Tuberculosis Disease Forecasting Among Indian Patients

Tuberculosis is a conspicuous syndrome for all individuals in developing countries including India. It is an uttermost causation of bereavement in personage. It is an ailment triggered by bacteria which strikes hominid body parts, primarily lungs. The desideratum of this paper is to foretell tuberculosis disease using data mining techniques, which tends to make a medical diagnosis of tuberculosis rigorous. Data Mining Techniques will help to glean that whether it is plausible to start tuberculosis treatment on suspected victims or not, without waiting for pernickety medical test outcomes. This scrutiny emphasis on patients health and provides treatment at low outlay through forecasting systems. There are assorted parameters such as Cough, Chest Pain, Night Sweats, Age, Weight Loss, Gender and Fever, Coughing up Blood, No Appetite which are used for predicting tuberculosis. Both Genetic algorithm and Neural network backwash better than other techniques. Tuberculosis disease forecasting is accomplished by soft computing technique. Genetic algorithm offers best fitness value, disembroil optimization problems whereas Neural Network takes parameters as input and also utilize genetic operators to train the neural network and spawn an output for presaging tuberculosis disease. This research outlines the main review and technical papers on tuberculosis detection that are implemented using multifarious data mining techniques. Review of papers surmises that soft computing technique acquires the highest accuracy

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Evaluating epidemic forecasts in an interval format

For practical reasons, many forecasts of case, hospitalization and death counts in the context of the current COVID-19 pandemic are issued in the form of central predictive intervals at various levels. This is also the case for the forecasts collected in the COVID-19 Forecast Hub (https://covid19forecasthub.org/). Forecast evaluation metrics like the logarithmic score, which has been applied in several infectious disease forecasting challenges, are then not available as they require full predictive distributions. This article provides an overview of how established methods for the evaluation of quantile and interval forecasts can be applied to epidemic forecasts in this format. Specifically, we discuss the computation and interpretation of the weighted interval score, which is a proper score that approximates the continuous ranked probability score. It can be interpreted as a generalization of the absolute error to probabilistic forecasts and allows for a decomposition into a measure of sharpness and penalties for over- and underprediction

Investigation of TOM-CAST, Staking, and Mulch for Managing Tomato Diseases

NYS IPM Type: Project ReportThe objective of one experiment conducted in 1997 was to compare two versions of TOM-CAST, a weather-based disease forecasting system, to a weekly spray program for managing early blight in fresh-market tomatoes. Bravo Ultrex and Bravo C/M were used

BMC Public Health

BackgroundInfectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts.Main bodyFor forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013\u201314 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication.ConclusionsThese efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.NU38OT000297-01-00/CC/CDC HHS/United States2019-12-10T00:00:00Z31823751PMC6902553789

Hop Biofungicide Trial

Downy mildew has been identified as the primary pathogen plaguing northeastern hop yards. This disease causes reduced yield, poor hop quality, and, in severe cases, plant death. Control measures that reduce disease incidence and have a low environmental impact are desperately needed for the region. Regular application of protectant fungicide sprays is an effective method for managing downy mildew pressure in hop yards. However, regular chemical applications can lead to residual toxicity in the soil and have a negative effect on beneficial organisms. Extended use of protectant and curative fungicides can also lead to pathogen resistance. The goal of this project was to evaluate the efficacy of organic approved biofungicides with a variety of active ingredients for control of downy mildew in hops

Aeromycology: studies of fungi in aeroplankton

Air is a natural environment for spores of many genera and species of fungi. Despite its small size and a significant dispersion they have a great impact on human health and different areas of our activities, such as agricultural production. The study on spores of fungi that belong to aeroplankton or bioaerosole is called aeromycology. The most frequent fungi present in the air are Cladosporium and Alternaria species. Their numbers are abundant regardless of latitude and height above the sea level and above the ground. They mostly originate from agricultural environment. Other frequently listed species of fungi, whose spores are present in the air include of Aspergillus, Penicillium, Fusarium, Sclerotinia and Ganoderma. The concentration of spores in the air strongly depends on the abundance of their formation during the studied period. This in turn relates to geobotanical region, vegetation, degree of urbanization, climatic conditions, season, current weather, wind force and direction, local microclimate, and many other factors. Changes in humidity affect the concentration of different types of fungal spores. In general they are divided to ‘dry’ (Alternaria, Cladosporium, Puccinia, Ustilago, Melampsora, Epicoccum, Drechslera) and ‘wet’ (Didymella, Fusarium, Ganoderma, Gliocladium, Leptosphaeria, Verticillium). Study of the composition of species and genera are being done using different types of spore samplers, mostly volumetric instruments. Visual identification is based on colony morphology of the fungus and the shape and size of spores. The identification at the species level is possible with molecular tools. Methods based on DNA/RNA amplification are very sensitive and accurate. They allow the identification below the species level, e.g. chemotypes, mating types or isolates with genes or alleles of interest. Aerobiological monitoring is widely used in the epidemiology of human diseases (inhalant allergies) and infections of arable crops (decision support systems for the protection of cultivated plants). Aeromycology is interconnected with such diverse areas as industrial aerobiology, bioterrorism, ecology, climatology or even speleology and cultural heritage.Powietrze jest naturalnym środowiskiem dla zarodników licznych rodzajów i gatunków grzybów. Pomimo niewielkich rozmiarów i znacznego rozproszenia mają one wielki wpływ na zdrowie ludzi i różne kierunki ich działalności, w tym w szczególności na produkcję rolniczą. Badania nad zarodnikami grzybów stanowiącymi część aeroplanktonu są przedmiotem aeromykologii. Niezależnie od szerokości geograficznej i wysokości nad poziomem morza w powietrzu szczególnie często występują grzyby z rodzajów Cladosporium i Alternaria, a ich źródłem jest najczęściej środowisko rolnicze. Innymi często notowanymi rodzajami grzybów, których zarodniki występują w powietrzu są m.in. Aspergillus, Penicillium, Fusarium, Sclerotinia i Ganoderma. Stężenie zarodników w powietrzu jest ściśle uzależnione od obfitości ich tworzenia w danym okresie, co jest pochodną regionu geobotanicznego, szaty roślinnej, stopnia zurbanizowania danej lokalizacji, warunków klimatycznych, pory roku, aktualnej pogody, siły i kierunku wiatru, lokalnego mikroklimatu i wielu innych czynników. Zmiany wilgotności powietrza wpływają na stężenie zarodników różnych rodzajów grzybów, określanych na tej podstawie jako „suche” (Alternaria, Cladosporium, Puccinia, Ustilago, Melampsora, Epicoccum, Drechslera) lub „mokre” (Didymella, Fusarium, Ganoderma, Gliocladium, Leptosphaeria, Verticillium). Badania składu rodzajowego i gatunkowego prowadzone są przy zastosowaniu różnego rodzaju chwytaczy zarodników, a identyfikacja wizualna na podstawie morfologii kolonii grzyba oraz kształtu i wymiarów zarodników uzupełniana jest obecnie przez wyjątkowo czułe metody detekcji molekularnej, specyficzne względem rodzajów, gatunków, chemotypów, a nawet składu genów i kompozycji poszczególnych alleli. Monitoring aerobiologiczny znajduje bezpośrednie wykorzystanie w epidemiologii chorób ludzi (alergologia) i roślin uprawnych (systemy wspierania decyzji w ochronie roślin uprawnych). Badania z zakresu aeromykologii znajdują zastosowanie w tak różnych kierunkach jak aerobiologia przemysłowa, bioterroryzm, ekologia, dziedzictwo kulturowe, klimatologia lub speleologia

Apple Disease Forecasting Models: When Climate Changes the Rules

With a changing global climate, plant pathologists must understand the impact aberrant weather events may have on the development of plant diseases. Fungal plant infections are largely dependent on temperature and precipitation, climate parameters that are predicted to change more in this century. Venturia inaequalis causes apple scab, one of the most destructive apple diseases of temperate growing regions. Temperature and precipitation drive apple scab infections and forecast models, which guide growers in efficient, effective fungicide applications. In some recent years in the Northeast, these models have failed to accurately predict when ascospores of this fungus are available to cause primary infections, prompting more fungicide intensive management. Identifying cause(s) of model failures will restore confidence in them, enabling growers to reduce fungicide use. As technology becomes an increasingly important component of on farm decision-making, so does educating new farmers and agricultural students in the benefits of Integrated Pest Management and challenges associated with models early on in their college educational experience. This research attempts to identify reasons for ascospore maturity model failures, determine to what degree critical ascospore maturity parameters have changed and create a tool that educators may use to engage undergraduate students in the complexities of Integrated Pest Management research and modern farming. It will more specifically do the following: 1) Dry periods will be analyzed to determine if frequency and duration are increasing, causing the fungus to mature over a longer period of time than models currently estimate. 2) Degree-days during fall and winter will be examined to estimate what effect a warming climate may have on ascospore and tree development, and ultimately apple scab occurrence. The research will use lab and field observations to track the development of V. inaequalis ascospores, the source of primary apple scab infections. These observations will be compared to infection events and spore maturation forecasts from models currently used by apple growers in the Northeast. 3) A case study developed for publication in American Phytopathological Societies’ Plant Health Instructor will provide early career college students with an introduction to forecasting models, Integrated Pest Management and the challenges associated with climate variability