21-EEID Cross-scale dynamics of LASV spillover within human-driven ecosystems

The natural world is expected to undergo a significant change over the coming century, driven by climate change, habitat loss, human population increases and increased globalisation. Many animal-borne or zoonotic human diseases (e.g. Ebola, Plague, Anthrax) are caught from wild animal species and these host species will likely alter what they do and where they are found in response to global changes.

The recent emergence of several zoonotic diseases has caused significant social and economic disruption (e.g. SARS-COV-2, Zika, Ebola). One such disease is Lassa Fever, which is found throughout West Africa and has yearly annual widespread outbreaks causing hundreds of deaths a year.

It is caught from the widespread, agricultural pest species Mastomys natalensis, also called the Multimammate Rat. Recent evidence has pointed to an increase in cases and, therefore, it is vital we act now to better understand this disease.

In this context, working closely with anthropologists, we will create a comprehensive model of the transmission of Lassa Fever virus between the animal hosts and human populations. Using a modelling approach that examines individual rodent and human behaviour, we will look to understand how the seasonal, geographical and sociocultural differences to the conditions that host species experience, alter their chances of transmitting their pathogens to humans.

We will also include specific differences in the behaviours of groups of people, such as farm labourers and household workers. From these models, will make and test management recommendations that disrupt contact between people and the host species.

We will then use other, simpler methods to summarise the outputs of these complex models. This will allow us to understand more about what we need to know about diseases, to model them across different spatial scales. For instance, to predict the number of cases within a village we would likely need to know lots of information about individual human and rodent behaviour, but to predict the same information at, for instance, a district level we might just need to know how often people and infected rodents meet each other.

Uncovering the relationship between drivers of disease risk and spatial scale, would allow us to more easily make risk maps for policy makers that we know are accurate.

Overall, using our different approaches we can help predict which areas of West Africa are at risk of Lassa Fever and many other poorly-known animal-borne diseases. By incorporating local-scale processes we can better create measures that prevent disease, while reducing negative impacts on the livelihoods of poor and vulnerable human communities. Furthermore, with large ongoing changes to demography and the environment expected in West Africa over the coming decades, it is important to predict how zoonotic diseases will likely respond to environmental change, to better understand where these diseases may spread in the future.

Lastly, we will create two software tools. The first will bring together researchers from different subjects to work more effectively, by providing a bespoke, digital framework for use in participatory mapping. The second, will provide an online framework for untrained users to run our broad-scale disease risk models.