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Related Concept Videos

Malaria01:29

Malaria

Malaria pathogenesis in humans reflects a delicate interplay between parasite biology and host response. Clinical illness reflects a host’s immune response to the parasite’s asexual replication cycle, which is often asymptomatic in individuals with partial immunity. From the parasite's perspective, transmission between mosquito and human with minimal host pathology is evolutionarily advantageous. Among the six Plasmodium species infecting humans, P. falciparum and P. vivax dominate in global...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...

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Updated: Jun 23, 2026

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Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models.

Abdul-Karim Iddrisu1, Dominic Otoo1, Gordon Hinneh1

  • 1Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani 233, Ghana.

Infectious Diseases & Immunity
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Malaria risk hotspots were identified in Ghana, with significant variations across regions. This study highlights key areas for targeted malaria control interventions, emphasizing the need for localized public health strategies.

Keywords:
Bayesian modelingConditional auto-regressiveDisease hotspotIntegrated Nested Laplace ApproximationMalariaSpatial and spatiotemporal models

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Area of Science:

  • Epidemiology
  • Public Health
  • Spatial Analysis

Background:

  • Malaria poses a significant public health challenge in Ghana, necessitating accurate risk assessment and mapping.
  • Understanding geographical variations in malaria risk is crucial for resource allocation and targeted interventions.
  • Identifying disease hotspots and controllable risk predictors is essential for effective malaria control.

Purpose of the Study:

  • To identify regions with elevated malaria risk in Ghana.
  • To map malaria risk hotspots for targeted public health interventions.
  • To identify controllable predictors of malaria risk.

Main Methods:

  • Utilized laboratory-confirmed malaria case data from 2015-2021.
  • Employed Bayesian spatial and spatiotemporal models to analyze malaria risk patterns.
  • Mapped regional malaria risk and identified hotspots using correlated and uncorrelated structures.

Main Results:

  • Both spatial and spatiotemporal models indicated increased malaria risk in several regions, including North East, Upper West, Bono East, Central, and Ahafo.
  • Identified specific regions as malaria risk hotspots.
  • Found substantial regional variations in malaria risk, with no statistically significant influence from climatic or economic factors.

Conclusions:

  • Malaria risk is clustered and varies significantly across Ghana's regions.
  • Several regions are identified as malaria hotspots requiring focused control efforts.
  • Climate and economic factors did not show a significant influence on malaria risk in this study, suggesting a need for further investigation into other drivers.