The present study concentrated on the prediction of Malaria risk zones in the study area. According to WHO 2022 report, the disease claimed the lives of almost 274,000 kids under the age of five, or 67% of all malaria deaths worldwide. Major causes of death among children vary by age. It reflects that “Every two minutes, a child dies from malaria”. Also, it emphasizes third Sustainable Development Goal (SDG-3), which Ensure healthy lives and promote well-being for all at all ages, the world is not on a trajectory to achieve the SDG 3 target of ending malaria by 2030. Beside many Malaria reduction programs initiated by the local government and WHO, that reduced the impact of Malaria in many parts of the world. But the UN and WHO objective the Malaria should be endemic by 2030. In addition, The Institute of Health Metrics and Evaluation (IHME) world malaria statistics also shows that the malaria fatality is reduced from 8,92,032 to 6,26,909 during the years 2001-2020. The study area comprises of 12 Tribal population impacted mandals that covers 6,519.9 Sq. Km and chosen study area is prone to malaria disease. In order to reduce the Malaria hazard impact in the study area a right, the hotspot prediction method is needed which is of high importance. The present research proposed and developed a novel Spatial Analysis for Malaria Risk Reduction (SAMRR). The prediction accuracy of the SAMRR is very high compared with other Machine Learning (ML) algorithms. This work focuses on six objects related to ‘Malaria Health Hazard Risk Reduction’ with GIS and Machine Learning (ML) procedures. Data collected from various national and international research and academic repositories such as APSAC, APSDPS and DMFW dept. related to demographic, health and environmental aspects that are help to evaluate the malaria incidence in the study area.
At a glance
- Spatial analysis of 2016–2020 malaria records from 37 health centers across 12 mandals
- Develops the Spatial Analysis for Malaria Risk Reduction model, SAMRR
- Uses Landsat-8 imagery and SRTM elevation data for mapping
- 120 sources in the bibliography
- 48 figures and 40 tables
Contents
1. Preface
2. Prologue/Introduction
3. INTRODUCTION
4. REVIEW OF LITERATURE
5. MATERIALS AND METHODS
6. RESULTS
7. DISCUSSION
8. SUMMARY AND CONCLUSIONS
9. PLATES
Objectives and Themes
This study examines how geospatial analysis and environmental information can be used to identify areas at risk of malaria in the Paderu tribal region and Golugonda mandal in Andhra Pradesh. It develops a spatial risk-reduction approach using malaria records, satellite imagery and climatic data to identify potentially high-risk mandals.
- Spatial and temporal patterns of malaria cases
- Primary health center and mandal-level risk mapping
- Rainfall, temperature, vegetation and water bodies
- Satellite imagery and geographic information systems
- Environmental correlations and the SAMRR prediction approach
Excerpt from the Book
Methodology
The methodology is explained step-wise and represented pictorially below (Figure 3). Data collected from various resources was subjected to the Arc GIS 10.3 to generate various thematic maps based on Satellite Imagery, SRTM Digital Elevation Model data and statistical data. The administrative boundaries of ITDA Paderu area and Golugonda mandal were delineated and generated 12 mandals map according to Andhra Pradesh mandal boundary data (Source: APSAC, 2022) and Location a map of study area is prepared based on Landsat-7 Imagery. Annual Parasite Indices (API) for Study area and Country was computed to know the impact of the malaria of the study area.
Demographic information maps such as Population and Literacy were generated to the demographic impact of malaria in the study area. Land Use/ Land Cover (LU/LC) map generated and it used to classify and estimate the land features such as forest, agriculture, built-up area, graze/grass, waste lands, wet lands and water bodies of the study area. Furthermore malaria data (2016-2020) and five geo-environmental (Normalized Difference Vegetation Index (Moderate Vegetation and Healthy Vegetation), Rainfall, Temperature and Water Bodies) information was used to generate various overlay maps to find the correlational analysis between environmental parameters. Preliminarily, malaria overlay maps were generated based on Year-wise and Mandal-wise malaria data (2016-2020). A correlational analysis was performed between environmental parameters and malaria cases such as Rainfall (Rf) data was taken into consideration and generated Monthly Mean Actual Rainfall (MMAR) map and Monthly Mean Malaria Cases (MMMC) with four classification Low, Moderate, High and Very rainfall of the study area mandals. Similarly the second environmental parameter Temperature (T) data was taken into consideration and generated Monthly Mean Minimum Temperature (MMMnT) map and Monthly Mean Maximum Temperature (MMMxT) map correlated with malaria cases during 2016-2020, of the study area mandals and extracted Monthly Mean Malaria Temperature (MMMT). Likewise the third environmental parameter Normalized Difference Vegetation Index (NDVI) data was taken into consideration and classified into four themes; they are Land/Rock, Water Bodies, Moderate Vegetation (MV) and Healthy Vegetation (HV) and calculated the area for all four classes of each mandal.
Chapter Summaries
Preface: Introduces the study area, its malaria burden and the proposed spatial prediction approach.
Prologue/Introduction: Outlines the study’s data, environmental factors, risk model and intended public-health applications.
INTRODUCTION: Places malaria in the context of communicable disease and tribal health, and states the research objectives.
REVIEW OF LITERATURE: Reviews tribal health, malaria transmission and previous geospatial and statistical prediction studies.
MATERIALS AND METHODS: Describes the study area, malaria records, satellite and environmental data, mapping procedures and statistical methods.
RESULTS: Presents malaria risk maps, environmental analyses and the development and assessment of SAMRR.
DISCUSSION: Interprets the study in relation to previous research and compares the proposed approach with other predictive models.
SUMMARY AND CONCLUSIONS: Brings together the study’s approach and conclusions and considers applications for malaria control.
PLATES: Presents field photographs of the study area, health centers, land cover and research activities.
Keywords
malaria risk prediction, geospatial analysis, GIS, remote sensing, Andhra Pradesh, Paderu, tribal health, primary health centers, Landsat-8, NDVI, rainfall, temperature, water bodies, SAMRR
Frequently Asked Questions
What is the study about?
It investigates the geographic prediction of malaria risk in tribal areas of Andhra Pradesh.
What are its central themes?
The study connects malaria records with geography, climate, vegetation, water bodies and public-health planning.
What is the primary objective?
It aims to identify potentially high-risk malaria areas using geospatial and environmental analysis.
What methods does the study use?
It combines GIS mapping, remote sensing, correlation analysis, statistical tests and a threshold-based spatial prediction method.
What does the main analysis cover?
It maps malaria cases by health center and mandal, examines environmental factors and assesses the proposed SAMRR approach.
Which keywords characterize the work?
Key terms include malaria risk prediction, GIS, remote sensing, NDVI, environmental factors and SAMRR.
What is SAMRR?
Spatial Analysis for Malaria Risk Reduction is the study’s proposed method for classifying malaria risk using environmental thresholds.
Which malaria records are examined?
The spatial analysis uses records from 37 primary health centers across 12 mandals for 2016–2020.
How are water bodies incorporated?
The study maps drainage and uses buffer zones around water bodies to examine the proximity of health centers and villages.
Which other prediction methods are considered?
The discussion compares SAMRR with Maxent, generalized linear models, Bayesian decision networks and SARIMA.
- Citar trabajo
- Dr. Kodamala Prathyusha (Autor), Dr. Rajesh Duvvuru (Autor), 2024, Malaria Risk Prediction, Múnich, GRIN Verlag, https://www.grin.com/document/1496925
