Difference between revisions of "Team:Uppsala/Model"
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Revision as of 14:24, 15 October 2018
Modeling
Survey modeling
One of this years model projects was to create a model to interpret answers from a survey. The survey dealt with horse owners awareness of the large and small strongyle, and everything related to them. The goal of the model was to process the answers from the survey into usable data and to make it possible to look after correlations between different variables.
Anthelmintic Resistance Model
To make sure the unnecessary use of anthelmintics is minimized we have created a model that compares regular and optimal usage of anthelmintics. When referring to the optimal use of anthelmintics, this means that the horse only receives treatment when the amount of parasites exceeds a certain threshold. The results shows that an optimized use of anthelmintics would lead to less anthelmintic use. This model shows us that a worm buster would be a needed tool to combat the issue of growing resistance.
Anthelmintic Resistance Model
Due to excessive use of anthelmintics against various parasites in cattle, the resistance to anthelmintics in nematodes has grown and has become more extensive than the current situation in horses.[1] In the early stages of our project we knew that the current problems brought by small strongyles were not yet as extensive in Sweden.[2] Therefore we made a model of the growth of nematodes in horses and on pastures, to see the difference between regular use and an optimized use of anthelmintics. The mean usage of anthelmintics in sweden is 3.2 times per year, and there is no common time to use the anthelmintics.[3] However, the treatment occurence that was later used in the model, was information that was retrieved from our own conducted market analysis, which states that the most prevalent use of anthelmintics is two time per year. The optimized use would be to only use anthelmintics when it’s needed, e. g. when the amount of parasites in the horse exceeds a threshold. Some of the variables that the model takes in to account is the usage of anthelmintics and the amount of horses, the temperature dependence of the parasite egg to develop into a larva and the amount of horses on a pasture to receive the results.
The model was inspired by a model that looks into how effective anthelmintic treatments are on sheep at pasture.[4] Due to the overuse of anthelmintics in sheep, the efficiency of the medicines have decreased resulting in sheep dying of starvation because of the excessive amount of parasites in the gastro-intestinal area. This causes big economical losses for sheep owners.
With this model we intend to improve our worm buster project since it would prevent anthelmintics overuse by determining the optimal amount of treatments. This would be complementary to the worm buster, which helps avoid wrong dosage of anthelmintics while treating the horse, in combination both tools help to decrease the overuse of anthelmintics and thus prevent resistance development.
Design
The model is built upon an ordinary differential equation (equation 1). Matlab is used for the calculation of the model, where Matlab's inbuilt function ode45 is used. The programs that were made and used for the calculations can be found here. At each step of the calculation, the program is looking at given temperature data for a year and other variables that will differ depending on the environment. These variables are described in table 1. Two extreme situations were analyzed, one where a horse has a parasite count that hits the threshold of maximum amount (100 000) of parasites and the second where the horse doesn’t have any parasites (although this is a very unlikely scenario). [5] The months at which anthelmintics were used in the regular use, was set to April and October, which are two months when it's likely that a horse owner gives anthelmintics to its horse. [4] The MATLAB code for the model can be found here.
Because the development of eggs to larvae are temperature dependent, an linear equation for calculating the probability of an egg to develop into a larva was created. The equation is based on information about how long it takes for an egg to develop into an larva.[6] This equation was created by first dividing all the given temperatures by the highest temperature. A linear regression was made with these values and the temperature. The probability of an egg to develop into a larva in the earlier mentioned study was at 0.0275. If this value is assumed to be the mean probability, then it’s 20 times smaller than the mean probability in this trend line that was received. Therefore the constants in the equation was divided with 20 to match it. The temperature data was used was from the Uppsala Aut weather station measurement of mean temperatur during 2017. [7]
β is the amount of square meters grass a horse eats per day, a horse eats approximately five times more than a sheep. [8][9] Beta decreases if the amount of parasites in the gastrointestinal area of the horse exceeds an amount that is non-detrimental to the horses health. The model takes into account a 70% decrease in food intake.[4] However, the only information about when a horse should be treated with anthelmintics is based on the egg count in horse feces and not on the amount of parasites in the horse. Therefore an arbitrary threshold was set to 100 000.
The constants µ, mortality rate of adult parasites, λ, mean rate at which an adult worm produced eggs and d, the probability of an larva to develop into a adult worm, were the same as in the model that served as inspiration for this one.[4]
Results
With the same starting conditions, and only varying when anthelmintics are used, four datasets were obtained. The four different data sets show how the density of parasites per hectare pasture and how the amount of parasites per horse varies for both the regular and the optimized use of anthelmintics. As mentioned in the method, two extreme points of starting values were used, which was when L0, the density of worms per ha pasture, is 1000, and A0 is either 10000 and 0. The results of the calculations are shown in the graphs.
How the density of parasites per hectare changes with optimized use of anthelmintics. The data fluctuates because the temperature changes day to day, which affects the development of an egg turning into a larvae. This can also be seen during the summer months, where the parasite density increases due to higher temperatures.
The amount of parasites per horse with optimized use of anthelmintics. Every time anthelmintics are used there is a sharp decline in the graph. Over a five year period, the regular use of anthelmintics, would be 10 times. For the optimized use, anthelmintics would be used 7 times. The amount of parasites in the horse are lower for the regular use of anthelmintics, but that is understandable, because anthelmintics is used more often. However, in the optimized use, the amount of parasites never exceed an amount which will affect the horse negatively.
The model shows that with an optimized use of anthelmintics, only seven cures would be used, which is three cures less than with the regular use. The anthelmintics resistance in parasites increases with the use of anthelmintics, which can be unnecessary in some cases. Which also can be seen in these model. However, this model doesn’t take into account the encysted larva in the mucosa, this is due to lack of information about when the larva choses to encyst, and when it decides to burst to from the cyst. The communication between the adult parasites and the encysted larva is still a mystery that has to be solved to be able to form a even more accurate model. Although there’s some shortcomings, the model clearly shows that using anthelmintics only when it’s needed, would decrease the usage of the anthelmintics, and in the long run restrain the growing resistance against different anthelmintics in the parasites.
Survey modeling
Introduction
When we had chosen this years iGEM project, we wanted to investigate how a potential bacteria based product could be integrated in the community and therefore a survey was conducted and sent out to people in the horse field. Based on the answers we would get more insight into people's awareness of the large and small strongyle, and everything related to them. The marketing analysis was done in collaboration with the human practice part of the project.
In the modeling part of the project, we have created a model to interpret the answers we received from the survey. The purpose of the model is to look for correlation between different variables, for example; is there any correlation between how concerned people are about resistance to strongyle and if they are using any preventive treatments against strongyles?
The knowledge acquired from the model could be used to, for example, investigate how knowledge about anthelmintic resistance could be disseminated in a efficacious manner. Also, to find a potential target market and to give an idea of how a potential product could be marketed.
Design
In order to turn the results from the survey into usable data, we first needed to transform all the answers that were in text form to numerical form, and filter out incomplete values, as well. This was done to be able to analyse it further. The survey consisted of several questions that gave answers as discrete data. If we instead, had worked with continuous data, a regression analysis could have been performed [10]. Now, when using discrete data, we needed to find a suitable way to handle this. We chose to use a type of discrete scatter plot with valued points [11]. Every time a coupling occurs the size of the points will increase, see an example of a plot in figure 1. In this case, a coupling refers to when a x and a y value exist together. In this way the importance of a couple can easily be visualized. To suspect a correlation, the largest sized points should appear in some kind of linear, exponential or other interesting pattern.
Result
To download the model written in R, click here. Figure 1 shows a descriptive discrete scatter plot with valued points and figure 2 shows an application of the latter plot based on real survey data.
Figure 1. Descriptive discrete scatter plot with valued points. The numbers in the points describes the number of couplings.
Figure 2. An example of an discrete scatter plot with valued points based on data from the survey.
[1] Traversa D, Klei TR, Iorio R, Paoletti B, Lia RP, Otranto D, et al. Occurrence of anthelmintic resistant equine cyathostome populations in central and southern Italy. Prev Vet Med. 2007;82: 314–320.
[2] Lind EO, Kuzmina T, Uggla A, Waller PJ, Höglund J. A field study on the effect of some anthelmintics on cyathostomins of horses in sweden. Vet Res Commun. 2007;31: 53–65.
[3] Lind EO, Rautalinko E, Uggla A, Waller PJ, Morrison DA, Höglund J. Parasite control practices on Swedish horse farms. Acta Vet Scand. 2007;49: 25.
[4] Pech CL, Doole GJ, Pluske JM. Economic management of anthelmintic resistance: model and application. Aust J Agric Resour Econ. 2009;53: 585–602.
[5] Karlsson J. Parasite detection in extensively hold Gotland ponies. Uppsala: SLU, Institutionen för biomedicin och veterinär folkhälsovetenskap; 2015; 42.
[6] Rupasinghe D, Ogbourne CP. Laboratory studies on the effect of temperature on the development of the free-living stages of some strongylid nematodes of the horse. Zeitschrift f�r Parasitenkunde. 1978;55: 249–253.
[7] SMHI Meteorologiska observationer [Internet]. Lufttemperatur, dygnsvärde, Historiskt granskande. Available: http://opendata-download-metobs.smhi.se/explore/?parameter=1
[8] Council NZS. A guide to feed planning for sheep farmers. Beef+ Lamb New Zealand. 2012; 1–55.
[9] The Rules of Feeding Your Horse. In: The humane society of United States [Internet]. [cited 12 Oct 2018]. Available: http://www.humanesociety.org/animals/horses/tips/rules_horse_feeding.html
[10] Schneider, Astrid, Gerhard Hommel, och Maria Blettner. ”Linear Regression Analysis”. Deutsches Ärzteblatt International107, nr 44 (november 2010): 776–82. https://doi.org/10.3238/arztebl.2010.0776.
[11] The R graphs gallery (n.d.). #5 CORRELATION OF DISCRETE VARIABLES, http://www.r-graph-gallery.com/5-correlation-of-discrete-variables/ [2018-10-14]
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