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] “Weekly Surveillance Briefing: New York State*Week 13: Ending March 29, 2026**the week ending March 29, 2026, the three diseases with the highest reported incidence in New York State were Chlamydia (579 cases), Gonorrhea (168 cases), and Campylobacteriosis (136 cases). Overall weekly trends were driven by a substantial decrease in reported sexually transmitted infections, alongside a notable increase in Giardiasis cases, which climbed by 57.9% (+22 cases) to 60 total cases. Surveillance data also suggest the beginning of the seasonal emergence of tick-borne illnesses, with the first cases of Anaplasmosis and Babesiosis reported this calendar year.patterns are particularly notable in this week’s data. First, reported cases of Chlamydia and Gonorrhea dipped substantially. Chlamydia cases fell by 44.4% (a decrease of 463 cases from 1,042 to 579), while Gonorrhea cases declined by 56.9% (a decrease of 222 cases from 390 to 168). A concurrent drop of this magnitude is uncommon and may reflect an administrative or reporting artifact, such as a data processing delay from a large provider network, rather than a true short-term reduction in transmission. Subsequent weeks of data will be necessary to determine if this represents a reporting anomaly or a sustained change in trend., the data signal the seasonal onset of tick-borne diseases. This week marked the first reports of Anaplasma phagocytophilum infection (9 cases) and Babesiosis (5 cases) since late last year. While these absolute numbers are low, their appearance after months of no reported activity is a reliable, expected indicator of increasing tick activity as temperatures rise. This early signal could indicate the start of the primary transmission season. Continued monitoring is warranted to confirm this seasonal trend and inform timely public health messaging on prevention measures.”

Summary Table