Weak signal detection: A discrete window of opportunity for achieving ‘Vision 90:90:90’?

Figures & data

Fig. 1. The conceptual foundations. Source: authors’ contribution.

Table 1. Complex Adaptive Systems.

Agents – People, human processes (rumour, gossip, logical reasoning) and computer systems are examples of agents; the more diverse the agents, the more likely it is that novel behaviour will result from information exchanges (Cilliers 1998; McDaniel & Driebe 2001)

Interconnections – Agents interact: Agents interact and exchange information through relatively rich means (Cilliers 1998), creating connection among all agents in the system (Capra 2002; Kauffman 2005) Interactions are non-linear means that small ‘causes’ might have large effects and large ‘causes’ might have small effects (Axelrod & Cohen 2000; Capra 2002; Kauffman 2005)

Self-Organisation – is the process by which people mutually adjust their behaviours in ways needed to cope with changing internal and external environmental demands (Cilliers 1998); agents have ‘freedom to interact, align and organize into related configurations’ over time (Lee 1997); thus, agents self-organise (McDaniel & Driebe 2001)

Emergence – ‘Agents interacting in a non-linear fashion may self-organize and cause system properties to emerge’; however, the emergent properties of the whole are distinct from the properties of the agents (Capra 2002; Holland 1998; Johnson 2001)

Co-evolution – Complex adaptive systems are open, and thus agents interact with others in the environment extending the interactions and information exchanges beyond the system boundaries (Boisot & Child 1999; Camazine, Deneubourg, Franks, Sneyd, Theraulaz & Bonabeau 2001; Stacey 1995); both the system and world change through these interactions; the system's current and future behaviour is intricately linked to its history (McDaniel & Driebe 2001)

Source: adapted from Anderson, Crabtree, Steele and McDaniel (2005).

Fig. 2. The virus–human–environment ‘system’ – complex patterned interactions create emergent phenomena associated with HIV/AIDS. Source: Burman, Aphane & Delobelle (2015:16).

Table 2. Phenomena that emerge from within components or between components of the human–virus–environment heuristic.

Emergent phenomena/intervention typology	Primary relationships/feedback loops	Purpose of intervention	Examples of patterns & effectiveness
Biomedical – such as treatment as prevention (TasP)	Between the virus–human components	Suppressing the viral load because an undetectable viral load makes it almost impossible to transmit the virus	Example: consistent use of medication once diagnosed HIV+ and a CD4 count of <500
			Impact: in the laboratory – very effective (Rodger et al. 2014); in naturalistic, ‘real world’ context – mixed/problematic (Grimsrud, Cornell, Egger, Boulle & Myer 2013; Nguyen et al. 2011) [involves complex interdependencies]
Structural –changing laws or change in social norms/values	Between the virus–human–environment components	Different contexts constrain/enable different behaviours enabling decreased/increased health promoting behaviour	Example: legalise sex work (Shannon et al. 2014) and same-sex relationships (UNAIDS 2014b) and environmental/co-infections (Modjarrad 2013)
			Impact: legal – problematic (UNAIDS 2014b); environmental/co-infections – treating TB/STIs – mixed problematic (Shisana et al. 2014) [involves complex interdependencies]. Managing other co-infections – hardly on the radar screen in RSA (Mkhize-Kwitshana & Mabaso 2014)
Behavioural – barriers to transmission of the virus, such as condoms or microbicides	Between the virus–human components	Put a physical barrier between HIV+ people and other people so virus cannot move from one body to another (containment)	Example: knowledge of status & appropriate use of particular barrier method
			Impact: variable/problematic (Shisana et al. 2014) [involves complex interdependencies]
Outlier – window period	Within the virus component of the virus–human–environment heuristic	No intervention to date	Example: during the window period, the body does not recognise that the virus is in the body; so the virus is able to replicate exponentially (Abu-Raddad et al. 2013)
			Impact: tackling the window period – which accounts for over 40% of new infections (Pettifor et al. 2011) could have a significant impact on the trajectory of the epidemic (Parkhurst & Whiteside 2010) [but it would involve managing complex interdependencies]

Source: authors’ contribution.

Fig. 3 The Cynefin framework. Source: http://en.wikipedia.org/wiki/Cynefin.

Table 3. The Cynefin framework and HIV/AIDS.

Quadrant	Knowledge/decision-making domain → management response	Examples
Obvious problems (solutions are known) – such as following a protocol, may encompass some basic issues of technique and terminology, but once these are mastered, following the ‘recipe’ carries with it a very high assurance of success	Analytical/reductionist evidence-based action/s → Sense-categorise-respond (a known best practice)	TasP; PMTCT; pre- and post-exposure prophylaxis; reduction of co-infections; correct and consistent condom use
Complicated problems (solutions are knowable) – contain subsets of simple problems but are not merely reducible to them. Their complicated nature is often related not only to the scale of a problem such as open heart surgery, but also to issues of coordination or specialised expertise. Complicated problems, although their solutions are generalisable, are not simply an assembly of simple components	Potentially ascertainable by application of evidence-based methods → Sense-analyse-respond (good practices exist but they are not 100% fool-proof)	Respond to drug resistance with an alternative medication and monitor for side-effects then adapt as necessary; management of the intersection of HIV with other chronic conditions; periconception decision-making for serodiscordant couples
Complex problems (solutions are understandable with time) – can encompass both complicated and simple subsidiary problems, but are not reducible to either, as they too have special requirements, including an understanding of unique local conditions. Historical adaptive, self-organisational social networks with observable patterns that are neither predictable nor generalisable, yet can open up numerous possibilities	Non-predictable, but potentially understandable by pattern observation → Probe-sense-respond (pattern management/safe-fail experimentation)	Carefully controlled interventions such as the microfinance initiative that amalgamated novel combinations to influence the structural drivers of HIV/AIDS (Pronyk et al. 2006)
Chaotic problems (solutions are not understandable until stabilised) – implies being out of control, with no discernible order. Yet chaos can break old restrictive connections and liberate space for a new order. Retrospective analysis of how chaos is managed and capitalised on is essential evidence for understanding systems	True chaos represents a major system failure and requires charisma or dictatorship to stabilise the situation → Act-sense-respond (crisis management)	The emergency response to HIV/AIDS in the Philippines by Health Minister Flavier in the 1990s which ushered in the ‘abstain, be faithful, condomise’ campaign to raise awareness that unprotected sex was risky (Hardee et al. 2013)
Disordered problems – applies to situations where there is no consensus about the knowledge type that is the focus of the challenge	Intuitive debate/argument. People tend to favour future actions based on their particular frame	A national South African report recommending that ABC can ‘equip the population with basic knowledge about HIV prevention’ (Shisana et al. 2014:xl) compared with other suggestions that the global mind-set needs to shift towards managing the epidemic as a chronic disease (Dybul 2014; UNAIDS 2014a)

Source: adapted from Martin and Sturmberg (2005).

Table 4. Themes that were surfaced through the Archetype Extraction technique.

Surfaced themes	Management response typology (the Cynefin framework)
Community ‘assets’	Obvious	Complicated	Complex	Chaos
Some people adhering to medication		✓	✓
Dedicated health care workers (including nurses and home-based carers)	✓	✓
Dedicated/supportive community members	✓	✓
Human rights activism		✓
Voluntarism		✓
Young women committed to education	✓	✓
Dedicated drop-in-centre workers	✓	✓
Community ‘challenges’	Obvious	Complicated	Complex	Chaos
Loss to follow-up/non-adherence		✓	✓
Mental health challenges			✓	✓
Sense of hopelessness amongst youth		✓	✓
Alcohol abuse		✓	✓
Stigma (including clinic staff members, community members and children)		✓	✓
Conflict between traditional medication and traditional medication		✓	✓
People supplying ‘fake medicine’ in the community			✓
Intergenerational sex (Ministers of Transport/Finance)			✓
Multiple partners			✓
Teenage pregnancy			✓
School drop-out due to pregnancy		✓	✓
Violence within the community (including knives being brought into drop-in-centres by children)			✓	✓
Conflict between traditional values and modern values			✓

Source: authors’ contribution.

Fig. 4. The Vicious Tollgate: surfacing Whoonga in 2011. Source: authors’ contribution.

Fig. 5. The ‘Opinionated Idiot’ archetype. Source: authors’ contribution.

Fig. 6. The Cycle of Treatment-Seeking Behaviour in Ga-Dikgale. Source: authors’ contribution.

Fig. 7. Development themes coded by ‘regularity’ (n = 1087 micro-narratives). Source: authors’ contribution.

Fig. 8. The same data filtered by ‘something the community will never accept. Source: authors’ contribution.

Fig. 9. The same data when viewed through ‘Landscape’. Source: authors’ contribution.

Fig. 10. Themes from Uganda and Kenya and the number of beds per clinic represented by the size of the circle. Source: Maxmeister (2014).

Fig. 11. The number of times different themes were mentioned in micro-narratives in the GlobalGiving project. Source: Maxmeister (2014).

Fig. 12. Historical data using Landscape. Source: Cognitive Edge training materials, available at http://cognitive-edge.com/uploads/presentations/CE%20Accreditation%20SENSEMAKER%20feb%2008.pdf. Reproduced with permission.

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