Predicting Epidemics

Dr. Hiroshi Nishiura, Editor-in-Chief of Theoretical Biology and Medical Modelling, discusses what the current biggest epidemics are, where the next big epidemic will come from and how we will cope with it and if we will be able to successfully predict and prevent epidemics in the future.

nishiura_picture_uprevProfessor Hiroshi Nishiura is an Associate Professor at Graduate School of Medicine, The University of Tokyo. After working 10 years for different infectious disease modeling groups at Imperial College London, University of Tuebingen (Germany), University of Utrecht (The Netherlands) and the University of Hong Kong, he returned to Japan in 2013, launching a real-time epidemic modelling unit and starting to building up research capacity and intensify collaborations with governmental entities using mathematical models.

His research interests span the areas of statistical epidemiology of infectious diseases, epidemiological modeling and biomathematical formulation of the transmission dynamics of infectious diseases.

He aims to answer policy-relevant questions by integrating various mathematical models with empirically observed data. He is the Editor in Chief of Theoretical Biology and Medical Modelling, an open access, peer-reviewed, online journal that adopts a broad definition of ‘biology’ and focuses on theoretical ideas and models associated with developments in biology and medicine.

 What are the biggest epidemics at the moment?

The ongoing worst epidemic is AIDS which has continuously grown since 1981. HIV infection has been gradually brought under control in many industrialized countries, but there remains a number of countries with steadily growth of HIV incidence.

The epidemic in Sub-Saharan African countries have been particularly large, although the ongoing ‘test-and-treat’ strategy, i.e., an expanded diagnosis and treatment promoted by the United Nations Programme on HIV/AIDS (UNAIDS), has just been demonstrated to be very successful in Africa.

Apart from HIV/AIDS, we have to remember that the timespan of observing recent emerging epidemics has become shorter than before. In addition, the growth of recent emerging disease outbreak has been much faster than slow diseases such as HIV/AIDS and tuberculosis.

As human beings come to experience greater opportunities to be exposed to animal reservoir host in natural settings than before, and because of intensified international travel, the chance to encounter emerging disease epidemic has grown with time, and the spread of epidemic has become faster than in the past. Considering these points, the epidemic of Ebola virus disease in West Africa has been certainly the biggest threat to mankind in the first two decades of the 21st century.

What do you think will be the next epidemic and where is it likely to come from?

The reason why we do not have a clear idea is that the ecology of microbial organisms in wild life has been poorly understood.

I have no idea as to what pathogen will likely cause the next epidemic of social concern. That can be caused by a known virus, but can also be due to a completely unknown novel bacterium. The reason why we do not have a clear idea is that the ecology of microbial organisms in wild life has been poorly understood.

Rather than studying the transmission dynamics in humans only, many disease prediction could be dramatically improved by elucidating the mechanisms by which a novel pathogenic strain of a virus or bacteria species emerges. For similar reason, human-animal interface, i.e. the mechanism of spill-over of pathogen from animals to human, has also been just started to be seriously explored.

Not only efficient vaccination campaigns in humans but also for the sake of improved understanding of the emergence of novel zoonosis, mathematical models exert their great power. I would highly rate future modelling studies that focus on animal reservoirs and contribute to our understanding in wild life and livestock.

How do we cope with epidemics?

Countermeasures against epidemics can be classified into specific and non-specific measures. Specific intervention includes the use of antibiotics/antivirals and vaccines. They can act very specifically and directly against the pathogen or elicit specific immunity, and currently approved specific interventions have been all demonstrated to be substantially effective through epidemiological studies.

Nevertheless, specific interventions have to be prepared in advance of any epidemics, while it is frequently the case that the future strain that will be responsible for a coming epidemic remains unknown. In the case of a pandemic influenza, it takes at least six months to produce substantial amount of vaccines following an emergence and isolation of the novel virus strain.

Non-specific interventions include the case isolation, contact tracing and quarantine. Behavioural intervention and the so-called “social distancing” such as school closure are also classified as non-specific interventions. Due to the non-specific nature, these countermeasures are applicable to a variety of diseases, e.g., Ebola virus disease, especially in the absence of specific countermeasures.

Nevertheless, the quantitative evidence of non-specific (non-pharmaceutical) interventions are in general very scarce. During the last decade, I have been personally very interested in measuring the effectiveness of non-pharmaceutical interventions, but even still not halfway to the goal.

Is global travel turning localised outbreaks into epidemics/pandemics? How can we control this?

There is no doubt that the global travel has contributed to accelerating the international spread of emerging infectious diseases. This point is well supported by the success of the use of airline travel network data coupled with epidemic modelling in predicting the geographic/global spread of the epidemics including severe acute respiratory syndrome (SARS) from 2002-3 and influenza H1N1-2009.

Full travel restriction (not a partial restriction) could effectively delay the epidemic, but it’s difficult to fully shut down communication between two countries.

An associated problem is that it is not very hopeful to manually control such global spread. Screening airline passengers at exit or entry has been demonstrated to be of limited effectiveness, and there has been a difficulty in justifying the cost and effort for such screening.

Full travel restriction (not a partial restriction) could effectively delay the epidemic (e.g. in the case of Ebola virus disease), but it’s difficult to fully shut down communication between two countries and such a countermeasure (i.e. complete shut-down of a border) may not be fully justified.

Rather than intending to control the inter-country spread, experts presently recognize the fact that we must face the risk of any infectious diseases arising from anywhere in the world. That is, rather than expecting the border control to detect and protect the society, each country/community should possess a good capacity of surveillance, and if necessary, small outbreak caused by an importation should be detected early and contained locally.

What are your thoughts on technological advances to predict epidemics?

When it comes to the prediction, infectious disease modelling has seldom been successful in forecasting past epidemics. This is partly due to the fact that the expectation for our forecasting in the public has been too large compared to other applications of forecasting, e.g. compared to weather forecast that predicts tomorrow or day after tomorrow, epidemic models are frequently expected to predict a moth or even a year later.

The other issue is that there are heterogeneities in many aspects of the transmission dynamics, contributing to increased uncertainty. For instance, the uncertainty bound (e.g. 95% prediction interval) of HIV incidence in 2000 based on 1988 study was extremely large, because initially it was very hard to see if the epidemic widely spread among heterosexual individuals.

There has been great progress in three particular technical subjects in drastically improving the prediction.

Besides, there has been great progress in three particular technical subjects in drastically improving the prediction. First, the use of pathogen genome data could shed light on the recognition of emerging pathogen strain in animal(s).Using data mining or machine learning techniques, it has become realistic to detect an early signature of the evolution from genomic data.

Second, examining the airline transportation network data and running a metapopulation model on that complex network, it has been shown that the real time forecasting of the geographic spread is quite feasible.

Third, increasingly many mathematical models are fitted to empirical data, and previously unquantifiable epidemic dynamics have become quantifiable. This trend is of course supported by the propagation of novel Bayesian techniques including particle filtering and approximate Bayesian computation, but in addition, I should stress out the fact that many modelling techniques have become widely available (such as backcalculation technique and renewal equation).

Do you think we will be able to successfully predict and prevent epidemics from happening in the not so distant future?

I am sure that we will eventually be able to predict emerging epidemics at a certain acceptable success probability. That will greatly assist associated prevention and control programs. We are on the clear path to the eventual full recognition of emerging infectious diseases, and moreover, the control of existing vaccine-preventable infectious diseases are also on the path to be well controlled by respective immunization programs.

But we should always consider potential drawbacks of such successful future. Once a serious infectious disease is fully cleared out from our society, human susceptible host will be left as completely naive to that pathogen.

Sarah Theissen

Sarah Theissen has a BSc in Applied Biosciences (Cork Institute of Technology) and a PhD in Endocrine Oncology (Royal College of Surgeons in Ireland). She worked as a Postdoc at Baylor College of Medicine in Houston before joining BioMed Central in 2014. In her role as Senior Journal Development Editor, she works on a portfolio of oncology journals.

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