Prevalence of gastrointestinal parasitic load in animals measured by egg per gram (EPG) of faeces was found to be linear with the level of precipitation in that region. A vital component of this study was to quantitatively evaluate the comparative importance of the agroclimatic factors in the EPG spread, with particular importance given to rainfall and temperature.
All figures have been prepared using the open sourced gnuplot package. Figure 1 shows the fluctuation pattern of the infection level (expressed in units of EPG) due to variations in rainfall (in mm) – Figure 1a - and temperature (in Centigrade) – Figure 1b - for Jowai, in the year 2002. Both plots show oscillatory infection patterns suggesting diurnal dependence. Figure 2 shows similar pattern for the year 2003. Comparative analysis indicates that the specific year of observation does not affect the oscillatory patterns. Figures 3, 4 and 5, 6 show similar statistics for Kyrdemkulai and Upper Shillong respectively. The temperature versus infection plot in Figure 6 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 represents the 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 portrays 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 represents the fluctuations in the infection level (expressed in units of EPG) due to variations in rainfall (in mm) and temperature (in Centigrade) for Kyrdemkulai. As opposed to Upper Shillong data, shown in Figure 8, the oscillatory pattern in Figure 9 clearly shows a dominating peak in the infection level around the monsoon time, although local highs are always there. Figure 10 is a representative case of 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. Probability density functions of infection level (expressed in EPG) with varying rainfall levels (expressed in mm) give us a picture of the probabilistic change in the infection pattern with rainfall variations (assuming temperature remains unchanged). More specifically, Figure 11 quantifies the probability that infection level will change with variations in the rainfall margin. For this comparison, 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 is the crux of our statistical analysis. This is an extrapolated linear fit between infection (expressed in EPG) versus rainfall (expressed in mm) in the log10-log10 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. It is important to note that although this power law exponent changes slightly if the data (whether from Jowai or Upper Shillong) changes, the change in percentage is never more than 10%. Given this, we are reasonably placed in hypothesizing that the power-law exponent indicates the preponderance of a non sub-diffusive universality class. At present, we are studying possibilities of correlating jumps in the infection levels (Figures 8 & 9) with statistical phase transitions.
Influence of rainfall on parasitic burden in animals (EPG level in animals) is non-equivocally depicted in all these figures. EPG level was found to be low (50 to 150) when rainfall level was less than 50 mm which was not pathogenic and hence did not directly influence the production level of (infected) animals. Moderate pathogenic effects were found to decrease the production level in animals when rainfall level was 100 to 500 mm (corresponding EPG level 200 to 350). With rainfall levels exceeding 500 mm, severe pathogenic manifestations were recorded in animals with simultaneous increase of EPG over more than 500 (per c.c.) in the faeces of animals.
Irrespective of the year chosen and the region of infection, results (Figures 7, 8, 9) indicate oscillatory seasonal fluctuations for all three regions with peak values for each of these around the monsoon period. The infection spread is seen to be strongly dependent on the amount of rainfall received though. Kyrdemkulai with the highest monsoon rainfall records the lowest possible infection during that period while Upper Shillong with the least rainfall in the aforementioned period shows the strongest effect of infection. There is, however, an underlying component of over simplification in this data based analysis. The effects of temperature on infection, especially during the arid times, can be a vital determining factor that could not be separately studied here (independent of the rainfall effects), a fact that can only be remedied through multivariate analysis of data and/or through model based studies, one of our present works in progress.
The above analysis gives an impression of the site variation of statistics. In the following, we present the effects of time variation, including seasonal fluctuations. Histograms (splined) obtained from the data were normalized to obtain the respective probability density functions (PDFs) of EPG infection against rainfall in all three regions over the years 2002–2003 across a range of rainfall measures. Clearly low rainfall precipitates a larger infection irrespective of the region of infection (Figure 12). Interestingly, the PDFs too show an oscillatory behavior, clearly indicating the seasonal effects on the infection spread.
To summarise, we conclude that rainfall directly influences the pathogenicity of the parasitic infection level. Unlike other bacterial and viral infections, the gastrointestinal parasites maintain a steady host sustainable relationship, which prevents the host from dying out of this infection. Simultaneously, the parasite contributes to a severe loss of production level in animals through nutrition sharing with the host. Therefore, to prevent production loss, animal parasite controlling anthelmintic treatment should be implemented at the time when precipitation levels of a region are within 100 mm to 500 mm. Our analysis also clearly indicates the presence of seasonal oscillations in the infection level at all points of these cut-off thresholds while also making clear quantitative suggestions contrasting the degree of virility of a strongyle infection against a coccidia infection, with the former type dominating. A lateral derivative of this analysis is the temperature dependence of the infection level (Figures 1, 2, 3, 4, 5, 6) further details of which would be presented through an analysis of a relevant mathematical model in a publication soon to follow.