Objetivo. Estimar los riesgos potenciales de salud debidos a la ingestión crónica de arsénico (As) en agua en Colima, México. Material y métodos. Se muestrearon aleatoriamente 36 pozos en 10 acuíferos locales. El análisis se hizo mediante ICP-OES siguiendo estándares internacionales. Se realizó una interpolación geoestadística con ArcGIS, implementando un modelo de ponderación del inverso de la distancia, para estimar la ruta de exposición de consumo en cada localidad. Se calcularon los coeficientes de peligro (HQ)y riesgo carcinogénico (R). Resultados. El HQ promedio ponderado de As para Colima es 2.41. Existen valores de HQ>1 para As que indican efectos adversos no carcinogénicos para la salud por ingestión continua y prolongada de agua; esto podría afectar a 183 832 individuos en el estado. El riesgo calculado de desarrollar cáncer debido a las concentraciones de arsénico en aguas subterráneas (R) es de 1.089E-3; estadísticamente esto podría ocasionar 446 casos de cáncer. Conclusiones. Los niveles actuales de arsénico en el agua de pozo incrementan los riesgos carcinogénicos y no carcinogénicos de salud humana en Colima.

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Epidemic models are used to analyze the progression or outcome of an epidemic under different control policies like vaccinations, quarantines, lockdowns, use of face-masks, pharmaceutical interventions, etc. When these models accurately represent real-life situations, they may become an important tool in the decision-making process. Among these models, compartmental models are very popular and assume individuals move along a series of compartments that describe their current health status. Nevertheless, these models are mostly Markovian, that is, the time in each compartment follows an exponential distribution. Here, we introduce a novel approach to simulate general stochastic epidemic models that accepts any distribution for the sojourn times.

Epidemic models are used to analyze the progression or outcome of an epidemic under different control policies like vaccinations, quarantines, lockdowns, use of face-masks, pharmaceutical interventions, etc. When these models accurately represent real-life situations, they may become an important tool in the decisionmaking process. Among these models, compartmental models are very popular and assume individuals move along a series of compartments that describe their current health status. Nevertheless, these models are mostly Markovian, that is, the time in each compartment follows an exponential distribution. In epidemic models, exponential sojourn times are most of the times unrealistic, for instance, they imply that the probability that a patient will recover from some disease in the next time unit is independent of the time the patient has been sick. This is an important restriction that prevents these models from being widely accepted and trusted by decision-makers. In spite of the need to incorporate algorithms to tackle the problem, literature on the topic is scarce. Here, we introduce a novel approach to simulate general stochastic epidemic models that accepts any distribution for the sojourn times that is efficient.