.tpr-box {
background-color: #f8f9fa;
border: 1px solid #e9ecef;
border-radius: 8px;
padding: 30px;
box-shadow: 0 4px 6px rgba(0,0,0,0.05);
margin-bottom: 40px;
}
.tpr-title {
color: #2c3e50;
margin-top: 0;
margin-bottom: 20px;
text-align: center;
font-size: 24px;
font-weight: 700;
}
.tpr-input-group {
margin-bottom: 20px;
}
.tpr-label {
display: block;
margin-bottom: 8px;
font-weight: 600;
color: #495057;
}
.tpr-input {
width: 100%;
padding: 12px;
border: 2px solid #ced4da;
border-radius: 6px;
font-size: 16px;
box-sizing: border-box;
transition: border-color 0.2s;
}
.tpr-input:focus {
border-color: #007bff;
outline: none;
}
.tpr-btn {
background-color: #007bff;
color: white;
border: none;
padding: 14px 20px;
font-size: 16px;
font-weight: bold;
border-radius: 6px;
cursor: pointer;
width: 100%;
transition: background-color 0.2s;
}
.tpr-btn:hover {
background-color: #0056b3;
}
.tpr-result-container {
margin-top: 25px;
padding: 20px;
background-color: #ffffff;
border-radius: 6px;
display: none;
text-align: center;
border: 1px solid #dee2e6;
}
.tpr-result-value {
font-size: 36px;
font-weight: 800;
color: #2c3e50;
margin: 10px 0;
}
.tpr-result-label {
font-size: 14px;
color: #6c757d;
text-transform: uppercase;
letter-spacing: 1px;
}
.tpr-interpretation {
margin-top: 15px;
font-size: 15px;
line-height: 1.5;
padding: 10px;
border-radius: 4px;
}
.status-low { background-color: #d4edda; color: #155724; border: 1px solid #c3e6cb; }
.status-moderate { background-color: #fff3cd; color: #856404; border: 1px solid #ffeeba; }
.status-high { background-color: #f8d7da; color: #721c24; border: 1px solid #f5c6cb; }
.article-section h2 { color: #2c3e50; border-bottom: 2px solid #007bff; padding-bottom: 10px; margin-top: 30px; }
.article-section p { line-height: 1.6; color: #333; margin-bottom: 15px; }
.article-section ul { margin-bottom: 20px; }
.article-section li { margin-bottom: 8px; line-height: 1.6; }
.formula-box { background: #eef2f7; padding: 15px; border-left: 4px solid #007bff; font-family: monospace; margin: 20px 0; }
function calculatePositivityRate() {
var totalInput = document.getElementById('total_tests');
var positiveInput = document.getElementById('positive_tests');
var resultContainer = document.getElementById('result_display');
var percentageDisplay = document.getElementById('final_percentage');
var interpretationDisplay = document.getElementById('interpretation_text');
var total = parseFloat(totalInput.value);
var positive = parseFloat(positiveInput.value);
// Reset display style
resultContainer.style.display = 'block';
interpretationDisplay.className = 'tpr-interpretation';
// Validation
if (isNaN(total) || isNaN(positive)) {
percentageDisplay.innerHTML = "–";
interpretationDisplay.innerHTML = "Please enter valid numbers for both fields.";
interpretationDisplay.classList.add('status-high');
return;
}
if (total <= 0) {
percentageDisplay.innerHTML = "Error";
interpretationDisplay.innerHTML = "Total tests must be greater than zero.";
interpretationDisplay.classList.add('status-high');
return;
}
if (positive total) {
percentageDisplay.innerHTML = "Invalid Data";
interpretationDisplay.innerHTML = "Number of positive tests cannot exceed total tests performed.";
interpretationDisplay.classList.add('status-high');
return;
}
// Calculation
var rate = (positive / total) * 100;
// Display Result
percentageDisplay.innerHTML = rate.toFixed(2) + '%';
// Interpretation Logic
var message = "";
if (rate < 5) {
interpretationDisplay.classList.add('status-low');
message = "
A rate below 5% generally indicates that the spread of infection is under control or that there is adequate testing capacity relative to the outbreak size.";
} else if (rate >= 5 && rate < 10) {
interpretationDisplay.classList.add('status-moderate');
message = "
A rate between 5% and 10% is a warning sign. It suggests increased transmission or that testing may not be keeping up with the spread of the virus.";
} else {
interpretationDisplay.classList.add('status-high');
message = "
A rate above 10% indicates widespread transmission. It strongly suggests that testing is insufficient to capture all cases and the virus is spreading significantly.";
}
interpretationDisplay.innerHTML = message;
}
What is Test Positivity Rate?
The test positivity rate (also known as the percent positive rate) is a critical epidemiological metric used to monitor the spread of infectious diseases, such as COVID-19 or Influenza. It represents the percentage of all tests performed that yield a positive result.
This metric helps public health officials understand two key factors: how widespread the infection is within the community and whether the current level of testing is adequate to detect the spread of the virus.
How to Calculate Test Positivity
The calculation for test positivity is straightforward. It requires two data points: the total number of tests conducted (specimens) and the number of those tests that returned a positive result.
Positivity Rate = (Positive Tests / Total Tests) × 100
Example Calculation:
If a laboratory processes 5,000 tests in a week, and 250 of them come back positive:
- Positivity Rate = (250 ÷ 5000) × 100
- Positivity Rate = 0.05 × 100
- Result = 5.00%
Why is Positivity Rate Important?
Looking at raw case numbers alone can be misleading. If confirmed cases go down, it might simply mean fewer people are being tested. The positivity rate provides context to the raw numbers:
- High Positivity Rate: Indicates that only the sickest people are being tested and many asymptomatic or mild cases are likely being missed. It suggests high viral transmission.
- Low Positivity Rate: Indicates that testing capacity is sufficient to find cases and that the outbreak is likely being contained.
Interpreting the Results
While thresholds can vary by disease and organization, the World Health Organization (WHO) has historically used a threshold of 5% for COVID-19.
- Below 5%: Generally considered the benchmark for reopening or relaxing restrictions. It implies sufficient surveillance.
- Above 5%: Suggests the virus is spreading faster than the testing strategy can contain it.
- Above 20%: Indicates a severe outbreak where testing is likely completely overwhelmed.
Limitations of the Metric
When using this calculator, keep in mind that the accuracy of the rate depends on "clean" data. Factors that can skew the results include lag times in reporting, duplicate testing of the same individual, and the specific type of test used (e.g., PCR vs. Antigen tests).