Near-term forecasting of Covid-19 cases and hospitalisations in Aotearoa New Zealand.

Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand's unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.

Ethical frameworks should be applied to computational modelling of infectious disease interventions.

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic.

To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.

Sensitivity of Reverse Transcription Polymerase Chain Reaction Tests for Severe Acute Respiratory Syndrome Coronavirus 2 Through Time.

BACKGROUND: Reverse transcription polymerase chain reaction (RT-PCR) tests are the gold standard for detecting recent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Reverse transcription PCR sensitivity varies over the course of an individual's infection, related to changes in viral load. Differences in testing methods, and individual-level variables such as age, may also affect sensitivity. METHODS: Using data from New Zealand, we estimate the time-varying sensitivity of SARS-CoV-2 RT-PCR under varying temporal, biological, and demographic factors. RESULTS: Sensitivity peaks 4-5 days postinfection at 92.7% (91.4%-94.0%) and remains over 88% between 5 and 14 days postinfection. After the peak, sensitivity declined more rapidly in vaccinated cases compared with unvaccinated, females compared with males, those aged under 40 compared with over 40s, and Pacific peoples compared with other ethnicities. CONCLUSIONS: Reverse transcription PCR remains a sensitive technique and has been an effective tool in New Zealand's border and postborder measures to control coronavirus disease 2019. Our results inform model parameters and decisions concerning routine testing frequency.

Modern Data Acquisition Approaches in Proteomics Based on Dynamic Instrument Control.

Traditionally, data acquisition in mass spectrometry-based proteomics is directed by user-defined parameters and relatively simple decision making, such as selection of the highest MS1 peaks for fragmentation. In recent years, access to two-way-communication with instrument codebases has led to a surge in algorithms instructing more complex decision processes on-the-fly. A closer matching between the time windows for monitoring peptides in targeted proteomics and their actual chromatographic elution peaks has been addressed through dynamic retention time scheduling and through triggered acquisition. Strategies based on real-time database searching and spectral matching have, among others, been used to adjust acquisition parameters for selected peptides for improved quantitative accuracy. While initial studies were mainly performed on a proof-of-concept level, dynamic acquisition approaches recently became more broadly available through software and increasing integration into standard instrument control and are likely to transform the field of proteomics in the coming years.

Worldwide border interceptions provide a window into human-mediated global insect movement.

As part of national biosecurity programs, cargo imports, passenger baggage, and international mail are inspected at ports of entry to verify compliance with phytosanitary regulations and to intercept potentially damaging nonnative species to prevent their introduction. Detection of organisms during inspections may also provide crucial information about the species composition and relative arrival rates in invasion pathways that can inform the implementation of other biosecurity practices such as quarantines and surveillance. In most regions, insects are the main taxonomic group encountered during inspections. We gathered insect interception data from nine world regions collected from 1995 to 2019 to compare the composition of species arriving at ports in these regions. Collectively, 8,716 insect species were intercepted in these regions over the last 25 yr, with the combined international data set comprising 1,899,573 interception events, of which 863,972 were identified to species level. Rarefaction analysis indicated that interceptions comprise only a small fraction of species present in invasion pathways. Despite differences in inspection methodologies, as well as differences in the composition of import source regions and imported commodities, we found strong positive correlations in species interception frequencies between regions, particularly within the Hemiptera and Thysanoptera. There were also significant differences in species frequencies among insects intercepted in different regions. Nevertheless, integrating interception data among multiple regions would be valuable for estimating invasion risks for insect species with high likelihoods of introduction as well as for identifying rare but potentially damaging species.

Considering unseen arrivals in predictions of establishment risk based on border biosecurity interceptions.

Assessing species establishment risk is an important task used for informing biosecurity activities aimed at preventing biological invasions. Propagule pressure is a major contributor to the probability of invading species establishment; however, direct assessment of numbers of individuals arriving is virtually never possible. Inspections conducted at borders by biosecurity officials record counts of species (or higher-level taxa) intercepted during inspections, which can be used as proxies for arrival rates. Such data may therefore be useful for predicting species establishments, though some species may establish despite never being intercepted. We present a stochastic process-based model of the arrival-interception-establishment process to predict species establishment risk from interception count data. The model can be used to estimate the probability of establishment, both for species that were intercepted and species that had no interceptions during a given observation period. We fit the stochastic model to data on two insect families, Cerambycidae and Aphididae, that were intercepted and/or established in the United States or New Zealand. We also explore the effects of variation in model parameters and the inclusion of an Allee effect in the establishment probability. Although interception data sets contain much noise due to variation in inspection policy, interception effort and among-species differences in detectability, our study shows that it is possible to use such data for predicting establishments and distinguishing differences in establishment risk profile between taxonomic groups. Our model provides a method for predicting the number of species that have breached border biosecurity, including both species detected during inspections but also "unseen arrivals" that have never been intercepted, but have not yet established a viable population. These estimates could inform prioritization of different taxonomic groups, pathways or identification effort in biosecurity programs.

The effects of cerebral curvature on cortical spreading depression.

Neuronal activity evokes a localised increase in cerebral blood flow through neurovascular coupling (NVC), a communication system between a group of cells known as a neurovascular unit (NVU). Dysfunctional NVC can lead to pathologies such as cortical spreading depression (CSD), characterised by a slowly propagating wave of neuronal depolarisation and high extracellular potassium (K+) levels. CSD is associated with several neurological disorders such as migraine, stroke, and traumatic brain injury. Insight into the spatial dynamics of CSD in humans is mainly deduced from animal experiments on the smooth lissencephalic brain (in particular murine experiments), however the human cortex is gyrencephalic (highly folded) and is considered likely to exhibit different and more complex patterns of CSD. In this study a large scale numerical NVC model of multiple NVUs is coupled to a vascular tree simulating a two-dimensional cerebral tissue slice. This model is extended with a spatial Gaussian curvature mapping that can simulate the highly folded nature of the human cortex. For a flat surface comparable to a lissencephalic cortex the model can simulate propagating waves of high extracellular K+ travelling radially outwards from a stimulated area at approximately 6.7 mm/min, corresponding well with multiple experimental results. The high K+ concentration induces a corresponding wave of vasoconstriction (with decreased blood flow) then slight vasodilation, achieved through cellular communication within the NVU. The BOLD response decreases below baseline by approximately 10% followed by an increase of 1%. For a surface with spatially varied curvature comparable to a section of gyrencephalic cortex, areas of positive Gaussian curvature inhibit wave propagation due to decreased extracellular diffusion rate. Whereas areas of negative curvature promote propagation. Consequently extracellular K+ is observed travelling as wave segments (as opposed to radial waves) through flat or negatively curved "valleys" corresponding to folds (sulci) in the cortex. If the wave size (defined as the activated area of high K+ concentration) is too small or diffusion rate too low then wave segments can cease propagation. If the diffusion rate is high enough the wave segments can grow from open ends forming loose spiral waves. These results may provide some insight into the differences seen between human and animal experiments.

Limiting Effect of Self-Shading on the Height of Tradescantia fluminensis Mats.

Tradescantia fluminensis is an invasive plant species in New Zealand, Australia and parts of the USA. It reproduces vegetatively and can grow to form dense mats up to 60 cm deep. Growth is limited by available light, and shading is one of the few effective methods of control. In this paper, we develop a dynamic model of a vertical cross section of a T. fluminensis mat, capturing vertical variation in its biomass and internal light intensity. We measure both variables at different heights in experimental mats of the species and use these data to parameterize the model. The model produces realistic vertical biomass and light intensity profiles. We show that the mat grows to a steady-state biomass that depends only on: (i) the light absorption coefficient, which we estimate from experimental data and (ii) the ratio of photosynthesis to respiration rate. This steady state undergoes a transcritical bifurcation; when the ambient light intensity falls below a critical level, the biomass shrinks to zero and the mat cannot survive.

Integrated models of neurovascular coupling and BOLD signals: Responses for varying neural activations.

A state-of-the-art integrated model of neurovascular coupling (NVC) (Dormanns et al., 2015b; Dormanns et al., 2016; Kenny et al., 2018) and the BOLD response (Mathias et al., 2017a; Mathias et al., 2017b) is presented with the ability to simulate the fMRI BOLD responses due to continuous neuronal spiking, bursting and cortical spreading depression (CSD) along with the underlying complex vascular coupling. Simulated BOLD responses are compared to experimental BOLD signals observed in the rat barrel cortex and in the hippocampus under seizure conditions showing good agreement. Bursting phenomena provides relatively clear BOLD signals as long as the time between bursts is not too short. For short burst periods the BOLD signal remains constant even though the neuron is in a predominantly bursting mode. Simulation of CSD exhibits large negative BOLD signals. Visco-elastic effects of the capillary bed do not seem to have a large effect on the BOLD signal even for relatively high values of oxygen consumption. While the results of the model suggests that potassium ions released during neural activity could act as the main mediator in NVC, it suggests the possibility of other mechanisms that can coexist and increase blood flow such as the arachidonic acid to epoxyeicosatrienoic acid (EET) pathway. The comparison with experimental cerebral blood flow (CBF) data indicates the possible existence of multiple neural pathways influencing the vascular response. Initial negative BOLD signals occur for all simulations due to the rate at which the metabolic oxygen consumption occurs relative to the dilation of the perfusing cerebro-vasculature. However it is unclear as to whether these are normally seen clinically due to the size of the magnetic field. Experimental comparisons for different animal experiments may very well require variation in the model parameters. The complex integrated model is believed to be the first of its kind to simulate both NVC and the resulting BOLD signal.

The role of astrocytic calcium and TRPV4 channels in neurovascular coupling.

Neuronal activity evokes a localised change in cerebral blood flow in a response known as neurovascular coupling (NVC). Although NVC has been widely studied the exact mechanisms that mediate this response remain unclear; in particular the role of astrocytic calcium is controversial. Mathematical modelling can be a useful tool for investigating the contribution of various signalling pathways towards NVC and for analysing the underlying cellular mechanisms. The lumped parameter model of a neurovascular unit with both potassium and nitric oxide (NO) signalling pathways and comprised of neurons, astrocytes, and vascular cells has been extended to include the glutamate induced astrocytic calcium pathway with epoxyeicosatrienoic acid (EET) signalling and the stretch dependent TRPV4 calcium channel on the astrocytic endfoot. Results show that the potassium pathway governs the fast onset of vasodilation while the NO pathway has a delayed response, maintaining dilation longer following neuronal stimulation. Increases in astrocytic calcium concentration via the calcium signalling pathway and/or TRPV4 channel to levels consistent with experimental data are insufficient for inducing either vasodilation or constriction, in contrast to a number of experimental results. It is shown that the astrocyte must depolarise in order to produce a significant potassium flux through the astrocytic BK channel. However astrocytic calcium is shown to strengthen potassium induced NVC by opening the BK channel further, consequently allowing more potassium into the perivascular space. The overall effect is vasodilation with a higher maximal vessel radius.

Macro scale modelling of cortical spreading depression and the role of astrocytic gap junctions.

Neuronal activity evokes a localised increase in cerebral blood flow in a response known as neurovascular coupling (NVC), achieved through communication between a group of cells known as a neurovascular unit (NVU). Dysfunctional NVC can lead to pathologies such as cortical spreading depression (CSD), characterised by a slow moving wave of neuronal depolarisation and high extracellular K+ levels. This phenomenon can be affected by the presence of an astrocytic gap junction network which is able to transport K+ away from areas of high concentrations, however the precise role of these gap junctions remains controversial. In this study, a large scale numerical NVC model of a vascular tree coupled with multiple NVUs comprising a two-dimensional cerebral tissue slice is extended through extracellular K+ and Na+ electrodiffusion and K+ transport via an astrocytic gap junction network. An updated NVU model has been utilised that contains complex neuronal and extracellular dynamics and is able to simulate various pathologies such as CSD and the effect on the vascular response. Under pathological conditions (determined by model parameters) and with extracellular electrodiffusion the model is able to simulate a propagating wave of high extracellular K+ travelling at 6.7 mm/min as can occur in CSD. This wave travels outward from the neuronally stimulated area and is followed by a wave of vasoconstriction (with corresponding decreased blood flow) then slight vasodilation in agreement with multiple experimental results. The vasoconstrictive wave peaks after the K+ wave due to the delayed vascular response. Increasing the density of astrocytic gap junctions reduces the duration and amplitude of the vasoconstrictive wave and for high enough density the vasoconstrictive behaviour outside the stimulated area is eliminated. Gap junctions also reduce the area that is initially affected by vasoconstriction. This in silico model provides a complex and experimentally validated test bed for a variety of neurological phenomena.

Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data.

Collective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate.

Disentangling nestedness from models of ecological complexity.

Complex networks of interactions are ubiquitous and are particularly important in ecological communities, in which large numbers of species exhibit negative (for example, competition or predation) and positive (for example, mutualism) interactions with one another. Nestedness in mutualistic ecological networks is the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species. Recent mathematical and computational analysis has suggested that such nestedness increases species richness. By examining previous results and applying computational approaches to 59 empirical data sets representing mutualistic plant­pollinator networks, we show that this statement is incorrect. A simpler metric­the number of mutualistic partners a species has­is a much better predictor of individual species survival and hence, community persistence. Nestedness is, at best, a secondary covariate rather than a causative factor for biodiversity in mutualistic communities. Analysis of complex networks should be accompanied by analysis of simpler, underpinning mechanisms that drive multiple higher-order network properties.

Spatial Moment Description of Birth-Death-Movement Processes Incorporating the Effects of Crowding and Obstacles.

Birth-death-movement processes, modulated by interactions between individuals, are fundamental to many cell biology processes. A key feature of the movement of cells within in vivo environments is the interactions between motile cells and stationary obstacles. Here we propose a multi-species model of individual-level motility, proliferation and death. This model is a spatial birth-death-movement stochastic process, a class of individual-based model (IBM) that is amenable to mathematical analysis. We present the IBM in a general multi-species framework and then focus on the case of a population of motile, proliferative agents in an environment populated by stationary, non-proliferative obstacles. To analyse the IBM, we derive a system of spatial moment equations governing the evolution of the density of agents and the density of pairs of agents. This approach avoids making the usual mean-field assumption so that our models can be used to study the formation of spatial structure, such as clustering and aggregation, and to understand how spatial structure influences population-level outcomes. Overall the spatial moment model provides a reasonably accurate prediction of the system dynamics, including important effects such as how varying the properties of the obstacles leads to different spatial patterns in the population of agents.

Importance of Altered Levels of SERCA, IP3R, and RyR in Vascular Smooth Muscle Cell.

Calcium cycling between the sarcoplasmic reticulum (SR) and the cytosol via the sarco-/endoplasmic reticulum Ca-ATPase (SERCA) pump, inositol-1,4,5-triphosphate receptor (IP3R), and Ryanodine receptor (RyR), plays a major role in agonist-induced intracellular calcium ([Ca2+]cyt) dynamics in vascular smooth muscle cells (VSMC). Levels of these calcium handling proteins in SR get altered under disease conditions. We have developed a mathematical model to understand the significance of altered levels of SERCA, IP3R, and RyR on the intracellular calcium dynamics of VSMC and to understand how variation in protein levels that arise due to diabetes contribute to different VSMC behavior and thus vascular disease. SR is modeled as a single continuous entity with homogeneous intra-SR calcium. Model results show that agonist-induced intracellular calcium dynamics can be modified by changing the levels of SERCA, IP3R, and/or RyR. Lowering SERCA level will enable intracellular calcium oscillations at low agonist concentrations whereas lowered levels of IP3R and RyR need higher agonist concentration for intracellular calcium oscillations. This research suggests that reduced SERCA level is the main factor responsible for the reduced intracellular calcium transients and contractility in VSMCs.

Evaluating random search strategies in three mammals from distinct feeding guilds.

Searching allows animals to find food, mates, shelter and other resources essential for survival and reproduction and is thus among the most important activities performed by animals. Theory predicts that animals will use random search strategies in highly variable and unpredictable environments. Two prominent models have been suggested for animals searching in sparse and heterogeneous environments: (i) the Lévy walk and (ii) the composite correlated random walk (CCRW) and its associated area-restricted search behaviour. Until recently, it was difficult to differentiate between the movement patterns of these two strategies. Using a new method that assesses whether movement patterns are consistent with these two strategies and two other common random search strategies, we investigated the movement behaviour of three species inhabiting sparse northern environments: woodland caribou (Rangifer tarandus caribou), barren-ground grizzly bear (Ursus arctos) and polar bear (Ursus maritimus). These three species vary widely in their diets and thus allow us to contrast the movement patterns of animals from different feeding guilds. Our results showed that although more traditional methods would have found evidence for the Lévy walk for some individuals, a comparison of the Lévy walk to CCRWs showed stronger support for the latter. While a CCRW was the best model for most individuals, there was a range of support for its absolute fit. A CCRW was sufficient to explain the movement of nearly half of herbivorous caribou and a quarter of omnivorous grizzly bears, but was insufficient to explain the movement of all carnivorous polar bears. Strong evidence for CCRW movement patterns suggests that many individuals may use a multiphasic movement strategy rather than one-behaviour strategies such as the Lévy walk. The fact that the best model was insufficient to describe the movement paths of many individuals suggests that some animals living in sparse environments may use strategies that are more complicated than those described by the standard random search models. Thus, our results indicate a need to develop movement models that incorporate factors such as the perceptual and cognitive capacities of animals.

Evolutionarily Stable Strategies for Fecundity and Swimming Speed of Fish.

Many pelagic fish species have a life history that involves producing a large number of small eggs. This is the result of a trade-off between fecundity and larval survival probability. There are also trade-offs involving other traits, such as larval swimming speed. Swimming faster increases the average food encounter rate but also increases the metabolic cost. Here we introduce an evolutionary model comprising fecundity and swimming speed as heritable traits. We show that there can be two evolutionary stable strategies. In environments where there is little noise in the food encounter rate, the stable strategy is a low-fecundity strategy with a swimming speed that minimises the mean time taken to reach reproductive maturity. However, in noisy environments, for example where the prey distribution is patchy or the water is turbulent, strategies that optimise mean outcomes are often outperformed by strategies that increase inter-individual variance. We show that, when larval growth rates are unpredictable, a high-fecundity strategy is evolutionarily stable. In a population following this strategy, the swimming speed is higher than would be anticipated by maximising the mean growth rate.

Collective Cell Behaviour with Neighbour-Dependent Proliferation, Death and Directional Bias.

Collective cell migration and proliferation are integral to tissue repair, embryonic development, the immune response and cancer. Central to collective cell migration and proliferation are interactions among neighbouring cells, such as volume exclusion, contact inhibition and adhesion. These individual-level processes can have important effects on population-level outcomes, such as growth rate and equilibrium density. We develop an individual-based model of cell migration and proliferation that includes these interactions. This is an extension of a previous model with neighbour-dependent directional bias to incorporate neighbour-dependent proliferation and death. A deterministic approximation to this individual-based model is derived using a spatial moment dynamics approach, which retains information about the spatial structure of the cell population. We show that the individual-based model and spatial moment model match well across a range of parameter values. The spatial moment model allows insight into the two-way interaction between spatial structure and population dynamics that cannot be captured by traditional mean-field models.

Information on biotic interactions improves transferability of distribution models.

Predicting changes in species' distributions is a crucial problem in ecology, with leading methods relying on information about species' putative climatic requirements. Empirical support for this approach relies on our ability to use observations of a species' distribution in one region to predict its range in other regions (model transferability). On the basis of this observation, ecologists have hypothesized that climate is the strongest determinant of species' distributions at large spatial scales. However, it is difficult to reconcile this claim with the pervasive effects of biotic interactions. Here, we resolve this apparent paradox by demonstrating how biotic interactions can affect species' range margins yet still be compatible with model transferability. We also identify situations where small changes in species' interactions dramatically shift range margins.