Impact of the presence of heart disease, cardiovascular medications and cardiac events on outcome in COVID-19

Background: Cardiovascular risk factors and usage of cardiovascular medication are prevalent among coronavirus disease 2019 (COVID-19) patients. Little is known about the cardiovascular implications of COVID-19. The goal herein, was to evaluate the prognostic impact of having heart disease (HD) and taking cardiovascular medications in a population diagnosed of COVID-19 who required hospitalization. Also, we studied the development of cardiovascular events during hospitalization. Methods: Consecutive patients with definitive diagnosis of COVID-19 made by a positive real time- -polymerase chain reaction of nasopharyngeal swabs who were admitted to the hospital from March 15 to April 14 were included in a retrospective registry. The association of HD with mortality and with mortality or respiratory failure were the primary and secondary objectives, respectively. Results: A total of 859 patients were included in the present analysis. Cardiovascular risk factors were related to death, particularly diabetes mellitus (hazard ratio in the multivariate analysis: 1.810 [1.159– –2.827], p = 0.009). A total of 113 (13.1%) patients had HD. The presence of HD identified a group of patients with higher mortality (35.4% vs. 18.2%, p < 0.001) but HD was not independently related to prognosis; renin–angiotensin–aldosterone system inhibitors, calcium channel blockers, diuretics and beta-blockers did not worsen prognosis. Statins were independently associated with decreased mortality (0.551 [0.329–0.921], p = 0.023). Cardiovascular events during hospitalization identified a group of patients with poor outcome (mortality 31.8% vs. 19.3% without cardiovascular events, p = 0.007). Conclusions: The presence of HD is related to higher mortality. Cardiovascular medications taken before admission are not harmful, statins being protective. The development of cardiovascular events during the course of the disease is related to poor outcome

Economics of Carbon Sequestration Projects Involving Smallholders

Afforestation and reforestation projects have the potential to help mitigate global warming by acting as sinks for CO². However, participation in carbon-sink projects may be constrained by high costs. This problem may be particularly severe for projects involving smallholders in developing countries. Of particular concern are the transaction costs incurred in developing projects and measuring, certifying, and selling the carbon-sequestration services generated by such projects. This chapter addresses these issues by analyzing the implications of transaction and abatement costs in carbon-sequestration projects. A typology of transaction costs is presented, and estimates of the five cost types are derived based on a review of existing projects. The influences of project design on abatement costs and transaction costs are explored, and the critical values of a set of three project-design variables (farm price, number of participating farms, and minimum farm area) are identified for any given combination of transaction costs

When is it optimal to eradicate a weed invasion?

When a weed invasion is discovered a decision has to be made as to whether to attempt to eradicate it, contain it or do nothing. Ideally, these decisions should be based on a complete benefit-cost analysis, but this is often not possible. Partial analysis, combining knowledge of the demographics of the weed and economic techniques, can assist in making the best decision. This paper presents a general conceptual model to decide when eradication of a weed should be attempted. Decision rules are derived based on a few parameters that represent the rate of spread, the cost of controlling the invasion, and the cost of damage caused by the invasion. These decision rules are then used to identify the 'switching point' - the invasion size at which it is no longer optimal to attempt eradication. The decision rules are used to estimate the optimal duration of the eradication effort depending on the current size of the invasion. Sensitivity analysis is undertaken and the possibility of characterising an invasion based on five parameters is discussed

Bioeconomics of Invasive Species in Aquatic Ecosystems

When a biological invader is identified in an aquatic ecosystem, rapid response is critical, particularly if the invasive organism has the ability to spread rapidly. The first response should be to attempt to contain the invasion while further information is gathered to evaluate whether eradication is feasible. The eradication versus containment decision is studied by developing and using a stochastic dynamic programming model based on a simple biological spread function. Information requirements to calibrate the decision model for a specific invader are discussed and numerical analysis is undertaken to illustrate how the model can be used when only rough estimates of benefits and costs are available

Deriving efficient frontiers for effort allocation in the management of invasive species

Invasive species cause significant losses through their effect on agriculture, human health and the environment. Their importance has increased with time owing to globalisation, as the spread of invasive species is facilitated by the increased movement of people, cargo and genetic material around the world. There is a vast literature on the economics of invasive species and their management. Here, we contribute to this literature by applying a spatio-temporal model to the allocation of surveillance resources. We focus on three questions regarding resource allocation to control a newly discovered invasion: the budget, which determines the amount of search effort available; the duration of the control program; and the allocation of surveillance and control in time and space. We also explore the complementary role of passive surveillance by members of the public. We derive efficient frontiers for effort allocation that represent the trade-off between cost and probability of eradication after inefficient strategies have been eliminated. We use the results to illustrate how to evaluate whether introduction of passive surveillance is desirable based on cost and eradication probability. We conclude by discussing the implications of our findings in the design of control programs

Green Power voluntary purchases: Price elasticity and policy analysis

Green Power schemes offer electricity from renewable energy sources to customers for a higher price than ordinary electricity. This study examines the demand characteristics of Green Power in Australia and policies which could increase its sales. A sample of 250 pooled time series and cross sectional observations was used to estimate a statistically significant elasticity of demand for Green Power with respect to price of -0.96 with a 95% confidence interval of ± 68%. The wide variation in market penetration between jurisdictions and between countries for Green Power, and the low awareness of Green Power found by surveys indicate that Green Power sales could be increased by appropriate marketing and government policies. The most cost effective means to increase sales was found to be advertising campaigns although only one Australian example was found, in the state of Victoria in 2005. It was also found that full tax deductibility of the Green Power premium to residential customers, exemption from the Goods and Services Tax and a tax rebate for Green Power are all probably less cost effective for promoting sales than direct government purchase of Green Power, in terms of cost per unit of increased sales

The spread of a biological invasion in space and time: Modelling active and passive surveillance

Invasive species are an important threat to global biodiversity and cause considerable economic losses. Modelling the spread of invaders can assist in mitigating the impacts of biological invasions by allowing us to identify strategies that are most likely to be effective in slowing or reversing their spread. In many situations, the main constraint to controlling or eradicating invaders is finding them rather than eliminating them after they are located. Once an invasion is found it can be treated and killed with a high probability of success. Searching large areas actively is expensive and therefore enlisting the help of the public through 'passive surveillance' is increasingly being used by pest-management agencies. The roles of active and passive surveillance and their interaction are investigated here using a spatially-explicit simulation model of the spread of an invasive species. The landscape is represented as a raster map consisting of square cells. Each cell in the landscape is characterised by various attributes, including habitat suitability and ownership type (private or public). The probability that a given site will be invaded depends on both habitat suitability and the number of propagules landing on it. Dispersal of propagules across the landscape is assumed to follow a Cauchy kernel. Long-distance dispersal may also occur independently, such as when propagules are transported by road or water. An invasion may be detected as a result of a report from the public or through active searching by a pest-control agency. Over time, the pest control agency uses passive detections, repeat searches and information about cell attributes to undertake additional searches in an attempt to eradicate the invader. The model is applied to a hypothetical invasion. Measures of success, such as cost and probability of eradication, are incorporated as fitness measures within an evolutionary algorithm that identifies optimal search and control strategies. Strategies are defined in terms of search effort applied per cell, the size of the neighbouring radius that is searched when an infestation is discovered, and the number of repeat visits to previously treated sites. Results demonstrate that increases in passive detection can reduce eradication costs and increased the probability of eradication. Although it is impossible to ensure that the global optimum is identified for a given scenario, the evolutionary algorithm helps identify quasi-optimal solutions that may be difficult to find through trial and error

Dynamic optimization for evaluating externalities in agroforestry systems

Integrating trees in cropping and grazing systems – agroforestry – can provide many benefits in the Australian agricultural context. These include the production of timber and non-timber products such as oils and flowers, fodder, windbreak protection, shade and shelter, wildlife habitat, flood mitigation, soil-erosion control, improved water quality, and reduced dryland-salinity emergence. Prinsley (1992), – Cleugh, Prinsley, Bird, Brooks, Carberry, and Crawford et al. (2002) and other authors in the same volumes present some examples for Australia. Many of the benefits from trees are off-farm environmental services, which are public goods or externalities and which landholders may not take into account. The social benefits from trees may therefore exceed the private benefits, and given such a divergence, landholders may conserve and plant too few trees from a social perspective. Without perfect information, landholders may even underestimate their private benefits from trees and further exacerbate this divergence. Concern for this issue is not new in Australia (see Tisdell, 1985). Market failure due to externalities and imperfect information provides a rationale for government intervention to encourage landholders to invest in vegetation management and reforestation. Regulatory, extension, and market-based approaches are all being used to this end by governments in Australia. State Governments have established regulatory controls on land clearing of private native vegetation (Walpole, 1999; Stoneham, Chaudhri, Ha, & Strappazzon, 2003), such as the Native Vegetation Conservation Act in New South Wales (NSW) and The Planning and Environment Act in Victoria. State and Federal Governments have also implemented extension programs to provide funds and/or assistance to landholders and community groups to manage native vegetation on private land. At the State level, these programs include Land for Wildlife and Trust for Nature in Victoria, and the Voluntary Conservation Agreement Program in Queensland. At the Federal level, there is Landcare, One Billion Trees, Save the Bush, Bushcare, National Heritage Trust, and the National Action Plan for Salinity and Water Quality. Stoneham et al

Spatially-explicit modelling for catchment-level salinity management

In this paper, a spatially-explicit model to undertake catchment-level analysis of dryland salinity is discussed. The model uses a raster-based approach where a catchment is represented as a grid of neighbouring cells. Each cell is defined by a set of seven attributes: land use, elevation, soil type, rainfall, aquifer thickness, groundwater-table depth and groundwater salinity. Cells receive and transmit groundwater information to and from neighbouring cells through a simplified hydrology model. The hydrology model is combined with an economic model and can be used to analyse the effect of alternative spatial patterns of land use. The model is implemented in the MATLAB programming environment and is designed to allow users to test any arbitrary pattern of land use and explore its long-term consequences. This facility permits the analysis of tradeoffs between financial (profit) and environmental (salt-affected area, water yield and water quality) outcomes. The model is illustrated in an application to a small agricultural catchment in central-west NSW, Australia. Attribute maps for elevation and soil type are read directly from ASCII grid files generated by GIS software. Rainfall is assumed to be uniform across the catchment, and monthly time steps ensure water movements between neighbouring cells in this small catchment are mimicked accurately. The model is initialised by reading a look-up table for land uses and their respective parameters, and a look-up table for soil types and their respective parameters. The user can change the number and types of land uses or soils and their associated parameters simply by changing the relevant look-up table in a spreadsheet. Three experiments were run where the catchment was entirely planted to a single land use, either tree belts, grazing on perennial pasture or annual cropping. Preliminary results demonstrate that the model responds to the different land-use scenarios in accordance with apriori expectations, with tree and pasture land uses generally resulting in lower-groundwater tables. The model is in the process of being calibrated and validated using time-series data for groundwater-table depth for several piezometers located across the catchment and some issues are discussed

A growth model for giant clams 'Tridacna crocea' and 'T. derasa'

In this paper, a model is presented for the average growth of an individual giant clam. The model describes the clam’s basic metabolic processes of photosynthesis, filter-feeding, respiration and 'surplus' energy demand for unaccounted metabolic processes such as reproduction, and incorporates the effects of environmental variables including temperature, solar radiation, depth, visibility and nutrients in the seawater. A detailed mathematical description of the model is provided, and its operation is investigated by fitting it to growth data collected for 'Tridacna crocea' and 'T. derasa' from smallholder-mariculture trials in Solomon Islands. Model calibration is described and the sensitivity of the simulation results to parameter values is considered. Growth predictions for both species are found to closely fit the observed data and to be very sensitive to small errors in the value of those parameters that regulate photosynthesis, respiration, surplus energy intake and how respiration responds to temperature. These results lend support for scientific research to better understand the relationships between these parameters and clam growth. The effect of environmental conditions on predicted growth is also investigated. Results indicate that growth has a substantial positive correlation with both visibility and nutrients. These environmental factors are likely to be negatively correlated with each other, hence nutrient-rich environments are likely to have low visibility and the benefits from nutrients may be outweighed by the costs of turbidity and clams may grow poorly. Although the mathematical model presented here could be applied to wild-stock clams, parameterisation with data from mariculture trials restricts its use to predicting growth over only a few years