One of the things about my research over the years is that I have rarely received much attention or notoriety for my methodologies. In the beginning, this was a bit of a problem. But in the long run, this behavior taken by others has enabled me to perfect whatever methods I developed and tried sharing twenty or thirty years ago, enough to apply them new ways in order to come up with new discoveries. That is what this page is all about.

The basic outline for Part II of this work will be is pulled from the NIAID List of Emerging and Re-emerging Diseases that follows.  Part II is the results of my research of the medical geography of these diseases, to be presented much later on another page.

The Rhetorical ‘Why?’

Because this is apparently a hot topic in medical GIS, based on some past job application experiences I have had and a number of calls I received over the years from agencies about this particular disease mapping issue.

Right now, it is possible that the individuals participating in emergent and re-emergent disease surveillance work probably ask the following rhetorical, mental exercise questions:

  1. what will the next pandemic disease be?
  2. who will it most likely impact?
  3. where will it strike?
  4. what age groups will be most affected?
  5. what are the environmental and topographic features possibly related to it?
  6. what are the demographic features related to it?
  7. are certain cultural or sociocultural groups of higher risk?
  8. how will it come in? (air, water, animals, people)
  9. what are the specific signs to watch for?
  10. where will it first touch land?
  11. am I or my family at risk?


Both my population pyramid mapping analyses and my 3D mapping techniques can be applied to this work.

The method I used for analyzing population pyramid age-gender relationships with a disease helps pinpoint those age groups most likely to be impacted by specific diseases we pay heed to. Age is important due to the possibility that some diseases, like the Asian Flu of the early 1900s with fears returning most recently, are fatal mostly to adults instead of the two normally nighest risk groups–the young, and the old. With increased life spans now possible for chronic disease victims we could possibly add the sick and disabled to the groups of those most likely impacted in some morbid or mortal way by infectious disease.

This page also applies my grid mapping/zip-mapping small area techniques with False 3D images, still or rotating, used to present the most important results.  Diseases of a foreign nature have features that can only be effectively displayed using the 3D modeling.  Pacific Rim and Atlantic or Gulf Coast immigration behaviors can be seen for a number of the diseases I evaluated.  For others, it helps to refer back to some historical maps as well to better understand the disease.  In situations where I have applied neither of these techniques the discussion (on Part II page(s)) will remain missing or turn to the standards for discussing such diseases that are already in use.

Note: the mapping presentation portion of this work is not expected to be done until at least next June (2013).


. . . .

List of NIAID
Emerging and Re-emerging Diseases


Group I—Pathogens Newly Recognized in the Past Two Decades

  • Acanthamebiasis
  • Australian bat lyssavirus
  • Babesia, atypical
  • Bartonella henselae
  • Ehrlichiosis [3D]
  • Encephalitozoon cuniculi
  • Encephalitozoon hellem
  • Enterocytozoon bieneusi
  • Helicobacter pylori
  • Hendra or equine morbilli virus
  • Hepatitis C [3D]
  • Hepatitis E
  • Human herpesvirus 8
  • Human herpesvirus 6
  • Lyme borreliosis [3D]
  • Parvovirus B19


Group II—Re-emerging Pathogens

  • Enterovirus 71
  • Clostridium difficile [3D]
  • Mumps virus [3D]
  • Streptococcus, Group A [3D]
  • Staphylococcus aureus  [3D]


Group III—Agents with Bioterrorism Potential

Category A pathogens are those organisms/biological agents that
pose the highest risk to national security and public health
because they

  • Can be easily disseminated or transmitted from person to person
  • Result in high mortality rates and have the potential for major public health impact
  • Might cause public panic and social disruption
  • Require special action for public health preparedness


Category A
Priority Pathogens

  • Bacillus anthracis (anthrax)
  • Clostridium botulinum toxin (botulism)
  • Yersinia pestis (plague) [3D?]
  • Variola major (smallpox) and other related pox viruses [3D]
  • Francisella tularensis (tularemia) [3D]
  • Viral hemorrhagic fevers [3D?]
  • Arenaviruses
    • LCM, Junin virus, Machupo virus [3D], Guanarito virus
    • Lassa Fever
  • Bunyaviruses
    • Hantaviruses [3D]
    • Rift Valley Fever [3D?]
  • Flaviruses
    • Dengue [3d]
  • Filoviruses
    • Ebola [3D]
    • Marburg



Category B pathogens are the second highest priority organisms/biological agents.


  • Are moderately easy to disseminate
  • Result in moderate morbidity rates and low mortality rates
  • Require specific enhancements for
    diagnostic capacity and enhanced disease surveillance


Category B Priority Pathogens

  • Burkholderia pseudomallei
  • Coxiella burnetii (Q fever) [3D]
  • Brucella species (brucellosis)  [3D?]
  • Burkholderia mallei (glanders)
  • Chlamydia psittaci (Psittacosis) [3d]
  • Ricin toxin (from Ricinus communis)
  • Epsilon toxin of Clostridium perfringens
  • Staphylococcus enterotoxin B [3D?]
  • Typhus fever (Rickettsia prowazekii) [3D]
  • Food- and Waterborne Pathogens
    • Bacteria
      • Diarrheagenic E.coli [3D]
      • Pathogenic Vibrios  [3D]
      • Shigella species  [3D]
      • Salmonella  [3D]
      • Listeria monocytogenes [3d?]
      • Campylobacter jejuni [3D?]
      • Yersinia enterocolitica [3D?]
    • Viruses (Caliciviruses, Hepatitis A)
    • Protozoa
      • Cryptosporidium parvum [3D]
      • Cyclospora cayatanensis
      • Giardia lamblia [3D]
      • Entamoeba histolytica [3D]
      • Toxoplasma [3D?]
    • Fungi
      • Microsporidia [3D?]
  • Additional viral encephalitides
    • West Nile Virus [3D]
    • LaCrosse [3D]
    • California encephalitis [3D]
    • Venezuelan equine encephalitis [3D]
    • Eastern equine encephalitis [3D]
    • Western equine encephalitis [3D}
    • Japanese Encephalitis Virus [3D}
    • Kyasanur Forest Virus [3D]


Category C pathogens are the third highest priority and include emerging pathogens that could be engineered for mass dissemination in the future because of

  • Availability
  • Ease of production and dissemination
  • Potential for high morbidity and mortality rates and major health impact


Category C Priority Pathogens

  • Emerging infectious disease threats such as Nipah virus and additional hantaviruses
  • Tickborne hemorrhagic fever viruses
    • Crimean-Congo Hemorrhagic fever virus [3D]
  • Tickborne encephalitis viruses [var. 3D]
  • Yellow fever [3D]
  • Tuberculosis [3D], including drug-resistant TB [3D]
  • Influenza
  • Other Rickettsias [3D?}
  • Rabies [3D]
  • Prions
  • Chikungunya virus
  • Severe acute respiratory syndrome associated coronavirus (SARS-CoV) [3D?]
  • Antimicrobial resistance, excluding research on sexually transmitted organisms*
    • Research on mechanisms of antimicrobial resistance
    • Studies of the emergence and/or spread of antimicrobial resistance genes within pathogen populations
    • Studies of the emergence and/or spread of antimicrobial-resistant pathogens in human populations
    • Research on therapeutic approaches that target resistance mechanisms
    • Modification of existing antimicrobials to overcome emergent resistance
  • Antimicrobial research, as related to engineered threats and naturally occurring drug-resistant pathogens, focused on development of broad-spectrum antimicrobials
  • Innate immunity, defined as the study of nonadaptive immune mechanisms that recognize, and respond to, microorganisms, microbial products, and antigens
  • Coccidioides immitis (added February 2008)
  • Coccidioides posadasii (added February 2008)


NIAID Category C Antimicrobial Resistance—Sexually Transmitted Excluded Organisms:

  • Bacterial vaginosis,
  • Chlamydia trachomatis [3D],
  • Cytomegalovirus [3D],
  • Granuloma inguinale,
  • Hemophilus ducreyi,
  • Hepatitis B virus,
  • Hepatitis C virus [3D],
  • Herpes Simplex virus,
  • Human immunodeficiency virus [3D?],
  • Human papillomavirus [3D?],
  • Neisseria gonorrhea [3D?],
  • Treponema pallidum [3D],
  • Trichomonas vaginalis [3D]


The goal of implementing a GIS for the surveillance of the above diseases is basic.

With a good GIS we can:

  • discover new spatiotemporal relationships and then test them using traditional protocols and paradigms
  • understand local disease behaviors
  • uncover periodic patterns or recurring spatial patterns not discovered previously
  • correlate more details of the complex environment with the diseases that prevail and recur
  • understand disease influx,  and flow behaviors
  • develop ways to predict theoretical disease patterns
  • come up with ways to analyze two areas normally not reviewed together due to distance or some other very unique difference
  • develop better preparations and prevention plans
  • standardize analytic approaches and the production of protocols to reacting to diseases as if they were natural disasters



Some of the following questions are actual research questions I answered using this routine.  Others are more rhetorical.

  1. Why is Poliomyelitis clustered around the eastern Great Lakes?
  2. Why are the peaks for Yellow Fever so far north of the southern borders and even north of the Mason-Dixon line?
  3. What is the most likely disease to makes its way into this country from Mexico? Central America?  South America?  U.S.S.R?  China?  Australia?
  4. Where is the most susceptible town or city for disease in-migration by way of inland routes?
  5. Kuru, Chiclero’s Ear and Ebola  all demonstrate what kind of unexpected spatial behavior?
  6. How do you best explain two completely different outbreak area for Rhinosporidiosis?  Define how human ecology and natural ecology or environment could each be involved in these behaviors.
  7. Which disease pattern demonstrates a very strong longitudinal barrier?  What natural features might this be related to?
  8. Which disease best demonstrates the role of a mountain range in preventing its diffusion?
  9. Brazilian Blastomycosis and Asiatic Cholera El Tor share a unique feature.  Geographically and based on political country features, how might you explain this?  Give at least one each for:  human focused explanation(s) and topographical-climatic-ecological explanation(s).
  10. The Pacific Northwest peak in Hanta Virus cases originated in Arizona.  This displacement is due to what natural phenomenon?
  11. Which of the following pairs of disease name-city name has the greatest spatial correlation?  Arizona ; St. Louis Encephalitis; Chicago Illness; California encephalitis; Rocky Mountain Spotted Fever.
  12. Of the diseases mentioned in the above questions, which one would be of highest concern were you performing studies just on people migration into the U.S., in particular those who bypass inspection sites from each of the following:  Peru?   Eastern Mexico?  Eastern South America?  Africa?
  13. In spite of a strong relationship to the Pacific Rim, Russian born diseases predominantly enter by way of the east coast.  This differs from the patterns noted for some Chinese diseases.  Define several sociological reasons for why this is the case.
  14. Considering the causes and mechanisms of transfer for each of these diseases in the above questions, when poverty and drought prevail, which disease is most likely to be impacted and demonstrate migration due to these two changes?
  15. Bimodal cities demonstrate land and water, animal and people in-migration patterns.  In what city do both inland and maritime routes merge to make this one of the most infectious cities in the U.S.?

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