Sofonea, M. T. | bioRxiv Science

bioRxiv2018

Modelling coinfections to detect within-host interactions from genotype combination prevalences

Parasite genetic diversity can provide information on disease transmission dynamics but most methods ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelling of coinfections and regression Approximate Bayesian Computing (ABC) to detect within-host interactions. Using genital infections by different types of Human Papillomaviruses (HPVs) as a test case, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data and that this detection is robust even in the face of host heterogeneity in behaviour. These results suggest that the combination of mathematical modelling and sophisticated inference techniques is promising to extract additional epidemiological information from existing datasets.

epidemiology↗

bioRxiv2018

Why multiple infections favour virulent parasites

It is now a fact that several strains/species (hereafter types) of parasites circulate in natural host populations. Parasite polymorphism can even occur within the same host, where distinct parasite types can interact in various ways. This can affect their transmission and, therefore, their evolution. We still lack general predictions regarding the evolution, in such multiple infection contexts, of virulence - the infection-induced host mortality, essentially because its emanation from within-host growth was often ignored so far. Here, we explicitly investigate within-host interactions, within-host competition outcomes, epidemiological dynamics and evolutionary invasibility using a formalism as general as possible. Focusing on chronic dimorphic infections caused by horizontally-transmitted microparasites, we apply both dynamical systems and probabilistic approaches to this bottom-up sequence of dynamics to explore the evolutionary outcomes. We show that within-host growth traits are under strong selective pressure and when small mutations affect them, most of the surviving mutants are more virulent than their resident. We thus identify a robust and unavoidable selection bias towards higher virulence.

evolutionary biology↗

bioRxiv2017

Can Ebola Virus evolve to be less virulent in humans?

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology ( ). Understanding Ebola Virus (EBOV) virulence evolution is not only timely but also raises specific questions because it causes one of the most virulent human infections and it is capable of transmission after the death of its host. Using a compartmental epidemiological model that captures three transmission routes (by regular contact, via dead bodies and by sexual contact), we infer the evolutionary dynamics of case fatality ratio (CFR) on the scale of an outbreak and on the long term. Our major finding is that the viruss specific life cycle imposes selection for high levels of virulence and that this pattern is robust to parameter variations in biological ranges. In addition to shedding a new light on the ultimate causes of EBOVs high virulence, these results generate testable predictions and contribute to informing public health policies. In particular, burial management stands out as the most appropriate intervention since it decreases the R0 of the epidemics, while imposing selection for less virulent strains.\n\nImpact SummaryThe severe haemorrhagic fever caused by Ebola Virus (EBOV) usually kills more than one infected individual out of two in the absence of treatment, which makes this pathogen one of the most virulent known to humans. The recent outbreak in West Africa (2013-2016) revealed that the virus is able to spread and persist for months across countries. It is often thought that virulence could be due to the fact that the virus is adapted to its reservoir host. Given that microbes evolve rapidly, it is important to determine whether EBOV virulence is likely to decrease as the virus adapts to its human host. To address this problem, we developed a novel mathematical model tailored to EBOVs life cycle, notably by capturing its three main transmission routes (by regular contact, sexual contact and via dead bodies). We investigated the evolutionary trends of EBOVs virulence on different time scales (outbreak initiation, short term and long term). Our results reveal that the virulence of EBOV might not be due to the maladaptation of the virus, but could rather originate from its unique life cycle. These results are robust to the parameter values chosen. From a public health perspective, burial management stands out as the main leverage to fight the virulence of EBOV, both on the short and long terms.

evolutionary biology↗