SELF-MANAGEMENT DIET AND RANDOM PLASMA GLUCOSE CONTROL OF PATIENT WITH TYPE 2 DIABETES MELLITUS AT PUSKESMAS ALUN-ALUN GRESIK

Background: Diabetes Mellitus (DM) is a non-communicable metabolic disease characterized by the pancreas not being able to produce insulin and a decrease in insulin receptor sensitivity. Epidemiological data show the prevalence of type 2 diabetes mellitus is high in Indonesia and estimated to increase by more than 2.5 times in 2030 compared to 2020. Several factors play an important role in the development and management of diabetes cases, including the management of a good independent diet. People with DM who do not pay attention to their diet can trigger complications and disability. Objective: This study aims to determine the correlation between independent diet management and blood sugar control in patients with type 2 diabetes mellitus. Methods: This study used an observational analytic method with a cross sectional design. The sampling technique was consecutive sampling. Self-management assessment of diet used the Self-Management Diabetic Diet Questionnaire (SMBDQ) which has been adjusted and tested for validity and reliability. Blood sugar measured using a glucometer. Data processing was conducted using Kendall's tau C with a significance level of 95% (α=0.05). Results: There was 79 respondents. There was a correlation with a significance value (P= 0.002) with a low correlation (τ=0.255) between independent diet management and current blood sugar control in patients with type 2 diabetes mellitus. Conclusion: Independent diet management has a significant correlation with blood sugar control in patients type 2 diabetes mellitus. Keywords: Diabetes mellitus, independent diet management, Random Plasma Glucose, Self-Management Diabetic Diet Questionnaire

A multivariate spatiotemporal spread model of COVID-19 using ensemble of ConvLSTM networks

AbstractThe high R-naught factor of SARS-CoV-2 has created a race against time for mankind and it necessitates rapid containment actions to control the spread. In such scenario short term accurate spatiotemporal predictions can help understanding the dynamics of the spread in a geographic region and identify hotspots. However due to the novelty of the disease there is very little disease specific data generated yet. This poses a difficult problem for machine learning methods to learn a model of the epidemic spread from data. A proposed ensemble of convolutional LSTM based spatiotemporal model can forecast the spread of the epidemic with high resolution and accuracy in a large geographic region. A data preparation method is proposed to convert available spatial causal features into set of 2D images with or without temporal component. The model has been trained with available data for USA and Italy. It achieved 5.57% and 0.3% mean absolute percent error for total number of predicted infection cases in a 5day prediction period for USA and Italy respectively.</jats:p

On nonlinear incidence rate of Covid-19

AbstractClassical Susceptible-Infected-Removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread. On top of that transmission rate may vary widely in a large region due to non-stationarity of spatial features which poses difficulty in creating a global model. We modified discrete global Susceptible-Infected-Removed model by using time varying transmission rate, recovery rate and multiple spatially local models. No specific functional form of transmission rate has been assumed. We have derived the criteria for disease-free equilibrium within a specific time period. A single Convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a 10-day prediction period. Local interpretations of the model using perturbation method reveals local influence of different features on transmission rate which in turn is used to generate a set of generalized global interpretations. A what-if scenario with modified recovery rate illustrates rapid dampening of the spread when forecasted with the trained model. A comparative study with current normal scenario reveals key necessary steps to reach baseline.</jats:p