Epidemics最新文献

The transmission of infectious diseases involves complex interactions across multiple biological scales, from within-host immunological processes to between-host transmission dynamics. While multiscale models have the potential to capture these interactions more accurately, they are often hindered by increased complexity and limited data availability. In this study, we develop a multiscale epidemic model linking host-vector population-level transmission dynamics to within-host and within-vector pathogen dynamics. Our model captures key features of within-vector viral progression and allows bidirectional coupling between within-host and between-host processes. The scales are linked under the assumption of dose-dependent transmission, with the functional form informed by empirical viremia-infectiousness data obtained from mosquito feeding experiments on live hosts. Focusing on Dengue, Zika, and West Nile viruses as case studies, we assess how different functional forms of the coupling affect the number of equilibria of the epidemic model. We find that when the transmission is modeled using linear coupling functions, the multiscale model yields the same bifurcation structure of the simpler, uncoupled model, indicating that the linking of scales does not alter the range of possible long-term epidemiological states in such cases. However, nonlinear coupling can induce complex behaviors such as multiple endemic equilibria, which the uncoupled model does not capture. These results underscore the importance of carefully selecting coupling functions and provide guidance on when multiscale modeling is essential for understanding and managing vector-borne diseases.

Previous pandemics, including influenza pandemics and Covid-19, have disproportionately impacted Māori and Pacific populations in Aotearoa New Zealand. The reasons for this are multi-faceted, including differences in socioeconomic deprivation, housing conditions and household size, vaccination rates, access to healthcare, and prevalence of pre-existing health conditions. Many mathematical models that were used to inform the response to the Covid-19 pandemic did not explicitly include ethnicity or other socioeconomic variables. This limited their ability to predict, understand and mitigate inequitable impacts of the pandemic. Here, we extend a model that was developed during the Covid-19 pandemic to support the public health response by stratifying the population into four ethnicity groups: Māori, Pacific, Asian and European/other. We include three ethnicity-specific components in the model: vaccination rates, clinical severity parameters, and contact patterns. We compare model results to ethnicity-specific data on Covid-19 cases, hospital admissions and deaths between 1 January 2022 and 30 June 2023, under different model scenarios in which these ethnicity-specific components are present or absent. We find that differences in vaccination rates explain only part of the observed disparities in outcomes. While no model scenario is able to fully capture the heterogeneous temporal dynamics, our results suggest that differences between ethnicities in the per-infection risk of clinical severe disease is an important factor. Our work is an important step towards models that are better able to predict inequitable impacts of future pandemic and emerging disease threats, and investigate the ability of interventions to mitigate these.

Real-time forecasting of infectious diseases is essential for public health decision-making. Traditional forecasting methods for epidemics exhibiting repeated waves, such as influenza and RSV, rely on strongly regular patterns in the incidence data, with stable duration (i.e., one year), peak timing, magnitude and initial incidence. In 2022-2023, COVID-19 epidemic waves became more regular compared to early in the pandemic, but they were nonetheless characterised by an absence of seasonality that shaped their overall trajectories, rendering existing methods unfit for use. Furthermore, the total number of waves with which to train the models was highly limited (as few as two). Leveraging an observed regularity in the dynamics of the growth rate, as opposed to the incidence, we developed a Bayesian framework for epidemic forecasting in situations where traditional forecasting methods would struggle. Our method learns from past epidemic waves to construct priors on the shape of the growth rate trajectory and updates the forecast as new data become available. We report a 27%-61% improvement in the weighted interval score for 14-day ahead forecasts compared to baseline models, as well as the ability to predict medium-term statistics such as peak timing and magnitude. We also introduce Gaussian processes for real-time smoothing and growth rate estimation, leading to a 41% reduction in root mean squared error on a simulated dataset over a popular, traditional technique. Our work highlights a promising approach for forecasting infectious diseases that do not follow strict seasonal patterns and reveals opportunities for further research into nonmechanistic time series models.