Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

Journal article

The 1918 influenza pandemic is one of the most devastating infectious disease epidemics on record, having caused approximately 50 million deaths worldwide. Control measures, including prohibiting non-essential gatherings as well as closing cinemas and music halls, were applied with varying success and limited knowledge of transmission dynamics. One hundred years later, following developments in the field of mathematical epidemiology, models are increasingly used to guide decision-making and devise appropriate interventions that mitigate the impacts of epidemics. Epidemiological models have been used as decision-making tools during outbreaks in human, animal and plant populations. However, as the subject has developed, human, animal and plant disease modelling have diverged. Approaches have been developed independently for pathogens of each host type, often despite similarities between the models used in these complementary fields. With the increased importance of a One Health approach that unifies human, animal and plant health, we argue that more inter-disciplinary collaboration would enhance each of the related disciplines. This pair of theme issues presents research articles written by human, animal and plant disease modellers. In this introductory article, we compare the questions pertinent to, and approaches used by, epidemiological modellers of human, animal and plant pathogens, and summarize the articles in these theme issues. We encourage future collaboration that transcends disciplinary boundaries and links the closely related areas of human, animal and plant disease epidemic modelling.

Authors: Thompson, R; Brooks-Pollock, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society
• Journal: Philosophical Transactions B: Biological Sciences
• Volume: 374
• Issue: 1775
• Article number: 20190038
• Publication date: 2019-05-06
• Acceptance date: 2019-05-01
• DOI: 10.1098/rstb.2019.0038
• EISSN: 1471-2970
• ISSN: 0962-8436
• Keywords: public health; plant disease; animal disease; human disease; mathematical modelling; FFR