Dengue is an increasing threat in Malaysia, particularly in the more densely populated regions of the country. We present an Artificial Intelligence driven model in predicting Aedes outbreak, using predictors of weather variables and vector indices sourced from the Ministry of Health. Analysis and predictions to estimate Aedes populations were conducted, with its results being used to infer the possibility of dengue outbreaks at pre-determined localities around the Klang Valley, Malaysia. A Bayesian Network machine learning technique was employed, with the model being trained using predictor variables such as temperature, rainfall, date of onset and notification, and vector indices such as the Ae. albopictus count, Ae. aegypti count and larval count. The interfaces of the system were developed using the C# language for Server-side configuration and programming, and HTML, CSS and JavaScript for the Client Side programming. The model was then used to predict the population of Aedes at periods of 7, 14, and 30 days. Using the Bayesian Network technique utilising the above predictor variables we proposed a finalised model with predictive accuracy ranging from 79%-84%. This model was developed into a Graphical User Interface, which was purposed to assist and educate the general public of regions at risk of developing dengue outbreak. This remains a valuable case-study on the importance of public data in the context of combating a public health risk via the development of models for predicting outbreaks of dengue which will hopefully spur further sharing of data by all parties in combating public health threats.

Objective: The COVID-19 pandemic disrupted disease surveillance systems globally, leading to reduced notifications of other infectious diseases. This study aims to estimate the impact of the COVID-19 pandemic on the infectious disease surveillance system in Klang district, Selangor state, Malaysia. Methods: Data on notifiable diseases from 2014 to 2022 were sourced from the Klang District Health Office. The 11 diseases with more than 100 notifications each were included in the study. For these 11 diseases, a negative binomial regression model was used to explore the effect of the pandemic on case notifications and registrations by year, and a quasi-Poisson regression model was used to explore the changes by week. Results: The results showed a reduction in the number of notifications and registrations for all 11 diseases combined during the pandemic compared with previous years. Changes between expected and observed notifications by week were heterogeneous across the diseases. Discussion: These findings suggest that restrictive public health and social measures in Klang district may have impacted the transmission of other infectious diseases during the COVID-19 pandemic. The differential impact of the pandemic on disease notifications and reporting highlights the large ancillary effects of restrictive public health and social measures and the importance of building resilience into infectious disease surveillance systems.