Urbanization influenced malaria prevalence has escalated over the past two decades in India. According to the UN Department of economics and social affairs, 55% of the world’s population lives in urban areas, which is expected to increase up to 68% by the year 2050 (UN Department of Economics and Social Affairs, 2018).
Forecasting public health expenditure by the Government of India is an important aspect to assess the government’s effectiveness towards disease control and policy implications. Assessing the trend in the public healthcare expenditure by the central government, predicted that the public health expenditure will get doubled in the next five years.
Plasmodium genus causes an estimated 438,000 global deaths annually. In India, mainly two species of Plasmodium is prevalent, Plasmodium falciparum and Plasmodium vivax (Siwal, et al., 2018). P. vivax accounts for 53% of the total malaria cases in India.
Associated malarial risk factors are largely favoured by the climatic and economic conditions. It largely occurs in the regions having high rates of precipitation, humidity, and rainfall making it optimum for the malaria vector to breed and flourish.
Meta-analysis study was conducted by using Comprehensive Meta-Analysis (CMA) to assess the government policy effectiveness under the 5-year plan scheme in accordance with the malaria incidence and deaths over the time span of last five year plans.
This article presents state-wise malaria incidence data to test the patterns of prevalence of malaria in India between 2011 and 2015. Geospatial malarial modelling 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.
This article presents a predictive assessment on the basis of secondary data available for malaria incidences in the period of 1998-2017. The predictive assessment uses the Autoregressive integrated moving average (ARIMA) model.