Welcome

Welcome to my personal data science website, where I focus on epidemiology and public health. On this page, I present data analyses on communicable disease incidence in New York State. The data used in these analyses are obtained from the Centers for Disease Control and Prevention (CDC) through the data.CDC.gov open data platform. The latest data are provisional and subject to updates.

I updated this page on a weekly basis.

Data Overview

The data presented on this page are weekly cases of selected infectious national notifiable diseases in the United States, including New York State. These cases are reported to the National Notifiable Diseases Surveillance System (NNDSS). The NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables by the CDC.

It’s important to note that the case counts listed in the tables are provisional and may change as additional information becomes available. State health departments report cases to the CDC for weekly publication, and ongoing revisions and delayed reporting can impact the numbers listed in later weeks. For a comprehensive understanding of interpreting these data, please refer to the Guide to Interpreting Provisional and Finalized NNDSS Data.

How to Use These Data

The summary data are now presented in a comprehensive table format with the following columns: * Disease: The name of the notifiable disease. * First MMWR Week with Any Cases Reported: The earliest week of the year when any cases were reported. * Latest MMWR Week with Any Cases Reported: The most recent week with reported cases. * Current Week Reported Cases: The number of cases reported for the current week. * Cumulative Reported Cases: The total number of cases reported so far for the year. * Trend: A sparkline visualization summarizing the weekly longitudinal trends in reported cases.

This table provides an at-a-glance overview of disease incidence, highlighting patterns over time and identifying changes in reporting. The trend column’s sparklines enable quick visual assessments of fluctuations or seasonality in disease activity.

The longitudinal trend graphs, displayed as line graphs, show the incidence of each disease over time. Each disease is represented by a separate line on the graph, allowing you to observe changes in disease occurrence and identify any seasonal patterns or long-term trends.

Why Are These Data Important?

These data play a crucial role in monitoring and understanding communicable diseases in New York State. By tracking the incidence of notifiable diseases, public health officials can identify outbreaks, assess the impact of interventions, and allocate resources effectively. These data inform public health policies and interventions aimed at preventing and controlling the spread of infectious diseases.

What Do These Data Show?

The table and graphs together provide a comprehensive view of the temporal and cumulative patterns of disease incidence: * First and Latest Weeks with Cases: These columns in the table help identify the duration of disease activity over the year. * Current Week Reported Cases: This column offers a snapshot of the most recent case data. * Cumulative Cases: This total highlights the overall burden of each disease. * Trend (Sparklines): The sparkline visualizations in the table illustrate how the weekly incidence has varied, revealing potential patterns, seasonality, or anomalies. * Longitudinal Trend Graphs: These graphs provide a detailed visualization of weekly case trends for each disease, allowing for a deeper understanding of changes over time and enabling the identification of specific weeks with spikes or declines.

What Do These Data Not Show?

While these data provide valuable information on the incidence of notifiable diseases, it’s important to note their limitations. The data only include cases that are reported to the CDC and may not capture the complete picture of disease incidence in New York State. Some cases may go unreported or may not meet the criteria for being included in the notifiable diseases list.

Additionally, the data are provisional and subject to updates. As more information becomes available and reporting is finalized, the case counts may change. Therefore, it’s crucial to interpret these data with caution and consider them as a snapshot of disease incidence at a specific point in time.

Implications for Public Health Practice

These data have significant implications for public health practice in New York State. By analyzing the trends and patterns in disease incidence, public health professionals can identify priority areas for intervention, allocate resources effectively, and develop targeted strategies to prevent and control communicable diseases. The insights gained from these data can inform decision-making, guide surveillance efforts, and contribute to evidence-based public health policies.

Thank you for visiting my website and exploring the data analyses on communicable disease incidence in New York State. I hope these insights contribute to your understanding of the public health landscape and support efforts to improve population health.

Executive Summary¹

[1] “This week’s surveillance data are led by reports of Chlamydia (1,180 cases), Gonorrhea (452 cases), and confirmed chronic Hepatitis B (251 cases). Overall, the data show a substantial week-over-week increase in reported sexually transmitted infections and viral hepatitis, alongside the expected seasonal emergence of tick-borne diseases. The two most notable patterns in this week’s report are the sharp, concurrent increases across several STIs and forms of hepatitis, and the first reported cases of anaplasmosis and babesiosis for the year. These trends warrant close monitoring to distinguish reporting artifacts from true changes in disease transmission.diseases saw substantial increases compared to the previous reporting week, a pattern most pronounced for STIs and viral hepatitis. Gonorrhea cases climbed by 191.6% (+297 cases), and Chlamydia reports rose by 87.9% (+552 cases). Similarly, confirmed chronic Hepatitis B cases jumped from 23 to 251 (+228 cases, an increase of 991.3%). This simultaneous surge across multiple diseases could suggest a reporting artifact, such as the clearing of a data backlog from a large laboratory or health system, rather than a sudden change in transmission. The large four-week increases for these same diseases may support this hypothesis, suggesting the data correction was recent. Continued monitoring is required to determine if these levels represent a new, higher baseline.data also signal the beginning of the season for tick-borne illnesses, an expected development for this time of year. After many months with no reported cases, surveillance detected 9 cases of anaplasmosis and 5 cases of babesiosis this week. This re-emergence is consistent with typical seasonal onsets as warmer weather increases both tick activity and human exposure. These initial reports serve as an important and timely indicator for public health messaging on tick-bite prevention measures. Future surveillance data will be crucial for assessing whether the 2026 season’s trajectory is tracking above or within historical norms.”

Summary Table