# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Pathogen Prevalence Studies  
As technology has advanced, the quantification of pathogens has become easier. However, there are various situations in which it is desired that the pathogen presence within a set of samples be compared. The most common set of studies that use pathogen prevalence make inferences about a population are epidemiological studies. In this context, we must define the difference between incidence and prevalence. Incidence measures the frequency of new cases of a particular disease in a population over a period of time. In general, they are reported as \(x\) number of cases in a set of population, for example, 23 cases in 100,000. Comparatively, prevalence refers to the proportion of a population which presents the evaluated characteristic, for example, a percentage of Salmonellaicsics cases in Mexico. For the scope of this review, the proportion of pathogen presence in a set of samples will be compared; however, the prevalence of pathogens in a set population will not be reported. For instance, pathogen presence variation due to seasonality has been compared using a chi-square test of independence, to determine if the presence of a pathogen is significantly greater in the rainy season when compared to the dry season [62; 63]. Similarly, differences in pathogen presence at different stages and times of a processing environment have been evaluated [64; 65; 66; 67]. Studies that evaluate epidemiological data and are large in size need more complex analysis that can be carried out by running logistic regression models, which associate different factors with the prevalence of the pathogen of interest. Therefore, understanding which factors may have a bigger impact in the prevalence of the pathogen can more effectively predict the risk of detecting a pathogen in a given environment [69; 70]. Multinomial logistical regression analysis will not be included in the scope of this review. Data Visualization and Data Analysis  
When comparing sets of samples with different foodborne pathogen presences, the distribution of the data compared will follow a binomial distribution (presence or non-detection in a sample). In this kind of study, the objective is to identify if a set of samples contains significant differences in pathogen presence. The most common comparison used involves the use of contingency tables, in which the statistical comparison used is a chi-square test of independence; however, other ways of presenting results, such as odds ratios with 95% confidence intervals, are alternatives widely used in food safety. For discussion purposes, a 2 \(\times\) 2 contingency table will be presented. In this contingency table, the presence and non-detection of the foodborne pathogen of interest throughout different samples was evaluated (Table 3). The table analyzes the counts of the pathogen presence and non-detection and compares the different samples. Percentages or proportions were not used to perform this comparison. Table 3 shows an example of a 2 \(\times\) 2 contingency table in the context of pathogen presence comparison between 2 seasons (rainy vs. dry). In this case, the chi-squared comparison will compare the distribution of the rows (rainy season against dry season). The chi-squared comparison is best for comparing a large number of observations within each category. An alternative method (rarely used) is [PERSON]'s exact test, which calculates \(p\)-values for small sample sizes; however, it is only suitable when the marginal row and \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Season**} & \multicolumn{2}{c}{_Salmonella_ **Presence**} & \multirow{2}{*}{**Row Total**} \\ \cline{2-3} \cline{5-3}  & **Presence** & & **Non-Detected** \\ \hline Rainy a  & 112 & 458 & 570 \\ Dry b  & 55 & 530 & 585 \\ \hline Column Total & 167 & 988 & 1155 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of 2 \(\times\) 2 contingency table for _Salmonella_ presence in dry and rainy seasons on a beef harvest floor.