ABSTRACT A variety of models have been developed to forecast dengue cases to date. However, it remains a challenge to predict major dengue outbreaks that need timely public warnings the most. In this paper, we introduce CrossLag, an environmentally informed attention that allows for the incorporation of lagging endogenous signals behind the significant events in the exogenous data into the architecture of the transformer at low parameter counts. We use TimeXer, a recent general-purpose transformer distinguishing exogenous-endogenous inputs, as the baseline for this study. Our proposed model outperforms TimeXer by a considerable margin in detecting and predicting major outbreaks in Singapore dengue data over a 24-week prediction window.

In light of the rising death toll from the coronavirus, which this week spread to the U.S. and was declared a health emergency by the World Health Organization (WHO), it's worth looking at AI's role in curbing the spread of other diseases. Algorithms have not only informed superior intervention and prevention strategies, they've helped optimize the allocation of resources to fight the spread of infection. Algorithms have even detected preliminary signs of an outbreak well before it came to human pathologists' attention. In a study back in 2014, investigators used statistical modeling to evaluate the testing and treatment of HIV in the U.K. and locate people living with the virus who weren't aware of their disease status. The team found that -- even without behavioral changes on the part of people living with HIV -- their approach could reduce new infections by 5%.

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed using an artificial dataset. The TM is further applied to forecast dengue outbreaks of all the seventeen regions in Philippines using the spatio-temporal properties of the data. Experimental results show that dengue outbreak forecasts made by the TM are more accurate than those obtained by a Support Vector Machine (SVM), Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting precision and F1-score.

PETALING JAYA: Malaysian Integrated Medical Professional Association (Mimpa) president Dr Dhesi Baha Raja has won the Pistoia Alliance Life Science Award for developing a mobile app that predicts dengue outbreaks. Dr Dhesi developed an AIME (Artificial Intelligence in Medical Epidemiology), which is a disease-prediction mobile platform that employs technology and data to give people prior warning of when disease outbreaks might occur. Dr Dhesi, who led the team of six people, which developed the app, said that winning the award, organised by the Pistoia Alliance of King's College London, proves and validates the technology used as a tool for dengue prevention. He told The Star Online on Wednesday that he is looking to bring the app to Malaysia within the next three months and believes that the technology would be useful to combat Malaysia's dengue problem. Dr Dhesi said that he would be working with mobile digital service provider Webe and the Health Ministry soon.