Machine learning for emerging infectious disease field responses.

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

Severe watery diarrhoea and bacteraemia caused by Vibrio fluvialis.

Individual cases and outbreaks of Vibrio fluvialis-associated gastroenteritis have been reported sporadically, with just one fatal bacteraemia case complicated with shigellosis described. We present a patient that suffered from severe watery diarrhoea requiring parenteral hyperalimentation. V. fluvialis simultaneously cultured from stool and blood proved to be the same strain by random amplified polymorphic DNA (RAPD) analysis. The patient was cured with intravenous administration of antibiotics and supportive treatment. To the best of our knowledge, this is the first case of gastroenteritis and bacteraemia caused by V. fluvialis.

Quantitation of severe acute respiratory syndrome coronavirus genome by real-time polymerase chain reaction assay using minor groove binder DNA probe technology.

The ability to rapidly recognize severe acute respiratory syndrome coronavirus (SARS-CoV) as a cause of infections is critical to quickly limiting further spread of the disease. A rapid, sensitive, and specific laboratory diagnostic test is needed to confirm outbreaks of SARS-CoV infection and distinguish it from other diseases that can cause similar clinical symptoms. An improved TaqMan technology using minor groove binder (MGB) probes was used to detect and quantify SARS-CoV in suspected patients. SARS-CoV primers and probe were designed based on the open reading frame 1b sequence, which encodes coronavirus replicase protein. A linear standard curve with R2 > 0.99 was obtained, and the threshold sensitivity was 10 genome equivalents per reaction. Interassay coefficients of variation were 1.73 to 2.72%, indicating good reproducibility of this method. A total of 228 specimens from 151 suspected patients were quantified by this method, 13 specimens from 6 patients were positive with viral load range from 362 to 36,240,000 genome equivalents/mL. In conclusion, the high sensitivity and reproducibility of the real-time polymerase chain reaction SARS-CoV RNA quantitation using MGB probe allowed the screening of large numbers of clinical samples.