Transmission
Dynamics
Environmental
determinants |
Human
epidemiology
A
goal of our transmission modeling is to discern whether parasite
abundance is mediated more by environmental forces or human activity
patterns, and thus whether ecological studies or human activity
surveys are more informative in directing selective control.
Our Transmission model is a synthesis of our research
efforts and integrates past and present empirical and theoretical
data from two primary research areas: environmental risk and human
epidemiology. Our model gives us insight into the complex
interactions between environmental determinants of risk and human
behavior
Multi-risk group transmission model
Flowchart to show structure of a multi-risk group model,
which specifically incorporates the field data. The host population
is divided into a number of groups in terms of risk of infection
defined by residence and occupation.
Disease
Modeling
Central to our quantitative understanding of schistosomiasis
transmission is our worm-burden model, which uses ordinary differential
equations of disease transmission in risk groups defined by residence
and occupation. The model incorporates temperature- and
precipitation-dependent seasonality of infectious stages, snail
population dynamics, and seasonal patterns of human water contact
specific to the local agricultural setting. The model’s parameters are
separated into two main subsets, those associated with the general
biology of the parasite and its lifecycle in the human and the snail and
those associated with directly measurable features of disease status in
the local population or relevant aspects of the local environment.
In this regard, the model is structured and parameterized to take
maximum advantage of data that can be collected in rural
China
by conventional methods.
For
example, it includes a statistical model for egg excretion to the
environment by each risk group which is based on local population
surveys of the prevalence and intensity of infection. The second
element of the framework of analysis relates to the strategy for
parameter estimation and calibration to local conditions. We
propose a Bayesian approach in which parameter estimates are refined
over time by methods employing extensive computer simulations. An
early analysis of data collected between 1987-1989 in endemic villages
near
Xichang
City
in southwestern
Sichuan
provides encouragement that parametric uncertainty can be reduced to
levels adequate to explore effective control strategies.