Uncrewed aircraft systems versus motorcycles to deliver laboratory samples in west Africa: a comparative economic study
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.
Public health laboratories in low-income and middle-income countries face operational constraints because of poor funding and infrastructure. Transferring patient samples from clinics to reference laboratories often requires costly, unreliable transportation methods across difficult terrain with poor road conditions, traffic congestion, and the requirement for cold-chain methods. These limitations become serious during disease outbreaks when rapid diagnoses are required for timely implementation of effective epidemic control strategies.1
Uncrewed aircraft systems (UAS)—ie, drones—can mitigate some of these limitations. UAS comprise aircraft and operating equipment that include ground-control consoles and communication systems, with or without launching apparatus. UAS are classified on the basis of criteria such as flight range, power source, size, operating altitude, and flight mechanism. 2 In this study, we used the flight mechanism classifications of fixed-wing, rotor-wing, and hybrid (both fixed-wing and rotor-wing mechanisms).3 Fixed-wing UAS have longer operational ranges, large payloads, and better weather tolerance. They are more expensive than rotor-wing systems, usually require dedicated launching and landing apparatus (ie, catapults or runways), and do not have the capability to transport goods on a return trip.3 By contrast, rotor-wing UAS have vertical takeoff and landing capabilities, limited flight range, smaller payloads, and poor weather tolerance, but they are substantially cheaper. Hybrid systems incorporate both rotor-wing and fixed-wing components and maximise the advantages of both systems.
Research in context
Evidence before this study
We searched the databases PubMed, MEDLINE, Scopus, Google Scholar, EMBASE, CINAHL, Econlit, WHO, and PATH (formerly Program for Appropriate Technology in Health) for studies published between Jan 1, 1990, and June 30, 2019. The search terms included combinations of “drones”, “unmanned aerial vehicles”, “unmanned aerial systems”, “unmanned aircraft”, “economics”, “costs”, “effects”, “benefits”, “utilities”, “motorcycles”, “laboratory”, “specimens”, and “samples”. We mined the references from articles identified during the primary search for related and pertinent articles, dissertations, and programme reports. This study was partly informed by US Centers for Disease Control and Prevention experiences during the west Africa Ebola virus disease epidemic. We therefore included actual programme data from the charity Riders for Health, who transported blood samples during the Ebola virus disease epidemic for the motorcycle comparator. We held a series of telephone and Skype calls with various uncrewed aircraft system (UAS) manufacturers about the limitations of current studies and pointers to other relevant literature including field trial reports. Through this consultative process, we also obtained access to unpublished and confidential industry operational documents.
Added value of this study
This is the first comprehensive analysis on use of UAS that examined the effect of major factors such as purchase price, operational lifespans, and flight ranges on costs. Previous studies have omitted these factors. Through the use of detailed maps, we also showed the strengths and limitations of different types of UAS, and challenged some of the commonly held assumptions regarding their operations. We also developed a user-friendly Microsoft Excel-based program that can be used by anyone to undertake a UAS-related cost evaluation.
Implications of all the available evidence
Our study showed that additional field performance data are required before UAS can be scaled-up for routine public health use. Future studies should address the major cost drivers of UAS including effective operation ranges, failure rates under various operational conditions, maintenance, weather performance, and actual field implementation.
Since 2010, use of civilian UAS for public health has increased.4 UAS have been used for aerial surveillance of mosquito vector breeding sites,5 and for real-time monitoring during major disasters. 6, 7 Pilot tests in Rwanda and Lesotho suggested that substantial savings in time and costs were achieved by the use of UAS for blood-transfusion services. 8 Samples delivered by UAS have been shown to be of similar quality to samples delivered by road.9 Simulation studies suggest that UAS can solve some problems in the vaccine-supply chain by reducing point-of-care inventory holding costs and stock-outs.10 These studies 9, 10 also showed that UAS are cost-effective compared with traditional land-based transport systems, and recommended their use in health programmes.
However, these studies did not indicate the procurement costs of the systems used or the amortisation factor (ie, operational UAS lifespan in terms of flight hours) used in the cost analyses. Furthermore, they did not account for costs related to UAS training and certification, insurance, personnel, management, preventive maintenance, and accidents or failures. 11 The studies also did not investigate UAS operability in inclement weather, extreme temperatures, and arid conditions that might cause dust interference. On the basis of these studies, it remains unclear whether UAS are useful substitutes or complements to traditional land-based transport services.
One costing study has compared the use of UAS with motorcycles for transportation of laboratory specimens within a 25 km radius of Lilongwe, Malawi, using a single-stop hub-and-spoke strategy. 12 The investigators showed that UAS were less cost-effective than motorcycles in most modelling scenarios. This study, like others referenced here, did not include procurement costs, weather operability, operational lifespan, or maintenance costs in the analysis. 12 Here we present a simulation model for west Africa to identify cost drivers that should be included in future comparative cost analyses for emergency and routine use of UAS for transportation of laboratory specimens. We estimated per-sample costs for each scenario and estimated average cost-effectiveness ratios. The aim of this study was to use these analyses to contribute to the knowledge base and inform the ongoing debate about the use of UAS in public health programmes in developing countries.
Methods
Study design
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 a public health emergency condition (based on the 2013–16 west African Ebola virus disease epidemic). Our unit of effectiveness was the number of specimens transported under both scenarios.
Our baseline model assumed kerosene-powered hybrid UAS that have an operational range of 65 km (UAS65), an optimal flight speed of 65 km/h, and a lifespan of 3000 flight hours. We did sensitivity analyses with 30 km (UAS30) and 100 km (UAS100) operational radii, and lifespans of 1000 to 10 000 flight hours. The motorcycle transport system assumptions were based on the Yamaha AG-200—a two-stroke engine motorcycle most commonly used by the Liberian Ministry of Health and the international charity, Riders for Health.
The analysis was done from a health system perspective and included costs for procurement, personnel, training, maintenance, and replacement. We included direct costs of treatment for motorcycle-related injuries but omitted costs for community sensitisation and post-crash recovery operations of UAS. We used 2014 as our base year and used a discount rate of 3% 13 for the analyses over a 3-year horizon. Liberia is a US dollarised economy, therefore all local costs were collected in US$.14 Table 1 lists the main assumptions used in this paper.
We obtained motorcycle operational data from Riders for Health, a non-profit charity that was contracted to transport samples in Liberia during the Ebola virus disease epidemic. The Riders for Health motorcycles are fitted with global positioning system (GPS) devices linked to a specialised fleet management application called Fulcrum that captures detailed operational data including distances travelled, fuel consumption, accident rates, breakdowns, and maintenance.
We obtained UAS data inputs from three qualified expert sources: Skyfire (Atlanta, GA, USA), a UAS consultancy specialising in emergency response; Vayu (Ann Arbor, MI, USA), a UAS manufacturer that has done studies across Africa; and Sandia National Laboratories (Albuquerque, NM, USA), a federally-funded private enterprise with civilian and military UAS research experience. Each of these has generated data that cover operational performance, training needs, and putative prices.
Geography
Liberia is an equatorial country in west Africa covering 111 369 km2 with a population of 4·7 million. A quarter of the population lives in the capital, Monrovia. Tropical and mangrove forests cover approximately 29% of the country. Liberia is subdivided into 15 counties, each with a county referral hospital and health administrative structure. 15
The annual precipitation ranges from 2200 mm in the interior to 5000 mm in the capital, Monrovia. Following a protracted civil war, road conditions are poor and few are paved. Some roads, especially in the southeast and northwest of the country, become impassable during the May to October rainy season. The heavy forest cover and poor road infrastructure in Liberia are ostensibly ideal for UAS services. 15
There are approximately 789 government and missionary health facilities in Liberia. These facilities have substantial resource constraints, including understaffing, with little equipment, electricity, and running water. Figure 1 shows the Liberia road network and clinic distribution. Facilities in the southeast and northwest of the country are isolated since there are few roadways, whereas facilities in the central part of the country are more accessible because of denser road networks that offer alternative land routes when some roads are impassable.
Figure 1Road networks and distribution of clinics in Liberia
We used the actual road distances covered by Riders for Health, who operated a fleet of 70 motorcycles, for our base analysis. These motorcycles collected 40 624 laboratory samples across 302 collection points over 14 months covering 1·88 million km (appendix p 1). We used the peak Ebola virus reference laboratory capacity in Liberia as the baseline and assumed that the UAS would be operated and maintained in those laboratories. 16 In our public health emergency simulation scenarios, for areas with insufficient UAS geographical coverage, we added motorcycles to ensure that all patient samples were collected.
We envisaged a well defined weekly sample collection schedule under the routine transportation model and assumed that these systems were solely dedicated to laboratory sample transport. We assumed a spoke-hub transport topology where transportation services are located at county hospitals (hubs) with radiating connections (road and air) to peripheral clinics (spokes). 16 Our model followed the Liberian health strategic plan assuming that each county would in future have its own well staffed, well equipped, referral laboratory service. 17, 18 We estimated round-trip Euclidean (straight-line) distances from county referral laboratories to each peripheral clinic within a county. We also estimated road distances between each clinic and the county laboratories (appendix pp 1,4). The combined average road and Euclidean distances (appendix pp 1–2) between each county laboratory and the peripheral clinics are in the appendix (pp 1–3). These distances were used to calculate the minimum number of motorcycles and UAS needed per county per year (appendix pp 4–9).
Number of UAS needed per year
The minimum number of aircraft needed per county was estimated as a function of total return distances travelled (based on the collections schedule), operational speed, daily working hours, and operational lifespan in flight hours. In the base case, we assumed minimal time for preflight checks, and route programming, and no accidents or failures, but undertook sensitivity analyses around these conditions. We assumed that both motorcycles and UAS were dedicated to laboratory transport during the public health emergency and were subsequently reallocated to other services once the emergency was contained. We amortised the procurement costs of all equipment across their useful lifespan.
Costs
Motorcycle training cost estimates were based on the Riders for Health curriculum that includes defensive riding, motorcycle self-inspection and performance checks, biosafety, specimen identification, infection prevention and control, and data entry into the Fulcrum app. UAS training costs included didactic, simulator-based, and supervised practicum. We added a one-off post-training UAS certification check to our estimates, and assumed that all trainings were done in-country to minimise international travel costs. We assumed that the logistical costs of setting up UAS training sites were trivial and omitted in-country pilot licensing costs, since these are unknown. We omitted curriculum development, post-accident training, and recertification costs. We assumed that peripheral clinic staff attended a half-day training to learn about proper procedures for loading and off-loading specimens from the UAS.
We used Riders for Health salaries and benefits for riders, project managers, mechanics, and support staff. We prorated managerial costs for the UAS models based on the overall number of personnel needed for operations. Since UAS operator salaries are unknown, we used salaries for Liberian air traffic controllers ($800 per month) as a proxy wage. We assumed that one loader would assist each UAS operator.
We included costs for cold-chain equipment at relay points, and for rider gear in the motorcycle system model. Since most African countries waive licensing costs for equipment and vehicles used for dedicated public health programmes, we did not include import duty and licensing costs for UAS in our model. All maintenance work for UAS and motorcycles were assumed to be performed in-house at no additional logistical personnel costs but include costs of replacement parts. We assumed that all maintenance work did not interfere with regular working hours and that there was no need for backup aircraft.
Sample estimates and analyses
We used the actual number of samples transported by Riders for Health for the public health emergency models. For the routine scenario, we projected sample transport needs as a function of outpatient and inpatient attendance. We assumed that half the samples would be from referral hospitals and would not need to be transported. We also assumed that half the remaining samples would need to be transported or roughly 1·8 million samples per year. 17, 19 We assumed there was no deterioration of samples under either transport method because UAS transfers were rapid, while cold-chain relay systems were used in the motorcycle scenario.9
We did univariate and probabilistic sensitivity analyses by varying the purchase price, estimated lifespan, range, and number of samples. For every reported scenario, we used 10 000 Monte Carlo simulations and presented the results in cost-effectiveness analyses planes. We entered data into Microsoft Excel (v 16.0) but did our analyses and visualisations in Stata (v 14.2).
Role of the funding source
There was no funding source for this study. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Results
In the public health emergency scenario, 357 (45·2%), 104 (13·2%), and 50 (6·3%) of the clinics were outside the aircraft operational collection ranges of UAS30, UAS65, and UAS100 (figure 2). The geographical coverage under routine programming was 92% (UAS65), 62% (UAS30), and 100% (UAS100) (figure 3).
The total number of vehicles needed was a function of total return distances travelled, lifespan, operational range, operating speed, base station location, transportation schedule, and equipment lifespan. The total number of aircraft needed per annum is in the appendix (p 9), assuming average UAS operating speeds of 65 km/h, with 10 operating h per day. For example, under a daily operating schedule, Lofa County, which has a total Euclidean return distance of 86500 km between the country referral hospital and all health facilities, would need a minimum of 11 aircraft under a pure UAS transport scenario. The minimum number of UAS needed increase with increasing operational radii because a greater number of facilities are covered and longer average distances travelled. The number of motorcycles needed to cover clinics outside the UAS’s operating range decreased with increasing UAS range (appendix p 7). The geographical coverage of UAS30 was limited to urban and periurban areas with a high concentration of clinics.
If UAS were to substitute motorcycles under a weekly collection schedule (figure 3), between 19 and 38 aircraft would be required per year based on their operational lifespans. Aircraft with 1000-h lifespans would need to be replaced every 4 months, while 3000-h aircraft would need to be replaced annually. Changing the sample collection schedule from weekly to daily will triple the number of requisite aircraft (appendix pp 5–6).
The effective flying time—ie, projected time in air plus time on ground to reprogramme the UAS for a new destination, change the battery or refuel, check flight path weather conditions, alert the receiving clinic, do pre-flight checks, and launch is also crucial. 11 The number of aircraft needed increased by 40% when pre-flight time checks were considered.
The average cost per sample transported under routine conditions was $0·65 (95% CI 0·01–2·85) with the motorcycle transport system and $0·82 (0·56–5·05) with UAS65 (table 2; figure 4). The cost-effectiveness planes in the appendix (pp 2,3) show the sensitivity analysis results of 10 000 Monte Carlo simulations of different UAS scenarios compared with motorcycles. Motorcycles dominated—ie, were more cost-effective than—UAS30 (left upper quadrant) under all scenarios that we modelled. The probability of UAS being cost-effective increased with increasing UAS ranges, lifespans over 1000 h, and prices less than $15 000 (appendix pp 16–18). The incremental cost-effectiveness ratios varied by scenario: −0·1 (–0·4 to −0·003) for UAS65, −0·03(–0·05 to −0·01) for UAS30, and −1·67 (–77·88 to −0·01) for UAS100.
Table 2Selected per-sample costs with different transport methods
Routine ($)
PHE pure UAS ($)
PHE UAS plus motorcycle ($)
Motorcycle
1·1 years lifespan
0·65 (0·01–2·85)
24·06 (21·14–28·2)
..
UAS30
1000 flight h lifespan
1·11 (0·82–2·82)
46·32 (34·75–115·81)
47·04 (41·31–81·47)
3000 flight h lifespan
0·78 (0·65–1·51)
30·89 (26·76–55·48)
39·40 (37·35–51·58)
10 000 flight h lifespan
0·66 (0·61–1·05)
25·38 (23·95–33·95)
36·67 (35·96–40·91)
UAS65
1000 flight h lifespan
1·37 (0·94–4·00)
46·15 (32·31–129·14)
49·20 (38·03–116·13)
3000 flight h lifespan
0·82 (0·66–1·80)
27·42 (22·66–55·96)
34·09 (30·25–57·11)
10 000 flight h lifespan
0·63 (0·56–1·03)
20·81 (19·25–37·59)
28·75 (27·50–42·29)
UAS100
1000 flight h lifespan
1·68 (1·11–5·05)
48·05 (33·27–136·75)
51·36 (38·69–127·40)
3000 flight h lifespan
0·96 (0·75–2·18)
28·19 (23·03–59·14)
34·33 (29·91–60·87)
10 000 flight h lifespan
0·71 (0·63–1·17)
20·92 (19·29–30·73)
28·10 (26·70–36·51)
PHE=public health emergency. UAS=uncrewed aircraft systems.
The mean per-sample cost for motorbikes in the emergency scenario was $24·06 (95% CI 21·14–28·20; table 2, figure 5). Per-sample costs were $27·42 (95% CI 19·25–136·75) for the unadjusted UAS scenario, and $34·09 (26·70–127·40) when motorcycles were added to maximise geographical coverage (table 2). Using UAS65, 75% of the simulation fall in the left upper and left lower quadrants—ie, would cost more to implement than motorbikes and would transport fewer samples under most conditions (Figure 3, Figure 4; appendix pp 14–15). The UAS30 scenarios were dominated by the motorcycle transport system (figure 5). 30% of the simulated incremental cost-effectiveness ratios in the UAS100 scenario were more cost-effective than motorcycles in 30% of the simulated scenarios.
Figure 5Cost-effectiveness plane (A) and per-sample costs over lifespan (B) under emergency scenario
The major cost categories for both systems were capital, maintenance, and personnel. In sensitivity analyses, purchase price, lifespan, and failure rates were the main determinants of UAS costs. The break-even point for UAS occurred at a purchase price of $10 000, operational range of 65 km, and lifespan of 3000 flight hours. Systems costing over $30 000 were not cost-effective under all scenarios. Per sample costs were most sensitive to lifespans. Assuming a 100 km operational radius, a purchase price of $30 000, the base case assumptions, and varying the lifespan between 1000 and 3000 h, the weighted sample transport costs ranged from $23·72 to $187·81 (appendix p 7).
Discussion
Our simulations suggest that short-range UAS are less cost-effective than motorbikes for transportation of laboratory samples under most scenarios in Liberia; however, there is potential scope for longer-range UAS, especially if prices decrease and operational lifespans increase.
There have been suggestions for using UAS as complements, rather than substitutes, for land-based transport systems. 4 UAS could be deployed to cover hard-to-reach areas, while motorcycles and trucks would cover more proximal locations. This scenario would be highly dependent on the operational range of the UAS, the location of the UAS base stations, road density, and the location and number of remote clinics. Most of the hard-to-reach facilities fall outside the range of the UAS30. For scenarios where remote locations fall outside UAS ranges, additional means of transportation (air, water, and land) will be required, increasing operational costs. Programmatic tradeoffs would need to be made to defray costs associated with underuse of UAS personnel and equipment under these mixed transport systems if the UAS cannot be reallocated to other duties.
The operational lifespan and accident rates of most civilian UAS aircraft are unknown. There is also the risk of interference, including theft. In field trials, WeRobotics, a non-governmental organisation, had a failure rate of 7% in 44 flights, including the permanent loss of an aircraft over the Peruvian Amazon. 11 Additional research is required to address concerns about useful operational lifespans, failure rates, weather operability, multistop functionalities, and operational aircraft mix. Such studies can also provide evidence for lifting of line-of-sight UAS piloting restrictions that are in place in some countries. We suggest that laboratory specimen transport systems, land-based or air-based, should have robust sample recovery protocols to mitigate losses. This is vital if biohazardous materials are to be transported.
The issues raised in this study suggest that cost-effectiveness of UAS depend on a country’s geographical and health-system design context. For example, South Pacific islands with high road densities might still need UAS to serve isolated islands, even if the intra-island road networks are reliable. The operational assumptions we made for laboratory sample transport might be inappropriate for UAS use in transport of time-sensitive medical supplies or for disease surveillance.
Our analyses had several limitations. We did not account for cost savings that could accrue from bulk purchasing or equipment rentals. We assumed single-programme use under both systems and did not consider potential concurrent uses. We also used a single-stop spoke-hub model, whereas most programmes dynamically optimise their transport models by using variations of multistop strategies. We did not consider downstream benefits of UAS including faster diagnostic turnaround times during public health emergencies. It is unclear whether temporal savings in the magnitude of minutes to several hours will be relevant in bending the epidemic curve conditional on laboratory capacity. 20 Temporal benefits also have to be viewed in the context of fundamental public health protocols such as early case definitions and rapid institution of isolation measures, which might translate to more lives saved. These comparative speed advantage arguments also extend to transportation of long-tail, time-sensitive supplies such snake antivenom, where health systems face realities such as an absence of technical expertise and infrastructural capacities for administering potentially risky treatments in remote clinics.
To make our models tractable, we assumed existence of well-established UAS infrastructure within a country at the start of a public health emergency. This helped us abstract from the uncertainties around the duration of the public health emergency, the inherent logistical challenges around procurement and deployment of the transport systems and learning curve effects. In reality, the procurement and deployment realities and uncertainties about the nature of the public health emergency effectively preclude last-minute emergency buys.
Programmes considering the use of UAS could adopt the aviation industry practice of having a mix of short-haul, medium-haul, and long-haul aircrafts to serve different geographical areas and needs. This will reduce costs since longer-range UAS are more expensive than short-range ones. We anticipate that greater UAS technological diffusion will result in lower prices and better performances, making them more attractive for both emergency and routine use.
Contributors
WOO, TY, CS, VK, and KK conceived the project and worked out the technical details for the analysis. SLY and WOO collected the data. WOO did the simulations and designed the figures. WOO wrote the manuscript with input from all the authors. All authors reviewed the results and made corrections on multiple iterations of the manuscript.
Declaration of interests
We declare no competing interests.
Acknowledgments
We thank Riders for Health Liberia, Jolie Dennis, and the Centers for Disease Control and Prevention Liberia Office for their tremendous assistance with motorcycle operational data including prices, distances, and road conditions; Sandia National Laboratories, Skyfire, and Vayu for unmanned aerial systems operational information; and Suzanne Friesen for her insights about Ebola virus disease reference laboratory operations. The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention or the Agency for Toxic Substances and Disease Registry.
Funding and services needed to achieve universal health coverage: applications of global, regional, and national estimates of utilisation of outpatient visits and inpatient admissions from 1990 to 2016, and unit costs from 1995 to 2016.
Establishing Ebola virus disease (EVD) diagnostics using GeneXpert technology at a mobile laboratory in Liberia: impact on outbreak response, case management and laboratory systems strengthening.
NHS could use DRONES to transport life-saving blood and chemotherapy kits between hospitals and surgeries in ground-breaking trial in four UK areas
Partnership of four councils has launched bid to carry out the first UK trials
Unmanned aerial vehicles would be used to transport vital medical equipment
Drones could be used at serious incidents involving police and fire services
The NHS could start delivering life-saving blood samples and chemotherapy kits using drones, under groundbreaking new proposals.
A partnership of four councils has launched a bid to carry out the first UK trials using unmanned aerial vehicles (UAVs) to transport vital medical equipment between hospitals and GP surgeries.
Taking to the skies to deliver the kits would dramatically transform the way emergency services operate – with it also hoped drones could be used at serious incidents involving police and fire services.
The NHS could start delivering life-saving blood samples and chemotherapy kits using drones, under groundbreaking new proposals.
The Department of Transport has received a bid from Solent Transport – which is made up of four south coast authorities – to use drones for carrying blood and chemotherapy kits.
The bid must gain ‘ethical approval’ after experts raised concerns about privacy, security and safety in regard to using the devices.
Solent Transport’s bid follows research by innovation foundation Nesta which showed using this technology would save the UK public sector £1.1 billion and boost the economy by almost £7 billion.
Nesta’s study of five UK cities found traffic congestion and long journey times are causing unnecessary delays to the NHS as well as emergency services.
A partnership of four councils has launched a bid to carry out the first UK trials using unmanned aerial vehicles (UAVs) to transport vital medical equipment between hospitals and GP surgeries. Pictured: One of the drones being tested at Southampton Hospital
In a statement, Solent Transport said: ‘Our proposed live trials would use equivalent dummy payloads to replicate pathology and treatment kit shipments moving between the various consignors and consignees.
‘Subject to ethical approval, trials of live samples would be undertaken.’
Drones would be flown nearly 15 miles between three Hampshire hospitals under the proposals; Southampton General Hospital [SGH], Portsmouth’s QA Hospital and the Isle of Wight’s St Mary’s Hospital.
Solent Transport said: ‘This project identified that congestion and long journey times by road and ferry [between the three hospitals] resulted in high delivery costs and inefficiencies to the NHS which could be significantly reduced if certain products were distributed by UAV.
‘[On the Isle of Wight] patients currently have to travel to SGH for chemotherapy treatment on a regular basis.
‘The concept would involve the transport of specialised kits by drone from SGH to recognised locations where they would be taken to the patient’s home and administered by local medical staff.’
Solent Transport says it hopes to eventually use drones to transport time-critical medicines and treatments.
Taking to the skies to deliver the kits would dramatically transform the way emergency services operate – with it also hoped drones could be used at serious incidents involving police and fire services.
Rick Allen, operations manager for SGH’s laboratories, said: ‘As soon as blood is taken from a patient’s vein, the clock is ticking. We have four hours to get it from the vein to us and then we’ve got a couple of hours to process that sample.
‘If we can be assured of getting samples to us quicker, then we can be that much more assured that the results are accurate and the correct result for that patient. Drones are already being used to deliver blood in developing parts of the world, such as Rwanda and Ghana, but the congested nature of Britain’s airspace make it more difficult.’
Hollie Jamieson, head of future cities at Nesta, said: ‘Our research showed that people did have concerns, the obvious concerns: privacy, security. Despite those concerns, the public are interested and accepting of drones when they are being used for publicly beneficial uses.’
The areas Nesta conducted its research were Bradford, London, Preston, Southampton and the West Midlands. In London and Southampton Nesta looked at the use of drones by hospitals, leading to the latest bid for funding by Solent Transport.
The Department of Transport has received a bid from Solent Transport – which is made up of four south coast authorities – to use drones for carrying blood and chemotherapy kits. Pictured: One of the drones being tested at Southampton Hospital.
In Bradford, the trial looked at the possibility of its fire service launching drones from a fire station, flying ahead to the scene and beaming images back. These could then be used to dispatch the right number of crews and equipment, avoid false alarms, and save valuable time.
In the West Midlands the trial examined how drones could help police and ambulance services respond to road traffic collisions.
Nesta said using these potential innovations could reduce costs by £1.1bn in the public sector in urban areas by 2035 and the use of drones to support delivery of public services could increase GDP by £6.9bn.
Tris Dyson, Executive Director of Nesta, said: ‘Drones delivering public services in cities could be part of our reality in the near future, bringing major benefits for the public sector.’
Solent Transport is made up Southampton City Council, Portsmouth City Council, Isle of Wight Council, and Hampshire County Council.
Altitude Angel named UTM provider for African Drone Forum
London, UK: Altitude Angel, the industry-leading UTM (Unmanned Traffic Management) technology provider, has been chosen as the lead and ‘umbrella’ UTM provider for the African Drone Forum and Lake Kivu Challenge 2020, which will take place in February 2020 on the shores of Lake Kivu, Rwanda.
The 2020 African Drone Forum comprises a symposium, expo, business plan competition and a series of automated drone flying competitions which are designed to showcase how emerging technology can improve the lives of people in hard-to-reach rural communities.
It is a multi-stakeholder initiative supported by the World Bank, UK Department for International Development (DFID)/UK Aid, World Economic Forum, World Food Programme, UNICEF, Danida and other partners in collaboration with the Government of Rwanda. The symposium and expo will be taking place in Kigali from February 5 to 7, 2020, with competitive flights commencing on February 8, 2020.
The Lake Kivu Challenge flying competitions are designed to illustrate the real-world applications of airspace management, delivery and autonomous flight.
Altitude Angel’s role will be as the foundational UTM Service Provider (SP) for the African Drone Forum and Lake Kivu Challenge 2020, providing the FIMS (Flight Information Management System). During the Challenge, all other UTM SPs, UAS manufacturers and developers will interface with Altitude Angel’s UTM system ensuring all operations can be monitored and tracked for regional ATC, safely integrating flights into local airspace. In doing so, the Challenge will demonstrate the full capability of drones to enhance humanitarian and commercial operations.
Rwanda’s Lake Kivu region has been specially selected for this symposium, as it presents logistical challenges which unmanned aerial vehicles are uniquely positioned to address. The region is densely populated and located around an expanse of water, surrounded by hilly conditions which are notoriously difficult to traverse with conventional land vehicles.
On being selected as the lead UTM provider for the Lake Kivu Challenge 2020, Richard Parker, Altitude Angel, CEO and founder, said: “The potential for drones to be used to transform lives and revolutionise businesses across all of Africa is immense. The continent has a unique opportunity to embrace a new technology and bring about fundamental, positive change to all. The African Drone Forum Lake Kivu Challenge 2020 will showcase this potential and we’re thrilled to be part of this important project.”
Jonty Slater from IMC Worldwide, supporting the Lake Kivu Challenge 2020, added: “As the industry-leading UTM provider, we couldn’t be happier having Altitude Angel on board. The company has time and again demonstrated its credentials and this challenge has attracted the world’s best-in-class across all the technology providers who will be taking part. I’m very much looking forward to getting out to Rwanda and seeing the technology put through its paces!”
About Altitude Angel:
Altitude Angel is an aviation technology company delivering solutions which enable the safer integration and use of fully automated drones into airspace. Through its Airspace Management platform, GuardianUTM O/S, they deliver the essential software platform which enable national deployments of USpace compatible services, safely unlocking the potential of drones and helping national aviation authorities and air navigation service providers to establish new services to support the growth in the drone industry.
The foundation components of GuardianUTM O/S are also available to enable third-party UTM developers to incorporate enterprise-grade data and services into their UTM solutions.
Altitude Angel was founded by Richard Parker in 2014 and is headquartered in Reading, UK.
Altitude Angel’s developer platform is open and available to all at https://developers.altitudeangel.com.
Police Exemptions activated to find a high risk missing person
In the early hours of January 11th 2019 Lincolnshire Police found a high risk missing person near Hykeham, on the edge of Lincoln. To do so they had to use their Emergency Services Exemptions to the Air Navigation Order, extending their operator distance from 500m to 750m.
There is a common misconception that the Police can fly where and when and how they want. This is not the case, as clearly laid out on the CAA website. There are exemptions though, as mentioned in ANO Article 266.
The use of these exemptions is monitored carefully both on an internal basis by the relevant Force and externally by the CAA in order to ensure the criteria are met. It is a decision usually made by the highly trained remote pilot and the on scene supervisors.
In this particular case, the decision was the correct one and the missing person was located faster than by traditional methods. This means help can be provided more quickly and the Police redirected to the next case more quickly too – a great result all round.
In the UK, Emergency Services hold about 50 PfCOs between them. They do an incredible job keeping the UK people safe in a variety of scenarios: from missing persons, to mapping traffic accidents quickly in order to reopen roads, crowd control at a variety of events, thermal imaging heat sources at fires and more.
They are also at the forefront of what the General Public deem acceptable. According to a PwC survey carried out in April 2019, more than 80% had positive feelings towards the Emergency Services using drones. The engagement that individual Emergency Services have with the Public is helping to improve the Public’s Perception of Drones. They regularly post about how they use the drones, in what circumstances, and will engage with the Public to correct misconceptions and encourage safe drone flying in accordance with the CAA regulations.
ARPAS-UK recognises the skills, competence and training that the Emergency Service UAV teams bring to the UK. We work with Emergency Services teams all around the UK. If you are a member of one an Emergency Services using drones, do get in touch if you would like to know more about what we do. membership@arpas.uk
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ARPAS-UK brings Industry Insight to CPD for Teachers & Trainers for Construction T-Level
When: 27th January 2020 Part 1: 10.30am-12.30pm Part 2: 1pm-3pm
Where: Grimsby Institute, Nuns Corner, Grimsby, DN34 5BQ
CPD for teachers, trainers and managers
These FREE two two-hour employer-led sessions will provide an update on Drone use within the Construction sector. This workshop will be delivered by experts from ARPAS-UK.
Part 1: Participants will gain an understanding of the knowledge, skills and behaviours required to progress into skilled employment, and the challenges faced by the sector to better inform students when offering careers advice and guidance. To Book
Part 2: Participants will gain an insight into current working practices ensuring that they, and subsequently their learners, have a clear line of sight to work. To Book
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The commercial UAV shift – from toy to industrial inspection tool
The IMechE South Yorkshire Area is hosting their first event of 2020
When: Thursday 16th January 2020 @ 6.30pm
(light refreshments from 6pm)
Where: AMP Technology Centre, Rotherham
For further details and to book your place, visit: http://nearyou.imeche.org/near-you/…/Yorkshire/event-detail…
or alternatively, contact:
Nicky Baxter (Yorkshire Region Admin Officer) via email yorksadmin@imechenearyou.org or tel. 0113 391 0537.
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‘GPS glitch’ grounds GoPro Karma drones
GoPro’s first and only drone – the Karma – has been grounded by a technical glitch. The issue is GPS-related and it prevents the camera-carrying drones from starting.
The drone was discontinued in 2018. However, GoPro pledged to keep supporting existing machines with the necessary software updates.
A GoPro spokesperson told the BBC that the company has identified the issue. “We’ve identified the cause of this issue, are in the process of implementing and testing a fix, and expect to release a firmware update for Karma that will address the issue within the week,” they said. “We apologise to anyone who was inconvenienced by this issue.”
Tech website The Verge reported that the issue is linked to the recent GPS clock “rollover” phenomenon, which happens once every 1,024 weeks, or every 19.7 years. Software needs to be programmed to anticipate the rollover to zero weeks, otherwise it may stop working.
The majority of tech firms have averted problems with software updates over the last few months but GoPro has not updated Karma’s software since September 2018.
GoPro Karma owners started complaining about the issue last Thursday.
“I recently got a Karma and am having an issue with compass calibration,” wrote a GoPro owner on a forum. “For some reason this doesn’t work: as soon as I select compass calibration in the controller, within half a second (not enough time to pick up the drone and start calibrating), I get the message ‘calibration failed, try again’. It also gives a message about not being able to calibrate the compass without a GPS signal.”
Another owner said they had the same problem and it was stopping them working.
The backpack-sized Karma drone, which has a dedicated slot for the company’s Hero action cameras, was launched by GoPro in September 2016.
The product, which competes with DJI’s camera drones, proved popular with snowboarders, surfers, and other extreme sports enthusiasts who want to film themselves performing from the air.
Some YouTubers and Instagrammers also bought the device, which retailed for £720 at launch.
GoPro have since created an update: “If you still find yourself asking, “What the heck happened?” Without making this TL;DR, here’s some technical information on the root of the issue: Consumer electronics products, like Karma, which we stopped producing in January of 2018, rely on the World Magnetic Model to provide accurate positioning services. The first week of January, after ringing in the New Year, we began to receive reports of users not being able to calibrate the compass on Karma. After investigation, we found that the World Magnetic Model stored in Karma experienced an issue when we clicked over to 2020.
The updated firmware will allow Karma to resume the performance that was available prior to the date change, though there may still be areas of the world where, in rare instances, variations in magnetic fields will cause calibrations to fail. We want to hear from you as a user if you experience this, either on the GoPro Support Hub or directly to our Customer Support Team. But for the majority of our users, this new firmware will fix any calibration issues.”
The Drone Industry Action Group met on the following dates and the minutes and presentations are available. If you have any questions, please get in touch via membership@arpas.uk
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