Preface: 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'

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

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. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

How education did (and did not) accentuate partisan differences during the Ebola outbreak of 2014-15

Education exacerbates partisan gaps in scientific knowledge and attitudes. However, previous findings about the extent and symmetry of this moderation have been mixed. As a conceptual replication of previous research, this study examines whether education asymmetrically moderates the Democrat-Republican gap in attitudes about Ebola virus disease (EVD) and policies to combat EVD. Weighted data from a survey of 1,461 non-institutionalized adults drawn from a probability-based panel were collected during the 2015 EVD epidemic. The survey measured seven attitudes: fear of personal infection, estimated severity of Ebola, suspicion of exposed Africans, suspicion of exposed Americans, Western government preparedness, support for low-intensity interventions, and support for high-intensity interventions. Knowledge about EVD was also measured. As in prior studies, highly educated Democrats uniquely diverged from other respondents in some attitudes. However, in the other attitudes, there were main party and education effects but no evidence that education was a moderator of partisan differences. Overall, education moderated partisanship when attitudes were affect-laden and targeted toward immediate threats, but not when attitudes were policy-oriented.