Infectious diseases co-occurrence and co-infection: effects on epidemic dynamics.

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Nowadays, the co-occurrence of infectious diseases is poorly understood in terms of both geography and time. As many as 30% of infectious diseases may result from co-infections, climbing as high as 80% in certain populations. Several methodological challenges remain when it comes to understanding co-occurrences, in particular, detecting interaction among associations and understanding the complexity of host-pathogen-environment interactions. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. Therefore, understanding the ecology and transmission of co-occurring pathogens within populations is crucial to design suitable disease prevention programmes, but methods to gain insights into host-pathogen-environment interactions are currently underdeveloped. By modelling the prevalence of viruses in areas where pathogens co-occur and determining the risk for pathogens to co-occur and co-infect, the project aims to develop statistical frameworks for public health surveillance and control. The aim of this project is to create a platform capable of detecting the potential of infectivity in an area where co-infection of vector populations occurs and to develop a model that can determine co-occurrence attributed risk of infectious diseases. This could then lead to identification of hotspots or areas of high risk attributed to co-occurrence and inform policies.The project will start by working with two distinct types of data. The first is remote sensing data (satellite data) from MODIS (Moderate Resolution Imaging Spectroradiometer) and IRI Maproom (map data). The second is secondary data (mosquitoes sampling) pulled from the OIE animal health information database for Europe, since no (open) human health database has the same level of detail in the geographic and epidemiological characteristics of outbreaks. With spatial and temporal information about 116 animal diseases in the world (available from 2005 and updated in real time), the OIE dataset is one of the largest database freely available to use (containing hundreds of thousands disease locations). However, the methods will be equally applicable to human diseases should suitable data become available.The project will start by reviewing current methods, mainly applied to species co-occurrence, which often focus on bi-co-occurrence, and only implicitly consider interactions between species, and do not incorporate non-independence among hosts, and more importantly do not account for the biology inherent the disease transmission. Therefore, the project aims to develop a new statistical framework to model pathogens co-occurrence for transmission risk mapping by taking into account intra-relationships within pathogens and hosts, between pathogens and hosts and between pathogens, hosts and environment.The initial method will be a bivariate Gaussian process model, which is a geostatistical model, as it will allow us to combine the secondary data which contains surveys on mosquitoes sampled at arbitrary locations and the spatial data. Furthermore, the objective is to predict, over a certain location, the prevalence of viruses in the mosquitoes (presence/absence/abundance). This would help us to find out if an association exists between co-infection in mosquitoes and their potential of disease transmission to humans.