Uncrewed aircraft systems versus motorcycles to deliver laboratory samples in west Africa: a comparative economic study. Issue 1 (January 2020)

• Record Type: Journal Article
• Authors: Ochieng, Walter O; Ye, Tun; Scheel, Christina; Lor, Aun; Saindon, John; Yee, Sue Lin; Meltzer, Martin I; Kapil, Vikas; Karem, Kevin
• Is Part Of: Lancet. Volume 8:Issue 1(2020)
• Page Start: e143
• Page End: e151
• Publication Date: 2020-01
• Subjects: World health -- Periodicals; 362.105
• DOI: 10.1016/S2214-109X(19)30464-4
• Languages: English
• ISSNs: 2214-109X

Abstract

Summary: Background: Transportation of laboratory samples in low-income and middle-income countries is often constrained by poor road conditions, difficult geographical terrain, and insecurity. These constraints can lead to long turnaround times for laboratory diagnostic tests and hamper epidemic control or patient treatment efforts. Although uncrewed aircraft systems (UAS)—ie, drones—can mitigate some of these transportation constraints, their cost-effectiveness compared with land-based transportation systems is unclear. Methods: We did a comparative economic study of the costs and cost-effectiveness of UAS versus motorcycles in Liberia (west Africa) for transportation of laboratory samples under simulated routine conditions and public health emergency conditions (based on the 2013–16 west African Ebola virus disease epidemic). We modelled three UAS with operational ranges of 30 km, 65 km, and 100 km (UAS30, UAS65, and UAS100) and lifespans of 1000 to 10 000 h, and compared the costs and number of samples transported with an established motorcycle transportation programme (most commonly used by the Liberian Ministry of Health and the charity Riders for Health). Data for UAS were obtained from Skyfire (a UAS consultancy), Vayu (a UAS manufacturer), and Sandia National Laboratories (a private company with UAS research experience). Motorcycle operational data were obtained from Riders for Health. In our model, we included costs for personnel, equipment, maintenance, and training, and did univariate and probabilistic sensitivity analyses for UAS lifespans, range, and accident or failures. Findings: Under the routine scenario, the per sample transport costs were US$0·65 (95% CI 0·01–2·85) and $0·82 (0·56–5·05) for motorcycles and UAS65, respectively. Per-sample transport costs under the emergency scenario were $24·06 (95% CI 21·14–28·20) for motorcycles, $27·42 (95% CI 19·25–136·75) for an unadjusted UAS model with insufficient geographical coverage, and $34·09 (95% CI 26·70–127·40) for an adjusted UAS model with complementary motorcycles. Motorcycles were more cost-effective than short-range UAS (ie, UAS30). However, with increasing range and operational lifespans, UAS became increasingly more cost-effective. Interpretation: Given the current level of technology, purchase prices, equipment lifespans, and operational flying ranges, UAS are not a viable option for routine transport of laboratory samples in west Africa. Field studies are required to generate evidence about UAS lifespan, failure rates, and performance under different weather conditions and payloads. Funding: None.