This article presents state-wise malaria incidence data to test the patterns of prevalence of malaria in India between 2011 and 2015. Geospatial malarial modeling helps in comparing the intervention efficacy with respect to different states in India and the aspect that contribute to its inefficacy.
India accounts for 6% of all malaria cases in the world, 6% of the deaths, and 51% of the global P. vivax cases. Trend-based assessments estimate that the total cases of Malaria in 2017 in India tallies for 1.31 million and deaths at 23990. However, the cases of malaria in 2012 in India were estimated to be 9.7 million, with about 48,660 deaths.
The epidemiological purpose of pharmaceutical industry comprises mainly towards the provision of quality drugs for prevention and intervention of emerging infectious disease (EID). Data gets collected in form of mortality rate, incidence rate and prevalence of the infectious agents.
Epidemiology is a branch of study that predicts the occurrences and patterns of diseases in different groups of the population. It helps in assessing the reason and factors behind the occurrence of a disease. Epidemiological information helps plan and strategies to prevent and manage epidemic diseases or illness.
GM (1,1) modeling is a popular grey forecasting method because of its computational efficiency. There are many challenges in GM (1,1) modeling, but they are solvable using MS Excel. This article is a detailed guide on how to overcome these challenges.
This article explains the application of PINDIS separately in Hamlet II. It also presents an example using PINDIS analysis to understand the application in depth. Accessing PINDIS separately is possible only after creating the input file using ‘Select’ function.
PINDIS method applies a series of variations by increasing flexibility in each iteration. The aim is to maximize the optimization. It provides better specifics when compared to the INDSCAL results.