Effect of vaccination on SARS-CoV-2 reinfection risk: a case-control study in the Republic of Cyprus.

OBJECTIVES: We explored the effectiveness of COVID-19 vaccines in preventing reinfection in the Republic of Cyprus. STUDY DESIGN: This was a matched case-control study (1:2). METHODS: Cases were adults with a first episode of SARS-CoV-2 infection in 2020 and a second episode (i.e. reinfection) between June and August 2021. Controls were adults with only one infection episode in 2020 (i.e. not reinfected). Matching was performed by age, gender, and week of diagnosis for the first episode. The reinfection date of a case was applied to the matched controls for estimating full or partial vaccination status. Cases and controls were classified as unvaccinated, partially vaccinated (i.e. vaccination series not completed or final dose received ≤14 days before the reinfection date), or fully vaccinated (i.e. final dose received >14 days before the reinfection date). Conditional logistic regression was performed to calculate odds ratios and 95% confidence intervals for full or partial vaccination, against no vaccination, between controls and cases. RESULTS: This study showed that controls were more likely to be vaccinated (odds ratio for full vaccination: 5.51, 95% confidence interval: 2.43-12.49) than cases. CONCLUSIONS: This finding answers a pressing question of the public and supports the offer of vaccination to people with previous SARS-CoV-2 infection.

Integrated modelling of medical emergency response process for improved coordination and decision support.

The medical emergency response comprises a domain with complex processes, encompassing multiple heterogeneous entities, from organisations involved in the response to human actors to key information sources. Due to the heterogeneity of the entities and the complexity of the domain, it is important to fully understand the individual processes in which the components are involved and their inter-operations, before attempting to design any technological tool for coordination and decision support. This work starts with the gluing together and visualisation of the interactions of involved entities into a conceptual model, along the identified five workspaces of emergency response. The modelling visualises the domain processes, in a way that reveals the necessary communication and coordination points, the required data sources and data flows, as well as the required decision support needs. Work continues with the identification and modelling of the event-driven discrete-time-based dynamics of the emergency response processes and their compositions, using Petri nets as the modelling technique. Subsequently, an integrated model of the process is presented, which facilitates the parallelisation of the tasks undertaken in an emergency incident.