Health

A model to predict diseases that transfer from animals to humans

The study tested the model with Lassa fever, a disease endemic to western Africa and is caused by the Lassa virus which passes on to people from rats

 
By Kundan Pandey
Last Updated: Monday 13 June 2016 | 12:28:40 PM

The picture shows the Ebola virus. More than 60 percent of the emerging infectious diseases are zoonotic. Though Ebola and Zika have come to our notice, others like the Rift Valley fever and Lassa fever are predicted to spread with changing environmental factors
Credit: CDC Global/Flickr

Scientists have come up with a model that can predict the spread of zoonotic diseases such as Zika and Ebola. The model developed by University College London (UCL) researchers and based on climate change, population growth and land use can be used to prepare communities and respond to disease outbreaks efficiently, Kate Jones, the lead author of the study and professor in UCL Genetics, Evolution and environment and ZSL, said.

According to the World Health Organization, any disease or infection that is naturally transmissible from vertebrate animals to humans and vice-versa is classified as a zoonosis. 

It is known that more than 60 percent of the emerging infectious diseases are zoonotic. Though Ebola and Zika have come to our notice, others like the Rift Valley fever and Lassa fever, which affect thousands, are predicted to spread with changing environmental factors. (Read)

Jones added, “Our model can help decision makers assess the likely impact of any interventions or change in national or international government policies, such as the conversion of grasslands to agricultural lands, on zoonotic transmission. 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 be make.”

The study published in the journal, Methods in Ecology and Evolution, tested the new model with Lassa fever.

The disease is endemic across western Africa and is caused by the Lassa virus which passes on to people from rats. The model predicts the number of people with the disease will double from 195,125 to 406,725 by 2070 due to climate change and growing human population.

Like the Ebola virus, the Lassa virus causes haemorrhagic fever and can be fatal. It is unclear as to how many people are affected by Lassa fever every year as many do not show severe symptoms. Very often, patients are misdiagnosed as doctors think it to be malaria. Current estimates range from 100,000 to a million.

“Our new approach successfully predicts outbreaks of individual diseases by pairing the changes in the host's distribution as the environment changes with the mechanics of how that disease spreads from animals to people which hasn’t been done before. It allows us to calculate how often people are likely to come into contact with disease-carrying animals and their risk of the virus spilling over. Alongside population increases, the expected future changes to climatic patterns will drive an expansion of the areas of west Africa considered high risk, especially the western-most regions around Senegal and Guinea, the coastline of Cote d'ivoire and Ghana, and in Central Nigeria,” David Redding of UCL’s Genetics, Evolution and Environment department said.

The team used the locations of 408 known Lassa fever outbreaks in West Africa between 1967 and 2012 and the changes in land use and crop yields, temperature, rainfall, behaviour and access to healthcare.

They also identified the sub-species of the multimammate rat (Mastomys natalensis) which transmits the Lassa virus to humans to map its location against ecological factors.

The model was then developed using this information, along with forecasts on climate change, future population density and land use changes.

The approach has already proved successful by predicting the current disease patterns of Lassa fever, the researchers said in a statement. They said the model could be refined to consider zoonotic disease transmission within human populations by including the impact of travel infrastructure, human-to-human contact rates and poverty—factors that would have been of use in the Ebola and Zika outbreaks.

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