An Early Detection System for Disease Outbreaks Based on Density Entropy

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

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Predicting potential and actual distribution of sudden oak death in Oregon: Prioritizing landscape contexts for early detection and eradication of disease outbreaks

Forest Ecology and Management ◽

10.1016/j.foreco.2010.06.026 ◽

2010 ◽

Vol 260 (6) ◽

pp. 1026-1035 ◽

Cited By ~ 47

Author(s):

Tomáš Václavík ◽

Alan Kanaskie ◽

Everett M. Hansen ◽

Janet L. Ohmann ◽

Ross K. Meentemeyer

Keyword(s):

Early Detection ◽

Disease Outbreaks ◽

Sudden Oak Death ◽

Actual Distribution

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Metagenomic analysis of fecal and tissue samples from 18 endemic bat species in Switzerland revealed a diverse virus composition including potentially zoonotic viruses

PLoS ONE ◽

10.1371/journal.pone.0252534 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252534

Author(s):

Isabelle Hardmeier ◽

Nadja Aeberhard ◽

Weihong Qi ◽

Katja Schoenbaechler ◽

Hubert Kraettli ◽

...

Keyword(s):

Next Generation Sequencing ◽

Early Detection ◽

Disease Outbreaks ◽

Tissue Samples ◽

Zoonotic Virus ◽

Non Invasive ◽

Zoonotic Viruses ◽

Zoonotic Risk ◽

Generation Sequencing ◽

Diverse Virus

Many recent disease outbreaks in humans had a zoonotic virus etiology. Bats in particular have been recognized as reservoirs to a large variety of viruses with the potential to cross-species transmission. In order to assess the risk of bats in Switzerland for such transmissions, we determined the virome of tissue and fecal samples of 14 native and 4 migrating bat species. In total, sequences belonging to 39 different virus families, 16 of which are known to infect vertebrates, were detected. Contigs of coronaviruses, adenoviruses, hepeviruses, rotaviruses A and H, and parvoviruses with potential zoonotic risk were characterized in more detail. Most interestingly, in a ground stool sample of a Vespertilio murinus colony an almost complete genome of a Middle East respiratory syndrome-related coronavirus (MERS-CoV) was detected by Next generation sequencing and confirmed by PCR. In conclusion, bats in Switzerland naturally harbour many different viruses. Metagenomic analyses of non-invasive samples like ground stool may support effective surveillance and early detection of viral zoonoses.

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Early detection system using low-dose CT lung cancer screening and related SNP information

Medical Imaging 2021: Computer-Aided Diagnosis ◽

10.1117/12.2582092 ◽

2021 ◽

Author(s):

Yuta Azuma ◽

Yoshiki Kawata ◽

Noboru Niki ◽

Issei Imoto ◽

Masahiko Kusumoto ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Early Detection ◽

Low Dose ◽

Detection System ◽

Lung Cancer Screening ◽

Low Dose Ct

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Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks

Public Health and Welfare ◽

10.4018/978-1-5225-1674-3.ch026 ◽

2016 ◽

pp. 565-586

Author(s):

Elena Arsevska ◽

Mathieu Roche ◽

Pascal Hendrikx ◽

David Chavernac ◽

Sylvain Falala ◽

...

Keyword(s):

Early Detection ◽

Web Mining ◽

Clinical Signs ◽

Disease Outbreaks ◽

Expert Elicitation ◽

Comprehensive Method ◽

Statistical Measures ◽

Animal Pathogens ◽

The Web ◽

Selection Of

In a context of intensification of international trade and travels, the transboundary spread of emerging human or animal pathogens represents a growing concern. One of the missions of the national veterinary services is to implement international epidemiological intelligence for a timely and accurate detection of emerging animal infectious diseases (EAID) worldwide, and take early actions to prevent their introduction on the national territory. For this purpose, an efficient use of the information published on the web is essential. The authors present a comprehensive method for identification of relevant associations between terms describing clinical signs and hosts to build queries to monitor the web for early detection of EAID. Using text and web mining approaches, they present statistical measures for automatic selection of relevant associations between terms. In addition, expert elicitation is used to highlight the most relevant terms and associations among those automatically selected. The authors assessed the performance of the combination of the automatic approach and expert elicitation to monitor the web for a list of selected animal pathogens.

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