title: Quantifying the ecology of Anopheles funestus and its implications for improved malaria control
Creators: name: Ngowo, Halfan
Publisher: name: University of Glasgow
Year: 2023

description: The malaria burden is highest in African countries where more than 95% of deaths and cases occur. There was a consistent decline in malaria deaths and cases in Africa between 2000 and 2015 but progress has since stalled. Due to biological changes in vector populations, notably insecticide resistance and behavioral adaptations such as outdoor-biting, the primary vector control measures are no longer as successful as they once were. In recent years, mosquito species considered to be the primary vector of malaria, (e.g. Anopheles gambiae s.s) have declined, and even disappeared from some communities. In settings such as rural south-eastern Tanzania, the residual transmission is now being maintained by An. funestus followed by An. arabiensis. Currently, An. funestus mediates a high proportion of malaria transmission events in east and southern Africa. The resilience of this vector may be linked to its high insecticide resistance; though recent evidence suggests that it may also be capable of shifting its biting behaviors to avoid contact with insecticidal interventions. Yet, our ability to tackle this vector species is impeded by the limited knowledge of its basic ecology and population dynamics, the difficulties in colonizing it under laboratory conditions and the many uncertainties about appropriate surveillance approaches. The overall aim of this PhD project was to quantify the ecology of An. funestus mosquitoes in Tanzania and assess the implications of its key attributes for improved malaria control in settings such as Tanzania where the vector species dominates. The work involved the following steps: 1) quantifying the fitness and behavioral attributes of wild An. funestus and their offspring during repeated colonization attempts under standard laboratory conditions, 2) developing and validating a framework for predicting human biting exposures from different exposure-free sampling methods, 3) developing and testing a population dynamics model to describe the ecology of the wild An. funestus populations, and 4) assessing the generalizability of the population dynamics model and its ability to reconstruct missing data. To achieve the first objective, I attempted to colonize a local population of An. funestus s.s. from southeastern Tanzania and assessed the key barriers which hinder laboratory establishment. Adult females (F0) from three wild An. funestus populations were brought into the laboratory for rearing. Their fecundity, and the development, survival, body size and mating success of their F1 offspring were measured to evaluate their fitness under laboratory conditions. While adult survival was relatively high, the mating success, poor hatching rate and poor larval survival and extended larval development periods were identified as key barriers to establishing a colony in the laboratory. Due to these factors, this colony was not sustained beyond the F1 generation in the laboratory, but the lessons were deployed for a subsequent and more successful colonization effort. To address the second objective, I analyzed data comparing the outdoor catch rates of An. funestus using six exposure-free trapping methods relative to the human landing catches (HLC), the gold standard method for estimating human exposures to mosquito bites. I tested different models for the relationship between HLC and other trapping methods while allowing flexibility for associations to be impacted by interspecific and intraspecific density dependence. This analysis indicated that that the association between catches in alternative traps and the HLC can best be explained by simple linear models; with minimal impact of intra and inter specific density dependence. A shiny app interface was developed to allow expanded use of this statistical calibration framework for future estimations of malaria vector biting risk in communities. For the third objective, I used the demographic parameters generated from the colonization attempt (described above) and published literature, to develop the first population dynamics model of wild An. funestus in Tanzania. I used a Bayesian framework to develop a state-space model and reconstruct the observed population dynamics of this species. I then used this model to assess the strength of evidence for intrinsic (density dependence) and extrinsic (environmental covariates) drivers of An. funestus population dynamics and how they drive seasonality in abundance and demographic variables (development periods and survival). This analysis indicated that density dependence has a minimal contribution on the overall dynamics of An. funestus in these settings. Daily larval and adult survival probabilities were marginally affected by changes in environmental covariates (temperature and rainfall), suggesting there is little seasonality in these fitness parameters. This study also revealed that An. funestus may be essential for sustaining year-round malaria transmission in settings such as rural south-eastern Tanzania. Finally, I interrogated the generalizability and sensitivity of this modelling framework for An. funestus by assessing its ability to predict missing time series data. Here, I refitted the model to a single population and assessed any unexplained features of population dynamics which is causing the density dependence to have small contributions. I also omitted portions of the time series data to assess model prediction capability. For example I first removed 25% and then 50% of the data, then reconstructed the missing sections. The single population model indicated that An. funestus demographic variables were much more sensitive to changes in environmental covariates compared to the preceding hierarchical model; suggesting that clear signals of environmental drivers may be lost by fitting the model to multiple populations that may have distinct drivers. The model was able to reconstruct the observed population trajectory poorly when 50% of the data was removed as compared to when 25% was removed. Overall, the model was only able to predict for the missing data if the training set included some representation of data from both dry and wet seasons. In conclusion, this PhD work contributes to a general understanding of the key barriers to colonization and the population dynamics of An. funestus. While An. funestus was not successfully colonised in this study, the lessons learned by documenting which fitness traits are impeded in the laboratory led to progress in further work at the Ifakara Health Institute, where a stable colony of An. funestus has now been established. Additionally, the model of An. funestus dynamics and demography developed here will underpin further research to evaluate and select optimal vector control packages for crashing these populations in southern Tanzania and other settings where they are the major source of residual malaria transmission.