**7. Global inequities of data handling and analytical capacity**

So why are most reported entomological data not linked to explicit species identification data, and why are insightful analytical approaches so underutilized by control programmes? The simple answer is that most of the existing global capacity for advanced analysis of malariarelated data is in the wrong places, predominantly located at centres of excellence in high income countries with no local malaria transmission (**Figure 7**).

Entomological Surveillance as a Cornerstone of Malaria Elimination: A Critical Appraisal http://dx.doi.org/10.5772/intechopen.78007 417

their contact with LLINs, even nocturnal species like *An. arabiensis* can continually search around from one house to the next until an unprotected non-user is located [68]. By combining endophagy with exophily in this way, *An. arabiensis* can achieve feeding success rates despite high LLIN coverage that are only a quarter lower than in the absence of nets [68]. Furthermore, the resilience of such nocturnal but behaviourally plastic species may be further enhanced by physiological resistance to insecticides and opportunistically feeding upon animals, resulting in redistribution of feeding activity onto a combination of livestock and humans who either lack nets or are encountered outdoors at times when they are unpro-

However, life history analyses of how resilient mosquito species survive despite high LLIN coverage also identifies some exciting intervention opportunities that would not otherwise be obvious. For example, the most direct corollary of the observation that mosquitoes forage cautiously through several houses to find an unprotected human is that this creates enhanced opportunities to kill them if more effective indoor control methods can be deployed [7, 68, 69]. Emerging options for doing just that range from insecticidal eave tubes [70] and eave baffles

More detailed consideration of life history distributions for the same vector population also reveals an even more counter-intuitive opportunity for such housing modifications to have an impact upon residual transmission. By the time a female *An. arabiensis* is old enough to have incubated malaria parasites through to infectious sporozoites, she will usually have completed at least 4 gonotrophic cycles, during which time she will most probably have been inside a house at least once [69]. So even though approximately half of all transmission events occur outdoors, they are all preceded by at least one house-entry event during which the guilty mosquito may be killed [69]. It is therefore possible to reduce levels of malaria transmission occurring outdoors using interventions that target mosquitoes when they enter or

More strategically, this particular simulation analysis [69] also suggests a thematic perspective that may be useful to apply more broadly to life history analyses. It may often be more valuable to look for opportunities to intervene early in the life cycle of mosquitoes rather than targeting transmission events occurring when they are far older. The life histories of adult mosquitoes are cyclical so targeting mosquitoes when they engage in frequently repeated behaviours, in this case house entry, can have far greater impact than would be obvious from

So why are most reported entomological data not linked to explicit species identification data, and why are insightful analytical approaches so underutilized by control programmes? The simple answer is that most of the existing global capacity for advanced analysis of malariarelated data is in the wrong places, predominantly located at centres of excellence in high

face-value interpretation of the fraction of single feeding events that occur indoors.

**7. Global inequities of data handling and analytical capacity**

income countries with no local malaria transmission (**Figure 7**).

[71] to untreated entry traps [72] and three-dimensional window screening [73].

tected [68, 69].

416 Towards Malaria Elimination - A Leap Forward

attempt to enter houses [69].

**Figure 7.** The global geographic distribution of current members and collaborators in the Malaria Modelling Consortium (MMC), as well as analytical contributors to the World Health Organization 2015–2017 World Malaria Report (WHO-WMR), overlaid upon a map with contemporary malaria endemicity (white).

To a large extent, these geographic inequities of data analysis capacity are an understandable consequence of pre-existing global poverty, education and opportunity patterns. However, accepting the *status quo* illustrated in **Figure 7** is not consistent with the 'think global, act local' ethos of successful malaria elimination programmes, and these global capacity inequities need to be addressed urgently if malaria is ever to be eradicated.

If the strategic vision presented by the global modelling community in **Figure 8** continues to be implemented in the context of the world map in **Figure 7**, several consequences are inevitable:


**5.** Ongoing geographic separation of data collection and analysis functions will continue to exacerbate recent trends towards overspecialization and excessive compartmentalization of entomologists, epidemiologists and mathematical modellers. Generalist but nevertheless expert *malariologists*, as exemplified by the working competence in entomology, epidemiology and process-explicit modelling of Ross or Garrett-Jones (**Figure 9B**), will remain a rare breed.

A particularly worrisome issue, which we doubt will spontaneously self-resolve, is the inability of programmes in malaria endemic countries to critically appraise the reliability and relevance of advanced modelling studies carried out at a distance. Some of the greatest mistakes in the history of global malaria policy and practice have arisen from over-confident interpretation of models that were very useful but nevertheless imperfect [75]. In the vast majority of endemic countries today, neither the national malaria control programmes nor the national universities and research institutes they should be able look to for locally-available expert

advice, currently have sufficient capacity to appraise the merits and limitations of state-of-

**Figure 9.** A schematic illustration of how data collection and analysis roles are currently distributed (A), and how they

Entomological Surveillance as a Cornerstone of Malaria Elimination: A Critical Appraisal

http://dx.doi.org/10.5772/intechopen.78007

419

While analytical and predictive models can add considerable value to any data interpretation exercise, they also have some fundamental limitations that need to be considered. Even the most complex mathematical model is a deliberately simplified conceptual representation of reality. It is therefore important to critically understand what the limitations of both the models and the data themselves are, and how those uncertainties limit confidence in their

*… fitting complex models to multiple types of data is challenging, and model predictions are always likely to be unreliable at very high spatial resolution. The twin objectives of understanding the dynamics and making quantitative predictions can also be in conflict, because the push to include all relevant factors in a locally calibrated predictive model rapidly leads to complex behaviour that can no longer* 

One of the most important reasons to develop a cadre of expert modellers in endemic countries is so they can advise their national programmes based on a full understanding of the uncertainties and inaccuracies of model-generated evidence. Expert modellers working at locally-owned and governed institutions in malaria-endemic countries have a vital role to play in guiding critical appraisal by their non-specialist colleagues who might otherwise be tempted to either disregard the results of modelling analyses they do not understand, or

*' … it is challenging for a non-specialist to distinguish modelling that is useful from poor quality* 

Mosquito dispersal, human movement, heterogeneities in the intensity of transmission, and over-dispersed distributions of parasite infection durations have all been recognized as factors

accept them at face value based on a level of trust that may not be warranted:

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

*modelling that may support misguided policies'* [23].

the-art modelling analyses.

should be actively reformed going forward (B).

interpretation:

*be explained* [23].

**Figure 8.** The schematic illustration of the comprehensive framework for malaria modelling presented by the malERA Consultative Group on Malaria Modelling in 2011 [74]. Consultations will allow policy makers, research scientists, and other stakeholders (U, users/stakeholders) from different country-specific health systems (HSM, country-specific health system models) to draw advice and analysis from multiple, independently derived models (M) grounded on data collected (D, data bases) from research on vector ecology, malaria epidemiology, and control through an interface that emphasizes direct engagement between modellers or modelling groups and end users.

Entomological Surveillance as a Cornerstone of Malaria Elimination: A Critical Appraisal http://dx.doi.org/10.5772/intechopen.78007 419

**5.** Ongoing geographic separation of data collection and analysis functions will continue to exacerbate recent trends towards overspecialization and excessive compartmentalization of entomologists, epidemiologists and mathematical modellers. Generalist but nevertheless expert *malariologists*, as exemplified by the working competence in entomology, epidemiology and process-explicit modelling of Ross or Garrett-Jones (**Figure 9B**), will remain

A particularly worrisome issue, which we doubt will spontaneously self-resolve, is the inability of programmes in malaria endemic countries to critically appraise the reliability and relevance of advanced modelling studies carried out at a distance. Some of the greatest mistakes in the history of global malaria policy and practice have arisen from over-confident interpretation of models that were very useful but nevertheless imperfect [75]. In the vast majority of endemic countries today, neither the national malaria control programmes nor the national universities and research institutes they should be able look to for locally-available expert

**Figure 8.** The schematic illustration of the comprehensive framework for malaria modelling presented by the malERA Consultative Group on Malaria Modelling in 2011 [74]. Consultations will allow policy makers, research scientists, and other stakeholders (U, users/stakeholders) from different country-specific health systems (HSM, country-specific health system models) to draw advice and analysis from multiple, independently derived models (M) grounded on data collected (D, data bases) from research on vector ecology, malaria epidemiology, and control through an interface that

emphasizes direct engagement between modellers or modelling groups and end users.

a rare breed.

418 Towards Malaria Elimination - A Leap Forward

**Figure 9.** A schematic illustration of how data collection and analysis roles are currently distributed (A), and how they should be actively reformed going forward (B).

advice, currently have sufficient capacity to appraise the merits and limitations of state-ofthe-art modelling analyses.

While analytical and predictive models can add considerable value to any data interpretation exercise, they also have some fundamental limitations that need to be considered. Even the most complex mathematical model is a deliberately simplified conceptual representation of reality. It is therefore important to critically understand what the limitations of both the models and the data themselves are, and how those uncertainties limit confidence in their interpretation:

*… fitting complex models to multiple types of data is challenging, and model predictions are always likely to be unreliable at very high spatial resolution. The twin objectives of understanding the dynamics and making quantitative predictions can also be in conflict, because the push to include all relevant factors in a locally calibrated predictive model rapidly leads to complex behaviour that can no longer be explained* [23].

One of the most important reasons to develop a cadre of expert modellers in endemic countries is so they can advise their national programmes based on a full understanding of the uncertainties and inaccuracies of model-generated evidence. Expert modellers working at locally-owned and governed institutions in malaria-endemic countries have a vital role to play in guiding critical appraisal by their non-specialist colleagues who might otherwise be tempted to either disregard the results of modelling analyses they do not understand, or accept them at face value based on a level of trust that may not be warranted:

*' … it is challenging for a non-specialist to distinguish modelling that is useful from poor quality modelling that may support misguided policies'* [23].
