Theoretical investigation of malaria prevalence in two Indian cities using the response surface method

Abstract

BackgroundElucidation of the relationships between malaria incidence and climatic and non-climatic factors in a region is of utmost importance in understanding the causative factors of disease spread and design of control strategies. Very often malaria prevalence data is restricted to short time scales (months to few years). This demands application of rigorous statistical modelling techniques for analysis and prediction. The monthly malaria prevalence data for three to five years from two cities in southern India, situated in two different climatic zones, are studied to capture their dependence on climatic factors.MethodsThe statistical technique of response surface method (RSM) is applied for the first time to study any epidemiological data. A new step-by-step model reduction technique is proposed to refine the initial model obtained from RSM. This provides a simpler structure and gives better fit. This combined approach is applied to two types of epidemiological data (Slide Positivity Rates values and Total Malaria cases), for two cities in India with varying strengths of disease prevalence and environmental conditions.ResultsThe study on these data sets reveals that RSM can be used successfully to elucidate the important environmental factors influencing the transmission of the disease by analysing short epidemiological time series. The proposed approach has high predictive ability over relatively long time horizons.ConclusionsThis method promises to provide reliable forecast of malaria incidence across varying environmental conditions, which may help in designing useful control programmes for malaria.

Study on Temporal Effects of Urban Malaria Incidences

“…Thus Malaria-incidence may be inaccurate due to limited health-care resources [43] or wrong population-size [47]. To plan public-health interventions, estimation of malaria burden is a necessary requirement, for which SPR may be used as a surrogate measure for malaria-incidence [43,45,48,49], to express the quantum of malaria endemicity in any region [47], plus to identify high risk areas [50]. In annual reporting system, Annual Parasite Index (i.e., malaria-incidence rate) is the principal monitoring indicator in malaria control programme in Kolkata for several years [50,52].…”

Devising a method towards development of early warning tool for detection of malaria outbreak

“…Existing regression or other mathematical forecasting models forecasted the malaria transmission in Africa 26 27 and in India 28 29 30 in one month advance using climatic variables such as rainfall and temperature. India has diversified climatic characteristics; therefore, implementation of climate-based model for the whole country is not possible.…”

“…Malaria outbreaks generally occur due to unexpected rainfall, resistance in vector and parasite towards insecticide and drugs, respectively 30 31 , migration, inadequate intervention, etc . The statistical method of mean+2 SD for detecting outbreak could detect major outbreaks in Karbi Anglong district but not the minor outbreaks.…”

A Retrospective Epidemiological Analysis of Malaria in South Indian Tertiary Care Hospital

“…A major challenge for malaria epidemiologists is to evaluate the strengths and weaknesses of both methods in estimating malaria incidence and time trends, especially as malaria control programmes are intensified worldwide [14][15][16][17][18][19][20][21] . Malaria is endemic in Mangalore, India which is rapidly undergoing industrialization, witnessing an unprecedented spurt in construction activities in recent years, thus facing the problem of mosquito breeding in man-made clear water sources like wells, overhead tanks, sumps, cisterns as well as other defective and illegal drainage systems 22 . It is observed that the number of cases belonged to migratory labors showing less resistance to malarial infection when compared to the local population, so it was difficult for us to discriminate the number of positive cases among migratory labor and local population as the occupational history in the record was not properly entered.…”