Seasonal variations of EPG Levels in gastro-intestinal parasitic infection in a southeast asian controlled locale: a statistical analysis

Abstract

We present a data based statistical study on the effects of seasonal variations in the growth rates of the gastro-intestinal (GI) parasitic infection in livestock. The alluded growth rate is estimated through the variation in the number of eggs per gram (EPG) of faeces in animals. In accordance with earlier studies, our analysis too shows that rainfall is the dominant variable in determining EPG infection rates compared to other macro-parameters like temperature and humidity. Our statistical analysis clearly indicates an oscillatory dependence of EPG levels on rainfall fluctuations. Monsoon recorded the highest infection with a comparative increase of at least 2.5 times compared to the next most infected period (summer). A least square fit of the EPG versus rainfall data indicates an approach towards a super diffusive (i. e. root mean square displacement growing faster than the square root of the elapsed time as obtained for simple diffusion) infection growth pattern regime for low rainfall regimes (technically defined as zeroth level dependence) that gets remarkably augmented for large rainfall zones. Our analysis further indicates that for low fluctuations in temperature (true on the bulk data), EPG level saturates beyond a critical value of the rainfall, a threshold that is expected to indicate the onset of the nonlinear regime. The probability density functions (PDFs) of the EPG data show oscillatory behavior in the large rainfall regime (greater than 500 mm), the frequency of oscillation, once again, being determined by the ambient wetness (rainfall, and humidity). Data recorded over three pilot projects spanning three measures of rainfall and humidity bear testimony to the universality of this statistical argument.

Keywords: EPG; Probability Density Function (PDF); anthelmintic; coccidia; histogram; least square fit; strongyle.

Figure 1

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Jowai, in the year 2002, are shown respectively in Figure 1 a and Figure 1 b. Both rainfall versus infection and temperature versus infection show oscillatory profiles.

Figure 2

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Jowai, in the year 2003, are shown respectively in Figure 2 a and Figure 2 b. Both rainfall versus infection and temperature versus infection show oscillatory profiles.

Figure 3

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Kyrdemkulai, in the year 2002, are shown respectively in Figure 3 a and Figure 3 b. Both plots show a jump in the infection level beyond critical values of rainfall and temperature.

Figure 4

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Kyrdemkulai, in the year 2003, are shown respectively in Figure 4 a and Figure 4 b. Plots indicate oscillatory transitions from a low infection level to a high infection level.

Figure 5

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Upper Shillong, in the year 2002, are shown respectively in Figure 5 a and Figure 5 b. Both rainfall versus infection and temperature versus infection show decaying oscillatory profiles.

Figure 6

Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Upper Shillong, in the year 2003, are shown respectively in Figure 5 a and Figure 5 b. The temperature versus infection plot (Figure 6b) indicates that beyond a critical temperature (approximately 30 C), infection grows enormously, although due to seasonal fluctuations in rainfall pattern, an oscillatory (increase–decrease) profile is omnipresent.

Figure 7

Comparative yearly Fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Jowai. Oscillatory pattern clearly shows a dominating peak in infection level around the monsoon time.

Figure 8

Comparative yearly fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Upper Shillong. Oscillatory pattern clearly shows a dominating peak in infection level around the monsoon time, although local highs are always there.

Figure 9

Comparative yearly fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Kyrdemkulai. Oscillatory pattern clearly shows a dominating peak in infection level around the monsoon time, although local highs are always there.

Figure 10

A comparative estimate of infection (expressed in EPG) variation against changes in the rainfall pattern (expressed in mm) for the three studied zones – Jowai, Upper Shillong and Kyrdemkulai. It is clearly seen that due to heavy rainfall, Kyrdemkulai and Upper Shillong show higher infection levels compared to Jowai. All three zones show oscillatory diurnal infection pattern.

Figure 11

Probability density functions of infection level (expressed in EPG) with varying rainfall levels (expressed in mm). We have chosen rainfall levels between 0–50 mm, 50–100 mm, 100–500 mm, 500–1000 mm and greater than 1000 mm. The plots clearly indicate the nature of diurnal variation with a sharp peak around monsoon.

Figure 12

Linear fit between infection (expressed in EPG) versus rainfall (expressed in mm) in the log ₁₀ -log ₁₀ scale for 2002 Kyrdemkulai data. The extrapolation clearly suggests a power-law (Pareto) form, where infection grows with rainfall. The precise nature of this growth is given as follows: Infection (EPG) ~ (Rainfall in mm)^0.55.