Measuring COVID-19 Vaccination Coverage to Support Healthcare Equity Decision-Making in Urban Areas (preprint)

Background: Limited studies have been conducted on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran. Methods: The age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different scenarios: only public hospitals, only public healthcare centres, and the top 20% healthcare centres were employed as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres, and distance to main roads) were used to choose potential vaccination centres with the highest suitability for residents. Results: Our findings indicate that including the both public hospitals and public healthcare centres can provide high accessibility to vaccination in central parts of the urban areas. However, using only public healthcare centres for vaccination can provide higher accessibility to vaccination sites in the eastern and north-eastern parts of the study area. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. Conclusions: Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems (GIS), provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available.

A Decision Support System for Prioritised COVID-19 Two-dosage Vaccination Allocation and Distribution (preprint)

Global distribution of COVID-19 vaccines is one of the world's most challenging logistics tasks. A timely mass vaccination during a pandemic is a matter of life and death. 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 disrupted supply, 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.

Spatial epidemiology of COVID-19 infection through the first outbreak (March-May 2020) in the city of Mashhad, Iran (preprint)

Background: Since December 2019, SARS-CoV-2 infection has converted to a severe threat to global health. It is now considered as the fifth worldwide pandemic problem. This study aims to explore spatial-time distribution of COVID-19 in the first outbreak of COVID-19 in the second major city of Iran (Mashhad). The results will pave the way for better tracking of COVID-19.Methods: Data were collected from two tertiary hospitals in Mashhad in June 2020. They included demographic findings and residential address of the patients with confirmed COVID-19 disease by polymerase chain reaction test. The univariate logistic regression model was used to assess the influence of age and sex on mortality. For spatial-time analysis, after calculating empirical Bayesian rate for every neighborhood, the local Moran's I statistic was used to quantify spatial autocorrelation of COVID-19 frequency at the city neighborhood level.Results: Of 1,535 confirmed cases of COVID-19 included in this study, 951 (62%) were male. Odds of death for patients over 60 years of age was more than three times higher (odds ratio [OR]: 3.7, CI [2.8-4.8]) than for those under the 60 years. In addition, the ratio of relative mortality for male patients was significantly higher than the female (OR: 1.58, CI [1.2-2]). The univariate regression model also revealed that odds of death increased along with increase in duration of hospitalization secondary to COVID-19 disease (OR: 1.02, IQR [1.01-1.02]). The downtown area had a significant high-high cluster throughout much of the study period (March-May 2020). Conclusions: Collection of geographic information system (GIS) map data on SARS-CoV-2 provides insight into clusters of infection and high risk places for COVID-19 transmission. GIS-