Scientists have developed a computer model that predicts outbreaks of zoonotic diseases – those that spread from animals to people – based on changes in climate, population growth, and land practices. They hope the tool will help governments and communities improve their decision-making.
Scientists estimate that 6 out every 10 infectious human diseases are zoonotic – they start in livestock or wildlife and spread to people. Zoonotic diseases can be caused by viruses, bacteria, parasites, and fungi.
Many people come into contact with animals in their daily lives. Animals are bred for food and kept in homes as pets. We also come into contact with animals at county fairs and petting zoos, and we can encounter wildlife when out hiking, camping, or clearing woodland.
Some zoonotic diseases are well known – such as Ebola and Zika. Others, such as Lassa fever and Rift Valley fever, are less familiar to the public, but they already affect thousands of people and are predicted to spread.
While the spread of a zoonotic disease is influenced by factors in the disease itself – such as how it moves from animal to human hosts – environmental factors also play an important role – for instance, by affecting opportunity for contact.
Now, researchers at University College London (UCL) in the United Kingdom have developed a computer model that predicts outbreaks of zoonotic diseases based on changes in climate, population growth, and land use.
The model brings together how often people are likely to come into contact with animals carrying the disease, with the risk of the disease spilling over.
Senior author Kate Jones, a professor in UCL’s Centre for Biodiversity and Environment Research, says:
“This model is a major improvement in our understanding of the spread of diseases from animals to people. We hope it can be used to help communities prepare and respond to disease outbreaks, as well as to make decisions about environmental change factors that may be within their control.”
The researchers hope that using the model, decision-makers will be able to assess the effect that planned changes on land use – such as converting grassland to agriculture – could have on spread of zoonotic diseases.
In a study published in the journal Methods in Ecology and Evolution, the researchers describe how they successfully used the model to predict current patterns of Lassa fever spread.
Lassa fever is a zoonotic viral disease that is endemic in many countries in West Africa and common in other countries in the region.
Like Ebola virus, Lassa virus causes an acute, potentially fatal hemorrhagic illness. The virus spreads to humans via contact with rat urine or feces, causing illness that lasts 2-21 days.
Estimates of how many people are affected by Lassa fever each year are highly varied as often the symptoms are not severe, and when they are, they can be mistaken for malaria. Current estimates range from 100,000 to 1 million.
In their study, Prof. Jones and colleagues predict that by 2070, the number of people with Lassa fever will rise from 195,125 to 406,725 as a result of climate change and human population growth.
The model brings together changes in the host’s distribution pattern as the environment changes with the mechanisms of how the disease spreads from animals to people. The researchers note this has not been done before.
For their calculations, the researchers used the locations of 408 known Lassa fever outbreaks in West Africa during 1967-2012, the changes in land use, crop yields, temperature, rainfall, behavior, and access to healthcare.
They also identified the sub-species of the rat that spreads Lassa virus to humans – Mastomys natalensis – to map its location against ecological factors.
Bringing this forecast information together, the model predicts the areas in West Africa considered high risk for Lassa fever will expand into the western-most regions around Senegal and Guinea, the coastline of Cote d’Ivoire and Ghana, and in Central Nigeria, note the researchers.
The team says the model could be fine-tuned to look at various factors influencing zoonotic disease spread within human populations.
The model could, for example, look at the effect of travel patterns, rates of contact between humans, and poverty on the spread of individual zoonotic diseases. Results of such an analysis would have been very useful in helping to contain recent outbreaks of Ebola and Zika.
“Importantly, the model also has the potential to look at the impact of global change on many diseases at once, to understand any trade-offs that decision-makers may have to make.”
Prof. Kate Jones