Health Life

Info-metrics for modeling and inference with complex and uncertain pandemic information

Figure. The probability of dying conditional on BCG vaccination policies (left). The probability of dying conditional on Pollution (right) show the death rate in the 10th percentile (dots) vs. those at the 90th percentile (continuous). The x-axis is the patients’ age. Credit: Santa Fe Institute

As the world faces the possibility of recurring waves of the current novel coronavirus pandemic, it is critical to identify patterns and dynamics that could be leveraged to decrease future transmission, infection, and death rates. At this stage in the pandemic, data on disease patterns and dynamics are emerging from almost all countries in the world. Variations across countries with respect to coronavirus infection rates, public health policies, social structure, norms, health conditions, environmental policy, climate, and other factors provide us with the data to investigate the impact of different underlying factors and governmental policies on COVID-19 transmission, infection, and death rates.

Despite the fact that millions have been infected and hundreds of thousands have died from COVID-19, the available information is still insufficient for reaching precise inferences and predictions. This is because the available data on each patient are very limited, the variables of interest are highly correlated, and great uncertainty surrounds the underlying process. In addition, though the death rate from COVID-19 is high relative to other infectious diseases, from an inferential point of view, it is still very small since the number of deaths relative to those who did not die is extremely small. As a result, the observations are in the tail of the survival probability distribution. In short, the available data for analysis of COVID-19 are complex, constantly evolving, and ill-behaved. Inferring and modeling with such data results in a continuum of explanations and predictions. We need to use a modeling and inferential approach that will yield the least biased

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Maturity-onset diabetes of the young – Genetics Home Reference

Maturity-onset diabetes of the young (MODY) is a group of several conditions characterized by abnormally high blood sugar levels. These forms of diabetes typically begin before age 30, although they can occur later in life. In MODY, elevated blood sugar arises from reduced production of , which is a hormone produced in the that helps regulate blood sugar levels. Specifically, insulin controls how much glucose (a type of sugar) is passed from the blood into cells, where it is used as an energy source.

The different types of MODY are distinguished by their genetic causes. The most common types are HNF1A-MODY (also known as MODY3), accounting for 50 to 70 percent of cases, and GCK-MODY (MODY2), accounting for 30 to 50 percent of cases. Less frequent types include HNF4A-MODY (MODY1) and renal cysts and diabetes (RCAD) syndrome (also known as HNF1B-MODY or MODY5), which each account for 5 to 10 percent of cases. At least ten other types have been identified, and these are very rare.

HNF1A-MODY and HNF4A-MODY have similar signs and symptoms that develop slowly over time. Early signs and symptoms in these types are caused by high blood sugar and may include frequent urination (polyuria), excessive thirst (polydipsia), fatigue, blurred vision, weight loss, and recurrent skin infections. Over time uncontrolled high blood sugar can damage small blood vessels in the eyes and kidneys. Damage to the light-sensitive tissue at the back of the eye (the ) causes a condition known as diabetic retinopathy that can lead to vision loss and eventual blindness. damage (diabetic nephropathy) can lead to kidney failure and end-stage renal disease (ESRD). While these two types of MODY are very similar, certain features are particular to each type. For example, babies with HNF4A-MODY tend to weigh more