Quantitative Polymerase Chain Reaction for the estimation of toxigenic microalgae abundance in shellfish production waters.

Certain species of marine microalgae produce potent biotoxins that pose a risk to human health if contaminated seafood is consumed, particularly filter feeding bivalve shellfish. In regions where this is likely to occur water and seafood produce are regularly monitored for the presence of harmful algal cells and their associated toxins, but the current approach is flawed by a lengthy delay before results are available to local authorities. Quantitative Polymerase Chain Reaction (qPCR) can be used to measure phytoplankton DNA sequences in a shorter timeframe, however it is not currently used in official testing practices. In this study, samples were collected almost weekly over six months from three sites within a known HAB hotspot, St Austell Bay in Cornwall, England. The abundance of algal cells in water was measured using microscopy and qPCR, and lipophilic toxins were quantified in mussel flesh using LC-MS/MS, focusing on the okadaic acid group. An increase in algal cell abundance occurred alongside an increase in the concentration of okadaic acid group toxins in mussel tissue at all three study sites, during September and October 2021. This event corresponded to an increase in the measured levels of Dinophysis accuminata DNA, measured using qPCR. In the following spring, the qPCR detected an increase in D. accuminata DNA levels in water samples, which was not detected by microscopy. Harmful algal species belonging to Alexandrium spp. and Pseudo-nitzschia spp. were also measured using qPCR, finding a similar increase in abundance in Autumn and Spring. The results are discussed with consideration of the potential merits and limitations of the qPCR technique versus conventional microscopy analysis, and its potential future role in phytoplankton surveillance under the Official Controls Regulations pertaining to shellfish.

Quantifying Spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach.

Harmful algal blooms (HABs) intoxicate and asphyxiate marine life, causing devastating environmental and socio-economic impacts, costing at least $8bn/yr globally. Accumulation of phycotoxins from HAB phytoplankton in filter-feeding shellfish can poison human consumers, prompting harvesting closures at shellfish production sites. To quantify long-term intoxication risk from Dinophysis HAB species, we used historical HAB monitoring data (2009-2020) to develop a new modelling approach to predict Dinophysis toxin concentrations in a range of bivalve shellfish species at shellfish sites in Western Scotland, South-West England and Northern France. A spatiotemporal statistical modelling framework was developed within the Generalized Additive Model (GAM) framework to quantify long-term HAB risks for different bivalve shellfish species across each region, capturing seasonal variations, and spatiotemporal interactions. In all regions spatial functions were most important for predicting seasonal HAB risk, offering the potential to inform optimal siting of new shellfish operations and safe harvesting periods for businesses. A 10-fold cross-validation experiment was carried out for each region, to test the models' ability to predict toxin risk at harvesting locations for which data were withheld from the model. Performance was assessed by comparing ranked predicted and observed mean toxin levels at each site within each region: the correlation of ranks was 0.78 for Northern France, 0.64 for Western Scotland, and 0.34 for South-West England, indicating our approach has promise for predicting unknown HAB risk, depending on the region and suitability of training data.

Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method.

The COVID-19 pandemic has highlighted delayed reporting as a significant impediment to effective disease surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. We discuss the four key sources of systematic and random variability in available data for COVID-19 and other diseases, and critically evaluate current state-of-the-art methods with respect to appropriately separating and capturing this variability. We propose a general hierarchical approach to correcting delayed reporting of COVID-19 and apply this to daily English hospital deaths, resulting in a flexible prediction tool which could be used to better inform pandemic decision-making. We compare this approach to competing models with respect to theoretical flexibility and quantitative metrics from a 15-month rolling prediction experiment imitating a realistic operational scenario. Based on consistent leads in predictive accuracy, bias, and precision, we argue that this approach is an attractive option for correcting delayed reporting of COVID-19 and future epidemics.

Household cooking fuel estimates at global and country level for 1990 to 2030.

Household air pollution generated from the use of polluting cooking fuels and technologies is a major source of disease and environmental degradation in low- and middle-income countries. Using a novel modelling approach, we provide detailed global, regional and country estimates of the percentages and populations mainly using 6 fuel categories (electricity, gaseous fuels, kerosene, biomass, charcoal, coal) and overall polluting/clean fuel use - from 1990-2020 and with urban/rural disaggregation. Here we show that 53% of the global population mainly used polluting cooking fuels in 1990, dropping to 36% in 2020. In urban areas, gaseous fuels currently dominate, with a growing reliance on electricity; in rural populations, high levels of biomass use persist alongside increasing use of gaseous fuels. Future projections of observed trends suggest 31% will still mainly use polluting fuels in 2030, including over 1 billion people in Sub-Saharan African by 2025.

Multivariate hierarchical frameworks for modeling delayed reporting in count data.

In many fields and applications, count data can be subject to delayed reporting. This is where the total count, such as the number of disease cases contracted in a given week, may not be immediately available, instead arriving in parts over time. For short-term decision making, the statistical challenge lies in predicting the total count based on any observed partial counts, along with a robust quantification of uncertainty. We discuss previous approaches to modeling delayed reporting and present a multivariate hierarchical framework where the count generating process and delay mechanism are modeled simultaneously in a flexible way. This framework can also be easily adapted to allow for the presence of underreporting in the final observed count. To illustrate our approach and to compare it with existing frameworks, we present a case study of reported dengue fever cases in Rio de Janeiro. Based on both within-sample and out-of-sample posterior predictive model checking and arguments of interpretability, adaptability, and computational efficiency, we discuss the relative merits of different approaches.

A modelling approach for correcting reporting delays in disease surveillance data.

One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.