ST283 is a zoonotic GBS clone associated with farmed freshwater fish, capable of causing severe disease in humans. It caused a large foodborne outbreak in Singapore and poses both a regional and potentially more widespread threat.

Vaccination helps reduce disease burden, particularly in the elderly, who are at higher risk for hospitalization and death.

Coxsackievirus A6 (CV-A6), coxsackievirus A16 (CV-A16) and enterovirus 71 (EV-A71) were the major enteroviruses causing nationwide hand, foot and mouth disease (HFMD) epidemics in Singapore in the last decade. We estimated the basic reproduction number (R 0) of these enteroviruses to obtain a better understanding of their transmission dynamics. We merged records of cases from HFMD outbreaks reported between 2007 and 2012 with laboratory results from virological surveillance. R 0 was estimated based on the cumulative number of reported cases in the initial growth phase of each outbreak associated with the particular enterovirus type. A total of 33 HFMD outbreaks were selected based on the inclusion criteria specified for our study, of which five were associated with CV-A6, 13 with CV-A16, and 15 with EV-A71. The median R 0 was estimated to be 5·04 [interquartile range (IQR) 3·57-5·16] for CV-A6, 2·42 (IQR 1·85-3·36) for CV-A16, and 3·50 (IQR 2·36-4·53) for EV-A71. R 0 was not significantly associated with number of infected children (P = 0·86), number of exposed children (P = 0·94), and duration of the outbreak (P = 0·05). These enterovirus-specific R 0 estimates will be helpful in providing insights into the potential growth of future HFMD epidemics and outbreaks for timely implementation of disease control measures, together with disease dynamics such as severity of the cases.

BACKGROUND:Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare subtype of nonsmall cell lung cancer (NSCLC) predominantly reported in East Asia. We aimed to evaluate clinical characteristics, diagnosis, treatment, and prognosis of PPLELC in Singapore.METHODS:Retrospective review of all patients diagnosed with PPLELC at our center between 2000 and 2014.RESULTS:All 28 patients were Chinese, 67.9% were female, and the median age was 58 years (range37–76 years). Majority (89.3%) were never smokers and 53.6% asymptomatic at diagnosis. About 28.6% presented with Stage I/II disease, 25% had Stage III disease, and 46.4% had Stage IV disease. All patients with Stage I/II disease underwent lobectomy without adjuvant treatment. Four out of 7 patients with Stage III disease underwent surgery with or without adjuvant therapy while the rest received chemoradiation. Twelve out of 13 patients with Stage IV disease received chemotherapy with or without radiotherapy. At the end of 2016, survival data were available for all 28 patients. Two-year survival rates for Stage I/II, Stage III, and Stage IV disease were 100%, 85.7%, and 61.5%, respectively, while survival was 100%, 85.7%, and 9.6%, respectively, at five years.CONCLUSION:The majority (46.4%) of patients presented with metastatic disease. For those with Stage I-III disease, 5-year survival for PPLELC was better than other NSCLC subtypes. Multimodality treatment including surgery could be considered in locally advanced disease. In Stage IV disease, it tended to approximate that of NSCLC.

ObjectiveTo develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems.IntroductionPneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. An unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation.The purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ED) admissions due to pneumonia in Singapore, a tropical country. We hypothesise that there is complementary information between hospital-based and community-based surveillance systems. The clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. Infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. Hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. Thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ED admissions due to pneumonia. We also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in Singapore and is a common aetiology for pneumonia.MethodsThis was a retrospective study using aggregated national surveillance data and meteorological data during the period 3 January 2011 to 1 January 2017.We compared the performance of autoregressive integrated moving average model (ARIMA) with multiple linear regression models with ARIMA errors, with and without the inclusion of influenza predictors at forecast horizons of 2, 4, 6 and 8 weeks in advance. Weekly data between the study period of 3 January 2011 and 1 January 2017 were split into training and validation sets, with the first three years of data used as the base training set. Time series cross validation was used to estimate the models’ accuracy and out-of-sample forecast accuracy was based on the calculation of the mean absolute error (MAE) and mean absolute percent forecast error (MAPE).ResultsThe multiple linear regression model with ARIMA errors that included influenza predictors was the best performing model while the basic ARIMA model was the worst performing model for all forecast horizons. The two multiple linear regression models with ARIMA errors had a MAPE of less than 10% for all forecast horizons.ConclusionsData from different multiple surveillance systems and the inclusion of influenza trends can be used to improve the forecast of ED admissions due to pneumonia in a tropical setting, despite the absence of large differences between seasons. Accurate forecasting at the national level can prepare healthcare facilities for an impending surge.