Public Health and Infectious Disease

This tutorial will introduce basic epidemiological concepts related to infectious disease and how GIS can be used in public health to analyze and communicate surveillance information about infectious disease.

Infectious Disease

Infectious diseases are illnesses caused by microorganisms, such as bacteria, viruses, parasites or fungi, that invade the human body (WHO 2020a). Many (but not all) infectious diseases are also communicable in that they can be spread directly or indirectly from one person to another (Correll 2019). Infectious diseases are a perennial source of misery and death for all living organisms, but public health interventions can mitigate the effects of infectious diseases.

The microorganisms that cause infectious diseases are called agents. Infectious diseases are transmitted when an agent leaves a reservoir through a portal of exit, is conveyed by a mode of transmission, and enters a portal of entry to infect a susceptible host (CDC 2020, 1:10).

The Chain of Infection (CDC 1992)

Epidemiology is "the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems" (Last 2001, 61; CDC 2012, 1:1).

The epidemiological approach analyzes diseases in a three step process (CDC 2012, 1:5):

The Epidemiological Approach

Disease Statistics

Incidence, Prevalence, and Mortality

There are three disease rates commonly used in epidemiology, based on what is being counted (Coggon, Rose, and Barker 2020):

Counts vs Rates

Data about diseases is collected as counts of new cases (incidence), existing cases (prevalence), or deaths (mortality). However, disease statistics are often expressed as rates calculated as the counts divided by the number of people living in the areas where the disease is being counted.

Rates for common disease are expressed as percents (per 100 people), while uncommon diseases are often expressed as rates per 100,000 people in order to keep from having to use very small numbers. For example, 30 cases per 100,000 residents is easier to read and compare than 0.03%.

Because older residents tend to have higher rates of disease than younger residents, rates are often age-adjusted to compensate for areas that have large numbers of older or younger residents. This allows clearer identification of areas where diseases are more common rather than just areas where there are more senior residents.

Rates are often more useful than counts when comparing different areas with different sized populations, or the same areas at different times when the population changes over time. While areas with larger populations will generally have higher counts of a disease, a small area with a higher rate (but lower count) of disease may be of special interest to public health official trying to identify areas where health problems are emerging or are most severe.

Counts are more useful than rates for proportionately distributing resources. For example, health clinics can service more people if they are located in areas that have the highest counts of conditions handled by those clinics. Locating a health clinic in a remote area with an unusually high rate of disease but a very small population may not (depending on the situation) be the most effective use of resources.

In the examples below, the map of tuberculosis counts is largely a map of the most populous states, while the map of cancer rates shows that tuberculosis is especially high in a handful of states.

Prevalence Count of Tuberculosis by State in 2018 (NCHHSTP 2020)
Prevalence Rate of Tuberculosis per 100,000 Population by State in 2018 (NCHHSTP 2020)

Levels of Prevalence

The ordinary prevalence of a disease in a community is called the endemic level of a disease. Some diseases (such as rabies) that occur infrequently are referred to as sporadic. Other diseases that persist at high levels (such as human papillomavirus) are referred to as hyperendemic (CDC 2020, 1:11).

There are a variety of terms for situations where rates of disease rise above the normal endemic level (CDC 2020, 1:11):

Outbreak vs. Epidemic vs. Pandemic

Disease Severity

Different disease agents affect the human body in different ways, and, likewise, move through human populations in different ways. Many of these characteristics can be rated or quantified to better understand how infectious diseases spread, and how those transmissions can be slowed or stopped.

How Many People Does It Kill: Case Fatality Rate

The deadliness of a disease is often expressed in terms of the case fatality rate (CFR), which is the percent of people diagnosed with a disease that eventually die from the effects attributable to that disease.

Higher CFR values are more severe. For example, the CDC (2015) lists the CFR for chickenpox as between 0.001% and 0.003% (meaning it is rarely fatal), while Tauenberger, Reid, and Fanning (2000) estimated the CFR for the deadly 1918 influenza pandemic was around 2.5%.

Although some diseases are generally more deadly than others, a CFR is specific to a particular time and place, and is not fixed for any particular disease agent. A CFR can be lowered by improved treatments and access to quality clinical care (CDC 2020 3:3).

Case fatality rates are also subject to variability due to issues with reporting. The number of reported cases will be different from the number of people who actually have the disease when some people go undiagnosed because their symptoms are mild or they do not have access to clinical care. And although fatalities are usually tracked fairly closely by public health officials, deaths attributable to a disease may be reported as caused by underlying or related conditions (such as pneumonia) that cannot be unambiguously tied to the disease in question (CDC 2020 3:3).

Note that CFR differs from mortality rates that are expressed as the proportion of deaths relative to the total population.

Case Fatality Rate Ranges for Notable Infectious Diseases
(click here for detailed data and sources)

How Readily Does It Spread: The Basic Reproduction Number R0

The basic reproduction number R0 is an estimate of how contagious a disease is in terms of the average number of people that will catch the disease from one infected person (Ramirez 2016).

Diseases with higher R0 values spread more quickly than diseases with lower R0 values unless there is some kind of intervention. For example, Cobern, Wagner, and Blower (2009) estimated the R0 for the 1918 global influenza pandemic at between 1.4 and 2.8. In contrast, Bacaër et al. (2006) found the R0 for HIV/AIDS among IV drug users in Kunming, China was 32.

The basic reproduction number (R0) only applies when no interventions are in effect to control the spread of a disease. As natural immunity, vaccinations, and other control measures are implemented, the effective reproduction number (R) can usually be driven down below one, which means the disease will eventually stop spreading and remain at a low level or disappear from the population (Chiew et al. 2013).

Delamater et al. (2019) notes that R0 values are affected by numerous biological, socio-behavioral, and environmental factors, and should not be misinterpreted as fixed properties of pathogens in all places and times. Li and Blakeley (2011) go even further to assert that "in almost every aspect that matters, R0 is flawed."

The following chart shows R0 estimates for a variety of infectious diseases:

R0 Ranges for Notable Infectious Diseases
(click here for detailed data and sources)

Factors Affecting The Spread of Infectious Disease

Typical Public Health Interventions

Infectious Disease Surveillance Maps

Surveillance has six common objectives with infectious diseases (WHO 2018):

One highly-visible application of GIS in epidemiology is the dissemination of current infectious disease surveillance data on web maps. These maps can inform healthcare providers, researchers, and the general public about the current state of epidemics and pandemics. That information can then be used to inform public health responses to those situations.

Global Maps

HealthMap is a web app created by a team of researchers, epidemiologists and software developers at Boston Children's Hospital as an informal online source for "disease outbreak monitoring and real-time surveillance of emerging public health threats"

Navigating the HealthMap Web App

ProMED is a disease news monitoring service with new articles displayed on a web map The Program for Monitoring Emerging Diseases (ProMED) is a program of the International Society for Infectious Diseases (ISID) that was launched in 1994 to identify unusual health events related to emerging and re-emerging infectious diseases and toxins affecting humans, animals and plants (ISIC 2002).

Navigating the ProMED Web App

The Metabiota Epidemic Tracker, a freely-accessible web app that provides current information on a number of specific pathogens. Metabiota is a private company that partners with businesses and governments worldwide to "identify and track pathogens from their source, then contextualize the risk based on socioeconomic, political, environmental and other factors" (Metabiota 2020).

Navigating the Metabiota Epidemic Tracker

For the COVID-19 pandemic in 2019-2020, Johns Hopkins University's Center for Systems Science and Engineering (CSSE) used the ArcGIS Online Dashboard to create a web app dedicated to sharing current information on worldwide infections and deaths.

One notable feature of this app was the ability to download the weekly data via GitHub. The video below shows how to download that data and create your own version of the map in ArcGIS Online. A CSV file of this data from 12 February 2020 can be downloaded here.

  1. Navigate through the GitHub directories to find the most recent report file.
  2. Download the Raw file to your hard drive.
  3. Create a new map in ArcGIS Online.
  4. Add -> Add Layer From File.
  5. Set the Addresses or Places Location Fields for the World so the app knows how to geocode the locations.
  6. Symbolize by the field of interest.
  7. Set transparency so overlapping bubbles are visible.
  8. Choose the desired base map.
  9. Show the legend.
  10. Save and share the map.
Recreating the JHU Coronavirus Map

US National Maps

FluView is an interactive web map created as part of the weekly influenza surveillance report prepared by the Centers for Disease Control and Prevention's (CDC) Influenza Division. This map can be animated to show the spread of seasonal influenza over the year.

This data can also be downloaded for map creation or further analysis. The video below demonstrates how FLUVIEW data can be downloaded, joined to a state polygon layer, and mapped as a choropleth in ArcGIS Online.

  1. Navigate in the FluView map to the week of interest.
  2. Download Data and Data as Shown On Map and save the CSV file to your local hard drive
  3. Create a new map in ArcGIS Online
  4. Add -> Add Layer From File with the CSV file
  5. For Locations, choose Addresses and Places in the United States and select the state name column.
  6. Add the Living Atlas Layer USA States (Generalized).
  7. On the state layer, click the perform analysis icon and select Summarize Data -> Join Features.
  8. The Target Layer should be the states and the Join Layer should be the points from the CSV file
  9. Give the layer a meaningful name
  10. Run the analysis
  11. Symbolize the activity estimate categories using a sequence of colors that reflects increasing severity.
  12. Save and share the map.
Navigating FLUVIEW and Downloading Data for Mapping

US State Maps

While state and local health departments collect significant amounts of infectious disease data as part of their surveillance responsibilities, they rarely make current infectious disease information available as web maps or downloadable geospatial data.

However, there are portals that make historical data available. Emory University’s Rollins School of Public Health in partnership with Gilead Sciences, Inc. and the Center for AIDS Research at Emory University (CFAR) make recent data on HIV infections available through their AIDSVu web map, and recent information on Hepatitis B infections on their HepVu web map. Both HIV and HBV are spread through bodily fluids and have the same risk factors (unprotected sex and shared needles), so these conditions are commonly addressed together by public health officials (NIH 2019).

The AIDSVu map has a number of features for viewing and sharing information:

Navigating the AIDSVu Map

Reference List

Bacaër, Nicolas, Xamxinur Abdurahman, and Jianli Ye. 2006. "Modeling the HIV/AIDS epidemic among injecting drug users and sex workers in Kunming, China." Bulletin of Mathematical Biology 68 (3): 525-550.

Boston Children's Hospital. 2020. "About." Accessed 10 February 2020.

Centers for Disease Control and Prevention (CDC). 1992. "Principles of Epidemiology, Second Edition." Atlanta, GA: U.S. Department of Health and Human Services.

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Chiew, May, Heather F Gidding, Aditi Dey, James Wood, Nicolee Martin, Stephanie Davis, and Peter McIntyre. 2013. "Estimating the measles effective reproduction number in Australia from routine notification data." Bulletin of the World Health Organization 92, 171-177.

Chowell, Gerardo, Hiroshi Nishiura, and Luis MA Bettencourt. 2007. "Comparative estimation of the reproduction number for pandemic influenza from daily case notification data." Journal of the Royal Society Interface 4 (12): 155-166.

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Coggon, D., Rose, Geoffrey, and Barker, DJP. 2020. "Epidemiology For the Uninitiated." London: British Medical Association.

Correll, Robyn. 2019. "Differences Between Communicable and Infectious Diseases." Accessed 10 February 2020.

Delamater, Paul L., Erica J. Street, Timothy F. Leslie, Y. Tony Yang, and Kathryn H. Jacobsen. 2019. "Complexity of the basic reproduction number (R0)." Emerging Infectious Diseases 25 (1): 1.

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Data References

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Anderson, Roy M., B. Anderson, and Robert M. May. 1992. "Infectious diseases of humans: Dynamics and control." Oxford University Press.

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Centers for Disease Control and Prevention. 2016. "Smallpox."

Centers for Disease Control and Prevention. 2015. "Middle East Respiratory Syndrome (MERS)." Accessed 16 February 2020.

Chowell, G., C. E. Ammon, N. W. Hengartner, and J. M. Hyman. 2006. "Estimation of the reproductive number of the Spanish flu epidemic in Geneva, Switzerland." Vaccine 24 (44-46): 6747-6750.

Coburn, Brian J., Bradley G. Wagner, and Sally Blower. 2009. "Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1)." BMC medicine 7, no. 1 (2009): 30.

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