## 4 Conclusions  
In conclusion, by applying Monte Carlo simulation, we can evaluate the probabilities of occurrence of different scenarios related to the availability of health personnel. For this evaluation, we determined a number of inputs related to the number of personnel available as well as the probabilities of contagion and the minimum number of personnel necessary to be able to attend patients.  
By means of Monte Carlo simulation, we can estimate the probability of occurrence of the different scenarios. In the calculation, we find three results of the probability of occurrence, such as high probability, average probability, and absolute probability. Hence, it is recommended to work with the average probability, as a safer option of occurrence.  
In relation to the analysis of the results obtained, we can indicate that the strong point of the method is related to the identification and determination of the necessary inputs to model the simulation. In the results obtained, we can indicate that it is necessary to know the probability of occurrence of contagion, which can be the daily contagion rate. In addition, it is also necessary to know the availability of health personnel required to be able to attend the patients, as well as the contagion recovery rates and a probability of acceptance of the contagion of the health center, which can ensure the continuity of care to patients. Below, we present the input data to be able to simulate through Monte Carlo. These data correspond to real situations that make possible the continuity of care. The model presented presents 21 possible cases that may occur:  
* **Number of personal available for attention**: 100
* **Number of interactions**: 1000
* **Accepted percentage of contagion**: 20%
* **Average percentage of contagion**: 50%
* **Average percentage of personnel recovered from infection**: 80
* **Average percentage of personal need for care**: 80  
This indicates that on a pandemic day, the most likely scenario of occurrence is that an average rate of infection of health personnel can occur at 50%, with a probability of acceptance of infection of 20%, keeping constant the minimum number of personnel for care with 80% of the total personnel, and with a probability of recovery of infected personnel who will return to work with 80%, as evaluated with 100 workers and 1000 iterations.  
In the model presented, we have as variables the percentage of accepted contagion and the average percentage of contagion. In both cases, we consider from 10% to 50%, with increments of 10%. It is recommended to perform evaluations considering higher values, which will depend on the type of health facility, so we can indicate that these values can vary if we analyze in reference health centers that attend to large numbers of patients per day, as well as health facilities that attend to a limited number of patients. Likewise, if more cases need to be evaluated, 1% increments can be considered in order to obtain different values and be sensitive to the model.  
Investigation, T.P.-E., W.A., E.R.S.A., E.A.R.P., E.R.G.P.d.L., S.O.C., R.O.F.C. and L.H. Thanks to the authors for their collaboration in data analysis, design of simulation models and interpretation of results. All authors have read and agreed to the published version of the manuscript.  
This research work was financed entirely by the authors.  
Not applicable.  
The data analyzed in this research work are worked with simulation models; therefore, the authors do not work directly with people, and therefore the authors consider that informed consent should not be applied.  
The data worked in the present work, are described in the document, for its application for which the authors consider not to publish in any repository.  
The authors declare that they have no conflict of interest.