The term One Health surveillance (OHS) has recently begun to appear in the scientific literature (Bordier et al., 2018). Collaborative OHS has been defined and characterized (Bordier et ah, 2018; Berezowski et ah, 2015; Stark et ah, 2015). OHS transgresses traditional boundaries between disciplines and sectors. It is the collaborative efforts between the human and animal health sectors working together to define health events, conduct systematic collection of event data, analysis of this data, and a timely dissemination of information and knowledge to guide interventions aimed at preventing or managing human and animal disease (Karimuribo et ah, 2012; Stark et ah, 2015) defined OHS as “the systematic collection, validation, analysis, interpretation of data and dissemination of information collected on humans, animals and the environment to inform decisions for more effective, evidence and system-based health interventions.”

Proposed benefits of OHS include a greater understanding of the complex dynamics of zoonotic diseases, saving resources by reducing duplication of infrastructure and processes, and ultimately reducing the burden of disease in people and animals (Hattendorf et ah, 2017). These expected benefits are derived from the One Health principle that collaborating across sectors (human, animal, and environmental) is beneficial and that more is better. In other words, more or better information can be created by combining data from many sources. The value of combining data is made even more attractive by the promise of applying big data methods to health data (Mamlin and Tierney, 2016).

Methods and best practices for OHS have not been established. There is no consensus about what is or is not OHS, or what OHS can reasonably expected to accomplish. Methodological challenges are daunting, including how to integrate data that are collected, stored, and analyzed using different standards, definitions, scales, and techniques. Health surveillance practitioners have started thinking about the type of surveillance that will be needed to support decision-making for One Health programs (Berezowski et al., 2019). However, OHS is currently in its infancy.


All health surveillance systems are complex, involving different stakeholders, components, infrastructure, processes, policies, and regulations. They are often operationalized by government organizations that have jurisdiction over many people and animals. In this section, we describe common elements of health surveillance that exist across species. The intent is to focus on commonalities that might underlie the creation of surveillance systems using similar architectures and processes that ease data sharing and the creation of information and knowledge relevant to cross-sectoral decision-makers.

Public health surveillance is “the continuous, systematic collection, analysis and interpretation of health related data needed for the planning, implementation, and evaluation of public health practice” (WHO, 2020). Animal health surveillance has similarly been defined as “the systematic, continuous or repeated, measurement, collection, collation, analysis, interpretation and timely dissemination of animal health and welfare related data from defined populations.” These data are then used to describe health hazard occurrence and to contribute to the planning, implementation, and evaluation of risk mitigation actions (Hoinville et al„ 2013). A One Health surveillance system can be defined as “a system in which collaborative efforts exist between at least two sectors (among human health, animal health, plant health, food safety, wildlife and environmental health) at any stage of the surveillance process, to produce and disseminate information with the purpose of improving an aspect of human, animal or environmental health” (Bordier et ah, 2018).

The definitions above share a common purpose of creating information for health decision-making. Differences between them relate to the number of species that should be monitored to create information, nature, number, and diversity of domains that should collaborate and the number of species that should benefit from the decisions informed by the surveillance system. They agree that health surveillance should be a systematic and ongoing process that creates information that is used for decision-making in health (El Allaki et al„ 2012). Continuously creating information implies that these processes operate continuously or at least are repeated regularly. Information creation requires processes such as observation. and/or data collection, manipulation, cleaning, analysis, interpretation, and communication (El Allaki et ah, 2012). Figure 7.2 is a visualization of a generic

Attributes of a generic disease surveillance-control system

FIGURE 7.2 Attributes of a generic disease surveillance-control system.


Attributes of a Generic Health Surveillance System





Surveillance activities

Sampling and data collection that lead to the production of the required information; processing, cleaning, analysis, and interpretation to create information from observations and data and activities to disseminate the resulting information

Disease control activities

Responses, interventions, and other harm- reduction activities that have been indicated from wisdom resulting from surveillance information

Tangible and measurable outputs and outcomes

Surveillance information output(s)

Estimates of the current and predicted hazard risk in the population under surveillance. These outputs are often very specific and are defined by the decision(s) that must be made to control the disease

Harm reduction outcomes

Result from the actions that are set in motion because of a surveillance information output that reduce the harms of concern, such as mortality or economic or ecological impacts


Targets of surveillance

Agents upon which surveillance activities are directed to produce information

Targets of control activities

Agents upon which the control actions are directed

Targets of harm reduction

Agents that benefit from the disease control activity

disease surveillance-control system based on a general theory of surveillance. Table 7.1 defines the attributes of the model.

An example of an integrated disease surveillance-control program for West Nile Virus (WNV) in Italy will be used to illustrate the generic model (Paternoster et al„ 2017; Calzolari et al„ 2012; Angelini et al., 2010). WNV causes disease in humans, horses, birds, and other animals. It is transmitted by mosquitoes among birds, and from birds to horses and humans, who are dead-end hosts. The exception is that infected people can transmit the virus via blood and solid organ donations. The goal of this program was to mitigate the risk of WNV transmission to horses and humans and to reduce the risk of transmission between humans via blood transfusions and solid organ donations. The intended beneficiaries of the program (the agents of harm reduction in Table 7.1) were humans and horses living in the Emilia-Romagna region of Italy. WNV has a seasonal cycle in the region and the surveillance goal was early detection of increased risk of WNV circulation in the environment (surveillance outputs Table 7.1) that will trigger harm reduction actions. Surveillance activities that produced information included reporting and confirmation of cases of WNV in humans, horses, and birds, and screening of mosquito pools for the virus (Figure 7.2 surveillance targets). When increased risk was detected, specific preplanned actions were triggered, including informing the public about the risk so that people in the high risk area can reduce personal risk of mosquito bites and vaccinate horses; implementation of mosquito control activities; and testing of human blood donations for WNV (Figure 7.2 control activities). This was a continuous process that produced information for decision-making, involved multiple species, and was a collaborative effort involving human, animal, and environmental health. The program was reported to be very successful in reducing the health risk for the targets of harm reduction (people and horses) and demonstrated the benefit of a collaborative, multidisciplinary approach for dealing with a single-pathogen problem (Paternoster et ah, 2017). From a One Health perspective, the scope of the program was relatively limited as it considers only two species (humans and horses), ignoring other species such as wild birds, whose populations have been shown to be adversely affected when WNV is introduced (Byas and Ebel, 2020).

Many of the reported OHS systems have been single hazard systems with hazard targets that include zoonotic diseases, antimicrobial resistance, food-borne pathogens, and one report of an environmental hazard (Bordier et ah, 2018). Even though many are multisector collaborations, they were mainly designed with the goal of benefiting human populations. Ecosystem health and One Health have been promoted for dealing with health problems involving more than single hazards that benefit one population. Ecosystem health aims to achieve sustainable health for people, animals, and ecosystems (Buse et ah. 2018). Although the practice of One Health may be narrower in scope, the goals are similar (AVM A, 2008). The scope of practice of One Health is increasing as researchers and practitioners learn more about the interconnectedness between humans and non-human species and as they respond to new threats to health resulting from climate change and other global changes. There is an opportunity to start thinking about new approaches for dealing with current and impending health risks, especially those associated with global changes.

The scope of health surveillance will need to be expanded to meet the information needs of emerging One Health and EcoHealth programs. This may be accomplished by increasing the number of species targeted for information production and broader consideration of the harms and benefits from interventions. For example, control programs for mosquito-transmitted diseases can include spraying pesticides around homes and in cities to eliminate mosquitoes. This intervention can have harmful effects on people and the other species with which they live (Zikankuba et al„ 2019). To represent these species and the ecosystem as a whole, the processes for informing disease control and health promotion interventions requires knowledge and wisdom from a wide range of people. They could include discipline specialists who have practical knowledge and wisdom relating to the undesired or unintended effects of interventions; citizens and traditional knowledge holders such as farmers and gardeners who have local information on critical relationships that can influence the breadth of intervention impacts; and local residents who can inform decision-makers about prevailing values that can influence risk versus benefit determinations for intervention implementation decisions. In the preceding WNV example, surveillance targets and information production could be expanded to better inform current and future decisions about which type of control program would be most beneficial or desirable by the community. For example, surveillance could be expanded to produce continuous information about local pollinator insects and other species, as well as cases of respiratory or other syndromes in people that could be the result of pesticide spraying to control vector mosquitoes. This would allow the production of information about the non-target effects of WNV control programs and promote a more comprehensive view of the effects of interventions on the local healthscape (see Chapter 8 for more information on healthscapes).

Making It Happen

There are many governmental and non-governmental sources of surveillance and monitoring data, and information that could be used to create an integrated, realtime picture of the changing patterns of the target disease and the effects of the control measures on humans, animals, and the environment. Even though these data are available, there are challenges to combining or collating them (Wendt et al., 2014). Close collaboration will be required between many of the people involved in surveillance at all levels from data collectors to information technology technicians, to analysts, to epidemiologists, to policy and decision-makers (Bordier et ah, 2019; Houe et ah, 2019; Johnson et ah, 2018; Bordier et ah, 2018; Wendt et ah, 2014). One Health leaders will need to support these initiatives with policies, funding, and especially leadership that models collaborative behaviours (Johnson et ah, 2018).

Many surveillance roles will need to change to meet this vision of integrated, cross-sectoral surveillance. For example, data collected by one surveillance initiative in one domain may need to be expanded to capture additional data needed to meet the information needs of the overall surveillance-control program. Data cleaners and processors will need to create datasets that are standardized, allowing collation of multiple datasets (Wendt et ah, 2016). Disease control and response interventions across multiple species will need to be coordinated by a multi-domain, collaborative, overarching organization (Paternoster et ah, 2017). These changes will not be possible without changes in the attitudes of people working in disease surveillance and control in government departments (Antoine-Moussiaux et ah, 2019; Johnson et ah, 2018). Rethinking disease surveillance-control in these terms is not insurmountable. Changes are occurring, suggesting a shift in attitudes may be taking place. For example, some governments have established One Health offices and departments with the aim of promoting collaborative approaches within and outside of government (BLV/Food Safety and Veterinary Office, 2020; USDA-APHIS, 2020). There are examples of surveillance systems that integrate data from multiple sectors (Dente et ah,

2019; Hutchison et al.. 2019; Bordier et al., 2018), and tools have been developed to facilitate government collaborations (Errecaborde et al., 2017).

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