Chain: The Virgin Field

A. LESSON #1: The Virgin Field
Most diseases begin with what is called “the virgin field”—a scenario in which humans have no natural or man-made immunity to the disease. To see the progress of a disease in a particular community, start by predicting how many sick days will be reported when you run the Kold disease through a medium-sized population, and record your prediction in the data table. In this first run-through, we’ll assume that the population does not move around the field; they interact with their neighbors, but do not travel long distances. Make sure the tableau is set to virgin and run the simulator to 100 days (click on Run button) three times and answer the following:

1. Do you get the exact same results each time? How do the results compare to each other and to your prediction? What factors might contribute to susceptibility to the disease?

2. If the contagion rate is calculated as the number of new cases per day per total population, what would the average contagion rate be for Kold?

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Unlike some of the other interactive labs, this model has some randomness built in to reflect the real spread of a disease, which is a matter of probabilities. Despite this variability, you can get a sense for what effect each factor has on disease spread. Before running the simulator, predict whether the sick days per capita will be higher or lower with low population density. Record your prediction in the data table and then run the simulator to 100 days three times, recording the data each time. Make a prediction for high population density; record it in the data table, and run the simulator three times, recording that data in the table. Answer the following:

3. What could be done to prevent the spread of disease in a low population density?

4. What kinds of challenges would high population density present to these precautions?

5. If contagion rate is calculated as the number of new cases per day per total population, what would the average contagion rate be for Kold?

Population mixing in a contagious area is analogous to increasing population density. Both increased density and increased movement of people bring more contagious people into contact with susceptible people, thus increasing the spread of disease. The rate of spread also has a lot to do with the nature of the disease: how long a sick person is contagious, the method of transmission (air, water, food, diarrhea), the transmission rate (i.e. the chance that any particular encounter will transmit the disease), and the death rate due to the disease (Kold is nonlethal).

Contagion or transmission rate is considered epidemic if it exceeds the norm, which differs depending on the disease in question. Less lethal diseases will have higher contagion rates without a sense of emergency (such as the common cold or the common flu) while a small increase above the norm in diseases such as tuberculosis, HIV, Ebola, or other such highly lethal viruses, results in a state of emergency. In addition, there are major differences between bacterial and viral illnesses. Antibiotics work for bacterial disease, and sometimes vaccines can be developed for viral disease. There isn’t always a quick fix to an illness, however, since both bacteria and viruses mutate and alter their genetic makeup, making previous treatments non-effective.

B. Lesson #2: Vaccine
In this lesson we’ll look at “Impfluenza”. You’ll start by examining the disease’s effects in a virgin field. First, compare the disease details (found in Simulation Parameters) of Impfluenza and Kold so that you know the differences between the two diseases. Based on these differences and what you know about Kold, predict the sick days per capita for Impfluenza at medium population and medium mixing. Record your prediction in your data table. In the simulator, select Lesson Vaccination, then set Countermeasure to None to provide the virgin field effect. Then run the simulator to 100 days three times. Answer the following:

6. Was your prediction correct? If not, why not?

7. Notice that Impfluenza, unlike Kold, has a death rate. How many people
die, on average, when you run the simulator on the virgin field?

8. How does a death toll change precautionary factors? What kinds of precautions might you take with Impfluenza that you might not have taken with Kold?

9. Would you consider Impfluenza’s death toll to warrant a “state of emergency”? How high would the numbers have to be for this to happen?

In this step we’ll look at the effects of using a counter-measure by vaccinating a certain percentage of the population against Impfluenza. This represents a real-life scenario, where the country vaccinates a certain portion of its population against the expected influenza strains for that year. Change the tableau in the upper right corner of the simulator from Virgin to Vaccine. Predict and record the sick days per capita at medium population and medium mixing while the vaccine is in use. Run the simulator three times and record your data.

Compare your results to the table in Lesson 2 Step 1. Then change the parameters to high population and high mixing with vaccine in use. Predict what will happen and run the simulator three times, recording your data for each run. Answer the following:

10. For the first set of parameters (medium/medium), how does the vaccine reduce sick days? How large a percentage of the population would have to be immunized in order to bring the sick days per capita reliably below 0.1 per capita?

11. How does using vaccination compare to changing the mixing or population density of the field?

Vaccinating a population has a similar effect to changing the population density. An immune person is no longer a vehicle for transmitting the disease, thus lowering the effective density of the population. Since we can’t control population density in most cases, vaccination is one of the best means to prevent the spread of disease, not just to the vaccinated individuals, but to the population as a whole. Stopping or slowing the mixing of people, via quarantine, or closing businesses and schools, is also an option, with similar net effect. Although the common cold doesn’t have a vaccine available, you may choose to return to the simulator and experiment with the possibilities of immunizing a percentage of your population to Kold.

There are four main types of vaccine: those containing a killed pathogen, those containing live strains of a pathogen, those containing toxoids (the compounds produced by a pathogen that cause a human reaction, as opposed to injecting with the microorganism), and those containing subunits of the pathogen (such as antigens or other proteins that create part of the physical makeup of the microorganism). Newer genetically targeted vaccines are being developed, but although preliminary tests look very positive, the constantly mutating genetic makeup of the more dangerous diseases prevents us from distributing a vaccine without caution.

C. Lesson #3: Counter Virus
What if we were to face an outbreak of a disease such as avian flu? In 1918-1919, the world experienced a pandemic unlike anything seen since the Black Plague of the mid-14th century in Europe. The Spanish Flu, or La Grippe, killed somewhere between 20 and 40 million people worldwide. In America alone, 28% of the population was infected with the virus, the vast majority of whom where between the ages of 20 and 40. There was no method in place at that time to deal with a pandemic with such a high contagion rate as well as a high death rate. The disease was new to the world in both form and function. In this lesson, imagine a new disease for which there is no vaccine and the death rate might be very high. Examine the details of Red Death and predict how many sick days per capita and the death toll of this new disease in low population and low mixing and record the prediction in your table. To see if you’re correct, set the tableau to virgin, run the simulation three times, and record your data. What if you had a high population and high mixing?

Record your prediction, change the population and mixing settings to high, and run the simulator three times. Record your data and compare with your prediction. Calculate contagion rate for each scenario (rural: low/low,
urban: high/high). Would either of these scenarios be considered epidemic? Why or why not? What practical, precautionary measure would you suggest for each situation based on your calculated contagion rates?

Let’s assume that there hasn’t been time to develop and distribute a vaccine. However, we may not be dealing with an entirely virgin field. There may be a disease similar to Red Death, which would provide immunity without a high death rate. (A situation like this occurred in the days of smallpox, when it was discovered that the similar but less lethal cowpox made people immune.) If we release this “reduced” virus into the population before Red Death comes along, some people will become sick and may even die from the reduced virus, but would the immunity provided make up for that? This final scenario may not be very realistic, as we don’t fight diseases by releasing other diseases into a population. However, it does show how the different aspects of a disease (sick days, transmission rate, death rate, and immunity) interact. Change the tableau to countervirus.

Choose one of the “C-Viruses” from the Countermeasure pull-down and review the features of that virus with its details button. Which of the three Countermeasure viruses (slow, medium, or fast) will do the best job in reducing the death toll in the population while also minimizing sick days per capita? Make a prediction based on everything you’ve learned about the effects of vaccination and disease transmission, record it, and then run the simulation three times for each of the C-Virus choices. Answer the following:

12. Can you think of any environmental factors that might contribute to the spread of the disease?

13. How would a counter-virus affect these environmental factors and/or the environmental factors affect it?

14. Can counter-viruses be used to fight disease internationally or would they be most effective at a local level?

15. What could health officials do to insure that the highest number of those at risk around the globe are receiving the most effective preventative health care possible?

As you’ve seen, diseases travel through populations in fairly predictable ways. Population density, or other factors that have the same effect as changing density, is one of the key features of disease transmission. If you haven’t already done so, you should complete the Demographics lab and consider how exponential population growth in certain developing countries might affect disease propagation and how this might be countered. Diseases like HIV, hepatitis, and avian influenza are currently spreading rapidly in developing countries.

On average, the CDC maintains a list of 12 diseases that are epidemic or pandemic and highly lethal. Although the list does not change and a majority of the diseases are found in sub-Saharan Africa, a threat remains constant to the world as a whole. Differences in health care, the availability of clean water (or water in general), and socio-political agendas between first and third world countries often define how quickly disease spreads and to what extent those afflicted may find care and respite. Consider the following: 16. In addition to the efforts of the CDC and WHO, what might be done to either contain virulent disease or prevent its onset?

17. In your opinion, what is the greatest viral or bacterial threat to your local population and what precautions might be taken to avoid contagion?

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