Influenza seasonality in equatorial countries is little understood. Seasonal and alert influenza thresholds were determined for Malaysia, using laboratory-based data obtained from the Malaysia Influenza Surveillance System and a major teaching hospital, from 2011 to 2016. Influenza was present year-round, with no clear annual seasons. Variable periods of higher transmission occurred inconsistently, in November to December, January to March, July to September, or a combination of these. These coincide with seasons in the nearby southeast Asian countries or winter seasons of the northern and southern hemispheres. Changes in the predominant circulating influenza type were only sometimes associated with increased transmission. The data can provide public health interventions such as vaccines.
Influenza viruses, which are RNA viruses belonging to the family Orthomyxoviridae, cause respiratory diseases in birds and mammals. With seasonal epidemics, influenza spreads all over the world, resulting in pandemics that cause millions of deaths. Emergence of various types and subtypes of influenza, such as H1N1 and H7N9, requires effective surveillance to prevent their spread and to develop appropriate anti-influenza vaccines. Diagnostic probes such as glycans, aptamers, and antibodies now allow discrimination among the influenza strains, including new subtypes. Several sensors have been developed based on these probes, efforts made to augment influenza detection. Herein, we review the currently available sensing strategies to detect influenza viruses.
We evaluated the performance of four rapid influenza diagnostic test methods (RIDT) compared to real-time reverse-transcription polymerase chain reaction (rRT-PCR), for the detection of the novel swine-origin influenza A (H1N1) virus (S-OIV) in August 2009. A total of 270 respiratory specimens were tested with rRT-PCR, where 74 of these were tested by BinaxNow (Inverness), 80 by QuickVue (Quidel), 37 by Influenza A Antigen Rapid Test (Rockeby Biomed) and 79 by Directigen (BD). The sensitivities ranged from 4.4% to 37.0%, specificities 90.9% to 100.0%, positive predictive values 75.0% to 100.0% and negative predictive values 32.3% to 75.0%. RIDT were able to detect S-OIV but the sensitivities were low. The limitations of RIDT must be considered when interpreting results for clinical management.
Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient's lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor's diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.
Of the ≈400 cases of avian influenza (H7N9) diagnosed in China since 2003, the only travel-related cases have been in Hong Kong and Taiwan. Detection of a case in a Chinese tourist in Sabah, Malaysia, highlights the ease with which emerging viral respiratory infections can travel globally.
OBJECTIVES: The clinical impact of seasonal influenza is understudied in tropical countries. The aim of this study was to describe the clinical features and seasonal pattern of influenza in children hospitalized in Malaysia, and to identify predictors of severe disease.
METHODS: Children hospitalized with community-acquired, laboratory-confirmed influenza at a teaching hospital in Kuala Lumpur, Malaysia during 2002-2007 were identified retrospectively. Clinical data were collected, and predictors of severe disease were identified by multivariate logistic regression. All influenza cases from 1982 to 2007 were also analyzed for seasonal patterns.
RESULTS: A total of 132 children were included in the study, 48 (36.4%) of whom had underlying medical conditions. The mean age was 2.5 years and 116 (87.9%) were <5 years old. The most common presenting features were fever or history of fever, cough, rhinitis, vomiting, and pharyngitis. Severe influenza was seen in 16 patients (12.1%; nine previously healthy), including 12 (9.1%; eight previously healthy) requiring intensive care. There were three (2.3%) deaths. Severe disease was associated with age <12 months, female sex, and absence of rhinitis on admission. Influenza was seen year-round, with peaks in November-January and May-July.
CONCLUSIONS: Seasonal influenza has a considerable impact on children hospitalized in Malaysia, in both the healthy and those with underlying medical conditions.
Study site: University Malaya Medical Centre (UMMC)
Respiratory infections constitute the most widespread human infectious disease, and a substantial proportion of them are caused by unknown etiological agents. Reoviruses (respiratory enteric orphan viruses) were first isolated from humans in the early 1950s and so named because they were not associated with any known disease. Here, we report a previously unknown reovirus (named "Melaka virus") isolated from a 39-year-old male patient in Melaka, Malaysia, who was suffering from high fever and acute respiratory disease at the time of virus isolation. Two of his family members developed similar symptoms approximately 1 week later and had serological evidence of infection with the same virus. Epidemiological tracing revealed that the family was exposed to a bat in the house approximately 1 week before the onset of the father's clinical symptoms. Genome sequence analysis indicated a close genetic relationship between Melaka virus and Pulau virus, a reovirus isolated in 1999 from fruit bats in Tioman Island, Malaysia. Screening of sera collected from human volunteers on the island revealed that 14 of 109 (13%) were positive for both Pulau and Melaka viruses. This is the first report of an orthoreovirus in association with acute human respiratory diseases. Melaka virus is serologically not related to the different types of mammalian reoviruses that were known to infect humans asymptomatically. These data indicate that bat-borne reoviruses can be transmitted to and cause clinical diseases in humans.
The Canadian Acute Respiratory Illness and Flu Scale (CARIFS) is a parent-proxy questionnaire that assesses severity of acute respiratory infections in children. The aim was to (a) perform a cross-cultural adaptation and (b) prove that the Malay CARIFS is a reliable tool.
Rapid diagnosis of influenza is important for early treatment and institution of control measures. In developing tropical countries such as Malaysia, influenza occurs all year round, but molecular assays and conventional techniques (such as immunofluorescence and culture) for diagnosis are not widely available. Rapid influenza diagnostic tests (RIDTs) may be useful in this setting. A total of 552 fresh respiratory specimens were assessed from patients with respiratory symptoms at a teaching hospital in Kuala Lumpur, Malaysia from November 2017 to March 2018. Two digital immunoassays (DIAs), STANDARD F Influenza A/B Fluorescence Immunoassay (STANDARD F) and Sofia Influenza A + B Fluorescence Immunoassay (Sofia) and one conventional RIDT (immunochromatographic assay), SD Bioline Influenza Ag A/B/A(H1N1) Pandemic rapid test kit (SD Bioline) were evaluated in comparison with a WHO-recommended reverse transcription quantitative PCR (RT-qPCR). Of the 552 samples, influenza A virus was detected in 47 (8.5%) and influenza B virus in 7 (1.3%). The digital immunoassays STANDARD F and Sofia had significantly higher overall sensitivity rates (71.7% and 70.6%, respectively) than the conventional RIDT SD Bioline and immunofluorescence/viral culture (55.8% and 52.8%, respectively). Sensitivity rates were higher for influenza A than influenza B, and specificity rates were uniformly high, ranging from 98% to 100%. Digital readout RIDTs can be used in tropical settings with year-round influenza if PCR is unavailable.
The H7N9 subtype of avian influenza is an enzootic and airborne virus which caused an influenza outbreak in China. Infected individuals mostly worked with poultry, suggesting H7N9 virus-infected poultry as the primary source of human infection. Significantly increased levels of proinflammatory mediators (chemokines, cytokines) during virus infection could hamper the immune system and aggravate the infection. Severe cases are marked by fulminant pneumonia, acute respiratory distress syndrome (ARDS) and encephalopathy. Left untreated, the condition may rapidly progress to multi-organ failure and death. Reverse transcription polymerase chain reaction (rRT-PCR) is the gold standard diagnostic test for H7N9 avian influenza. Use of neurominidase inhibitor antivirals remain the main treatment. New antivirals are developed to counteract neurominidase inhibitor resistance H7N9 viral strains. Corticosteroid use in viral pneumonia may provoke mortality and longer viral shedding time. Subjects at high risk of contracting avian influenza H7N9 infection are recommended to receive annual seasonal influenza vaccination.
Trivalent inactivated influenza vaccine (TIV) is reformulated annually to contain representative strains of 2 influenza A subtypes (H1N1 and H3N2) and 1 B lineage (Yamagata or Victoria). We describe a sentinel surveillance approach to link influenza variant detection with component-specific vaccine effectiveness (VE) estimation.