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Updated: Jun 8, 2026

A Method for Studying the Temperature Dependence of Dynamic Fracture and Fragmentation
Published on: June 28, 2015
Refractory periods and climate forcing in cholera dynamics.
Katia Koelle1, Xavier Rodó, Mercedes Pascual
1Department of Ecology and Evolutionary Biology, 2045 Kraus Natural Science Building, University of Michigan, 830 North University Avenue, Ann Arbor, Michigan 48109-1048, USA.
Climate variability and temporary immunity critically explain yearly cholera outbreaks in Bangladesh. This interplay shows how environmental factors and host immunity drive disease cycles.
Area of Science:
- Epidemiology
- Climate Science
- Environmental Health
Background:
- Many infectious diseases, such as cholera, malaria, and dengue, exhibit yearly cycles.
- Linking climate variability to these disease cycles is challenging due to complex epidemiological dynamics like host immunity.
Purpose of the Study:
- To investigate the interplay between climate variability and host immunity in explaining interannual cholera outbreaks.
- To reconstruct and analyze the transmission rate of cholera over four decades.
Main Methods:
- Utilized a four-decade cholera time series from Matlab, Bangladesh.
- Employed a nonlinear population model to reconstruct transmission rates, accounting for host immunity.
- Correlated transmission rates with climate patterns like monsoon rains, river discharge, sea surface temperatures, and ENSO.
Main Results:
- Demonstrated a critical interplay between climate variability and temporary immunity in explaining cholera's interannual cycles.
- Transmission rates showed strong correlations with various climate patterns across different time scales.
- During periods of high immunity, climate forcing had a weaker impact on outbreak size.
Conclusions:
- Climate variability and host immunity are key drivers of interannual cholera dynamics.
- Understanding this interplay is crucial for predicting and managing infectious disease outbreaks.
- The findings highlight the importance of considering both environmental and intrinsic factors in epidemiological modeling.
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