Epidemiological modelling of the health and economic effects of COVID-19 control in Australia's second wave.

Background: We investigated the public health and economy outcomes of different levels of social distancing to control a 'second wave' outbreak in Australia and identify implications for public health management of COVID-19. Methods: Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes. Results: The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs. Conclusions: Better public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

Bayesian learning and predictability in a stochastic nonlinear dynamical model.

Bayesian inference methods are applied within a Bayesian hierarchical modeling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations.

Review of fluorescent standards for calibration of in situ fluorometers: recommendations applied in coastal and ocean observing programs.

Fluorometers are widely used in ecosystem observing to monitor fluorescence signals from organic compounds, as well as to infer geophysical parameters such as chlorophyll or CDOM concentration, but measurements are susceptible to variation caused by biofouling, instrument design, sensor drift, operating environment, and calibration rigor. To collect high quality data, such sensors need frequent checking and regular calibration. In this study, a wide variety of both liquid and solid fluorescent materials were trialed to assess their suitability as reference standards for performance assessment of in situ fluorometers. Criteria used to evaluate the standards included the spectral excitation/emission responses of the materials relative to fluorescence sensors and to targeted ocean properties, the linearity of the fluorometer's optical response with increasing concentration, stability and consistency, availability and ease of use, as well as cost. Findings are summarized as a series of recommended reference standards for sensors deployed on stationary and mobile platforms, to suit a variety of in situ coastal to ocean sensor configurations. Repeated determinations of chlorophyll scale factor using the recommended liquid standard, Fluorescein, achieved an accuracy of 2.5%. Repeated measurements with the recommended solid standard, Plexiglas Satinice® plum 4H01 DC (polymethylmethacrylate), over an 18 day period varied from the mean value by 1.0% for chlorophyll sensors and 3.3% for CDOM sensors.