Changes in environment, such as global climate change, are playing a starring role in the spread of zoonotic diseases. Image Source: Flickr User Shever & Kimmrs
In the past couple of years, there have been a few major illness outbreaks that have put the world on edge. Zoonotic diseases are diseases which normally exist in wild or domesticated animals but can transition into an illness affecting the human population. Ebola and Zika Virus are two recent and well-known zoonotic illnesses. With more than 60% of emerging infectious diseases being zoonotic,1 and with environments continuing to evolve to accommodate large-scale outbreaks, better predictive measures will need to be developed. Researchers will need to be able to collaborate more easily and better stratify their data to find commonalities between outbreak situations leading to more efficient predictive techniques.
Utilizing Environmental and Diversity Stressors
In recent years, zoonotic diseases have been a hot topic within the medical science community and beyond. You would be hard pressed to meet someone who didn’t hold some level of concern about Ebola or Zika. That said, this is not a new phenomenon; zoonotic diseases have long plagued humans, and there have been many varied environmental theories surrounding the transmission of these illnesses.
Theories that revolve around the loss of biological diversity driving the emergence and spread of infectious disease is both contradictory and controversial. That said, there is evidence supporting the theory that once a disease has emerged, it may be suppressed in areas of high biodiversity through the dilution effect.2 It is unlikely, however, that all zoonotic disease will respond in one particular manner to environmental and diversity stressors. This is hard data to track using pen and paper methods, especially when looking to share information with other researchers working on different zoonotic illnesses. Links between varying factors can be easily overlooked when data is widely spread across numerous notebooks and labs.
There are models that are commonly used to predict the impact of local and global environmental change on many species. These models help researchers better understand how different environmental changes, such as an increased human population or average temperature increase, will affect those populations. By tracking this data using innovative lab software, researchers will be able to see how this piece fits into the puzzle of overall emergence and transmission rates.
Innovative Changes in Zoonotic Disease Modelling
Researchers and University College London worked to create an environmental-mechanistic framework to model the impacts of environmental change on zoonotic spillover. They used a case study of the haemorrhagic zoonotic disease Lassa fever virus (LAS) and the rats in which the disease originated.3 Much like Ebola, this disease can be deadly in humans; however, not all cases are reported and they may be misdiagnosed as a bad case of malaria.
The team worked to create a model of LAS that successfully predicts outbreaks based on the changes in host distribution as environmental changes occur and the mechanics of that diseases transition from animal to people. They assessed the frequency with with people come into contact with disease-carrying rats, taking into account temperature increases, rainfall changes, people’s behaviour and their access to healthcare and then combined that with virus spillover risk to create a fairly comprehensive model of LAS.
This model should be able to translate well to a number of similar zoonotic diseases, but the researchers are also looking to further improve their model. Meanwhile, comprehensive lab software that allows researchers on the ground to connect doctors with their logs of commonly reported symptoms may assist researchers in gaining a better grasp of the breadth of this illness, and others like it.
As researchers move forward with this model, they are looking to potentially introduce a few more factors present in human populations, such as travel infrastructure, poverty and human-to-human contact rates; something which will be indispensable for the next occurrence of a large scale Ebola or Zika type illness outbreak.4 These factors will all need to be logged in one place to ensure that they can be accessed and accurately stratified between many teams.
In the coming years, there will be more outbreaks of illnesses like Ebola and Zika virus, and it would be best if decision-makers and pharmaceutical companies could predict and prepare properly to prevent catastrophic events. BIOVIA Pipeline Pilot enables scientists to rapidly create, test and publish scientific services that automate the process of accessing, analyzing and reporting scientific data. Using this innovative technology can provide the information-sharing capabilities you need for success in today’s globalized scientific community. Contact us today to learn more about this technology, as well as our other innovative software offerings.
- “Risk factors for human disease emergence,” July 29, 2001, http://rstb.royalsocietypublishing.org/content/356/1411/983 ↩
- “Biodiversity inhibits parasites: Broad evidence for the dilution effect,” May 15, 2015, http://www.pnas.org/content/112/28/8667 ↩
- “Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever,” June 13, 2016, http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12549/full ↩
- “Predicting disease outbreaks using environmental changes,” June 13, 2016, https://www.sciencedaily.com/releases/2016/06/160613090414.htm ↩