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In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.

In this study, we developed a computational algorithm under stochas- ticity by using Markov-cycle tree and Monte Carlo simulations for pa- tients coming into a hospital and being suspected of leptospirosis. Our mathematical model finds an optimal treatment strategy for the patients depending on whether they have severe or mild symptoms and whether they are late case patients who are coming to the hospital after seven days of onset of their symptoms or early case patients who are coming to the hospital within seven days of onset of their symptoms. The model is a useful tool to determine the treatment strategies during flood session for a large group of patients.