Disease outbreak response: why epidemiology plays a central role

requirements for a wide multi-disciplinary approach, and the importance of training and preparedness activities to facilitate rapid response


Introduction
The case for controlling outbreaks Transboundary animal diseases have been defined as highly contagious epidemic diseases that can spread extremely rapidly, irrespective of national borders [1]. The need to control them is recognised internationally. New contagious diseases introduced into naïve livestock populations can have serious public health, animal welfare and societal consequences and in extreme cases affect the national economy of a country. This led to the creation of the World Organisation for Animal Health (WOAH, founded as OIE) in 1924 to enhance preparedness and standardise control of transboundary livestock diseases at a global level. Today, the Organisation provides a standardised reference frame of diseases, surveillance and control mechanisms for the WOAH-listed diseases [2]. The Food and Agriculture Organization of the United Nations also recognises the potential disastrous consequences that animal diseases can have on society, and established the Emergency Prevention System for transboundary animal and plant pests and diseases to support early warning and early reaction to transboundary infectious animal diseases [3]. The argument for 'early' is to limit the consequences, including the potential for the pathogen to jump into populations, where control is more complicated e.g. wildlife, or has greater impact e.g. zoonoses.

The case for understanding outbreaks in order to control
Once a WOAH-listed transboundary disease is detected in a member country (outwith quarantine), an outbreak is declared, and emergency control plans are triggered. There is a need to quickly identify, contain 41_2_19_Avigad_preprint 3/19 and eradicate the infection, and in many cases the outbreak is controlled by managing the primary case and possibly a few traced contacts.
However, uncertainty is inherent in outbreak control and during the outbreak it is difficult to predict the ultimate size due to uncertainty around identification of infected holdings. Without an understanding of the primary (initial introduction) versus the index (first detected) case, mode of introduction, spread, pathogen and population, control efforts will be reactive rather than preventive. As understanding of the outbreak grows during the control stages, identification of infected information are high quality, they do not explain the outbreak and therefore further analysis, collation and interpretation of all strands of information are needed to transform it into epidemiological evidence and further communicate this.

Types of information
The types of information commonly generated during an outbreak include: − population data and maps showing animal density and location; − clinical history; − test results; − pathogen characteristics; − disease epidemiology and relevant risk factor information such as assessment of wild bird presence in relation to avian influenza; − premise information including production data, production chains, husbandry practices and farm layout; − disease timeline, tracing and contact information detailing movements on/off the farm; − predictive modelling.

Actions needed to understand outbreaks and inform control measures
From a disease control perspective, once the index case is identified and contained, a critical next step is to identify and contain further transmission of infection. A timeline for the likely source and spread period must be estimated so that high risk contacts for potential spread and likely source of infection can be identified. Surveillance must be designed to ensure the detection and containment of locally transmitted infection. Equally to help understand wider related impacts, modelling 41_2_19_Avigad_preprint 6/19 may be commissioned to predict the likely size of the outbreak and determine the relative impact of potential control options.

Interpretation and communication of the information
The information needed must be elicited from a variety of sources and may not be standardised. However, it needs to be rapidly collated and interpreted, acknowledging the existing assumptions and uncertainty.
The types of uncertainty often relate to the quality and timeliness of the data, test availability and characteristics, understanding of the pathogen including factors such as infectious dose, survival time and conditions, on-farm recording including personnel and vehicle movements. This collated evidence then needs to be communicated to decision-makers in a timely and easily assimilated way, including through the production of risk assessments to aid understanding of the uncertainties. Additional information may need to be sought to reduce outstanding uncertainty, such as further laboratory testing or interviews with the animal keepers about on-farm events or practices.

Results: the need for a central evidence team and the National Emergency Epidemiology Group example
The requirement for a central evidence team requirements will need to be adhered to but within this framework, adjustments may need to be made to account for resourcing, infrastructure and logistical limitations, as well as the trade-off between the cost and impact of different interventions and their potential to control disease. This is also needed to inform discussions on the wider impact of the outbreak on the primary production chain, animal welfare, international trade, public health and the wider economy and environment.
The case for epidemiology leading the central evidence team The epidemiology discipline provides a good framework for bringing together, weighting, comparing and challenging conflicting evidence, as well as understanding, interpreting and communicating uncertainty.
An epidemiological approach can also make best use of the varying levels of quantification in information from qualitative risk assessments to more in-depth quantitative analysis and modelling.
Epidemiologists are well placed to identify and bring these disparate sources of information together. Whilst understanding the various disciplines and information sources, they seldom generate the primary evidence e.g. test results or production data. This may help prevent confirmation bias, which could arise when scientists find evidence for a favoured theory, and may become insufficiently critical of their own results or cease searching for contrary evidence [8].

Example: the National Emergency Epidemiology Group
In the United Kingdom, a group called the National Emergency  multiple, independent sources of evidence and replication are much more convincing and disease specialists may not be best placed to bring all this together [8]. There is also the question as to whether separation from the production of the primary evidence is needed to ensure epidemiology in helping to define the parameters of an acceptable level of risk' [12]. The value of epidemiologists working alongside policymakers and as part of a wider multi-disciplinary team has also been highlighted in a number of papers [12,13,14,15,16] highlights the need for a multi-disciplinary team including social and political science input as well as the need for local investigators leading on operational research [21]. The key role of epidemiology as part of a wider multidisciplinary team in human disease outbreak response has also been identified in several other publications [22,23,24].

Types of epidemiologists
In the NEEG example, epidemiologists are separated into two key areas: − the field epidemiologists;

Maintaining expertise between outbreaks
To be able to rapidly respond to outbreaks of transboundary animal disease, sufficient training and preparedness activities need to continue between outbreaks [25].

Conclusions
The case for the central role of epidemiology in leading the provision of evidence to inform transboundary animal disease outbreak response has been presented in this paper. It underlines the relevance of the mix of different skills that are unique to epidemiology training and the importance of the impartiality that derives from not being a generator of the primary data. It illustrates how epidemiology binds together a wider multi-disciplinary team and enables collation, translation, and communication of the evidence to inform the rapid decision making often required during outbreaks. It is noted that whilst the rationale for the central role of epidemiology has been described, this does not mean that all epidemiological functions need be in one location with substantial benefit observed from some regional activities. Finally, the importance of preparedness and training activities is stressed to ensure rapid response. This not only includes epidemiological skills, expertise, information sources and outputs but also understanding of the linkages with other teams and disciplines that are key to effective outbreak response.