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.
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.
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.
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.
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.
Controlling and eliminating is never going to be easy and this issue of the SACEMA Quarterly is devoted to some of the recent developments that have been made in our attempts to manage TB.
Insufficient tuberculosis (TB) case finding constitutes a major barrier to effective TB control. Despite considerable progress in improving healthcare service availability and accessibility, many people worldwide who fall ill with TB have no access to quality care, particularly in countries with a high disease burden. Increasing efforts to close this enormous gap will be crucial in the forthcoming years to effectively reduce TB incidence and mortality worldwide. This article describes opportunities, current challenges and open questions towards intensifying TB case finding.
TB disease has been known by various names for thousands of years, and has of late been described in exquisite biological detail. Yet we still struggle to reliably answer the question: Does a particular person have ‘active’ TB? The available diagnostic tests have several limitations and perform poorly especially in developing countries where they are most needed. We need new point of care diagnostic tests, be able to accurately distinguish between TB infection and TB disease and have tests which accurately predict cure.
With the current situation in South Africa, showing only a modest decline in new TB cases since 2012, new avenues and strategies to identify TB cases need to be explored, tested and implemented. Systematic symptom screening in high risk populations, when this translates to screening everyone in the community, is not very sensitive or cost-effective. The TB programme might therefore consider screening all individuals at primary healthcare facility level, irrespective of their reason for attending. The use of a screening tool with improved sensitivity in comparison to symptom screening alone would be preferable, followed by the current diagnostic algorithm.
Models of HIV and TB are well established and it is tempting to model the combination of HIV and TB by repeating a suitable TB model a number of times corresponding to the various states of HIV. This can, however, lead to a very complex model with tens, if not hundreds of parameters, requiring considerable computing power to run. Fortunately, the time scales over which the two infections progress are very different, allowing us to greatly simplify the problem.