METHODOLOGY: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase).
RESULTS: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model.
CONCLUSIONS: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.
METHODS: The residential addresses of 3054 notified CHIKV cases in 2009-2010 were georeferenced onto a base map of Sarawak with spatial data of rivers and roads using R software. The spatiotemporal spread was determined and clusters were detected using the space-time scan statistic with SaTScan.
RESULTS: Overall CHIKV incidence was 127 per 100 000 population (range, 0-1125 within districts). The average speed of spread was 70.1 km/wk, with a peak of 228 cases/wk and the basic reproduction number (R0) was 3.1. The highest age-specific incidence rate was 228 per 100 000 in adults aged 50-54 y. Significantly more cases (79.4%) lived in rural areas compared with the general population (46.2%, p<0.0001). Five CHIKV clusters were detected. Likely spread was mostly by road, but a fifth of rural cases were spread by river travel.
CONCLUSIONS: CHIKV initially spread quickly in rural areas mainly via roads, with lesser involvement of urban areas. Delayed spread occurred via river networks to more isolated areas in the rural interior. Understanding the patterns and timings of arboviral outbreak spread may allow targeted vector control measures at key transport hubs or in large transport vehicles.
METHODS: Twenty seven HFpEF (clinical features of HF, left ventricular EF >50%, evidence of mild diastolic dysfunction and evidence of exercise limitation as assessed by cardiopulmonary exercise test) and 14 controls underwent 1H-cardiovascular magnetic resonance spectroscopy (1H-CMRS) to measure MTG (lipid/water, %), 31P-CMRS to measure myocardial energetics (phosphocreatine-to-adenosine triphosphate - PCr/ATP) and feature-tracking cardiovascular magnetic resonance (CMR) imaging for diastolic strain rate.
RESULTS: When compared to controls, HFpEF had 2.3 fold higher in MTG (1.45 ± 0.25% vs. 0.64 ± 0.16%, p = 0.009) and reduced PCr/ATP (1.60 ± 0.09 vs. 2.00 ± 0.10, p = 0.005). HFpEF had significantly reduced diastolic strain rate and maximal oxygen consumption (VO2 max), which both correlated significantly with elevated MTG and reduced PCr/ATP. On multivariate analyses, MTG was independently associated with diastolic strain rate while diastolic strain rate was independently associated with VO2 max.
CONCLUSIONS: Myocardial steatosis is pronounced in mild HFpEF, and is independently associated with impaired diastolic strain rate which is itself related to exercise capacity. Steatosis may adversely affect exercise capacity by indirect effect occurring via impairment in diastolic function. As such, myocardial triglyceride may become a potential therapeutic target to treat the increasing number of patients with HFpEF.