June 2018

Published on June 28, 2018 by

Editorial: Emerging, zoonotic, and vector-borne diseases: crossing boundaries to improve health

Emerging, zoonotic, and vector-borne diseases are often lumped together in a seemingly hodge-podge “other” category of infectious diseases. Although the pathogens causing these diseases are different, factors that they have in common are discussed here. In this issue of the Quarterly, you will find articles that represent a diverse array of scientific perspectives from around the world, bringing a wide range of epidemiological approaches to bear on emerging, zoonotic, and vector-borne diseases.

Published on June 28, 2018 by

Models to support policy to improve rabies prevention and guide elimination

Rabies has until very recently been very much a neglected disease, with thousands of deaths occurring every year in low- and middle-income countries. But recent in-country prioritization exercises have highlighted that rabies is a priority for countries like Kenya. By modelling the different tools that can be applied to help us to reach the target to eliminate human deaths from dog-mediated rabies by the year 2030.What this shows us for now is that we need to use both human and animal vaccines more effectively to deliver on this possibility.

Published on June 28, 2018 by

Modelling Ebola virus disease outbreak risk in Africa

Forecasting when and where Ebola outbreaks will occur is difficult, especially because the ‘reservoir hosts’ of the viruses that cause this disease are not known for certain. There has been a focus on modelling Ebola disease in people and this has informed epidemic control strategies. Less attention has been given to modelling the initial ‘spillover’ events from other species to people, or disease dynamics in reservoirs, because of a lack of data. However, because the wildlife reservoirs and mechanism of spillover are poorly understood, modelling approaches can be used to identify or exclude hypotheses even when data are limited.

Published on June 28, 2018 by

Zika virus outbreak: Challenges for research

From the onset of an infectious disease outbreak, there is a need for public health guidance. In order to inform this guidance, one needs to understand the potential risks that are associated with the outbreak. At this stage, however, large scale studies providing robust evidence for Zika virus are lacking, and evidence only slowly accumulates as the outbreak expands. This article discusses approaches to two challenges that the Zika virus outbreak presented: 1) Establishing causality between Zika virus and adverse neurological outcomes in the absence of high quality epidemiological studies; 2) Establishing the risk of sexual transmission in the presence of multiple transmission routes.

Published on June 25, 2018 by

Monitoring arboviruses in Venezuela: challenges and recent findings

In this article we use Zika virus in Venezuela as an example which is showing that international collaborations can be helpful when in-country public health and pathogen surveillance systems are in disrepair. The findings from the project shed light on the complexities of arbovirus outbreaks, and confirmed the presence of Zika virus infections in Barquisimeto. The international community should always take notice of the fact that uncontrolled outbreaks in one country potentially cause a domino effect, spreading to surrounding countries.

Published on June 25, 2018 by

Regression ABC phylodynamics to infer epidemiological parameters

The field of phylodynamics hypothesises that the way viruses spread leaves footprints in their genetic information. This opens great potential to provide insights into complex epidemiological dynamics, but in practice, there are many hurdles to be overcome. One of them is that the number of parameters to infer from raw DNA or RNA sequence data is huge, making likelihood-based methods challenging for non-trivial models. A promising alternative is a simulation-based approach called ABC for Approximate Bayesian Computation. Its downside is that it is computationally intensive, since thousands (if not millions) of simulation runs to obtain a good quality model fit. To avoid this, people have proposed all sorts of optimization schemes, such as regression ABC, which relies on state-of-the-art machine learning techniques.

Short item Published on June 24, 2018

Modelling the evolution of influenza

On 18 April 2018, Venetia Karamitsou, PhD student in the Disease Dynamics group at the University of Cambridge, held a talk at SACEMA on modelling the evolution of influence. Given that vaccination is the main control strategy against influenza outbreaks, it is worrisome that influenza mutates often, making reinfection possible even for vaccinated individuals. Existing models regarding the evolution of influenza focus on either changes within hosts or between hosts. The main motivation behind the current research is to find out how both types of models can be combined. The results from the study can be useful in reassessing vaccination policies.