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: A multi-centre, retrospective observational study was performed among children aged ≤12 years with laboratory-proven COVID-19 between 1 February and 31 December 2020.
RESULTS: In total, 261 children (48.7% males, 51.3% females) were included in this study. The median age was 6 years [interquartile range (IQR) 3-10 years]. One hundred and fifty-one children (57.9%) were asymptomatic on presentation. Among the symptomatic cases, fever was the most common presenting symptom. Two hundred and forty-one (92.3%) cases were close contacts of infected household or extended family members. Twenty-one (8.4%) cases had abnormal radiological findings. All cases were discharged alive without requiring supplemental oxygen therapy or any specific treatment during hospitalization. The median duration of hospitalization was 7 days (IQR 6-10 days). One (2.1%) of the uninfected guardians accompanying a child in quarantine tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) upon discharge.
CONCLUSIONS: COVID-19 in children was associated with mild symptoms and a good prognosis. Familial clustering was an important epidemiologic feature in the outbreak in Negeri Sembilan. The risk of transmission of SARS-CoV-2 from children to guardians in hospital isolation was minimal despite close proximity.
METHODS: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt
METHODS: A molecular epidemiological investigation of CVA21 was conducted among patients presenting with acute upper respiratory illnesses in the ambulatory settings between 2012 and 2014 in Kuala Lumpur, Malaysia.
RESULTS: Epidemiological surveillance of acute respiratory infections (n = 3935) showed low-level detection of CVA21 (0.08%, 1.4 cases/year) in Kuala Lumpur, with no clear seasonal distribution. Phylogenetic analysis of the new complete genomes showed close relationship with CVA21 strains from China and the United States. Spatio-temporal mapping of the VP1 gene determined 2 major clusters circulating worldwide, with inter-country lineage migration and strain replacement occurring over time.
CONCLUSIONS: The study highlights the emerging role of CVA21 in causing sporadic acute respiratory outbreaks.
METHODS: An Internet-based, cross-sectional survey was administered on 29 January 2020. A total of 4393 adults ≥18 y of age and residing or working in the province of Hubei, central China were included in the study.
RESULTS: The majority of the participants expressed a great degree of trust in the information and preventive instructions provided by the central government compared with the local government. Being under quarantine (adjusted odds ratio [OR] 2.35 [95% confidence interval {CI} 1.80 to 3.08]) and having a high institutional trust score (OR 2.23 [95% CI 1.96 to 2.53]) were both strong and significant determinants of higher preventive practices scores. The majority of study participants (n=3640 [85.7%]) reported that they would seek hospital treatment if they suspected themselves to have been infected with COVID-19. Few of the participants from Wuhan (n=475 [16.6%]) and those participants who were under quarantine (n=550 [13.8%]) expressed an unwillingness to seek hospital treatment.
CONCLUSIONS: Institutional trust is an important factor influencing adequate preventive behaviour and seeking formal medical care during an outbreak.
METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia.
RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks.
CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.