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Principles of Disease Surveillance01:26

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Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
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Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease

Justin Remais1, Adam Akullian, Lu Ding

  • 1Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA. justin.remais@emory.edu

Journal of the Royal Society, Interface
|February 19, 2010
PubMed
Summary

New spatial models reveal hidden disease spread risks by analyzing effective geographical distance, improving infectious disease surveillance and control in complex landscapes.

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Area of Science:

  • Environmental science
  • Epidemiology
  • Geospatial analysis

Background:

  • Infectious disease spread is influenced by parasite, vector, and host dispersal.
  • Current geospatial models offer limited insights into landscape heterogeneity and disease spread.
  • Schistosomiasis japonica and its snail host (Oncomelania hupensis) in China present a relevant case study.

Purpose of the Study:

  • To develop advanced functional spatial models for disease surveillance and control.
  • To identify transmission hotspots with higher connectivity than predicted by Euclidean distance.
  • To create ecologically meaningful surveillance units using non-Euclidean distance measures.

Main Methods:

  • Application of functional spatial models to Schistosoma japonicum and Oncomelania hupensis in China.
  • Development of a framework using effective geographical distance (EGD) to quantify connectivity.
  • Utilizing Monte Carlo rank product analysis and k-means clustering for node analysis and grouping.

Main Results:

  • Non-Euclidean EGD measures reveal novel connectivity patterns between transmission nodes.
  • These patterns are not apparent when using traditional Euclidean distance.
  • The study identified transmission nodes with higher connectedness and potential for disease spread.

Conclusions:

  • Ecologically informed distance measures are crucial for accurate connectivity assessment in heterogeneous landscapes.
  • Current Euclidean-based surveillance strategies may be suboptimal for disease control.
  • The developed framework provides a quantitative approach for effective, ecologically grounded disease surveillance systems.