Computing the Acclimatization Thermal Stress Index (ATSI) for Miami Dade, Florida

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Monday, 5 January 2015: 2:00 PM
228AB (Phoenix Convention Center - West and North Buildings)
David Quesada, Saint Thomas University, Miami Gardens, FL

Handout (4.7 MB)

The response of humans to weather changes might be estimated through the Acclimatization Thermal Stress Index (ATSI) introduced by Grigorieva, et.al. Such index is the ratio of the difference of heat exchanges and the exchange based on the normal for the specified region expressed as a percentage. It includes convective as well as radiative exchange and it is computed including main weather parameters as ambient temperature, humidity levels, saturated vapor pressure, wind speed, solar radiation at the geographical location. Negative values are indicative of a cold stress while positive values point into a hot stress. The merit of this index is that it unifies in a single body different approaches seem in the biometeorology community to describe the effect of extreme conditions on human health and that usually are treated separately. Additionally it is a relative index, which makes it useful for comparing different climatic zones and to find common pathways in the human response to adverse weather. The ATSI is used to find possible associations between the occurrences of respiratory diseases in South Florida and thermoregulation mechanisms in humans. Since thermoregulation in humans activates other physiological processes, ATSI is compared with results from a seasonally forced SEIR epidemic model, where seasonality is included via a sine function with a simple periodicity, a combination of sine functions leading to more than one period, and the Weierstrass function, a continuous, periodic and differentiable nowhere function which serves as an example of correlated noise. It is noteworthy, that ATSI might be used in conjunction with cluster analysis if a region is either stratified by sub regions with specific climatology and then compare the different microclimatological differences helping to point to conditions favorable for hot spots in disease outbreaks. ATSI might be easy implemented and might constitute a robust and reliable indicator for health managers.