Intelligent early warning system for avian influenza

University of Technology, Sydney. Faculty of Engineering and Information Technology.With the number of natural disasters has increased dramatically during the last decade, the early warning system (EWS) has become a necessary aid for all humankind in detecting incoming threats in good time, taking countermeasures beforehand and finally, mitigating the risks. This research focuses on an intelligent epidemic EWS in the context of avian influenza. Computational intelligence (CI) techniques can provide cutting edge for an efficient and effective avian influenza EWS. The literature review reveals that the use of CI techniques in EWS is neither balanced nor systematic. This research proposes a conceptual framework and a technical framework as a guideline for integrating suitable CI techniques into an EWS from the aspects of structure, function and process. Following this guideline, we provide a hybrid knowledge-based prediction method which seamlessly connects case-based reasoning (CBR) and a fuzzy logic system to apply both implicit case knowledge and explicit expert knowledge in early warning prediction. In order to establish early warning in both a specific time and area, this research also puts forward two methods to address the issue. The first method is a seasonal auto-regressive based support vector regressive (SAR-SVR) time series prediction method, which applies SAR and Fast Fourier Transformation as the heuristic feature selection, and applies SVR to improve prediction accuracy. The second method employs one class classification (OCC) models by revising model combining policy and joining sub-classifiers OCC (JSC-OCC) methodology to realize the area risk mapping. Each method is followed by a validation with real world dataset. Finally, an avian influenza intelligent early warning system (IEWS) prototype is implemented. The data used in the prototype system is real data collected from the Internet, and thus the system could act as a validation means for this research. This prototype instantiates all the proposed approaches which can both estimate a risk level at a concrete location and map risk in a specific area in a specific time. The system realizes the consideration of involving suitable CI techniques in an EWS to form an IEWS with efficiency and effectiveness

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

Risk, modernity and the H5N1 virus in action in Indonesia A multi‐sited study of the threats of avian and human pandemic influenza

This thesis examines the Influenza A/H5N1 virus in action through an ethnographic study focused on the entwined concepts of risk and modernity. The objective is to explain why the response to the virus has been challenged in Indonesia. Concerned with policy formulation, and everyday practice, the thesis argues that assemblages of historical, political, institutional and knowledge‐power processes create multiple hybrid constructions of risk and modernity, which challenge technical responses based on epistemological positions and institutional arrangements that do not allow for such hybridity. The thesis is organised into four sections. The first section (chapters 1 – 3) introduces the virus and its terrain, outlines a constructivist position, and argues that conceptually risk and modernity have multiple, dynamic, power‐laden forms. The second section (chapters 4 – 6) contrasts constructions of risk and modernity among the actors and networks responding to the emergence, spread and persistence of the H5N1 virus, with the constructions of affected people in Indonesia. The third section (chapters 7 – 9) investigates the multi‐directional processes that occur when ‘global’ policies and practices encounter ‘local’ social and political settings, and vice versa, through three empirical case studies of the response to H5N1 in Indonesia between 2005 and 2010. The final section (chapter 10) provides a set of reflections and conclusions. Given the conceptual plurality of risk and modernity, and the multiple overlapping interacting hybrid constructions that have been empirically demonstrated in the case of H5N1, it is concluded that reductive, science‐based, governmentally‐orientated responses which treat nature as a matter of separate, fixed identity do not allow for such hybridity. The virus in action in Indonesia shows that any divide between nature and society is artificial and deceiving. Technical disease control responses need to incorporate understandings which accept the dynamics of culture, politics, and powe

Joint sub-classifiers one class classification model for avian influenza outbreak detection

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset. © 2011 Imperial College Press

Emergence of infectious diseases

From SARS to avian influenza, Ebola virus and MERS-CoV, infectious diseases have received increasing attention in recent decades from scientists, risk managers, the media and the general public. What explains the constant emergence of infectious diseases? What are the related challenges? In five chapters, experts from different scientific fields analyse the ecological, social, institutional and political dynamics associated with emerging infectious diseases. This book discusses how the concepts, scientific results and action plans of international or governmental organizations are constructed and coordinated. In clear straightforward language, this book explores the continuities and discontinuities that occur with emerging infectious diseases, both in terms of collective action and in our relationship to the biological world

Inactivation of Coronaviruses in food industry: The use of inorganic and organic disinfectants, ozone, and UV radiation

Currently there is a worldwide pandemic due to Covid-19, which has caused a great impact on humanity in social, economic, psychological aspects and unfortunately on health. Due to the risk that food can also be a medium to cause virus diseases, the procedures in the food industry safety programs must be revised; and, to be more specific, to disinfect Covid-19. Some effective disinfectants that have been proved to inactivate the coronavirus are: chlorine dioxide, sodium hypochlorite, quaternary compound, ozone and UV-C (shortwave ultraviolet light). In this review, some treatments used to inactivate a virus, with an emphasis to the coronavirus family, and other influenza viruses, are reported. It has been concluded that the coronavirus could be inactivated using free chlorine solutions at 30 mg/L, sodium hypochlorite 0.25 %, or Chlorine Dioxide (99% purity) diluted at 1/2.5 relation. Also, alcohol is an effective disinfectant at concentrations of 62 to 71% of ethanol. With respect to the use of the quaternary compound, it can be used at concentrations of 0.10%. Ozone is another promising disinfectant to inactivate the coronavirus and Covid-19. Doses of ozone between 10 to 20 ppm for 10 to 15 minutes are recommended to inactivate the coronavirus with 3.5 log10 reductions. However, a warning should be reported to the use of high doses of exposure because it can be a risk to human health. UV-C can inactivate the coronavirus at a value of 67 J/m2 by 1 to 30 minutes of exposure.Currently there is a worldwide pandemic due to Covid-19, which has caused a great impact on humanity in social, economic, psychological aspects and unfortunately on health. Due to the risk that food can also be a medium to cause virus diseases, the procedures in the food industry safety programs must be revised; and, to be more specific, to disinfect Covid-19. Some effective disinfectants that have been proved to inactivate the coronavirus are: chlorine dioxide, sodium hypochlorite, quaternary compound, ozone and UV-C (shortwave ultraviolet light). In this review, some treatments used to inactivate a virus, with an emphasis to the coronavirus family, and other influenza viruses, are reported. It has been concluded that the coronavirus could be inactivated using free chlorine solutions at 30 mg/L, sodium hypochlorite 0.25 %, or Chlorine Dioxide (99% purity) diluted at 1/2.5 relation. Also, alcohol is an effective disinfectant at concentrations of 62 to 71% of ethanol. With respect to the use of the quaternary compound, it can be used at concentrations of 0.10%. Ozone is another promising disinfectant to inactivate the coronavirus and Covid-19. Doses of ozone between 10 to 20 ppm for 10 to 15 minutes are recommended to inactivate the coronavirus with 3.5 log10 reductions. However, a warning should be reported to the use of high doses of exposure because it can be a risk to human health. UV-C can inactivate the coronavirus at a value of 67 J/m2 by 1 to 30 minutes of exposure

Behavioral economic impact on animal health surveillance system in Thailand

Zoonotic diseases are a continuously significant threat to global human and livestock health (causing millions of deaths yearly). Zoonotic diseases are not only a human health threat, but also a threat to animal health and welfare. Moreover, they have a high impact on national economies and food security due to productivity and production reduction. Expanding worldwide travel and global trade increases the importance of the threat of zoonotic diseases. The increase in global meat consumption contrasts with the escalating instability of the global meat market, which is affected by the increase of livestock densities, changes in production intensity, and slaughtering systems, causing animal disease outbreaks to spread widely. This study focuses on the animal disease surveillance system in Thailand as an important world meat exporter. In 2014, the Participatory One Health Disease Detection project, or PODD was set up by the veterinary inspection authorities to test animal epidemic control systems using smartphone applications in the Chiang Mai province in northern Thailand The main objectives of this study are (i) to evaluate the economic impact of the PODD system on farmers by impact assessment (n = 177) (ii) to demonstrate the impact of monetary and non-monetary incentives on the PODD reporters by the experimental approach (n = 17), (iii) and to present the effect of the socioeconomic factors and behavioral bias on farmers animal disease reporting behavior with the logit model (n = 467). Focusing on the first objective, the results of this study concluded that there is an impact on the farmers. The technology alone cannot improve animal health security in the short-term. In the second objective, the results concluded that, in the case of the PODD reporters, the decision of using monetary incentives to motivate most of the PODD reporters has a negative impact in the long-term. Losing reporter motivation and effort reflected to the low efficiency of the digital surveillance system of PODD and no impact on farmers. Concerning In the last objective, the results concluded that the optimistic bias of farmers has a very high impact on their decision making about reporting animal diseases on their farm. Just one infected farm in the case of dairy milk farmers can spread the foot-and-mouth disease to other farms. The new digital animal health surveillance system alone is not enough to reduce the impact of animal diseases of farmers. Suitable motivation for the reports and awareness of farmers optimistic bias in animal disease reporting cannot be neglected in digital animal disease surveillance system improvement. Overall, it can be concluded that the digital animal disease surveillance system is a powerful instrument for reducing the impact of animal diseases and increasing food safety and security. However, application of this advanced technology still needs time to demonstrate the impact and to be broadly adopted by users. In terms of motivation, the monetary incentive can increase the effort of report in the short run but it comes at a high cost and has a negative impact in the long-term. While the social incentive costs less and is more effective in the long-term. Where farmers animal disease reporting behavior is concerned, the optimistic bias is the highest influential factor on the farmers reporting decisions, in an inverse correlation.Zoonotische Krankheiten stellen eine anhaltend große Bedrohung für die Gesundheit von Mensch und Tier dar (sie verursachen jährlich Millionen von Todesfällen). Zoonosen stellen nicht nur eine Gefahr für die menschliche Gesundheit dar, sondern auch für die Gesundheit und das Wohlergehen der Tiere. Darüber hinaus haben sie aufgrund von Produktivitätseinbußen einen hohen Einfluss auf die Volkswirtschaften und die Ernährungssicherheit. Der Anstieg des weltweiten Reiseverkehrs und des globalen Handels erhöht die Bedeutung der Bedrohung durch Zoonosen. Die Zunahme des weltweiten Fleischkonsums steht im Gegensatz zur eskalierenden Instabilität des globalen Fleischmarktes, der durch die gesteigerten Viehbestandsdichten, Veränderungen der Produktionssyteme und der Schlachtsysteme beeinflusst wird, was zu einer weiten Verbreitung von Tierseuchenausbrüchen führt. Diese Studie konzentriert sich auf ein Tierseuchenüberwachungssystem in Thailand, einem weiltweit wichtigen Fleischexporteur. Im Jahr 2014 wurde von den Veterinärinspektionsbehörden das Projekt Participatory One Health Disease Detection (PODD) ins Leben gerufen, um Tierseuchenkontrollsysteme mit Smartphone-Anwendungen in der Provinz Chiang Mai im Norden Thailands zu testen. Die Hauptziele dieser Studie sind (i) die Bewertung der wirtschaftlichen Auswirkungen des PODD-Systems auf die Landwirte durch eine Folgenabschätzung (n = 177), (ii) der Nachweis des Einflusses von monetären und nicht-monetären Anreizen auf die PODD-Berichterstatter durch einen experimentellen Ansatz (n = 17), (iii) und die Darstellung des Einflusses der sozioökonomischen Faktoren und Verhaltensverzerrungen auf das Meldeverhalten der Landwirte bei Tierseuchen mit dem Logit-Modell (n = 467). Gemäß dem erste Ziel kamen die Ergebnisse dieser Studie zu dem Schluss, dass es eines Einflusses auf die Landwirte gibt. Die Technologie allein kann die Sicherheit der Tiergesundheit kurzfristig nicht verbessern. Bezüglich des zweiten Ziels, konnte gefolgert werden, dass die Entscheidung, PODD-Berichterstatter durch monetäre Anreize zu motivieren, langfristig negative Auswirkungen hat. Der Verlust der Motivation und des Einsatzes der Berichterstatter konnte auf die geringe Effizienz des digitalen Überwachungssystems des PODD zurückgeführt werden. Beim letzten Ziels 105 kamen die Ergebnisse zu dem Schluss, dass die optimistische Voreingenommenheit der Landwirte einen sehr großen Einfluss auf ihre Entscheidungsfindung bei der Meldung von Tierkrankheiten auf ihrem Betrieb hat. Nur ein infizierter Betrieb kann im Falle von Milchviehhaltern die Maul- und Klauenseuche auf einen anderen Betrieb übertragen. Das neue digitale Tiergesundheitsüberwachungssystem allein reicht dabei nicht aus, um die Auswirkungen von Tierkrankheiten der Landwirten zu verringern. Bei der Verbesserung des digitalen Tierseuchenüberwachungssystems dürfen die Motivation für die Berichterstattung und das Bewusstsein für die optimistische Voreingenommenheit der Landwirte bei der Meldung von Tierseuchen nicht vernachlässigt werden. Insgesamt ist zu schlussfolgern, dass das digitale Tierseuchenüberwachungssystem ein wirksames Instrument zur Verringerung der Auswirkungen von Tierseuchen und zur Erhöhung der Lebensmittelsicherheit und -sicherheit darstellt. Allerdings wird noch Zeit benötigt, bis die Auswirkungen dieser fortschrittlichen Technologie abgeschätzt werden können und sie von den Anwendern adoptiert wird. Was die Motivation betrifft, kann der monetäre Anreiz die Motivation für die Berichterstattung erhöhen, aber er ist mit hohen Kosten verbunden und hat langfristig negative Auswirkungen. Der soziale Anreiz kostet hingegen weniger und ist auf lange Sicht wirksamer. Bezüglich des Meldeverhaltens der Landwirte auf Tierseuchen, ist die optimistische Verzerrung in umgekeheter Korrelation und der höchste Einflussfaktor auf die Meldeentscheidungen der Landwirte