Risk assessment for COVID-19 pandemic in Taiwan.

BACKGROUND AND OBJECTIVES: Event-based surveillance and rapid risk assessment for acute public health events are essential in emerging infectious disease control. Since detecting the unusual signal in Wuhan in December 2019, Taiwan has been aligning risk management to policy planning via conducting regular risk assessments to combat the coronavirus disease 2019 (COVID-19). This article aims to provide some insights into Taiwan's experiences and corresponding actions for the outbreak. RESULTS: The COVID-19 risk level in Taiwan was raised to "moderate-to-high" in mid-January 2020 when neighboring countries had reported cases and the human-to-human transmission became obvious. The risk level became "high" on 24 January due to China's escalating epidemic situation and imposed a lockdown in Wuhan. We learned that the commander recognized the importance of risk assessments and considered advice from the experts was crucial in making the correct decision at the early stage of the crisis. CONCLUSIONS: Given the surge of COVID-19 cases globally, understanding the evidence-driven mobilizations via detailed risk assessments in Taiwan may be an example worth considering for other countries. We believe that strengthening a global epidemic intelligence network and sharing information in a timely and transparent manner are essential for confronting new challenges of COVID-19 and other emerging infectious diseases.

Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images.

Importance: Decades of effort have been devoted to establishing an automated microscopic diagnosis of malaria, but there are challenges in achieving expert-level performance in real-world clinical settings because publicly available annotated data for benchmark and validation are required. Objective: To assess an expert-level malaria detection algorithm using a publicly available benchmark image data set. Design, Setting, and Participants: In this diagnostic study, clinically validated malaria image data sets, the Taiwan Images for Malaria Eradication (TIME), were created by digitizing thin blood smears acquired from patients with malaria selected from the biobank of the Taiwan Centers for Disease Control from January 1, 2003, to December 31, 2018. These smear images were annotated by 4 clinical laboratory scientists who worked in medical centers in Taiwan and trained for malaria microscopic diagnosis at the national reference laboratory of the Taiwan Centers for Disease Control. With TIME, a convolutional neural network-based object detection algorithm was developed for identification of malaria-infected red blood cells. A diagnostic challenge using another independent data set within TIME was performed to compare the algorithm performance against that of human experts as clinical validation. Main Outcomes and Measures: Performance on detecting Plasmodium falciparum-infected blood cells was measured by average precision, and performance on detecting P falciparum infection at the image level was measured using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The TIME data sets contained 8145 images of 36 blood smears from patients with suspected malaria (30 P falciparum-positive and 6 P falciparum-negative smears) that had reliable annotations. For clinical validation, the average precision was 0.885 for detecting P falciparum-infected blood cells and 0.838 for ring form. For detecting P falciparum infection on blood smear images, the algorithm had expert-level performance (sensitivity, 0.995; specificity, 0.900; AUC, 0.997 [95% CI, 0.993-0.999]), especially in detecting ring form (sensitivity, 0.968; specificity, 0.960; AUC, 0.995 [95% CI, 0.990-0.998]) compared with experienced microscopists (mean sensitivity, 0.995 [95% CI, 0.993-0.998]; mean specificity, 0.955 [95% CI, 0.885-1.000]). Conclusions and Relevance: The findings suggest that a clinically validated expert-level malaria detection algorithm can be developed by using reliable data sets.

Genotypes and phenotypes of Staphylococcus lugdunensis isolates recovered from bacteremia.

BACKGROUND: Staphylococcus lugdunensis is a member of coagulase-negative staphylococci, which has the potential to cause serious infections, such as endocarditis, bone and joint infections, and septicemia. Differences in phenotypic/genotypic characterization may be linked to different diseases. METHODS: Genotypes of 11 S. lugdunensis isolates from bacteremia were determined by pulsed field gel electrophoresis and accessory gene regulator (agr) typing. The SCCmec elements in two oxacillin-resistant isolates were sequenced. Phenotypes were tested by antimicrobial susceptibility testing, biofilm formation assessments, and virulence factor analysis (hemolytic and protease activities). RESULTS: Among the 11 isolates, six pulsotypes were found, and seven isolates belonged to two major pulsotypes. Two agr types (agr-1sl or agr-2sl) were found. The 11 isolates were susceptible to most antimicrobial agents tested. The SCCmec elements in two oxacillin-resistant isolates belonged to the SCCmec type V, but with additional ccrAB2 genes. The agr-2sl isolates (n = 7) displayed higher hemolytic and protease activities than the agr-1sl isolates. All isolates contained the icaA gene but with variable biofilm activities. The results suggest that protein might play an important part in S. lugdunensis biofilms, possibly through an ica-independent pathway. Of the 11 patients with S. lugdunensis bacteremia, one patient had a community-onset infection, and others had a hospital-acquired infection, which were mostly central venous catheter-related infections. CONCLUSION: The 11 S. lugdunensis bacteremia isolates displayed various genotypes and phenotypes. Two oxacillin-resistant isolates contained SCCmec type V and carried additional ccrAB2 genes. Correlation of genotypes and phenotypes with infections needs further studies.