Artificial intelligence in public health: the potential of epidemic early warning systems.

The use of artificial intelligence (AI) to generate automated early warnings in epidemic surveillance by harnessing vast open-source data with minimal human intervention has the potential to be both revolutionary and highly sustainable. AI can overcome the challenges faced by weak health systems by detecting epidemic signals much earlier than traditional surveillance. AI-based digital surveillance is an adjunct to-not a replacement of-traditional surveillance and can trigger early investigation, diagnostics and responses at the regional level. This narrative review focuses on the role of AI in epidemic surveillance and summarises several current epidemic intelligence systems including ProMED-mail, HealthMap, Epidemic Intelligence from Open Sources, BlueDot, Metabiota, the Global Biosurveillance Portal, Epitweetr and EPIWATCH. Not all of these systems are AI-based, and some are only accessible to paid users. Most systems have large volumes of unfiltered data; only a few can sort and filter data to provide users with curated intelligence. However, uptake of these systems by public health authorities, who have been slower to embrace AI than their clinical counterparts, is low. The widespread adoption of digital open-source surveillance and AI technology is needed for the prevention of serious epidemics.

Uncertainty Estimation With Neural Processes for Meta-Continual Learning.

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.

[Change in serum IgG antibody during the recovery stage of Omicron variant infection in children: an analysis of 110 cases]. / 110ä¾‹å„¿ç«¥å¥¥å¯†å ‹æˆŽå˜å¼‚æ ªæ„ŸæŸ“æ¢å¤æœŸè¡€æ¸ IgG抗体变化.

OBJECTIVES: To investigate the serum level of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific RBD IgG antibody (SARS-CoV-2 IgG antibody for short) in children with SARS-CoV-2 Omicron variant infection during the recovery stage, as well as the protective effect of SARS-CoV-2 vaccination against Omicron infection. METHODS: A retrospective analysis was performed on 110 children who were diagnosed with coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 Omicron variant infection in Tianjin of China from January 8 to February 7, 2022. According to the status of vaccination before diagnosis, they were divided into a booster vaccination (3 doses) group with 2 children, a complete vaccination (2 doses) group with 90 children, an incomplete vaccination (1 dose) group with 5 children, and a non-vaccination group with 13 children. The clinical data and IgG level were compared among the 4 groups. RESULTS: The complete vaccination group had a significantly higher age than the non-vaccination group at diagnosis (P<0.05), and there was a significant difference in the route of transmission between the two groups (P<0.05). There were no significant differences among the four groups in sex, clinical classification, and re-positive rate of SARS-CoV-2 nucleic acid detection (P>0.05). All 97 children were vaccinated with inactivated vaccine, among whom 85 children (88%) were vaccinated with BBIBP-CorV Sinopharm vaccine (Beijing Institute of Biological Products, Beijing, China). At 1 month after diagnosis, the booster vaccination group and the complete vaccination group had a significantly higher level of SARS-CoV-2 IgG antibody than the non-vaccination group (P<0.05), and at 2 months after diagnosis, the complete vaccination group had a significantly higher level of SARS-CoV-2 IgG antibody than the non-vaccination group (P<0.05). For the complete vaccination group, the level of SARS-CoV-2 IgG antibody at 2 months after diagnosis was significantly lower than that at 1 month after diagnosis (P<0.05). CONCLUSIONS: Vaccination with inactivated SARS-CoV-2 vaccine has a protective effect against Omicron infection in children. For children vaccinated with 2 doses of the vaccine who experience Omicron infection, there may be a slight reduction in the level of SARS-CoV-2 IgG antibody at 2 months after diagnosis. Citation:Chinese Journal of Contemporary Pediatrics, 2022, 24(7): 736-741.

Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images.

The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While RT-PCR is the most commonly used, it can take up to eight hours, and requires significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.