METHODS: This study proposed a deterministic, compartmental model with contact tracing and vaccination components. We defined contact tracing effectiveness as the proportion of contacts of a positive case that was successfully traced and the vaccination rate as the proportion of daily doses administered per population in Malaysia. Sensitivity analyses on the untraced and infectious populations were conducted.
RESULTS: At a vaccination rate of 1.4%, contact tracing with an effectiveness of 70% could delay the peak of untraced asymptomatic cases by 17 d and reduce it by 70% compared with 30% contact tracing effectiveness. A similar trend was observed for symptomatic cases when a similar experiment setting was used. We also performed sensitivity analyses by using different combinations of contact tracing effectiveness and vaccination rates. In all scenarios, the effect of contact tracing on COVID-19 incidence persisted for both asymptomatic and symptomatic cases.
CONCLUSIONS: While vaccines are progressively rolled out, efficient contact tracing must be rapidly implemented concurrently to reach, find, test, isolate and support the affected populations to bring COVID-19 under control.
METHODS: This descriptive cross-sectional study aimed to assess Al Ain University students' knowledge of Human Monkeypox. A validated questionnaire was distributed to students between lectures. The respondents' knowledge of human Monkeypox was assessed by 21 questions that examined the participants' knowledge of Monkeypox as follows: 5 items examined knowledge of the source, definition, and incubation time; 2items assessed the mechanism of transmission of human Monkeypox, 7 items assessed the signs and symptoms; 7 items assessed the preventative measures; and 6 items assessed the treatment modalities. A multivariate logistic regression model was used to identify the factors influencing respondents' knowledge of human Monkeypox among university students.
RESULTS: A total of five hundred and fifty-eight (558) students participated in the study. The average knowledge score was 70.1%, with a 95% confidence interval (CI) of 68.9 - 71.3. Of the total participants, 111 (19.9%) had poor knowledge about human Monkeypox, 320 (57.3%) had moderate knowledge, and 127 (22.8%) had good knowledge. The results of the statistical modelling showed that Old age (OR 0.681; 95% CI 1.005-1.016), female gender (OR 1.26; 95% CI 0.813 -0.961), participants from medical colleges (OR 1.22; 95% CI 1.13 -1.32) having a history of human chickenpox infection (OR 2.6; 95% CI 2.3-2.9) and receiving information on human Monkeypox during education (OR 1.14; 95% CI 1.05-1.2) were strong determinants for good knowledge about human Monkeypox.
CONCLUSION: knowledge of Monkeypox among the participants is relatively low, particularly regarding the epidemiology, symptoms and treatments. Therefore, increasing knowledge of Monkeypox will be key to enhancing the capacity to respond to human monkeypox cases and to relay pertinent data to a disease surveillance system.
METHODS: This cross-sectional study collected publicly available data on COVID-19 clusters occurring in Malaysia from 1 March 2020 to 31 May 2021. The characteristics of cases were described by category, and their associations with several outcomes were analysed. Descriptive analyses were performed to explore the method used to detect the index case and the mode of early transmission, according to cluster category.
RESULTS: A total of 2188 clusters were identified. The workplace cluster category had the largest proportion of clusters (51.5%, 1126/2188 clusters), while the custodial settings category had the largest median cluster size (178 cases per cluster) and longest median duration of cluster (51 days). The high-risk groups category had the highest mortality. There were significant differences in cluster size, duration and rate of detection across the categories. Targeted screening was most commonly used to detect index cases, especially in custodial settings, and in imported and workplace clusters. Household-social and social-workplace contacts were the most common modes of early transmission across most categories.
DISCUSSION: Targeted screening might effectively reduce the size and duration of COVID-19 clusters. Measures to prevent and control COVID-19 outbreaks should be continually adjusted based on ongoing assessments of the unique context of each cluster.
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.