The Impact of H1N1 Flu Cluster Geometry on Appropriate Intervention Methods in Developed versus Developing Nations Krishna Bhogaonker
The Focus • The Puzzle: How do systematic differences in the shape and size of flu clusters between developed and developing nations affect the choice of public health interventions and planning. • Significance: Differences in disease cluster structures impacts the efficiency and efficacy of public health interventions. By understanding national patterns in infectious disease clusters, countries can make better epidemic response plans. • Study: I compare flu cluster size, shape, and progression in the US and India to identify systematic patterns of variation. I also narrow my focus to comparable cities Pune, India and San Diego, California.
Roadmap • Demonstrate global Flu spread • Compare the structure of Flu clusters in the US and India • Compare the structure of Flu clusters in San Diego versus Pune. • Compare hospital coverage of Flu cases in San Diego versus Pune. • CONCLUSION: The structure of a flu cluster influences the effectiveness of public health interventions.
In 2009, Pandemic Swine Flu swept the world. Both developed and developing nations were affected. By Krishna B, Source Flutracker Inset map, Geocoding, Aggregating Attributes: incidents = confirmed + suspected
Hotspot and Structural Analysis of India Flu Clusters By Krishna B, Source Flutracker Modeling, Hotspots, Charts + Graphs, R mashup, measurment analysis
Hotspot and Structural Analysis of US Flu Clusters By Krishna B, Source Flutracker Modeling, Hotspots, Charts + Graphs, R mashup, measurment analysis
Case Comparison: San Diego versus Pune • National comparisons obscure data patterns because of scale. • Comparing 2 similar cities provides a better sense of clustering patterns. • Pune and San Diego are similar: • Both relatively coastal • Both 2-3 hours from large metropolitian city that was also a flu cluster • Both are separated from the neighboring metropolitian city by mountains • Similar population balance between metro city and secondary city.
Explanation: Effective hospital buffer size is different in developed versus developing nations
Conclusion • Implications for Policy: Developing nations need to extend hospital buffer sizes by mobile clinics. • Using the data model provided, we can estimate the optimal buffer size for each developing nation hospital to produce cluster covering.