess the quantitative conjectures and identify the trend of CD4 counts before inception of “HAART” therapy. A total 497 PLHIVs infected pre-ART and alive on ART cases were considered for the study with written consent. Retrospective cohort data, viz., baseline (start of ART treatment), first year, second year, third year, fourth year, and fifth year cohort data, was recorded systematically. As per the national ART guidelines, we have classified the CD4 count based on the mean and SD, namely, low CD4 count and medium CD4 count (threshold CD4 count <250 cells/microliter and >250 cells/microliter) (Detels et al. 2005). The collected data was compiled by using SPSS-16.50 version. Logistic regression forecasting model, Spearman rank correlation, and polynomial curve fitting models were used to draw the relevant conclusion about the population.