Leptospirosis modelling using hydrometeorological indices and random forest machine learning.

Leptospirosis is a zoonosis that has been linked to hydrometeorological variability. Hydrometeorological averages and extremes have been used before as drivers in the statistical prediction of disease. However, their importance and predictive capacity are still little known. In this study, the use of a random forest classifier was explored to analyze the relative importance of hydrometeorological indices in developing the leptospirosis model and to evaluate the performance of models based on the type of indices used, using case data from three districts in Kelantan, Malaysia, that experience annual monsoonal rainfall and flooding. First, hydrometeorological data including rainfall, streamflow, water level, relative humidity, and temperature were transformed into 164 weekly average and extreme indices in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). Then, weekly case occurrences were classified into binary classes "high" and "low" based on an average threshold. Seventeen models based on "average," "extreme," and "mixed" indices were trained by optimizing the feature subsets based on the model computed mean decrease Gini (MDG) scores. The variable importance was assessed through cross-correlation analysis and the MDG score. The average and extreme models showed similar prediction accuracy ranges (61.5-76.1% and 72.3-77.0%) while the mixed models showed an improvement (71.7-82.6% prediction accuracy). An extreme model was the most sensitive while an average model was the most specific. The time lag associated with the driving indices agreed with the seasonality of the monsoon. The rainfall variable (extreme) was the most important in classifying the leptospirosis occurrence while streamflow was the least important despite showing higher correlations with leptospirosis.

The effect of drying temperature and sodium caseinate concentration on the functional and physical properties of spray-dried coconut milk.

This study investigated the effect of drying temperature on the stability and quality of spray-dried coconut milk. A low concentration (1-2% w/w) of sodium caseinate (SC) was used as emulsifying agent with 8-9% of maltodextrin. The spray drying temperature was varied from 140 to 180 °C. Emulsions prepared at different SC concentration remained stable without phase separation for 24 h. Higher the SC concentration produced smaller-sized of droplet and powder particles. The spray dried coconut milk has a skin-forming structure. Emulsion with low concentration of SC (1% w/w) is unstable during atomisation process due to re-coalescence of fat. Adding SC to the emulsion reduce the moisture content to less than 5%. However, drying the emulsions at 180 °C gave negative impact to the powder properties. Some particles rupture and lead to high free fat content, high insolubility and larger fat droplet size. Presence of fleck is also noticed in the powder.