Potential for Hydroclimatically Driven Shifts in Infectious Disease Outbreaks: The Case of Tularemia in High-Latitude Regions
Hydroclimatic changes may be particularly pronounced in high-latitude regions and can influence infectious diseases, jeopardizing regional human and animal health. In this study, we consider the example of tularemia, one of the most studied diseases in high-latitude regions, which is likely to be im...
| Published in: | International Journal of Environmental Research and Public Health |
|---|---|
| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
MDPI AG
2019
|
| Subjects: | |
| Online Access: | https://doi.org/10.3390/ijerph16193717 https://doaj.org/article/a64689c9561f49e3acae6d6d11d3849c |
| Summary: | Hydroclimatic changes may be particularly pronounced in high-latitude regions and can influence infectious diseases, jeopardizing regional human and animal health. In this study, we consider the example of tularemia, one of the most studied diseases in high-latitude regions, which is likely to be impacted by large regional hydroclimatic changes. For this disease case, we use a validated statistical model and develop a method for quantifying possible hydroclimatically driven shifts in outbreak conditions. The results show high sensitivity of tularemia outbreaks to certain combinations of hydroclimatic variable values. These values are within the range of past regional observations and may represent just mildly shifted conditions from current hydroclimatic averages. The methodology developed also facilitates relatively simple identification of possible critical hydroclimatic thresholds, beyond which unacceptable endemic disease levels may be reached. These results call for further research on how projected hydroclimatic changes may affect future outbreaks of tularemia and other infectious diseases in high-latitude and other world regions, with particular focus on critical thresholds to high-risk conditions. More research is also needed on the generality and spatiotemporal transferability of statistical disease models. |
|---|