Using Remotely Sensed Data to Explore Spatial and Temporal Relationships Between Photosynthetic Productivity of Vegetation and Malaria Transmission Intensities in Selected Parts of Africa

Spatial and temporal variations in malaria transmission are naturally associated with prevailing climatic and environmental factors, for example rainfall, humidity, temperature and human activities. These factors influence malaria transmission mainly in non-deterministic ways, making them less appropriate for accurate geographical mapping of malaria risk. One distinctive phenomenon, ‘photosynthetic productivity of vegetation’, is similarly affected by these factors, yet it can be easily estimated from remotely sensed data using standardized indices. In this study, multiple linear regression techniques are used to explore spatial and temporal associations between photosynthetic productivity of vegetation (measured as Normalized Difference Vegetation Index (NDVI)) and malaria transmission intensities (measured as Entomological Inoculation Rate (EIR)). The study shows significant relationships between NDVI and EIR both at continental level and at a number of the selected study sites. Moreover, in three of four sites where temporal analysis was conducted, a similarity of linear trends is observed between EIRs and means of current and previous month NDVIs. Both NDVI and EIR are significantly associated with altitude as well as to a rural/urban dummy variable. It is concluded that spatial and temporal variations in photosynthetic productivity of vegetation are strongly related to variations in malaria transmission at respective places and periods. Results of this basic exploration imply that vegetation production is a potential indicator of situations favourable for malaria transmission, and can therefore be used to improve mapping of geographical extents of risk of malaria, and perhaps several other vector borne diseases

Variations in household microclimate affect outdoor-biting behaviour of malaria vectors

Background: Mosquito behaviours including the degree to which they bite inside houses or outside is a crucial determinant of human exposure to malaria. Whilst seasonality in mosquito vector abundance is well documented, much less is known about the impact of climate on mosquito behaviour. We investigated how variations in household microclimate affect outdoor-biting by malaria vectors, Anopheles arabiensis and Anopheles funestus. Methods: Mosquitoes were sampled indoors and outdoors weekly using human landing catches at eight households in four villages in south-eastern Tanzania, resulting in 616 trap-nights over 12 months. Daily temperature, relative humidity and rainfall were recorded. Generalized additive mixed models (GAMMs) were used to test associations between mosquito abundance and the microclimatic conditions. Generalized linear mixed models (GLMMs) were used to investigate the influence of microclimatic conditions on the tendency of vectors to bite outdoors (proportion of outdoor biting). Results: An. arabiensis abundance peaked during high rainfall months (February-May), whilst An. funestus density remained stable into the dry season (May-August). Across the range of observed household temperatures, a rise of 1ºC marginally increased nightly An. arabiensis abundance (~11%), but more prominently increased An. funestus abundance (~66%). The abundance of An. arabiensis and An. funestus showed strong positive associations with time-lagged rainfall (2-3 and 3-4 weeks before sampling). The degree of outdoor biting in An. arabiensis was significantly associated with the relative temperature difference between indoor and outdoor environments, with exophily increasing as temperature inside houses became relatively warmer. The exophily of An. funestus did not vary with temperature differences. Conclusions: This study demonstrates that malaria vector An. arabiensis shifts the location of its biting from indoors to outdoors in association with relative differences in microclimatic conditions. These environmental impacts could give rise to seasonal variation in mosquito biting behaviour and degree of protection provided by indoor-based vector control strategies

Participatory approaches for raising awareness among subsistence farmers in Tanzania about the spread of insecticide resistance in malaria vectors and the possible link to improper agricultural pesticide use

Background: Insecticide resistance is a key barrier to long-term malaria control, and it may be exacerbated by poor agricultural pesticide use. Current practices, however, do not link public health and agricultural pesticide use. This study investigated the perspectives of farmers and other stakeholders regarding the integration of agricultural and public health measures to address resistance. Additionally, the feasibility of participatory workshops to increase the farmers’ understanding and participation in pesticide stewardship was assessed. Methods: Four themes were investigated: pesticide awareness, practices, and opinions of; insecticide resistance in malaria vectors; the effectiveness of current malaria prevention tools; and the links between agricultural and public health pesticide usage. Participatory workshops and field training were held with entomologists, farmers, and agricultural specialists, focusing on agro-ecosystem practices related to pest control; and local farmers were involved in live-testing for insecticides resistance of local Anopheles mosquitoes. Results: Most farmers (94%) considered pesticides effective, and nearly half of them (n = 198, 46.4%) could identify and name crop pests and diseases, mostly using local names. Three quarters were unaware of mosquito larvae in their fields, and only 7% considered their fields as potential sources of mosquitoes. Two thirds were uninformed of any effects that agricultural pesticides may have on mosquitoes, and three quarters had never heard of resistance in malaria mosquitoes. Experts from various sectors acknowledged that agricultural pesticides might impact malaria control through increasing resistance. They did, however, emphasize the importance of crop protection and advocated for the use of pesticides sparingly and non-chemical approaches. Farmers learnt how to discriminate between malaria vectors and non-vectors, identify agricultural pests and diseases, choose and use pesticides effectively, and conduct resistance tests during the participatory workshops. Conclusion: This study emphasizes the significance of enhancing subsistence farmers’ awareness of mosquito ecology as well as merging public health and agricultural pest management measures. Participatory techniques have the potential to raise stakeholder awareness and engagement, resulting in more effective resistance management

Combining insecticide treated bed nets and indoor residual spraying for malaria vector control in Africa

Background: Insecticide treated nets (ITNs) and indoor residual spraying (IRS) are the preferred techniques for malaria vector control in Africa, where their application has already contributed to significant reductions in the burden of the disease. Even though both methods are commonly used together in the same households, evidence of greater health benefits due to these combinations as opposed to use of either ITNs or IRS alone has been minimal and inconclusive. Objectives and methods: The main aim of this research was therefore to contribute to this essential evidence, by way of experimental hut studies and mathematical simulations. I investigated whether there would be any added protective advantages when any of three selected long lasting insecticidal nets (LLINs) are combined with any of three selected IRS chemicals, as opposed to using any of the treatments alone. Data generated from the experimental but studies was then input into an optimised deterministic mathematical model, simulating a typical malaria endemic village. Results and conclusions: Both the field studies and the simulations showed that any synergies or redundancies resulting from LLIN/IRS combinations are primarily a function of modes of action of active ingredients used in the two interventions. Where LLINs are already present, addition of IRS would be redundant unless the IRS chemical is highly toxic, but where IRS is the pre-existing intervention, these combinations always confer improved protection. Therefore, IRS households should always be supplemented with nets, preferably LLINs, which not only protect house occupants against mosquito bites, but also kill additional mosquitoes. Finally, where resources are limited, priority should be given to providing everybody with LLINs and ensuring that these nets are consistently and appropriately used, rather than trying to implement both LLINs and IRS in the same community at the same time

Study areas.

<p>Map showing the three villages where the study was conducted (Kivukoni, Minepa and Mavimba) in rural Ulanga district, southeastern Tanzania.</p

Main stages in the process of crowdsourcing vector surveillance.

<p>Illustration of the five main steps when crowdsourcing for community knowledge and experiences to predict or approximate densities and distribution of outdoor-biting mosquitoes.</p

The M-Trap.

<p>Pictures of the odour-baited trap, the M-trap, used for comparative assessment of mosquito densities. Vertical envelope-shaped mosquito entry points are labelled. In our study, no human volunteer occupied the trap, and instead we relied on synthetic mosquito attractants complemented with carbon-dioxide gas.</p

Comparison of mosquito catches in areas classified by communities as having high, medium or low mosquito densities in dry season and wet season.

<p>Median nightly mosquito catches in areas marked by community members as having high mosquito densities, medium densities or low densities in all villages during wet season (upper panel), and dry season (lower panel). Data segregated by taxa, but combined over 12 months. The error bars in this graph represent the inter-quartile ranges, i.e. 25^th percentile and 75^th percentile on either side of the median nightly catch. Data for the wet season included months of December, January, February, March, April and May, while the dry season data included June, July, August, September, October and November.</p