Strategies for Using Antigen Rapid Diagnostic Tests to Reduce Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 in Low- and Middle-Income Countries: A Mathematical Modelling Study Applied to Zambia.

BACKGROUND: Increasing the availability of antigen rapid diagnostic tests (Ag-RDTs) in low- and middle-income countries (LMICs) is key to alleviating global SARS-CoV-2 testing inequity (median testing rate in December 2021-March 2022 when the Omicron variant was spreading in multiple countries: high-income countries = 600 tests/100 000 people/day; LMICs = 14 tests/100 000 people/day). However, target testing levels and effectiveness of asymptomatic community screening to impact SARS-CoV-2 transmission in LMICs are unclear. METHODS: We used Propelling Action for Testing and Treating (PATAT), an LMIC-focused agent-based model to simulate coronavirus disease 2019 (COVID-19) epidemics, varying the amount of Ag-RDTs available for symptomatic testing at healthcare facilities and asymptomatic community testing in different social settings. We assumed that testing was a function of access to healthcare facilities and availability of Ag-RDTs. We explicitly modelled symptomatic testing demand from individuals without SARS-CoV-2 and measured impact based on the number of infections averted due to test-and-isolate. RESULTS: Testing symptomatic individuals yields greater benefits than any asymptomatic community testing strategy until most symptomatic individuals who sought testing have been tested. Meeting symptomatic testing demand likely requires at least 200-400 tests/100 000 people/day, on average, as symptomatic testing demand is highly influenced by individuals without SARS-CoV-2. After symptomatic testing demand is satisfied, excess tests to proactively screen for asymptomatic infections among household members yield the largest additional infections averted. CONCLUSIONS: Testing strategies aimed at reducing transmission should prioritize symptomatic testing and incentivizing test-positive individuals to adhere to isolation to maximize effectiveness.

Primary healthcare seeking behaviour of low-income patients across the public and private health sectors in South Africa.

BACKGROUND: The proposed National Health Insurance (NHI) system aims to re-engineer primary healthcare (PHC) in South Africa, envisioning both private sector providers and public sector clinics as independent contracting units to the NHI Fund. In 2017, 16% of the South African population had private medical insurance and predominately utilised private providers. However, it is estimated that up to 28% of the population access private PHC services, with a meaningful segment of the low-income, uninsured population paying for these services out-of-pocket. The study objective was to characterise the health seeking behaviour of low-income, patients accessing PHC services in both the public and private sectors, patient movement between sectors, and factors influencing their facility choice. METHODS: We conducted once-off patient interviews on a random sample of 153 patients at 7 private PHC providers (primarily providing services to the low-income mostly uninsured patient population) and their matched public PHC clinic (7 facilities). RESULTS: The majority of participants were economically active (96/153, 63%), 139/153 (91%) did not have health insurance, and 104/153 (68%) earned up to $621/month. A multiple response question found affordability (67%) and convenience (60%) were ranked as the most important reasons for choosing to usually access care at public clinics (48%); whilst convenience (71%) and quality of care (59%) were key reasons for choosing the private sector (32%). There is movement between sectors: 23/76 (30%) of those interviewed at a private facility and 8/77 (10%) of those interviewed at a public facility indicated usually accessing PHC services at a mix of private and public facilities. Results indicate cycling between the private and public sectors with different factors influencing facility choice. CONCLUSIONS: It is imperative to understand the potential impact on where PHC services are accessed once affordability is mitigated through the NHI as this has implications on planning and contracting of services under the NHI.

Cost and Impact of Dried Blood Spot Versus Plasma Separation Card for Scale-up of Viral Load Testing in Resource-limited Settings.

BACKGROUND: Routine plasma viral load (VL) testing is recommended for monitoring human immunodeficiency virus-infected patients on antiretroviral therapy. In Zambia, VL scale-up is limited due to logistical obstacles around plasma specimen collection, storage, and transport to centralized laboratories. Dried blood spots (DBSs) could circumvent many logistical challenges at the cost of increased misclassification. Recently, plasma separation cards (PSCs) have become available and, though more expensive, have lower total misclassification than DBSs. METHODS: Using a geospatial model created for optimizing VL utilization in Zambia, we estimated the short-term cost of uptake/correct VL result using either DBSs or PSCs to increase VL access on equipment available in-country. Five scenarios were modeled: (1) plasma only (status quo); (2) plasma at high-volume sites, DBS at low-volume sites; (3) plasma at high-volume sites, PSC at low-volume sites; (4) PSC only; (5) DBS only. RESULTS: Scenario 1 resulted in 795 342 correct results due to limited patient access. When allowing for full and partial adoption of dried specimens, access increases by 19%, with scenario 3 producing the greatest number of correct results expected (929 857). The average cost per correct VL result was lowest in the plasma + DBS scenario at $30.90 compared to $31.62 in our plasma + PSC scenario. The cost per correct result of using dried specimens only was dominated in the incremental analysis, due primarily to fewer correct results. CONCLUSIONS: Adopting the partial use of dried specimens will help achieve improved VL access for patients at the lowest cost per correct result.

Understanding the costs and the cost structure of a community-based HIV and gender-based violence (GBV) prevention program: the Woza Asibonisane Community Responses Program in South Africa.

BACKGROUND: The Woza Asibonisane Community Responses (CR) Programme was developed to prevent HIV infections and gender-based violence (GBV) within four provinces in South Africa. The Centre for Communication Impact (CCI) in collaboration with six partner non-governmental organizations (NGOs) implemented the programme, which was comprised of multiple types of group discussion and education activities organized and facilitated by each NGO. To date, little information exists on the cost of implementing such multi-objective, multi-activity, community-based programmes. To address this information gap, we estimated the annual cost of implementing the CR Programme for each NGO. METHODS: We used standard methods to estimate the costs for each NGO, which involved a package of multiple activities targeted to distinct subpopulations in specific locations. The primary sources of information came from the implementing organizations. Costs (US dollars, 2017) are reported for each partner for one implementation year (the U.S. Government fiscal year (10/2016-09/2017). In addition to total costs disaggregated by main input categories, a common metric--cost per participant intervention hour--is used to summarize costs across partners. RESULTS: Each activity included in the CR program involve organizing and bringing together a group of people from the target population to a location and then completing the curriculum for that activity. Activities were held in community settings (meeting hall, community center, sports grounds, schools, etc.). The annual cost per NGO varied substantially, from $260,302 to $740,413, as did scale based on estimated total participant hours, from 101,703 to 187,792 participant hours. The cost per participant hour varied from $2.8-$4.6, with NGO labor disaggregated into salaries for management and salaries for service delivery (providing the activity curriculum) contributing to the largest share of costs per participant hour. CONCLUSIONS: The cost of implementing any community-based program depends on: (1) what the program implements; (2) the resources used; and (3) unit costs for such resources. Reporting on costs alone, however, does not provide enough information to evaluate if the costs are 'too high' or 'too low' without a clearer understanding of the benefits produced by the program, and if the benefits would change if resources (and therefore costs) were changed.

Implementation research: a protocol for two three-arm pragmatic randomised controlled trials on continuous glucose monitoring devices in people with type 1 diabetes in South Africa and Kenya.

BACKGROUND: Self-monitoring of glucose is an essential component of type 1 diabetes (T1D) management. In recent years, continuous glucose monitoring (CGM) has provided an alternative to daily fingerstick testing for the optimisation of insulin dosing and general glucose management in people with T1D. While studies have been conducted to evaluate the impact of CGM on clinical outcomes in the US, Europe and Australia, there are limited data available for low- and middle-income countries (LMICs) and further empirical evidence is needed to inform policy decision around their use in these countries. METHODS: This trial was designed as a pragmatic, parallel-group, open-label, multicentre, three-arm, randomised (1:1:1) controlled trial of continuous or periodic CGM device use versus standard of care in people with T1D in South Africa and Kenya. The primary objective of this trial will be to assess the impact of continuous or periodic CGM device use on glycaemic control as measured by change from baseline glycosylated haemoglobin (HbA1c). Additional assessments will include clinical outcomes (glucose variation, time in/below/above range), safety (adverse events, hospitalisations), quality of life (EQ-5D, T1D distress score, Glucose Monitoring Satisfaction Survey for T1D), and health economic measures (incremental cost-effectiveness ratios, quality adjusted life years). DISCUSSION: This trial aims to address the substantial evidence gap on the impact of CGM device use on clinical outcomes in LMICs, specifically South Africa and Kenya. The trial results will provide evidence to inform policy and treatment decisions in these countries. TRIAL REGISTRATION: NCT05944731 (Kenya), July 6, 2023; NCT05944718 (South Africa), July 13, 2023.

Impact and cost-effectiveness of SARS-CoV-2 self-testing strategies in schools: a multicountry modelling analysis.

OBJECTIVES: To determine the most epidemiologically effective and cost-effective school-based SARS-CoV-2 antigen-detection rapid diagnostic test (Ag-RDT) self-testing strategies among teachers and students. DESIGN: Mathematical modelling and economic evaluation. SETTING AND PARTICIPANTS: Simulated school and community populations were parameterised to Brazil, Georgia and Zambia, with SARS-CoV-2 self-testing strategies targeted to teachers and students in primary and secondary schools under varying epidemic conditions. INTERVENTIONS: SARS-CoV-2 Ag-RDT self-testing strategies for only teachers or teachers and students-only symptomatically or symptomatically and asymptomatically at 5%, 10%, 40% or 100% of schools at varying frequencies. OUTCOME MEASURES: Outcomes were assessed in terms of total infections and symptomatic days among teachers and students, as well as total infections and deaths within the community under the intervention compared with baseline. The incremental cost-effectiveness ratios (ICERs) were calculated for infections prevented among teachers and students. RESULTS: With respect to both the reduction in infections and total cost, symptomatic testing of all teachers and students appears to be the most cost-effective strategy. Symptomatic testing can prevent up to 69·3%, 64·5% and 75·5% of school infections in Brazil, Georgia and Zambia, respectively, depending on the epidemic conditions, with additional reductions in community infections. ICERs for symptomatic testing range from US$2 to US$19 per additional school infection averted as compared with symptomatic testing of teachers alone. CONCLUSIONS: Symptomatic testing of teachers and students has the potential to cost-effectively reduce a substantial number of school and community infections.

Cost of SARS-CoV-2 self-test distribution programmes by different modalities: a micro-costing study in five countries (Brazil, Georgia, Malaysia, Ethiopia and the Philippines).

OBJECTIVE: Diagnostic testing is an important tool to combat the COVID-19 pandemic, yet access to and uptake of testing vary widely 3 years into the pandemic. The WHO recommends the use of COVID-19 self-testing as an option to help expand testing access. We aimed to calculate the cost of providing COVID-19 self-testing across countries and distribution modalities. DESIGN: We estimated economic costs from the provider perspective to calculate the total cost and the cost per self-test kit distributed for three scenarios that differed by costing period (pilot, annual), the number of tests distributed (actual, planned, scaled assuming an epidemic peak) and self-test kit costs (pilot purchase price, 50% reduction). SETTING: We used data collected between August and December 2022 in Brazil, Georgia, Malaysia, Ethiopia and the Philippines from pilot implementation studies designed to provide COVID-19 self-tests in a variety of settings-namely, workplace and healthcare facilities. RESULTS: Across all five countries, 173 000 kits were distributed during pilot implementation with the cost/test distributed ranging from $2.44 to $12.78. The cost/self-test kit distributed was lowest in the scenario that assumed implementation over a longer period (year), with higher test demand (peak) and a test kit price reduction of 50% ($1.04-3.07). Across all countries and scenarios, test procurement occupied the greatest proportion of costs: 58-87% for countries with off-site self-testing (outside the workplace, for example, home) and 15-50% for countries with on-site self-testing (at the workplace). Staffing was the next key cost driver, particularly for distribution modalities that had on-site self-testing (29-35%) versus off-site self-testing (7-27%). CONCLUSIONS: Our results indicate that it is likely to cost between $2.44 and $12.78 per test to distribute COVID-19 self-tests across common settings in five heterogeneous countries. Cost-effectiveness analyses using these results will allow policymakers to make informed decisions on optimally scaling up COVID-19 self-test distribution programmes across diverse settings and evolving needs.

The integration of tuberculosis and HIV testing on GeneXpert can substantially improve access and same-day diagnosis and benefit tuberculosis programmes: A diagnostic network optimization analysis in Zambia.

Diagnostic network optimization (DNO), a geospatial optimization technique, can improve access to diagnostics and reduce costs through informing policy-makers' decisions on diagnostic network changes. In Zambia, viral load (VL) testing and early infant diagnosis (EID) for HIV has been performed at centralized laboratories, whilst the TB-programme utilizes a decentralized network of GeneXpert platforms. Recently, the World Health Organization (WHO) has recommended point-of-care (POC) EID/VL to increase timely diagnosis. This analysis modelled the impact of integrating EID/VL testing for children and pregnant/breastfeeding-women (priority-HIV) with TB on GeneXpert in Zambia. Using OptiDx, we established the baseline diagnostic network using inputs for testing demand (October 2019-September 2020), referrals, testing sites, testing platforms, and costs for HIV/TB testing (transport, test, device) respectively in Zambia. Next, we integrated priority-HIV testing on GeneXpert platforms, historically only utilized by the TB-programme. 228,265 TB tests were conducted on GeneXpert devices and 167,458 (99%) of priority-HIV tests on centralized devices at baseline, of which 10% were tested onsite at the site of sample collection. With integration, the average distance travelled by priority-HIV tests decreased 10-fold (98km to 10km) and the proportion tested onsite increased (10% to 48%). 52% of EID tests are likely to be processed within the same-day from a baseline of zero. There were also benefits to the TB-programme: the average distance travelled/specimen decreased (11km to 7km), alongside potential savings in GeneXpert device-operating costs (30%) through cost-sharing with the HIV-programme. The total cost of the combined testing programmes reduced marginally by 1% through integration/optimization. DNO can be used to strategically leverage existing capacity to achieve the WHO's recommendation regarding POC VL/EID testing. Through DNO of the Zambian network, we have shown that TB/HIV testing integration can improve the performance of the diagnostic network and increase the proportion of specimens tested closer to the patient whilst not increasing costs.

Optimising courier specimen collection time improves patient access to HIV viral load testing in South Africa.

Background: South Africa uses a courier network for transporting specimens to public laboratories. After the daily collection of specimens from the facility by the courier, patients not yet attended to are unlikely to receive same-day blood draws, potentially inhibiting access to viral load (VL) testing for HIV patients. Objective: We aimed to design an optimised courier network and assess whether this improves VL testing access. Methods: We optimised the specimen transport network in South Africa for 4046 facilities (November 2019). For facilities with current specimen transport times (n = 356), we assessed the relationship between specimen transport time and VL testing access (number of annual VL tests per antiretroviral treatment patient) using regression analysis. We compared our optimised transport times with courier collection times to determine the change in access to same-day blood draws. Results: The number of annual VL tests per antiretroviral treatment patient (1.14, standard deviation: 0.02) was higher at facilities that had courier collection after 13:36 (the average latest collection time) than those that had their last collection before 13:36 (1.06, standard deviation: 0.03), even when adjusted for facility size. Through network optimisation, the average time for specimen transport was delayed to 14:35, resulting in a 6% - 13% increase in patient access to blood draws. Conclusion: Viral load testing access depends on the time of courier collection at healthcare facilities. Simple solutions are frequently overlooked in the quest to improve healthcare. We demonstrate how simply changing specimen transportation timing could markedly improve access to VL testing.

Optimal use of COVID-19 Ag-RDT screening at border crossings to prevent community transmission: A modeling analysis.

Countries around the world have implemented restrictions on mobility, especially cross-border travel to reduce or prevent SARS-CoV-2 community transmission. Rapid antigen testing (Ag-RDT), with on-site administration and rapid turnaround time may provide a valuable screening measure to ease cross-border travel while minimizing risk of local transmission. To maximize impact, we developed an optimal Ag-RDT screening algorithm for cross-border entry. Using a previously developed mathematical model, we determined the daily number of imported COVID-19 cases that would generate no more than a relative 1% increase in cases over one month for different effective reproductive numbers (Rt) and COVID-19 prevalence within the recipient country. We then developed an algorithm-for differing levels of Rt, arrivals per day, mode of travel, and SARS-CoV-2 prevalence amongst travelers-to determine the minimum proportion of people that would need Ag-RDT testing at border crossings to ensure no greater than the relative 1% community spread increase. When daily international arrivals and/or COVID-19 prevalence amongst arrivals increases, the proportion of arrivals required to test using Ag-RDT increases. At very high numbers of international arrivals/COVID-19 prevalence, Ag-RDT testing is not sufficient to prevent increased community spread, especially when recipient country prevalence and Rt are low. In these cases, Ag-RDT screening would need to be supplemented with other measures to prevent an increase in community transmission. An efficient Ag-RDT algorithm for SARS-CoV-2 testing depends strongly on the epidemic status within the recipient country, volume of travel, proportion of land and air arrivals, test sensitivity, and COVID-19 prevalence among travelers.

Role of data from cost and other economic analyses in healthcare decision-making for HIV, TB and sexual/reproductive health programmes in South Africa.

An increasing focus on the use of the results of cost analyses and other economic evaluations in health programme decision-making by governments, donors and technical support partners working in low- and middle-income countries is accompanied by recognition that this use is impeded by several factors, including the lack of skills, data and coordination between spheres of the government. We describe our experience generating economic evaluation data for human immunodeficiency virus, tuberculosis and sexual/reproductive health programmes in South Africa alongside the results of a series of in-depth interviews (IDIs) among decision-makers within the South African government and implementing organizations (data users) and producers of economic evaluations (data producers). We summarize results across (1) the process of implementing a new intervention; (2) barriers to the use of cost data and suggested solutions and (3) the transferability of experiences to the planned South African implementation of universal health coverage (UHC). Based on our experience and the IDIs, we suggest concrete steps towards the improvement of economic data use in the planning and the establishment of structures mandated under the transition to UHC. Our key recommendations include the following: (1) compile a publicly available and regularly updated in-country cost repository; (2) increase the availability of programmatic outcomes data at the aggregate level; (3) agree upon and implement a set of primary decision criteria for the adoption and funding of interventions; (4) combine the efforts of health economics institutions into a stringent system for health technology assessments and (5) improve the link between national and provincial planning and budgeting.

Cost-effectiveness of adoption strategies for point of care HIV viral load monitoring in South Africa.

BACKGROUND: Viral load (VL) testing is recommended for monitoring people on ART. The National Health Laboratory Service (NHLS) in South Africa conducts >5million laboratory-based VL tests but faces challenges with specimen integrity and results delivery. Point-of-care (POC) VL monitoring may improve VL suppression (VLS). We assessed the cost-effectiveness of different strategies for POC testing in South Africa. METHODS: We developed a cost-outcome model utilizing NHLS data, including facility-level annual VL volumes, proportion with VLS, specimen rejection rates, turn-around-time, and the cost/test. We assessed the impact of adopting POC VL technology under 4 strategies: (1) status-quo; (2) targeted POC testing at facilities with high levels of viral failure; (3) targeted POC testing at low-performing facilities; (4) complete POC adoption. For each strategy, we determined the total cost, effectiveness (expected number of virally suppressed people) and incremental cost-effectiveness ratio (ICER) based on expected (>10%) VLS improvement. FINDINGS: Existing laboratory-based VL testing costs $126 m annually and achieves 85.2% VLS. Strategy 2 was the most cost-effective approach, with 88.5% VLS and $40/additional person suppressed, compared to the status-quo. Should resources allow, complete POC adoption may be cost-effective (ICER: $136/additional person suppressed), requiring an additional $49 m annually and achieving 94.5% VLS. All other strategies were dominated in the incremental analysis. INTERPRETATION: Assuming POC VL monitoring confers clinical benefits, the most cost-effective strategy for POC adoption in South Africa is a targeted approach with POC VL technologies placed at facilities with high level of viral failure. FUNDING: Funding support from the Bill & Melinda Gates Foundation.

Bringing Data Analytics to the Design of Optimized Diagnostic Networks in Low- and Middle-Income Countries: Process, Terms and Definitions.

Diagnostics services are an essential component of healthcare systems, advancing universal health coverage and ensuring global health security, but are often unavailable or under-resourced in low- and middle-income (LMIC) countries. Typically, diagnostics are delivered at various tiers of the laboratory network based on population needs, and resource and infrastructure constraints. A diagnostic network additionally incorporates screening and includes point-of-care testing that may occur outside of a laboratory in the community and clinic settings; it also emphasizes the importance of supportive network elements, including specimen referral systems, as being critical for the functioning of the diagnostic network. To date, design and planning of diagnostic networks in LMICs has largely been driven by infectious diseases such as TB and HIV, relying on manual methods and expert consensus, with a limited application of data analytics. Recently, there have been efforts to improve diagnostic network planning, including diagnostic network optimization (DNO). The DNO process involves the collection, mapping, and spatial analysis of baseline data; selection and development of scenarios to model and optimize; and lastly, implementing changes and measuring impact. This review outlines the goals of DNO and steps in the process, and provides clarity on commonly used terms.

Optimizing viral load testing access for the last mile: Geospatial cost model for point of care instrument placement.

INTRODUCTION: Viral load (VL) monitoring programs have been scaled up rapidly, but are now facing the challenge of providing access to the most remote facilities (the "last mile"). For the hardest-to-reach facilities in Zambia, we compared the cost of placing point of care (POC) viral load instruments at or near facilities to the cost of an expanded sample transportation network (STN) to deliver samples to centralized laboratories. METHODS: We extended a previously described geospatial model for Zambia that first optimized a STN for centralized laboratories for 90% of estimated viral load volumes. Amongst the remaining 10% of volumes, facilities were identified as candidates for POC placement, and then instrument placement was optimized such that access and instrument utilization is maximized. We evaluated the full cost per test under three scenarios: 1) POC placement at all facilities identified for POC; 2)an optimized combination of both on-site POC placement and placement at facilities acting as POC hubs; and 3) integration into the centralized STN to allow use of centralized laboratories. RESULTS: For the hardest-to-reach facilities, optimal POC placement covered a quarter of HIV-treating facilities. Scenario 2 resulted in a cost per test of $39.58, 6% less than the cost per test of scenario 1, $41.81. This is due to increased POC instrument utilization in scenario 2 where facilities can act as POC hubs. Scenario 3 was the most costly at $53.40 per test, due to high transport costs under the centralized model ($36 per test compared to $12 per test in scenario 2). CONCLUSIONS: POC VL testing may reduce the costs of expanding access to the hardest-to-reach populations, despite the cost of equipment and low patient volumes. An optimal combination of both on-site placement and the use of POC hubs can reduce the cost per test by 6-35% by reducing transport costs and increasing instrument utilization.

Monitoring viral load for the last mile: what will it cost?

INTRODUCTION: Routine viral load testing is the WHO-recommended method for monitoring HIV-infected patients on ART, and many countries are rapidly scaling up testing capacity at centralized laboratories. Providing testing access to the most remote populations and facilities (the "last mile") is especially challenging. Using a geospatial optimization model, we estimated the incremental costs of accessing the most remote 20% of patients in Zambia by expanding the transportation network required to bring blood samples from ART clinics to centralized laboratories and return results to clinics. METHODS: The model first optimized a sample transportation network (STN) that can transport 80% of anticipated sample volumes to centralized viral load testing laboratories on a daily or weekly basis, in line with Zambia's 2020 targets. Data incorporated into the model included the location and infrastructure of all health facilities providing ART, location of laboratories, measured distances and drive times between the two, expected future viral load demand by health facility, and local cost estimates. We then continued to expand the modelled STN in 5% increments until 100% of all samples could be collected. RESULTS AND DISCUSSION: The cost per viral load test when reaching 80% patient volumes using centralized viral load testing was a median of $18.99. With an expanded STN, the incremental cost per test rose to $20.29 for 80% to 85% and $20.52 for 85% to 90%. Above 90% coverage, the incremental cost per test increased substantially to $31.57 for 90% to 95% and $51.95 for 95% to 100%. The high numbers of kilometres driven per sample transported and large number of vehicles needed increase costs dramatically for reaching the clinics that serve the last 5% of patients. CONCLUSIONS: Providing sample transport services to the most remote clinics in low- and middle-income countries is likely to be cost-prohibitive. Other strategies are needed to reduce the cost and increase the feasibility of making viral load monitoring available to the last 10% of patients. The cost of alternative methods, such as optimal point-of-care viral load equipment placement and usage, dried blood/plasma spot specimen utilization, or use of drones in geographically remote facilities, should be evaluated.

Impact of a borderless sample transport network for scaling up viral load monitoring: results of a geospatial optimization model for Zambia.

INTRODUCTION: The World Health Organization recommends viral load (VL) monitoring at six and twelve months and then annually after initiating antiretroviral treatment for HIV. In many African countries, expansion of VL testing has been slow due to a lack of efficient blood sample transportation networks (STN). To assist Zambia in scaling up testing capacity, we modelled an optimal STN to minimize the cost of a national VL STN. METHODS: The model optimizes a STN in Zambia for the anticipated 1.5 million VL tests that will be needed in 2020, taking into account geography, district political boundaries, and road, laboratory and facility infrastructure. We evaluated all-inclusive STN costs of two alternative scenarios: (1) optimized status quo: each district provides its own weekly or daily sample transport; and (2) optimized borderless STN: ignores district boundaries, provides weekly or daily sample transport, and reaches all Scenario 1 facilities. RESULTS: Under both scenarios, VL testing coverage would increase to from 10% in 2016 to 91% in 2020. The mean transport cost per VL in Scenario 2 was $2.11 per test (SD $0.28), 52% less than the mean cost/test in Scenario 1, $4.37 (SD $0.69), comprising 10% and 19% of the cost of a VL respectively. CONCLUSIONS: An efficient STN that optimizes sample transport on the basis of geography and test volume, rather than political boundaries, can cut the cost of sample transport by more than half, providing a cost savings opportunity for countries that face significant resource constraints.

Investigating the potential of novel non-woven fabrics for efficient pollination control in plant breeding.

Plant breeding is achieved through the controlled self- or cross-pollination of individuals and typically involves isolation of floral parts from selected parental plants. Paper, cellulose or synthetic materials are used to avoid self pollination or cross contamination. Low seed set limits the rate of breeding progress and increases costs. We hypothesized that a novel 'non-woven' fabric optimal for both pollination and seed set in multiple plant species could be developed. After determining the baseline pollen characteristics and usage requirements we established iterative three phase development and biological testing. This determined (1) that white fabric gave superior seed return and informed the (2) development of three non-woven materials using different fibre and layering techniques. We tested their performance in selfing and hybridisation experiments recording differences in performance by material type within species. Finally we (3) developed further advanced fabrics with increased air permeability and tested biological performance. An interaction between material type and species was observed and environmental decoupling investigated, showing that the non-woven fabrics had superior water vapour transmission and temperature regulation compared to controls. Overall, non-woven fabrics outperformed existing materials for both pollination and seed set and we found that different materials can optimize species-specific, rather than species-generic performance.