1. Classifying infectious diseases Infectious Diseases Bacterial, e.g. cholera Viral, e.g. HIV/AIDS Other (helminths Protozoa, fungi), e.g. bilharzia ….one option is to classify diseases by the agent or pathogen involved. ….another option is to classify diseases according to their history Emerging, e.g. HIV/AIDS Re-emerging, e.g. TB Existing, e.g. measles

2. Classifying infectious diseases Infectious Diseases ….a third option is to classify diseases by their mode of transmission Vector-borneNon vector-borne Vector-borne diseases are transmitted to humans via an intermediate organism, e.g. malaria via mosquitoes or trypanosomiasis via the tsetse fly There is a substantial literature on vector-borne disease and GIS, but here we consider only non-vector borne disease. What about geographical patterns of infectious disease transmission?

3. Infectious disease diffusion: Imagine the entry of a given infectious disease into a country. For example, cholera-causing bacteria might have spread between countries in bilge water from ships or arrived via an infected individual. Cholera might hypothetically have entered Mozambique through shipping arriving in the port of Beira.

4. Three theoretical types of spatial diffusion are recognised. Contagion Diffusion is localised and related to Cartesian (straight-line) distances. Subsequent infection Initial infection In our Mozambican example, visitors to Beira might ingest ice contaminated with cholera at a drinks stall, then return to their homes in surrounding villages, so transporting the V. Cholerae bacteria and spreading the disease.

5. Alternatively, the visitors to Beira might have travelled from further afield. The disease might spread initially to the major cities in the urban hierarchy…. Mozambique Zimbabwe Beira Mutare Indian Ocean The initial person to contract cholera in Beira is referred to as the index case.

6. ..and then on to smaller neighbouring settlements. This hierarchical diffusion could be modelled by ranking settlements according to population size.

7. Alternatively, network diffusion may take place as infected individuals move by rail….

8. …or through the road network. Internationally, air travel can also be important

9. A given disease outbreak may exhibit one or more types of diffusion. Different phases of an outbreak may show different patterns of diffusion. A Mozambican cholera epidemic might show city-to-city spread initially (hierarchical diffusion), then contagion around these urban centres.

10. Distance: -Straight-line -Using roads, railways Settlement size So in theory disease spreads from an infected population to a susceptible population via 3 routes: contagion, network & hierarchical diffusion. From a GIS standpoint, the central factors affecting this spread can be modelled using settlement size (hierarchical diffusion) and distance (contagion / network diffusion). The relative balance of these factors depends on the specific socio-economic and cultural context of the outbreak. Infected population Susceptible population

11. In practice, all 3 types of diffusion can be expressed mathematically in a social gravity model. Imagine 2 towns – O, an origin town with disease cases and T, a destination town that is disease free. The key term in a social gravity model is typically as follows: Interaction with town T = (population T ) a / exp(-b. Distance OT ) So: Interaction with town T = (20,000) a / exp(-b. 50) Town 0 – with diseaseTown T – without disease Distance between O and T = 50km Town T has a population of 20,000.

12. Interaction with town T = (20,000) a / exp(-b. 50) If a = 0 and b > 0, then regardless of the population, the numerator is always 1. The population effect is always the same, so all that matters is distance – i.e. contagion diffusion: Interaction with town T = (20,000) 0 / exp(-b. 50) = 1 / exp (-b. 50) Town 0 – with diseaseTown T – without disease Distance between O and T = 50kmTown T has a population of 20,000. If b = 0 and a > 0, then the denominator is always 1, regardless of the distance between the two towns. The distance effect is constant, so all that matters is population size – i.e. hierarchical diffusion. Interaction with town T = (20,000) a / exp(0) = (20,000) a / 1 = (20,000) a

13. If both a = 0 and b = 0, then the interaction term is always 1, regardless of population or distance. So, disease spread is random: Interaction with town T = (20,000) a / exp(-b. 50) = 1 / 1 = 1 So, if you can estimate a and b statistically based on existing population, disease, and geographical data, you can distinguish between hierarchical and contagion diffusion. Town 0 – with diseaseTown T – without disease Distance between O and T = 50kmTown T has a population of 20,000.