SchistoSim : A CAL PROGRAM ABOUT SCHISTOSOMIASIS

Contents of this Page

• Introduction to schistosomiasis (bilharzia, bilharziasis).
• The Schistosoma lifecycle and SchistoSim.
• Suggestions of disease control simulations to try.
• Using SchistoSim.
• Summary of useful information to use when running SchistoSim.
• The SchistoSim program.
• Acknowledgements.

Introduction to Schistosomiasis (Bilharzia, Bilharziasis)

Schistosomiasis (also known as bilharzia or bilharziasis) is a major parasitic disease of humans. It is thought to infect some 200 to 300 million people across Africa, South America, the Caribbean, the Middle East, China and Southeast Asia. The major causes of the disease are Schistosoma mansoni, Schistosoma japonicum and Schistosoma haematobium. Transmission of the schistosomes to humans is via contact with fresh water that contains the parasite's intermediate snail host and that has been contaminated by urine or faecal material from infected individuals.

The aim of the introductory material on this page is not to discuss the biology of schistosomiasis (bilhazia, bilharziasis) in any great detail. The aim however is to use the epidemiology of the disease to bring out some of the important issues arising during the planning and execution of parasite disease control programs. For more information on the biological and medical aspects of bilharzia please consult the World Health Organisation's site. Additional support material can also be purchased from the Wellcome Trust.

Epidemiology and its mathematical models have long been a useful tool in the planning of disease control programs. Such models allow one to understand how a parasite population will respond to a treatment program before actually undertaking it. The results from the studies into the epidemiology of schistosomiasis has been no exception to this rule. One of the earliest models for schistosomiasis was developed by MacDonald (1965) and further refined by Anderson and May (1985). SchistoSim is based on the Anderson and May model and enables you to change a variety of parameters to model different transmission states and to explore the effects of different control strategies on the mean number of adult parasite worms/person versus time in years. We hope that this computer assisted learning (CAL) package will be of help to educators looking for schistosomiasis teaching material and will prove useful for those with an interest in epidemiology, parasite epidemiology and population biology in general.

The Schistosoma Lifecycle and SchistoSim

The basic life cycle of the schistosome parasite is as follows:

1. The adult parasitic worm resident in its human host sheds eggs into the human faeces or urine.
2. Urine or faecal contamination of water results in the eggs hatching to give miracidia.
3. The miracidia then infect the snail intermediate host present in the water.
4. After a delay the parasites in the snail start shedding cercariae into the water.
5. The cycle is completed when humans are exposed to the contaminated water and are infected by the cercariae which then develop into the adult worms.

As in all such models a number of simplifying assumptions have had to be made about the life cycle in order to simulate it. As well as the starting average number of adult worms/person, the main factors that can be altered in SchistoSim are the adult worm's lifespan in the human host, the contamination factor, the lifespan of the snail intermediate host and the exposure factor. The factors that you can change in SchistoSim are related to the parasite lifecycle in the diagram and accompanying descriptions below.

Worm lifespan

The average lifespan of the adult worm in its human host. Reducing this factor simulates control of the worm by chemotherapy. Thus to simulate an annual drug treatment regime, which kills the worms once a year, set this value to 1 year. A six monthly treatment requires a value of 0.5 years. Range the user is allowed to specify: 0.001 to 3.5 years (default = 3.5 years).

Contamination factor

The contamination factor represents the number of eggs passed from man to water, the lower the value the better the level of hygiene. Estimates from control programmes suggest that the contamination factor can be reduced by a factor of 10 by improved sanitation and education. Thus in a simulation of the effects of a hygiene program, try reducing the default value of this factor down to a tenth of its original value. Range the user is allowed to specify: 0-1 (default = 0.001)

Snail lifespan

The average lifespan of snails that are not shedding parasites. Reducing this factor models eradication methods directed at eliminating snails such as the use of molluscicides. Control programmes suggest that mollusciding can reduce the snail factor to 10 days. Thus when simulating the effect of controlling the parasite by killing the snail host, reduce this factor from its default value down to 10 days. Range the user is allowed to specify: 0.001 to 39 days (default 39 days).

Exposure factor

The exposure factor represents the fraction of the human population in water contact. This factor can be used to simulate control measures aimed at changing personal hygiene habits and providing safe water supplies. Surveys suggest that educational programmes can reduce the exposure factor by 50% or more. Thus when simulating the effects of control programs aimed at reducing human contact with contaminated water reduce this factor from the default value down to half its original value. Range the user is allowed to specify: 0-1 (default = 0.01).

Suggestions of Disease Control Simulations to Try

The default values in the program give a stable mean adult worm load of 25/person. The program enables you to investigate the effects of changing the different factors on the final worm burden (mean worms/person) and on the rate at which the changes take place. Some suggested things to try include:

• You can investigate the effects of systematically changing individual factors and the effects of combining factors as means of controlling the disease, e.g. look at the effect of adjusting the adult worm lifespan in humans to simulate the effect of drug control of the parasite.
• By reducing the initial adult worm population values you can model the effects of what happens when you stop a control programme or when the disease enters a new area.
• If the parasite population is reduced to a sufficiently low level (the break point), transmission can no longer be sustained and the parasite population will become extinct.

Using SchistoSim

User inputs relating to the worm lifecycle.

These were described above.

Start mean worms/person

The mean number of worms/person at zero time. Range the user is allowed to specify: 0.0001 to 900 (default value 25).

Years to display

The length of time over which to calculate (numerically integrate) and plot the results of your changes. Range the user is allowed to specify: 1-50 years (default = 20 years).

"Default values" button.

Pressing this button sets the inputs back to their default values and displays a message to this effect in the message area (bottom of the program screen). These values are for a population which is stable over time (i.e. at equilibrium) and provide a reference population for looking at the effects of changes in different parameters on the population dynamics. The defaults are also the values in the input fields when the program is first loaded.

"Plot on this graph" and "plot on new graph" buttons

Both of these buttons have basically similar effects except the first plots the calculated values on the currently displayed graph (in a new colour) to create an overlay whilst the second plots the results on a new graph.

When either button is pressed the following sequence occurs:

1. The values of each input field are read and checked. If an input is invalid a message is displayed in the message area and the calculations are aborted.
2. The calculations are started and as they progress, years and mean parasites/person data are displayed in the message area (bottom of the program screen).
3. If the mean parasites/person exceeds 900 this is outside the program's range and a message is displayed in the message area. The calculated data up to that point are plotted on the graph.
4. If the calculations run to completion then the year, mean parasites/person values at the end of the calculation are shown in the message area and the data are plotted.

Trouble shooting

We have tested SchistoSim on a wide variety of computer systems and found very few problems. The problems we have encountered and the cures for them are described on the Trouble Shooting page.

Summary of Useful Information to Use when Running SchistoSim

Please note that this table is included here for easy reference with the main program descriptions above. A copy of it is also supplied above the SchistoSim program itself.

The graph is a plot of the average number of adult worms per person versus time in years.

The SchistoSim Program

To ensure that SchistoSim is easy to use on monitors of various resolutions two copies of the program are supplied, one for lower resolution displays the other for higher resolutions. The lower resolution version runs SchistoSim in an area of 700 pixels across by 370 pixels down. At the higher resolution it runs in an area of 900 pixels across by 450 pixels down.

Please pick one of the links below to run the program.

Acknowledgements

This program was written in Java which was obtained free of charge from Sun Microsystems. The data generated by the simulation are plotted as 2D graphs using the Java package " Java Graph Class Library (Version 2.4)" obtained under GNU licence from "The Java Repository" which was at: http://java.wiwi.uni-frankfurt.de/ . The core program classes, GUI and numerical integrator etc etc were written by Dr. Christopher Davey (cld@aber.ac.uk).

We wish to thank the Wellcome Trust for financial support during this project. We also wish to thank the many people who tested and commented on this program.