The far-reaching, highly ambitious Sustainable Development Goals (SDGs) build upon the momentum generated by the Millennium Development Goals (MDGs) and are intended as a guide for health, social and economic initiatives until 2030. Implemented correctly, the STI agenda may well fit better within the SDGs than the MDGs, although that does not become directly clear at first glance. For refocusing attention on the control of STIs in the forthcoming years we propose a framework, most especially within low- and middle-income countries (LMICs).
Other infectious diseases
HIV and human papillomavirus (HPV) are two heavy hitting sexually transmitted infections (STIs). Meta-analyses of the association between HPV prevalence and HIV acquisition and the association between HIV prevalence and new HPV detection have estimated a two-fold increased risk in both directions, after adjusting for individual-level (sexual behavioural) factors. The studies argue that biological mechanisms may be responsible for these increased risks, but they also concur that residual confounding due to behaviour at the sexual network level cannot be ruled out. We used an individual based model to shed some light on the matter.
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.
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.
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.
Foot-and-mouth disease (FMD) can affect a range of animals, including livestock. In South Africa, successful control has eliminated the infection from most of the country, however, infection risk remains in the areas surrounding the Kruger National Park. African buffalo (Syncerus caffer) maintain a high prevalence of FMD. Understanding how buffalo maintain the infection, when transmission from buffalo is most likely, and why transmission occurs are important to understanding FMD in South Africa. This article discusses an individual-based model that is guiding our field and experimental data collection in Kruger National Park.