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The (Re)-emerging And ePidemic Infectious Diseases (RAPID) Stigma Scales: a cross-outbreak scale development and pyschometric validation study
Reducing stigma during infectious disease outbreaks is crucial for delivering an effective response. However, no validated stigma scales exist for use across outbreaks, and outbreak-specific scales are developed too slowly to guide timely interventions. To enable more real-time monitoring and mitigation of stigma across outbreak contexts, we developed and validated the (Re)-emerging and ePidemic Infectious Diseases (RAPID) Stigma Scales. Field testing and psychometric validation were conducted in communities affected by Ebola disease in Uganda, mpox in the UK, and Nipah virus disease in Bangladesh. Content validity was established through cognitive interviews and expert Delphi scoring. 1008 respondents were included across the three countries. The final RAPID Community Stigma Scale (12 items) captures initial social stigma, provider or authority-related stigma, structural stigma, and enduring social stigma. The RAPID Self Stigma Scale (4 items) is unidimensional. Both scales were found to have robust psychometric properties, including content validity, structural validity (factor loadings ≥0·6), and reliability (ordinal alphas 0·79–0·92). High scores on both scales predicted an increased hesitancy to report symptoms and seek care. The RAPID Stigma Scales are validated tools for real-time assessment of stigma across outbreak settings, enabling responders to design targeted interventions to improve health outcomes and promote equitable care.
Reemergence of Cosmopolitan Genotype Dengue Virus Serotype 2, Southern Vietnam.
We performed phylogenetic analysis on dengue virus serotype 2 Cosmopolitan genotype in Ho Chi Minh City, Vietnam. We document virus emergence, probable routes of introduction, and timeline of events. Our findings highlight the need for continuous, systematic genomic surveillance to manage outbreaks and forecast future epidemics.
Estimating epidemic dynamics with genomic and time series data.
Accurately estimating the prevalence and transmissibility of an infectious disease is an important task in genetic infectious disease epidemiology. However, generating accurate estimates of these quantities, that make use of both epidemic time series and pathogen genome sequence data, is a challenging problem. Phylogenetic birth-death processes are a popular choice for modelling the transmission of infectious diseases, but it is difficult to estimate the prevalence of infection with them. Here, we extended our approximate likelihood approach, which combines phylogenetic information from sampled pathogen genomes and epidemiological information from a time series of case counts, to estimate historical prevalence in addition to the effective reproduction number. We implement this new method in a BEAST2 package called Timtam. In a simulation study our approximation is seen to be well-calibrated and recovers the parameters of simulated data. To demonstrate how Timtam can be applied to real datasets, we carried out empirical analyses of data from two infectious disease outbreaks: the outbreak of SARS-CoV-2 onboard the Diamond Princess cruise ship in early 2020 and poliomyelitis in Tajikistan in 2010. In both cases we recover estimates consistent with previous analyses.