researchHeterogeneities
- Overview: disease in space and time
- Hetereogeneity in social space
- Differences between individual hosts
Overview: disease in space and time
CIDD researchers are investigating how the epidemiology and evolution of host-pathogen interactions are affected by geographical, behavioural, physiological and genetic variation in hosts and disease agents.
We are interested in how the spread and evolution of disease is affected by spatiotemporal heterogeneities in such variables as:
- Intrinsic characteristics of the disease agent and the host (e.g. host immunity)
- Extrinsic characteristics of the environment, and their effects on hosts and disease agents (e.g. contact rates of hosts may be affected by spatial distribution of their resources)
- Interactions between disease agents (e.g. a host's adaptive immunity may be boosted by prior infection with a related pathogen)
Mathematical modeling based on data from real epidemics allows us to identify how epidemic dynamics are affected by geographical variation in key parameters such as transmission rates. This in turn allows us to investigate the likely impact of alternative control strategies on the spread of disease.
For instance, Grenfell, Bjørnstad and coworkers have modeled measles epidemics in England and Wales to explain:
- The critical community size required for infection to persist during epidemic "troughs"
- How mass vaccination desynchronizes measles epidemics — after vaccination, epidemics in neighboring towns no longer occur concurrently
- How and when "waves" of disease travel from large urban centers to smaller towns
We are currently extending this work to examine the dynamics and control of measles in developing countries, where birth rates are higher and seasonal differences in transmission are likely to differ.
Hetereogeneity in social space
In many host species, the frequency and nature of contacts between infected and susceptible indiduals is affected by social interactions that in turn are affected by the host's environment (biotic and abiotic) — and in humans, culture and politics. All these variables may vary geographically, and through time.
We are using network models to explore the impact of host social interactions on disease spread and evolution:
- In human societies. Albert, Grenfell and colleagues are examining how diseases might spread across simple networks for human societal contacts. We are investigating interactions between network structure and epidemic dynamics: effects of network structure on epidemics, and vice versa. We are bringing models and empirical observations together using measles and influenza as case studies.
- In wildlife populations. Perkins and collaborators are characterizing social networks in rodents infected with up to five different parasite species. Infection status and contact rates of hosts are known — enabling us to examine transmission heterogeneities in detail.
November 2005: CIDD hosted a two-day workshop on the spread of pathogens of social animals, with particular reference to the morbillviruses
Differences between individual hosts
Host networks are often highly clustered, so some hosts are responsible for many more infections than others. This would be true even if all hosts were otherwise identical. However, hosts are not all the same: individuals differ from conspecifics in many ways, including gender, physiological characteristics and behavior. These differences can influence the dynamics and control of epidemics, and can affect host-pathogen coevolution.
Some hosts are much more likely to transmit infections. The existence of such superspreaders is perhaps best known in the human diseases SARS and HIV. However, superspreaders play an important role in many systems, including wildlife diseases. Superspreaders are implicated in the spread of vector-borne diseases as well as directly-transmitted ones. Hudson and coworkers are investigating the role that superspreaders play in the transmission of wildlife disease — including some emerging diseases — and the implications for control strategies.