**8. Epidemiological implications of the** *Portfolio Effect***: Malaria transmission systematically tends to be more stable than it appears**

Mosquito dispersal, human movement, heterogeneities in the intensity of transmission, and over-dispersed distributions of parasite infection durations have all been recognized as factors that stabilize malaria transmission and frustrate efforts to eliminate it [23]. Indeed, the importance of all these phenomena has been illustrated with a range of different mathematical models. However, all models are systematically biased to underestimate the stability of complex biological systems simply because they are models [23, 76, 77]. Conservation biologists have recently adopted the *portfolio effect* concept from economics, to guide their thinking in relation to ecosystem management. Diversification stabilizes investment portfolios, thereby reducing risks of catastrophic losses [78]. The same is true of complex, biologically and environmentally diverse ecosystems, which are always more stable than any of their component species, habitats or subsets thereof [79]. Mathematical models are deliberately designed to be simpler than the biological system they are intended to mimic [23, 76, 77], so they systematically underestimate their complexity, stability and resilience. Malaria transmission therefore tends to be more stable and less vulnerable to control than face-value interpretation of predictive mathematical models suggest (Killeen & Reed, Unpublished).

in wealthier, cooler, malaria-free countries, nor do we suggest that the capacities they bring to the table are anything less than invaluable. However, the existing *status quo* is neither effective nor fair, and will persist until it is deliberately addressed with far more south-centred funding schemes and productive data sharing mechanisms (**Figure 9**). The time has come for the systematic redistribution of funding investment, to unambiguously prioritize locally-owned and governed institutions in the low-income countries struggling with malaria on an ongoing

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And data governance structures that incentivize productive *south–south* and *south–north* (as distinct from *north–south*) collaborations are equally important. For many surveillance staff, investigators and institutions in developing countries, ownership of their data and the analytical opportunities it provides constitute their most important means leverage when negotiating fair conditions in collaborations through which they can develop their data handling and analytical capacities. South-centred platforms for archiving and sharing data, that empower data collectors and incentivize development-friendly collaborations with expert partners from high-income countries, are urgently needed. Looking beyond entomological surveillance, invaluable lessons may be learned from the encouraging experiences of regional and

The funding and data sharing policies that have shaped the global capacity distribution illustrated in **Figure 7** need to be progressively and aggressively reformed. Vocal advocacy for such strategic changes are a job for everyone in the malaria surveillance community. Each of us are, in our own way, responsible for the landscape as it stands today, and have no-one to blame but ourselves if such inequities and inefficiencies are allowed to persist. Unless we all play our part in actively finding solutions, we must accept that we are passively perpetuating the problem.

Considerable progress towards development and deployment of a much broader diversity of vector control tools can be achieved through far more widespread adaptation of established entomological field methods to programmatic surveillance platforms. However, ensuring such data are effectively collected, analyzed, interpreted and acted upon will require that current geographic inequities of analytical capacity are decisively addressed. Specifically, funding and data sharing systems need to be re-oriented to prioritize south-centred collaborations that enable low-income malaria-endemic countries to develop and institutionalize their own

We thank numerous colleagues for invaluable discussions over the years that have contributed to this perspective, particularly Tom Smith, Samson Kiware and John Gimnig for sharing their experiences with entomological informatics, as well as Mafalda Viana, Jason Matthiopoulos

and Heather Ferguson for their insights on life history modelling.

global networks for monitoring anti-malarial drug resistance [82].

basis.

**10. Conclusions**

expertise base.

**Acknowledgements**

The extent to which portfolio effects make malaria transmission resilient against vector control is probably impossible to quantify. Nevertheless, simply being mindful of the overall principle can help moderate expectations of intervention impacts upon guilds of multiple vectors distributed across highly heterogeneous environments. The diversity of overlapping transmission dynamics these complex biological and environmental interactions generate result in malaria transmission that is far more resilient to programmatic-scale control than any single characterized species or location. In many tropical settings, elimination of malaria will probably necessitate elimination of its most efficient vectors [80], possibly including key vectors of residual transmission which readily, flexibly and opportunistically feed upon either humans or animals [1]. Malaria parasite populations that spread their reproductive bets across two or more vectors with different behaviours, ecological niches, seasonal dynamics and vulnerability to specific control measures will always be more difficult to eliminate, and will usually require more diverse intervention packages, than in settings with a single vector species. Furthermore, where individual vector species spread their own reproductive bets across multiple aquatic habitat types, resting sites or blood sources, this creates refugia that limit the impact of any given vector control measure applied in any given time and place. No matter how much detail we try to capture in our mathematical models of vector biology and malaria transmission, they will always under-represent the full complexity and diversity of those interactions, so they are biased towards under-estimating the resilience of malaria transmission against vector control. Whatever vector population response trajectory is expected following introduction of a new vector control measure, the portfolio effect will tend to flatten it out to some unknown extent. The only sensible way to integrate the implications of the portfolio effect into our efforts is to interpret entomological surveillance data and simulation models with considerable restraint (Killeen & Reed, Unpublished).
