The Organic Research Centre - Elm Farm:Bulletin 87

Bulletin 87 with coverage of Avian Influenza H5N1 in Suffolk,commentary on Biofuels, a paper on the organic "transition to sustainable resilience",paper on participatory approach to agronomy trials,update on evolutionary breeding of wheat project,article on formation of new growers alliance in UK

Recent advances in biosensors for detection of COVID-19 and other viruses

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers

Recent advances in biosensors for detection of COVID-19 and other viruses

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers

Two resource distribution strategies for dynamic mitigation of influenza pandemics

As recently pointed out by the Institute of Medicine, the existing pandemic containment and mitigation models lack the dynamic decision support capabilities. We present two simulation-based optimization models for developing dynamic predictive resource distribution strategies for cross-regional pandemic outbreaks. In both models, the underlying simulation mimics the disease and population dynamics of the affected regions. The quantity-based optimization model generates a progressive allocation of limited quantities of mitigation resources, including vaccines, antiviral, administration capacities, and social distancing enforcement resources. The budget-based optimization model strives instead allocating a total resource budget. Both models seek to minimize the impact of ongoing outbreaks and the expected impact of potential outbreaks. The models incorporate measures of morbidity, mortality, and social distancing, translated into the societal and economic costs of lost productivity and medical expenses. The models were calibrated using historic pandemic data and implemented on a sample outbreak in Florida, with over four million inhabitants. The quantity-based model was found to be inferior to the budget-based model, which was advantageous in its ability to balance the varying relative cost and effectiveness of individual resources. The models are intended to assist public health policy makers in developing effective distribution policies for mitigation of influenza pandemics

Modelling the global spread of diseases: A review of current practice and capability

Mathematical models can aid in the understanding of the risks associated with the global spread of infectious diseases. To assess the current state of mathematical models for the global spread of infectious diseases, we reviewed the literature highlighting common approaches and good practice, and identifying research gaps. We followed a scoping study method and extracted information from 78 records on: modelling approaches; input data (epidemiological, population, and travel) for model parameterization; model validation data. We found that most epidemiological data come from published journal articles, population data come from a wide range of sources, and travel data mainly come from statistics or surveys, or commercial datasets. The use of commercial datasets may benefit the modeller, however makes critical appraisal of their model by other researchers more difficult. We found a minority of records (26) validated their model. We posit that this may be a result of pandemics, or far-reaching epidemics, being relatively rare events compared with other modelled physical phenomena (e.g. climate change). The sparsity of such events, and changes in outbreak recording, may make identifying suitable validation data difficult. We appreciate the challenge of modelling emerging infections given the lack of data for both model parameterisation and validation, and inherent complexity of the approaches used. However, we believe that open access datasets should be used wherever possible to aid model reproducibility and transparency. Further, modellers should validate their models where possible, or explicitly state why validation was not possible

Highly Pathogenic H5N1 Influenza A Virus Spreads Efficiently in Human Primary Monocyte-Derived Macrophages and Dendritic Cells

Influenza A viruses cause recurrent epidemics and occasional global pandemics. Wild birds are the natural reservoir of influenza A virus from where the virus can be transmitted to poultry or to mammals including humans. Mortality among humans in the highly pathogenic avian influenza H5N1 virus infection is even 60%. Despite intense research, there are still open questions in the pathogenicity of the H5N1 virus in humans. To characterize the H5N1 virus infection in human monocyte-derived macrophages (M phi s) and dendritic cells (DCs), we used human isolates of highly pathogenic H5N1/2004 and H5N1/1997 and low pathogenic H7N9/2013 avian influenza viruses in comparison with a seasonal H3N2/1989 virus. We noticed that the H5N1 viruses have an overwhelming ability to replicate and spread in primary human immune cell cultures, and even the addition of trypsin did not equalize the infectivity of H7N9 or H3N2 viruses to the level seen with H5N1 virus. H5N1 virus stocks contained more often propagation-competent viruses than the H7N9 or H3N2 viruses. The data also showed that human DCs and M phi s maintain 1,000- and 10,000-fold increase in the production of infectious H5N1 virus, respectively. Both analyzed highly pathogenic H5N1 viruses showed multi-cycle infection in primary human DCs and M phi s, whereas the H3N2 and H7N9 viruses were incapable of spreading in immune cells. Interestingly, H5N1 virus was able to spread extremely efficiently despite the strong induction of antiviral interferon gene expression, which may in part explain the high pathogenicity of H5N1 virus infection in humans

SARS as a 'health scare'

Epidemics of fear, perhaps also of disease: here are two of our most prominent anxieties. By ‘us’ I mean the various academics, experts and professionals, who in the ‘western’ nations of North America, Europe and Australia have ongoing conversations on these subjects. We certainly worry about disease. Health professionals genuinely fear the possibility of a vast outbreak of a new or re-emerging infectious disease; some are worried about being deliberately attacked with biological weapons; and there are many who fear the ‘urban health penalty’, the disease burdens consequent on polluted urban environments with weak social network resources to limit their occurrence (Fitzpatrick and LaGory, 2000). But they don’t just worry about these events – they (we) also worry about fear-mongering, as well. Many health professionals worry about the enormous impacts that public fears may have on economies and societies: ‘the problem with SARS,’ I have heard several in Canada say, ‘was not SARS itself, but fear’ (Skinner, 2003). By this they mean that the disruptions of SARS were vastly disproportionate to its body count of 44 deaths, a very small number in comparison with the mortality rate commanded by, inter alia, smoking, drinking, driving and not getting a flu vaccine. I know they recall, and would like to prevent, other situations where public worries unjustified by scientific evidence - say, of radiation from powerlines (Abt, 1994, Campion, 1997) - caused much social upheaval and a great expenditure of public money that could have saved many lives if it had been spent on hospital beds rather than on calming unfounded fears. Yet they seem to make little professional effort to trace the tensions between their fears and their fears of fear (Lupton, 1999, Gordon, 2003). And so we who sardonically observe the antics of public health from the padded balconies of the humanities worry and wonder about their (our) worrying: is ours a ‘risk society’ (Adam et al 2000, Beck, 1999), a ‘culture of fear’ (Furedi, 2002)? In this chapter I will join this - these - conversations to reflect on current concerns about, and responses to, the threats of infectious disease, specifically in an urban context. First, I will situate these concerns and reactions in a more general conceptual framework, concerns about ‘health scares’ – events in which there is a strong social reaction to a specific hazard that appears to threaten the health of a significant portion of the population. This is, if you will, the problem of not-disease-but-‘fear itself’ that was mentioned above. I briefly discuss concerns with epidemics of new and re-emerging infectious diseases as a particular category of health scare. In asking how we can analyse, predict and, perhaps, prevent or resolve health scares I then turn to the ‘social amplification of risk’ framework and to the role that networks – actor-networks that link humans and non-humans in specific responsive alliances, and social networks, including intra-urban networks, professional networks and global cities networks – may play in the amplification or attenuation of risk issue signals and so to the resolution or otherwise of health scare situations. In the remainder of the chapter I show how networked social amplification and attenuation effects played out in the outbreaks of SARS in Toronto in the spring of 2003. I conclude with comments on the central importance of social and professional informational networks and connectivity in the ecology of the urban landscape for successfully managing health scares in the future.Sidney Sax Travelling Postdoctoral Fellowship in Public Health. NHMRC 30771

Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Background: In order to prevent spread and improve control of infectious diseases, public health experts need to closely monitor human and animal populations. Infectious disease surveillance is an established, routine data collection process essential for early warning, rapid response, and disease control. The quantity of data potentially useful for early warning and surveillance has increased exponentially due to social media and other big data streams. Digital epidemiology is a novel discipline that includes harvesting, analysing, and interpreting data that were not initially collected for healthcare needs to enhance traditional surveillance. During the current COVID-19 pandemic, the importance of digital epidemiology complementing traditional public health approaches has been highlighted. Objective: The aim of this paper is to provide a comprehensive overview for the application of data and digital solutions to support surveillance strategies and draw implications for surveillance in the context of the COVID-19 pandemic and beyond. Methods: A search was conducted in PubMed databases. Articles published between January 2005 and May 2020 on the use of digital solutions to support surveillance strategies in pandemic settings and health emergencies were evaluated. Results: In this paper, we provide a comprehensive overview of digital epidemiology, available data sources, and components of 21st-century digital surveillance, early warning and response, outbreak management and control, and digital interventions. Conclusions: Our main purpose was to highlight the plausible use of new surveillance strategies, with implications for the COVID-19 pandemic strategies and then to identify opportunities and challenges for the successful development and implementation of digital solutions during non-emergency times of routine surveillance, with readiness for early-warning and response for future pandemics. The enhancement of traditional surveillance systems with novel digital surveillance methods opens a direction for the most effective framework for preparedness and response to future pandemics

An overview of technologies and devices against COVID-19 pandemic diffusion: virus detection and monitoring solutions

none5siThe year 2020 will remain in the history for the diffusion of the COVID-19 virus, originating a pandemic on a world scale with over a million deaths. From the onset of the pandemic, the scientific community has made numerous efforts to design systems to detect the infected subjects in ever-faster times, allowing both to intervene on them, to avoid dangerous complications, and to contain the pandemic spreading. In this paper, we present an overview of different innovative technologies and devices fielded against the SARS-CoV-2 virus. The various technologies applicable to the rapid and reliable detection of the COVID-19 virus have been explored. Specifically, several magnetic, electrochemical, and plasmonic biosensors have been proposed in the scientific literature, as an alternative to nucleic acid-based real-time reverse transcription Polymerase Chain Reaction (PCR) (RT-qPCR) assays, overcoming the limitations featuring this typology of tests (the need for expensive instruments and reagents, as well as of specialized staff, and their reliability). Furthermore, we investigated the IoT solutions and devices, reported on the market and in the scientific literature, to contain the pandemic spreading, by avoiding the contagion, acquiring the parameters of suspected users, and monitoring them during the quarantine period.openR. de Fazio, A. Sponziello, D. Cafagna, R. Velazquez, P. Viscontide Fazio, R.; Sponziello, A.; Cafagna, D.; Velazquez, R.; Visconti, P