A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Multi-resource scheduling and routing for emergency recovery operations.

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

Indian Cancer Patients' use of Traditional, Complementary and Alternative Medicine (TCAM) and delays in presentation to Hospital.

OBJECTIVES: A majority of Indian cancer patients are often presented with incurable diseases at the latest phase of disease progression. The use of traditional, complementary and alternative medicine (TCAM) has been identified by Indian oncologists as a potential factor for the delay in seeking health from medical practitioners but no research has been conducted to verify such claims. The aim of this study is to identify socio-demographic and disease status differences between TCAM and non-TCAM users among cancer patients in India and associated patterns of seeking professional medical help. METHODS: A random survey of 825 cancer patients in one public and one private hospital was conducted in Delhi, India. Using four interviewers, a list sampling technique was used to interview every patient over a four month period, with a response rate of 80%. RESULTS: The results showed that 34.3% of cancer patients had used TCAM. The results also demonstrated a statistically significant relationship between the use of TCAM and reported delay in seeking help from clinical medicine (p<0.001). On the other hand, 35.2% of TCAM users reported seeking help immediately after onset of symptoms, whereas 50% of non-users immediately sought help from conventional medicine. Furthermore, 11.5% of TCAM users reported waiting for six months or more after noticing cancer-related symptoms, while only 2.1% of non-users waited this long. CONCLUSION: Overall, early diagnosis and intervention is critical for effective treatment of many malignancies. Delays in presentation related to the use of TCAM may be an important factor relating to the high rates of advanced disease on presentation and low survival rates in the care of Indian cancer patients. Further research is needed to explore the reasons for using TCAM and to ensure existing issues of delays in help seeking are addressed.