Search

Abstract Background Larval Source Management (LSM) is an important tool for malaria vector control and is recommended by WHO as a supplementary vector control measure. LSM has contributed in many successful attempts to eliminate the disease across the Globe. However, this approach is typically labour-intensive, largely due to the difficulties in locating and mapping potential malarial mosquito breeding sites. Previous studies have demonstrated the potential for drone imaging technology to map malaria vector breeding sites. However, key questions remain unanswered related to the use and cost of this technology within operational vector control. Methods Using Zanzibar (United Republic of Tanzania) as a demonstration site, a protocol was collaboratively designed that employs drones and smartphones for supporting operational LSM, termed the Spatial Intelligence System (SIS). SIS was evaluated over a four-month LSM programme by comparing key mapping accuracy indicators and relative costs (both mapping costs and intervention costs) against conventional ground-based methods. Additionally, malaria case incidence was compared between the SIS and conventional study areas, including an estimation of the incremental cost-effectiveness of switching from conventional to SIS larviciding. Results The results demonstrate that the SIS approach is significantly more accurate than a conventional approach for mapping potential breeding sites: mean % correct per site: SIS = 60% (95% CI 32–88%, p = 0.02), conventional = 18% (95% CI − 3–39%). Whilst SIS cost more in the start-up phase, overall annualized costs were similar to the conventional approach, with a simulated cost per person protected per year of $3.69 ($0.32 to $15.12) for conventional and $3.94 ($0.342 to $16.27) for SIS larviciding. The main economic benefits were reduced labour costs associated with SIS in the pre-intervention baseline mapping of habitats. There was no difference in malaria case incidence between the three arms. Cost effectiveness analysis showed that SIS is likely to provide similar health benefits at similar costs compared to the conventional arm. Conclusions The use of drones and smartphones provides an improved means of mapping breeding sites for use in operational LSM. Furthermore, deploying this technology does not appear to be more costly than a conventional ground-based approach and, as such, may represent an important tool for Malaria Control Programmes that plan to implement LSM.

Abstract Background Once a mainstay of malaria elimination operations, larval source management (LSM)—namely, the treatment of mosquito breeding habitats–has been marginalized in Africa in favour of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS). However, the development of new technologies, and mosquitoes' growing resistance to insecticides used in LLINs and IRS raise renewed interest in LSM. Methods A digitally managed larviciding (DML) operation in three of the seven districts of São Tomé and Príncipe (STP) was launched by the Ministry of Health (MOH) and ZzappMalaria LTD. The operation was guided by the Zzapp system, consisting of a designated GPS-based mobile application and an online dashboard, which facilitates the detection, sampling and treatment of mosquito breeding sites. During the operation, quality assurance (QA) procedures and field management methods were developed and implemented. Results 12,788 water bodies were located and treated a total of 128,864 times. The reduction impact on mosquito population and on malaria incidence was 74.90% and 52.5%, respectively. The overall cost per person protected (PPP) was US$ 0.86. The cost varied between areas: US$ 0.44 PPP in the urban area, and US$ 1.41 PPP in the rural area. The main cost drivers were labour, transportation and larvicide material. Conclusion DML can yield highly cost-effective results, especially in urban areas. Digital tools facilitate standardization of operations, implementation of QA procedures and monitoring of fieldworkers’ performance. Digitally generated spatial data also have the potential to assist integrated vector management (IVM) operations. A randomized controlled trial (RCT) with a larger sample is needed to further substantiate findings.

Accurate malaria diagnosis is critical to prevent malaria fatalities, curb overuse of antimalarial drugs, and promote appropriate management of other causes of fever. While several diagnostic tests exist, the need for a rapid and highly accurate malaria assay remains. Microscopy and rapid diagnostic tests are the main diagnostic modalities available, yet they can demonstrate poor performance and accuracy. Automated microscopy platforms have the potential to significantly improve and standardize malaria diagnosis. Based on image recognition and machine learning algorithms, these systems maintain the benefits of light microscopy and provide improvements such as quicker scanning time, greater scanning area, and increased consistency brought by automation. While these applications have been in development for over a decade, recently several commercial platforms have emerged. In this review, we discuss the most advanced computer vision malaria diagnostic technologies and investigate several of their features which are central to field use. Additionally, we discuss the technological and policy barriers to implementing these technologies in low-resource settings world-wide.