Project Lead(s): Rachelle Desrochers
Issue
In Tanzania, arboviruses (e.g., dengue and chikungunya virus) contribute greatly to malaria misdiagnosis and subsequent overtreatment, due to lack of diagnostic resources and skills among health professionals. The rate of misdiagnosis of arboviral infections being malaria can be as high as 45%, reinforcing the need for increased awareness of the distribution of competing vectors in order to facilitate optimal management of malaria and arboviruses.
Solution
The proof of concept the team sought to employ involved advanced spatial modelling, to develop high-resolution habitat maps for the mosquito vectors that transmit malaria and arboviruses (specifically dengue fever and chikungunya).
These risk maps would be used to predict for cases of malaria and chikungunya virus.
The maps provide the vital first step for linking mosquito vector habitat to disease transmission, because the distribution of mosquito vectors – and consequently their associated infections – is determined in part by the distribution of their habitat.
These maps were then used to develop maps of malaria and arbovirus risk, to help inform local diagnoses of fevers by relating how much mosquito habitat is locally available to occurrences of malaria or arboviruses.
The team also sought to develop a prototype mobile phone-based interactive diagnostic tool of febrile illness for health workers that would include automatically updated local estimates of relative transmission risk of malarial and arboviral infections.
Outcome
The pre- and post-intervention malaria risk maps were developed using GLMM (general linear mixed models). The malaria rapid diagnostic test results (positive or negative) were related to the amount of An. arabiensis habitat in a two-kilometer neighborhood.
The resulting patterns of malaria risk and, in particular, the pattern of change in malaria risk pre- and post-intervention, are consistent with what researchers in the field have observed.
Having demonstrated the ability of Maxent habitat maps to predict malaria risk in Muleba District, they invested in procuring and processing higher-resolution, more precise environmental data from the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite. The maps are also useful for targeted spraying.
A manuscript of the results of this component of the grant, entitled “Spatial mapping of vector habitat predicts post-intervention malaria”, is in preparation for the International Journal of Health Geographics.
The ultimate goal was to create a prototype of a Smartphone-based app, with an interactive diagnostic tool for febrile illness, and with automatically updated local estimates of relative transmission risk of malaria and arboviral infections. Due to time and capacity constraints, this part of the project was not completed.
A prospective plan for the mobile app is currently being discussed among the principal researchers at HealthBridge and Kilimanjaro Christian Medical College (KCMUC), with further plans to release and validate the first prototype with guidance from experts in this area. However, there are no plans to apply for Phase II Transition To Scale funding at this time.