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: We conducted molecular detection, genetic characterization, and Bayesian time-scale evolution analyses of NiV using pooled Pteropid bat roost urine samples from an outbreak area in 2012 and archived RNA samples from NiV case patients identified during 2012-2018 in Bangladesh.
RESULTS: NiV-RNA was detected in 19% (38/456) of bat roost urine samples and among them; nine N gene sequences were recovered. We also retrieved sequences from 53% (21 out of 39) of archived RNA samples from patients. Phylogenetic analysis revealed that all Bangladeshi strains belonged to NiV-BD genotype and had an evolutionary rate of 4.64 × 10-4 substitutions/site/year. The analyses suggested that the strains of NiV-BD genotype diverged during 1995 and formed two sublineages.
CONCLUSION: This analysis provides further evidence that the NiV strains of the Malaysian and Bangladesh genotypes diverged recently and continue to evolve. More extensive surveillance of NiV in bats and human will be helpful to explore strain diversity and virulence potential to infect humans through direct or person-to-person virus transmission.
METHODS: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values.
RESULTS: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations.
CONCLUSIONS: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.