The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices.

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems. The QAOA utilizes a quantum-classical loop, consisting of a quantum ansatz and a classical optimizer, to minimize some cost function, computed on the quantum device. This paper presents an investigation into the impact of realistic noise on the classical optimizer and the determination of optimal circuit depth for the Quantum Approximate Optimization Algorithm (QAOA) in the presence of noise. We find that, while there is no significant difference in the performance of classical optimizers in a state vector simulation, the Adam and AMSGrad optimizers perform best in the presence of shot noise. Under the conditions of real noise, the SPSA optimizer, along with ADAM and AMSGrad, emerge as the top performers. The study also reveals that the quality of solutions to some 5 qubit minimum vertex cover problems increases for up to around six layers in the QAOA circuit, after which it begins to decline. This analysis shows that increasing the number of layers in the QAOA in an attempt to increase accuracy may not work well in a noisy device.

COVID-19: lessons and experiences from South Africa's first surge.

On 5 March 2020, South Africa recorded its first case of imported COVID-19. Since then, cases in South Africa have increased exponentially with significant community transmission. A multisectoral approach to containing and mitigating the spread of SARS-CoV-2 was instituted, led by the South African National Department of Health. A National COVID-19 Command Council was established to take government-wide decisions. An adapted World Health Organiszion (WHO) COVID-19 strategy for containing and mitigating the spread of the virus was implemented by the National Department of Health. The strategy included the creation of national and provincial incident management teams (IMTs), which comprised of a variety of work streams, namely, governance and leadership; medical supplies; port and environmental health; epidemiology and response; facility readiness and case management; emergency medical services; information systems; risk communication and community engagement; occupational health and safety and human resources. The following were the most salient lessons learnt between March and September 2020: strengthened command and control were achieved through both centralised and decentralised IMTs; swift evidenced-based decision-making from the highest political levels for instituting lockdowns to buy time to prepare the health system; the stringent lockdown enabled the health sector to increase its healthcare capacity. Despite these successes, the stringent lockdown measures resulted in economic hardship particularly for the most vulnerable sections of the population.

The impact of pharmaceutical tendering on prices and market concentration in South Africa over a 14-year period.

OBJECTIVE: We investigated the South African tendering system for medicines to (a) evaluate its impact on prices and market concentration over a 14-year period and (b) analyze the accuracy of government forecasts of drug demand. METHODS: We calculated Herfindahl-Hirschman indexes to measure market concentration levels based on all pharmaceutical tender contracts issued by the South African government between 2003 and 2016 (n = 8701). We estimated price indexes to track changes in medicine costs over this period. We compared prices set through tenders in the public health care system to the corresponding prices in the private system. We also analyzed government data on procurement in selected drug classes to assess the accuracy of demand forecasts. FINDINGS: Between 2003 and 2016, the prices of medicines in most tender categories in the public health care system dropped by an average of around 40% or more. The prices of medicines procured for the public system through tenders were almost always lower than those sold in the private system. Tenders generally remained moderately to highly competitive over time (i.e., Herfindahl-Hirschman indexes < 2500), although the number of different firms winning contracts decreased in many categories. There were large discrepancies between the drug need estimates by the government and the quantities it went on to procure, with estimates off by more than 50% in most drug classes (9/16 observations). CONCLUSION: Tendering may be an effective measure to lower drug costs. Because most tenders remained competitive over time, price decreases may be durable. South African government officials should monitor the availability and prices of medicines to ensure continued access to affordable medicines for patients, as it may be undermined by the decreasing number of firms winning contracts over time. Given the large discrepancy between forecasts and procurements, the government would benefit from improving the accuracy of its demand forecasts.

Health financing lessons from Thailand for South Africa on the path towards universal health coverage.

Five years after the release of its Green Paper on National Health Insurance (NHI),years after the institution of NHI pilot sites and following the recent release of the White Pa 4 per on NHI, South Africa (SA) needs to move beyond the phase 1 plans of policy making and healthening activities to phase 2 - putting into place the legal and institutional frameth system strengworks and systems for implementation of its universal health coverage (UHC) system. In doing so, SA can draw on considerable practical lessons from other countries' reforms in managing UHC with favourable equity outcomes over the past decade. We outline some potentially significant lessons from the Thai health financing system for SA.

An ontology for factors affecting tuberculosis treatment adherence behavior in sub-Saharan Africa.

PURPOSE: Adherence behavior is a complex phenomenon influenced by diverse personal, cultural, and socioeconomic factors that may vary between communities in different regions. Understanding the factors that influence adherence behavior is essential in predicting which individuals and communities are at risk of nonadherence. This is necessary for supporting resource allocation and intervention planning in disease control programs. Currently, there is no known concrete and unambiguous computational representation of factors that influence tuberculosis (TB) treatment adherence behavior that is useful for prediction. This study developed a computer-based conceptual model for capturing and structuring knowledge about the factors that influence TB treatment adherence behavior in sub-Saharan Africa (SSA). METHODS: An extensive review of existing categorization systems in the literature was used to develop a conceptual model that captured scientific knowledge about TB adherence behavior in SSA. The model was formalized as an ontology using the web ontology language. The ontology was then evaluated for its comprehensiveness and applicability in building predictive models. CONCLUSION: The outcome of the study is a novel ontology-based approach for curating and structuring scientific knowledge of adherence behavior in patients with TB in SSA. The ontology takes an evidence-based approach by explicitly linking factors to published clinical studies. Factors are structured around five dimensions: factor type, type of effect, regional variation, cross-dependencies between factors, and treatment phase. The ontology is flexible and extendable and provides new insights into the nature of and interrelationship between factors that influence TB adherence.