User:Dalastrascastrejon/Disease ecology

In relation to climate change
As climate change continues to disrupt ecosystems around the world it can make both human and non-human populations more or less vulnerable to disease depending on the specific effects of climate change on the disease. The subject of climate change and its impact on disease is increasingly attracting the attention of health professionals and climate-change scientists, particularly with respect to malaria and other vector-transmitted human diseases. More specifically, climate change can impact malaria transmissions by extending the season of transmission and creating more breeding sites due to increasing temperatures and rainfall, respectively. Increases in malaria transmissions and other vector-transmitted human diseases can have a devastating impact on communities that do not receive appropriate medical care and on people who have not had exposure to these diseases.

In relation to tropical, northern temperate zones, and the Arctic
It is thought that the effects of climate change on temperature will increase with latitude. This means that northern temperate zones will experience more temperature changes than tropical zones. Tropical zones experience less climate variability, so organisms in tropical zones have adjusted to a continuous climate. Therefore, slight disruptions in climate can dramatically affect the organisms in tropical zones. Climate change can affect organisms by elongating their reproductive cycles. In addition to this, climate change allows for pathogens to expand beyond tropical zones, dramatically impacting species because of the introduction of new pathogens. These impacted species include humans and human livestock.

Changes in northern temperate zones and the Arctic are also expected. More specifically, the effects of climate change on temperature increase with latitude, so the temperature in northern temperate zones is projected to increase and the temperature in the Arctic is projected to increase even more. Like tropical zones, climate change in northern temperate zones and the Arctic can also cause species to move beyond their original niche. For example, climate change has allowed elk to move north in areas that overlap with other species such as caribou. When the elk move, they introduce new pathogens into the area, thus harming the caribou.

Models and predicting disease ecology
There are numerous approaches when predicting the impacts of climate change on diseases. Static approaches use reproduction rates to find how climate change will affect vectors. An example of the use of static approaches is a process-based model called MIASMA. This model explores the relationship between different climate change scenarios and the reproduction rate of vectors. This model has been used specifically to look at mosquitoes in African highlands to make predictions about the future of the development and feeding of mosquitoes. Additionally, this model can be used to find the population of mosquitoes that bite, allowing predictions of diseases such as dengue fever.

Another approach includes statistical based models, which relies on observations unlike process-based models. An example of this type of model is CLIMEX, which maps vector species over geographical locations while accounting for climate factors. It is important to note that this approach does have limitations. CLIMEX does not include all factors that impact vector species.

Time-series models can also be used to find how climate change will modify disease dynamics. However this approach has a downside; only a limited number of locations and pathogens can be looked at simultaneously using time-series models.

Predictions of ENSO (El Niño Southern Oscillation) can also help predict diseases. ENSO events can create cooler temperatures in the Western Tropical Pacific and warmer temperatures in the Central and Eastern Tropical Pacific leading to intense precipitation and storms. Changes in climate due to ENSO can affect the dynamics of diseases and can affect the water sources humans use. For example, in 1991, cholera reappeared in Peru around the same time as an el Niño event occurred. ENSO events can be anticipated early on, and therefore by predicting ENSO, predictions about disease transmission peaks can be made up to two months before they occur.