HEALTH SURVEILLANCE IN COMPLEX DYNAMIC SITUATIONS

The generic model of health surveillance presented above is insufficient for capturing the complexity that is present in most operational disease surveillance- control systems. The model is not well suited for capturing the characteristics and contextually of the relationships and collaborations that can be present in One Health programs, or the different types of decisions being made in a One Health context. It also does not capture some of the other uses for health surveillance information in disease control, and. of particular importance, it does not capture the continuous nature of surveillance information production.

At a population level, the occurrence of new cases of most diseases is a continuous process. The incidence of new cases can vary considerably depending on the changing characteristics of the hazards and dynamic socio-ecological determinants of disease transmission or occurrence (in the case of non-infectious disease) (Da Costa et al.. 2019; Homer and Hirsch. 2006). To be effective, health surveillance must create information about changing disease conditions at a rate that is appropriate for the disease and the decisions that need to be made to deal with it. Making decisions for slowly changing processes like the obesity epidemic will require information at a much slower rate than fast moving processes such as the recent emergence of a novel coronavirus in China and its rapid intercontinental spread (Munster et al., 2020).

The rate of occurrence of new cases of disease in a population of any species is the result of complex processes made up of many interactions between many agents (humans, animals, plants, microbes, organizations, societies, governments, etc.) and the environment. These collections of agents and the environment are called complex adaptive systems (CAS) (see Chapter 13 for more information on complexity in health). CAS are ubiquitous. They are self-organized hierarchical structures of nested systems. For example, metabolic pathways are CAS nested within cells, which are CAS nested within organ systems, which are CAS nested within animals, which are CAS nested within farms, which are CAS nested within local ecosystems, and nested also within production systems, and so on. There are many interactions between individual actors within levels of the hierarchy and up and down the hierarchy. The constant interaction within and between the levels results in the system being in a constant state of change at all levels of the hierarchy. These systems are not isolated from their surroundings. For example, there is no physical structure that bounds the local ecosystem within which a farm is located. There are external inputs into the farm, such as changing weather conditions, and there is communication and exchange of materials over short and long distances. Farmers purchase animals, feed, and other commodities and receive visitors from locations that may be many kilometres distant from the farm. All of these are the channels through which information and hazards can be introduced onto the farm. Surveillance systems for the Anthropocene will need to be adaptable to these types of complex, dynamic situations.

An adaptive management system for health surveillance

FIGURE 7.3 An adaptive management system for health surveillance.

Adaptive management provides frameworks that can be tailored to health surveillance. Adaptive management is a continuous, cyclic process that has been extensively used to model continuous decision-making processes in business (Landstrom et al., 2018), ecology (Jokinen et ah. 2018), and it has recently been proposed for surveillance and disease control (Miller and Pepin, 2019; Stark et ah, 2018). Adaptive management cycles begin with a situation requiring improvement. Decision-makers wanting to improve the situation design and implement policies and actions to change the situation. The situation is then monitored to evaluate the effect of the policies and actions. This is followed by a critical re- evaluation of the situation to determine whether the desired improvement has been achieved (Jokinen et ah, 2018).

Figure 7.3 blends adaptive management and health surveillance concepts with decision-making for health management and disease control. We explain the model by considering a common situation such as when an endemic disease is present in a population at an acceptable level. If the disease increases to a certain level, control actions would be considered to reduce the level of disease in the population. We begin the description of the adaptive management cycle when surveillance activities are underway to assess the status of the disease (Surveillance Activities in Figure 7.3). As in the generic surveillance model (Figure 7.2), surveillance outputs are estimates of the current disease risk. The decision of importance (decision-making in Figure 7.3) is whether the current estimated disease risk (Surveillance Output in Figure 7.3) is high enough to warrant an intervention or not. If it remains at an acceptable level, no intervention is needed, and surveillance activities continue to generate risk estimates that are continuously evaluated by decision-makers. However, if the surveillance risk estimate exceeds an acceptable level, an intervention will be warranted, and control activities will be planned and implemented. In many cases, control activities are preplanned as in responses to known emergencies, where pre-established plans are activated when significant risks are identified by surveillance.

An example of this adaptive management model for health surveillance is the Kyasanur Forest Disease (KFD) control program implemented by Western Indian State Health Departments in the regions endemic for the disease (Shah et al., 2018). KFD virus is a tick-borne virus that causes severe, sometimes fatal, disease in humans (case fatality ranges from 2% to 10%) and monkeys in Western India (Kasabi et ah, 2013). There is currently no definitive treatment for the disease (Oliveira et ah, 2019). Virus reservoirs include small forest-dwelling rodents, shrews, bats, and birds (Oliveira et ah, 2019). Epidemics in humans and monkeys can occur when local environmental conditions support the proliferation of ticks. A surveillance system maintained by state health departments operates continually during the tick season. It targets humans (reports of suspect and confirmed clinical cases), monkeys (reports of dead monkeys by villagers), and ticks (population density estimates), outputting information about the human risk of infection. When the surveillance system detects an increase in risk in a geographic region (surveillance output), a decision is made whether to implement preplanned disease control activities or not. These can include vaccination, tick control, public awareness campaigns informing the public to take personal protective measures against tick bites, and avoiding dead monkeys (Gupta, 2020).

This cyclical adaptive management model is very simple; however, it can be generalized to most disease surveillance-control situations that have been reported (Hasler et ah, 2011):

1. A known pathogen of importance that is not currently present in the population and a response is required if the pathogen is introduced.

Surveillance activities will be targeted towards identifying a confirmed case as quickly as possible (surveillance output), and the decision will be to respond or not, to eliminate the pathogen from the population.

2. The emergence of a previously unknown pathogen that causes significant harm and requires an intervention.

Surveillance activities must be quite broad to detect the emergence of any new disease as quickly after the occurrence of the emergence as possible (surveillance output). The decision about whether to respond may be quite difficult, especially in the early stages of the disease emergence, as little will be known about the potential harm that may be caused by the new pathogen.

3. The occurrence of an epidemic of an important disease that escapes the current control activities.

The surveillance system produces a variety of information (incidence, geographic distribution, production types affected) that is used to develop a new more effective control strategy and decide about the type of intervention.

4. During the time when disease control activities are ongoing.

Surveillance produces information about the changing amount of disease in the population. This information is used to evaluate the effectiveness of control activities. In this case, the decision is whether to continue with the current control activities or to consider new ones.

 
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