Milestone 6: Infectious Disease Outbreak in California
Nilson Palma, Michelle Zhen Huang
2025-12-14
Introduction
Scenario 1: Infectious Disease Outbreak (Simulated) in California
The sim_novelid_CA.csv file and
sim_novelid_LACounty.csv file together represent simulated
morbidity for the entire state of California. This analysis combines and
analyzes these datasets to understand the temporal and demographic
patterns of the outbreak.
Problem Statement
There is a novel infectious respiratory disease outbreak in California. Data was collected from 2023 and included data on cases and case severity by demographic categories (age category, race, sex) and geographic categories (county) for California counties and population data. As part of the California Department of Public Health surveilling the infectious respiratory disease outbreak, we are interested in examining the course of the outbreak, if it is disproportionately affecting certain demographic or geographic populations and how prevention and treatment resources should be allocated.
Methods
Data for this analysis came from three simulated 2023 datasets. The sim_novelid_CA.csv file contained weekly morbidity data (new infections and severe cases) by county, age group, sex, and race/ethnicity for all California counties except Los Angeles. The sim_novelid_LACounty.csv file provided equivalent morbidity data for Los Angeles County, which operates a separate surveillance system. The ca_pop_2023.csv file provided 2023 population estimates by county and demographic categories, which served as denominators for rate calculations. Data preparation involved standardizing variable names and coding schemes across datasets. Race/ethnicity categories were recoded from numeric values to descriptive labels matching California Department of Finance classifications. Age categories in the population data (0-4, 5-11, 12-17) were collapsed to match the broader categories in the morbidity data (0-17). County names were standardized to enable successful joins. Rows with missing values were excluded to ensure complete records for stratified analyses. Weekly incidence rates per 100,000 population were calculated by dividing new infections by the population denominator and multiplying by 100,000. This standardization enables fair comparisons across demographic groups of different sizes. Attack rates (cumulative proportion infected) and severity rates (proportion of cases with severe outcomes) were calculated for summary statistics. All measures were stratified by age group and race/ethnicity to identify disparities. CDC epidemiological weeks (Sunday start) were used as the temporal unit for time series analysis.
Data Preparation
Load and Clean Morbidity Data
First, we loaded the data.
Standardize Column Names and Select Variables
We selected relevant variables to our analysis for each data source.
Handle Missing Data
We removed rows with missing values to ensure clean joins.
Dates and CDC Week
We fixed date format for LA County. CDC epidemiological weeks start on Sunday and are the standard for disease surveillance reporting. So we set the week to start on Sunday. We then created the CDC week for CA dataset from time_int variable and for LA County from diagnosis date.
Recode Race/Ethnicity Categories
We converted the numeric race codes to descriptive labels for CA data, and ensured race is factor type for LA data.
Combine Datasets
We merged the CA and LA County morbidity data.
Population Data Preparation
Clean and Standardize Population Data
We selected and renamed relevant columns to match morbidity data. We then standardized the race/ethnicity labels to match morbidity data. We reclassified the age categories to match morbidity data (e.g., 0-17 instead of 0-4, 5-11, 12-17).
Standardize County Names
To standardize the county names, we removed the “County” suffix from morbidity data to match population data. We also made sure there was consistent formatting in population data.
Join Morbidity and Population Data
We used a left join to add population data to morbidity data.
Time Series Analysis
Calculate Weekly Rates
We created a dataset with weekly cumulative incidence rates and dropped columns not relevant to our time series analysis.
Overall California Outbreak Trajectory
We aggregated to state level by week to determine the overall California outbreak trajectory.
Stratify by Age Group
We aggregated by age group and week to stratify by age group.
Stratify by Race/Ethnicity
We aggregated by race/ethnicity and week to stratify by race/ethnicity.
Results
Epidemic Curve by Age Group
The outbreak affected all age groups, with clear differences in disease burden by age.
Key Findings:
- All age groups peaked simultaneously around August-September 2023.
- Incidence increases with age, however, cumulative incidence for 18-49 > 50-64.
- Persons aged 65+ experienced the highest burden (~1,600 per 100,000 at peak).
- Persons aged 0-17 had the lowest incidence (~350 per 100,000 at peak).
Epidemic Curve by Race/Ethnicity
Key Findings:
- American Indian or Alaska Native (Non-Hispanic) populations experienced the highest peak incidence (~1,250 per 100,000).
- All racial/ethnic groups followed similar temporal patterns, peaking around the same time.
Summary Statistics
Summary by Age Group
| Age Group | Total Cases | Average Population | Peak Weekly CI (per 100k) | Total Severe Cases | Attack Rate (%) | Severe Rate (%) |
|---|---|---|---|---|---|---|
| 0-17 | 353210 | 8694111 | 354.22 | 363 | 4.06 | 0.10 |
| 18-49 | 2467284 | 17106505 | 1250.22 | 29325 | 14.42 | 1.19 |
| 50-64 | 540481 | 7086797 | 668.40 | 2822 | 7.63 | 0.52 |
| 65+ | 1188608 | 6221657 | 1662.00 | 94416 | 19.10 | 7.94 |
Key Findings:
- Overall, Cumulative Incidence (per 100k), Attack Rate, and Severe Rate generally increased with age, with the exception that the 18–49 age group had higher rates than the 50–64 group.
- Persons aged 65+ experienced the highest burden across all 3 measures: Cumulative Incidence (per 100k), Attack Rate, and Severe Rate .
- Persons aged 0-17 had the lowest burden across all 3 measures: Cumulative Incidence (per 100k), Attack Rate, and Severe Rate.
Summary by Race/Ethnicity
| Race/Ethnicity | Total Cases | Average Population | Peak Weekly CI (per 100k) | Total Severe Cases | Attack Rate (%) | Severe Rate (%) |
|---|---|---|---|---|---|---|
| American Indian or Alaska Native, Non-Hispanic | 22195 | 158672 | 1230.21 | 671 | 13.99 | 3.02 |
| White, Non-Hispanic | 1778774 | 13848282 | 1117.81 | 63448 | 12.84 | 3.57 |
| Black, Non-Hispanic | 271836 | 2211518 | 1060.00 | 7118 | 12.29 | 2.62 |
| Hispanic (any race) | 1796696 | 14829946 | 1052.14 | 36484 | 12.12 | 2.03 |
| Native Hawaiian or Pacific Islander, Non-Hispanic | 16921 | 153729 | 962.08 | 410 | 11.01 | 2.42 |
| Asian, Non-Hispanic | 546770 | 6295420 | 760.47 | 16568 | 8.69 | 3.03 |
| Multiracial (two or more of above races), Non-Hispanic | 116391 | 1611503 | 617.87 | 2227 | 7.22 | 1.91 |
Key Findings:
- American Indian or Alaska Native (Non-Hispanic) populations experienced the highest attack rate (14.0%), nearly double that of the lowest group (Multiracial, 7.2%).
- Asian (Non-Hispanic) populations showed low incidence (8.7% attack rate) but high case severity (3.03%), a pattern potentially explained by an older age distribution in this population.
Data Dictionary
Clean Dataset Variables
The final analytical dataset (fun1) contains the
following key variables:
| Variable | Data Type | Description |
|---|---|---|
| county | Character | California counties |
| age_cat | Character | Age category: 0-17, 18-49, 50-64, 65+ |
| sex | Character | Sex: FEMALE, MALE |
| race_eth | Factor | Race/ethnicity category defined by California Department of Finance |
| new_infections | Numeric | Number of newly diagnosed individuals during the week |
| new_severe | Numeric | Newly identified individuals with severe disease requiring hospitalization |
| cdc_week | Date | Start date of CDC epidemiological week (Sunday) |
| pop | Numeric | CA Dept of Finance population estimates |
| ci_per100k | Numeric | Weekly cumulative incidence rate per 100,000 persons |
| severe_per100k | Numeric | Weekly severe disease rate per 100,000 persons |
Discussion
The outbreak peaked in August–September 2023 before declining. The observed disparities have important implications for ongoing preparedness and resource allocation. Age was the strongest predictor of disease burden. Adults aged 65 and older experienced the highest attack rates (15.6%) and severity rates (5.0%), indicating this group should be prioritized for both prevention efforts and treatment. The unexpectedly high incidence among adults aged 18–49 compared to those aged 50–64 may reflect occupational exposures and warrants further investigation. Racial and ethnic disparities were also evident. American Indian or Alaska Native (Non-Hispanic) populations experienced attack rates nearly double those of the lowest-affected group, highlighting the need for targeted outreach and culturally appropriate prevention strategies. Interestingly, Asian (Non-Hispanic) populations showed low incidence but high case severity, a pattern potentially driven by age distribution within this population. Several limitations should be noted. This analysis used simulated data and may not capture real-world complexities. Socioeconomic factors, which often drive health disparities, were not available in the dataset. Additionally, rows with missing demographic data were excluded, which could introduce bias if missingness was not random. Finally, this analysis did not examine county-level variation, which could reveal geographic clustering of cases, requiring localized response efforts.