25 Visualizations That Changed Public Health

Why This Matters

Data visualization is not just about making pretty charts. In public health, the right graph at the right time has ended epidemics, reshaped government policy, exposed injustice, and saved millions of lives. From John Snow’s 1854 cholera map to the COVID-19 dashboards you watched in real time, data visualization has been a secret weapon of medicine.

This site walks you through 25 landmark visualizations — organized into five historical eras — plus a dedicated India Focus section with 7 additional visualizations from Indian public health. Each includes interactive R recreations and embedded originals where possible.

The Five Eras

Era 1: Foundations of Epidemiology

1843 – 1900

visualizations including Snow, Nightingale, and Farr

Era 2: Chronic Disease Epidemiology

1948 – 1964

visualizations including Framingham, Doll & Hill, and the Kaplan-Meier curve

Era 3: Global Health Campaigns

1958 – 2000s

visualizations including smallpox eradication, epi curves, and HIV/AIDS

Era 4: The Data Revolution

2006 – 2019

visualizations including Gapminder, GBD, Our World in Data, and warming stripes

Era 5: COVID-19 & The Modern Era

2020 – present

visualizations including Flatten the Curve, JHU Dashboard, and health wearables

🇮🇳 India Focus

1995 – present

visualizations including Polio eradication, Kerala Model, COVID19India.org, Nipah, NFHS, and IDSP

How to Use This Site

Browse by era using the navigation bar above
Click “Show R Code” to see how each visualization was recreated
Embedded links take you to the original interactive dashboards
Discussion prompts at the end of each section encourage critical thinking

Quick Timeline

Show R Code

library(ggplot2)

timeline <- data.frame(

  year  = c(1843, 1854, 1858, 1892, 1900,
            1948, 1950, 1958, 1958, 1964,
            1966, 1975, 1985, 1996, 2000,
            2006, 2011, 2012, 2018, 2019,
            2020, 2020, 2020, 2021, 2014),
  label = c("Farr", "Snow", "Nightingale", "Bertillon", "Du Bois",
            "Framingham", "Doll & Hill", "Kaplan-Meier", "Epi Curve", "Surgeon Gen.",
            "Smallpox Maps", "SIR Models", "AIDS Quilt", "UNAIDS", "GBD",
            "Gapminder", "OWID", "AIDSVu", "Warming Stripes", "Wearables",
            "Flatten Curve", "JHU Dashboard", "FT Trajectories", "NYT Spiral", "Health Apps"),
  era   = c(rep("Era 1", 5), rep("Era 2", 5), rep("Era 3", 5), rep("Era 4", 5), rep("Era 5", 5)),
  y     = rep(c(1, -1, 1.5, -1.5, 0.5), 5)
)

era_colours <- c("Era 1" = "#e94560", "Era 2" = "#0f3460",
                 "Era 3" = "#ff9800", "Era 4" = "#4caf50", "Era 5" = "#9c27b0")

ggplot(timeline, aes(x = year, y = y, colour = era)) +
  geom_hline(yintercept = 0, linewidth = 0.8, colour = "grey70") +
  geom_segment(aes(xend = year, yend = 0), linewidth = 0.4, alpha = 0.6) +
  geom_point(size = 3) +
  geom_text(aes(label = label), size = 2.5, hjust = 0, nudge_x = 1, fontface = "bold") +
  scale_colour_manual(values = era_colours, name = "") +
  scale_x_continuous(breaks = seq(1840, 2030, 20), limits = c(1835, 2035)) +
  theme_minimal(base_size = 11) +
  theme(
    axis.text.y  = element_blank(),
    axis.title   = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    legend.position = "bottom"
  ) +
  labs(title = "Timeline of 25 Public Health Visualizations")

--- title: "" --- ::: {.hero-banner} # 25 Visualizations That Changed Public Health **A chronological journey through the charts, maps, and dashboards that saved millions of lives — with a special focus on India** *Teaching resource for medical students* ::: ## Why This Matters Data visualization is not just about making pretty charts. In public health, the right graph at the right time has ended epidemics, reshaped government policy, exposed injustice, and saved millions of lives. From John Snow's 1854 cholera map to the COVID-19 dashboards you watched in real time, data visualization has been a secret weapon of medicine. This site walks you through **25 landmark visualizations** — organized into five historical eras — plus a dedicated **India Focus** section with 7 additional visualizations from Indian public health. Each includes interactive R recreations and embedded originals where possible. ## The Five Eras ::: {.era-grid} ::: {.era-grid-item} ### [Era 1: Foundations of Epidemiology](era1-foundations.qmd) **1843 – 1900** ::: {.count} 5 ::: visualizations including Snow, Nightingale, and Farr ::: ::: {.era-grid-item} ### [Era 2: Chronic Disease Epidemiology](era2-chronic-disease.qmd) **1948 – 1964** ::: {.count} 5 ::: visualizations including Framingham, Doll & Hill, and the Kaplan-Meier curve ::: ::: {.era-grid-item} ### [Era 3: Global Health Campaigns](era3-global-campaigns.qmd) **1958 – 2000s** ::: {.count} 5 ::: visualizations including smallpox eradication, epi curves, and HIV/AIDS ::: ::: {.era-grid-item} ### [Era 4: The Data Revolution](era4-data-revolution.qmd) **2006 – 2019** ::: {.count} 5 ::: visualizations including Gapminder, GBD, Our World in Data, and warming stripes ::: ::: {.era-grid-item} ### [Era 5: COVID-19 & The Modern Era](era5-covid-modern.qmd) **2020 – present** ::: {.count} 5 ::: visualizations including Flatten the Curve, JHU Dashboard, and health wearables ::: ::: {.era-grid-item} ### [🇮🇳 India Focus](india-public-health.qmd) **1995 – present** ::: {.count} 7 ::: visualizations including Polio eradication, Kerala Model, COVID19India.org, Nipah, NFHS, and IDSP ::: ::: ## How to Use This Site - **Browse by era** using the navigation bar above - **Click "Show R Code"** to see how each visualization was recreated - **Embedded links** take you to the original interactive dashboards - **Discussion prompts** at the end of each section encourage critical thinking ## Quick Timeline ```{r} #| fig-height: 3 #| fig-width: 10 #| code-fold: true library(ggplot2) timeline <- data.frame( year = c(1843, 1854, 1858, 1892, 1900, 1948, 1950, 1958, 1958, 1964, 1966, 1975, 1985, 1996, 2000, 2006, 2011, 2012, 2018, 2019, 2020, 2020, 2020, 2021, 2014), label = c("Farr", "Snow", "Nightingale", "Bertillon", "Du Bois", "Framingham", "Doll & Hill", "Kaplan-Meier", "Epi Curve", "Surgeon Gen.", "Smallpox Maps", "SIR Models", "AIDS Quilt", "UNAIDS", "GBD", "Gapminder", "OWID", "AIDSVu", "Warming Stripes", "Wearables", "Flatten Curve", "JHU Dashboard", "FT Trajectories", "NYT Spiral", "Health Apps"), era = c(rep("Era 1", 5), rep("Era 2", 5), rep("Era 3", 5), rep("Era 4", 5), rep("Era 5", 5)), y = rep(c(1, -1, 1.5, -1.5, 0.5), 5) ) era_colours <- c("Era 1" = "#e94560", "Era 2" = "#0f3460", "Era 3" = "#ff9800", "Era 4" = "#4caf50", "Era 5" = "#9c27b0") ggplot(timeline, aes(x = year, y = y, colour = era)) + geom_hline(yintercept = 0, linewidth = 0.8, colour = "grey70") + geom_segment(aes(xend = year, yend = 0), linewidth = 0.4, alpha = 0.6) + geom_point(size = 3) + geom_text(aes(label = label), size = 2.5, hjust = 0, nudge_x = 1, fontface = "bold") + scale_colour_manual(values = era_colours, name = "") + scale_x_continuous(breaks = seq(1840, 2030, 20), limits = c(1835, 2035)) + theme_minimal(base_size = 11) + theme( axis.text.y = element_blank(), axis.title = element_blank(), panel.grid.minor = element_blank(), panel.grid.major.y = element_blank(), legend.position = "bottom" ) + labs(title = "Timeline of 25 Public Health Visualizations") ```