Mathematical Sciences Discipline/School of Science, RMIT University, Melbourne, Victoria, Australia.
PeerJ. 2022 Oct 21;10:e14184. doi: 10.7717/peerj.14184. eCollection 2022.
Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.
估算漏报病例的数量对于确定疾病的真实负担至关重要。在 COVID-19 大流行期间，通过捕获真实的发病率来量化真实的疾病负担，以制定适当的措施和策略来对抗疾病，这是非常有必要的。本研究调查了澳大利亚维多利亚州在大流行第三波期间由于地理区域和时间的差异导致的 COVID-19 病例漏报情况。目的是确定潜在的漏报区域，并根据病例数量生成疾病的真实情况。采用双层贝叶斯层次模型方法通过贝叶斯模型平均来估计真实的发病率和检测率。该模型通过研究天气、疫苗接种率以及接种和检测中心的可及性等其他协变量（包括空间和时间之间的相互作用和变化），不仅检验了不同区域之间的不平等，还超越了这一范围。该模型旨在保持简约性，但同时允许更广泛的范围来捕捉报告的 COVID-19 病例的潜在动态。此外，它是一种数据驱动、灵活且可推广到全球范围的模型，例如跨国估计和在严格的大流行条件下跨时间点的估计。
