function calculateDiseaseSurvival() {
// Get input values
var totalDiagnosedInput = document.getElementById('totalDiagnosed');
var totalFatalitiesInput = document.getElementById('totalFatalities');
var errorMsg = document.getElementById('input-error');
var resultsDiv = document.getElementById('calc-results');
var diagnosed = parseFloat(totalDiagnosedInput.value);
var fatalities = parseFloat(totalFatalitiesInput.value);
// Reset error state
errorMsg.style.display = 'none';
// Basic Validation
if (isNaN(diagnosed) || isNaN(fatalities)) {
alert("Please enter valid numbers for both fields.");
return;
}
if (diagnosed <= 0) {
alert("Total diagnosed cases must be greater than zero.");
return;
}
if (fatalities diagnosed) {
errorMsg.style.display = 'block';
resultsDiv.style.display = 'none';
return;
}
// Calculations
var survivors = diagnosed – fatalities;
var survivalRate = (survivors / diagnosed) * 100;
var mortalityRate = (fatalities / diagnosed) * 100;
// Ratio Calculation (Simplified)
var gcd = function(a, b) {
return (!b) ? a : gcd(b, a % b);
};
// Normalize for display approx if numbers are large, but exact for stats
// We will just show raw numbers formatted if no common divisor makes sense,
// or just display "X survivors for every 1 death" if death > 0
var ratioText = "";
if (fatalities > 0) {
var ratio = survivors / fatalities;
ratioText = ratio.toFixed(1) + " survivors per 1 fatality";
} else {
ratioText = "100% Survival";
}
// Update DOM
document.getElementById('survivalRateResult').innerText = survivalRate.toFixed(2) + "%";
document.getElementById('mortalityRateResult').innerText = mortalityRate.toFixed(2) + "%";
document.getElementById('totalSurvivorsResult').innerText = survivors.toLocaleString();
document.getElementById('ratioResult').innerText = ratioText;
// Show Results
resultsDiv.style.display = 'block';
}
How to Calculate Survival Rate of a Disease
Understanding medical statistics is crucial for researchers, healthcare professionals, and patients navigating a diagnosis. The survival rate is one of the most fundamental metrics used in epidemiology to measure the severity of a disease and the effectiveness of treatments. This guide explains how to calculate the survival rate accurately using standard formulas.
The Survival Rate Formula
The basic observed survival rate is calculated by comparing the number of survivors to the total number of diagnosed cases within a specific timeframe (often 1, 5, or 10 years). The formula is:
Survival Rate (%) = [(Total Cases – Total Deaths) / Total Cases] × 100
Conversely, the Case Fatality Rate (CFR), or mortality rate for the specific disease, is calculated as:
Let's look at a practical example to clarify the process:
Step 1: Identify the total population diagnosed with the disease. For this example, let's assume a clinical study follows 500 patients.
Step 2: Determine the number of fatalities caused by the disease within the study period. Assume 25 patients passed away.
Step 3: Calculate the number of survivors. 500 (Total) – 25 (Deaths) = 475 Survivors.
Step 4: Divide survivors by total cases and multiply by 100. (475 ÷ 500) = 0.95 0.95 × 100 = 95%
In this scenario, the disease has a 95% survival rate.
Types of Survival Rates
When reading medical literature, it is important to distinguish between different types of rates:
Observed Survival Rate: The percentage of patients who are still alive after a specific time, regardless of cause of death.
Disease-Specific Survival Rate: Considers only deaths caused by the specific disease, excluding other causes (like accidents or other illnesses).
Relative Survival Rate: Compares the survival of people with the disease to the survival expected in the general population of the same age and sex. This is common in cancer statistics (e.g., 5-year relative survival rate).
Why This Metric Matters
Calculating the survival rate helps public health officials allocate resources, allows doctors to provide accurate prognoses, and helps researchers evaluate whether new drugs or therapies are improving patient outcomes. However, it is a statistical average and does not predict individual outcomes with certainty.