Bio-meteorology of Asthma<br />David Quesada<br />School of Science, Technology and Engineering Management,<br />St. Thoma...
How far weather variability influences seasonal asthma episodes? <br />Climatic and environmental changes occurring since ...
Why Asthma? Motivation of the study.
 Previous results within continental USA and Miami Dade.
 WeatherBug  Mesonet and Asthma – Weather connection.
 Statistical Processing
Minimal Bio-Physical model: Thermoregulation & Immunology
Future of the project: Computational Fluid Dynamics & Immunology, urban weather forecast model of asthma, atmospheric chem...
The spatial and temporal scales of various weather phenomena <br />Characteristic length L – defines the spatial range for...
Forms of Energy Transfer<br />Conduction:Particle by particle transfer of <br />thermal and electric energy. Heat transfer...
Composition of the Atmosphere<br />
World Physical Geography and Climatic Zones<br />UTC or Z – time = Universal  standard Time = It is the time measured at R...
Slopes, Trigonometric Functions, Average Values, and Global Warming<br />Range of <br />variation<br />Cloudiness and Rand...
Eagle Valley HS, Eagle Bend, MN<br />Boyd Buchanan, Chattanooga, TN<br />St. Thomas University, Miami Gardens, FL<br />
Periodic Patterns in Nature and its Graphical Representation<br />Time Series Analysis<br />Daily variations – Days and Ni...
Slopes, Trigonometric Functions, Average Values, and Global Warming<br />Trigonometric Interpolation<br />Case 1: The free...
Slopes, Trigonometric Functions, Average Values, and Global Warming<br />It is worth to notice how the trigonometric <br /...
Why to study Asthma? <br />Asthma Statistics Worldwide<br />Number of people diagnosed: more than 150 M<br />Europe: the #...
Why to study Asthma? How far Bio-Meteorology may help with?<br />
Why to study Asthma? How far Bio-Meteorology may help with?<br />
Why to study Asthma? How far Bio-Meteorology may help with?<br />
Seasonal Variations ofAsthma Hospital Admissions in the United States<br /><ul><li>Asthma seasonal variations confirmed
 Larger seasonal variation associated with a decrease in age. </li></ul>Aichatou Hassane, UNH; Robert Woodward, PhD, UNH; ...
Seasonal Variations ofAsthma Admissions in the United States<br />Regional seasonal variation exists: <br /><ul><li> Midwe...
 West region has the lowest rate of Asthma - Mountain division: Arizona and Colorado</li></li></ul><li>Miami Dade Asthma S...
Create a database of weather parameters and environmental triggers for asthma ( WeatherBug & WeatherBug Achieve)<br />
Zip codes patients came from<br />  WeatherBug Mesonet stations<br /> NWS stations, MIA & Tamiami<br />
Seasonal Variations of Asthma diagnosed cases <br />Kendall Medical Group in Miami Dade, FL<br />
Seasonal Variations of Asthma diagnosed cases in standard units <br />Z = (N – Nave)/S, Kendall Medical Group in Miami Dad...
Tmax<br />Tmin<br />Tmean=(Tmax+Tmin)/2<br />
ΔT=Tmax-Tmin<br />ΔT/Tmean<br />
Pmean<br />Pmax<br />Pmin<br />dPmean/dt<br />
Hmax<br />Hmin<br />Hmean<br />dHmean/dt = H[i+1] - H[i]<br />
Correlations between the number of cases and the given set of variables <br />(IBM-SPSS-19)<br />
90th Annual Meeting of AMS, Atlanta 2010<br />
91thAnnual Meeting <br />of AMS, Seattle 2011<br />
Future Directions<br />Statistical Correlations<br />--- Asthma Index ---<br />Urban Biometeorology<br />--- Experiment an...
Statistical Analysis<br />
Urban fluid dynamics and weather<br />Weather Research and Forecast - WRF<br />Microscale and Mesoscale meteorology<br />
Air Quality Modeling and Human Health<br />
Mathematical modeling of the episodes of <br />Asthma by using dynamical systems and<br />Complexity theory<br />Macroscop...
Mathematical Biology: Mathematical Modeling in Physiology and Anatomy<br />The dark outline on the left is an actual traci...
Upcoming SlideShare
Loading in …5
×

Biometeorology Asthma Stu April2011

800 views

Published on

An introduction to Biometeorology and its application in regards to asthma. It includes also, future ideas to be implemented along this line. Three consecutive years of asthma information and weather information are correlated together in order to find possible indicators to define an asthma index.

Published in: Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
800
On SlideShare
0
From Embeds
0
Number of Embeds
13
Actions
Shares
0
Downloads
18
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

Biometeorology Asthma Stu April2011

  1. 1. Bio-meteorology of Asthma<br />David Quesada<br />School of Science, Technology and Engineering Management,<br />St. Thomas University, Miami Gardens FL 33054<br />
  2. 2. How far weather variability influences seasonal asthma episodes? <br />Climatic and environmental changes occurring since the middle of the Twentieth Century as well as the aggravating pollution levels in megacities are exacerbating asthma episodes and the number of hospitalizations due to this disease. Since 1999, in Miami Dade County the hospitalization rates were doubling the Healthy People 2010 objectives in every age group. A comprehensive weather database including outdoor temperature (T), humidity (H), barometric pressure (P), wind direction (θw) and speed (vw) as well as the values of maximum and minimum and the range of all these variables has been created. As a result, a seasonal pattern emerged, with a maximum appearing around the middle of December and a minimum around the middle of March every year for the three years of analysis. <br />Tentative Outlook<br /><ul><li> What Biometeorology is? Weather & Climate.
  3. 3. Why Asthma? Motivation of the study.
  4. 4. Previous results within continental USA and Miami Dade.
  5. 5. WeatherBug Mesonet and Asthma – Weather connection.
  6. 6. Statistical Processing
  7. 7. Minimal Bio-Physical model: Thermoregulation & Immunology
  8. 8. Future of the project: Computational Fluid Dynamics & Immunology, urban weather forecast model of asthma, atmospheric chemistry modeling, modeling asthma – Students are invited to participate !!!</li></li></ul><li>WEATHER AND CLIMATE<br />Weather is defined as the state of the <br />atmosphere at a given time at a given <br />place. Weather is described by:<br />Temperature<br />Air pressure<br />Humidity<br />Cloudiness<br />Wind speed and direction<br />Visibility<br />Weatherisgoingtobedefined as the<br />intersection of aboveSix sets of <br />physicalparameters.<br />Weather is a short term event, whereas <br />Climate is a long-term one. Weather<br />Can change over a short time span. <br />Climate, on the other hand, must be <br />Measured over periods of years, <br />because climate Is the average weather<br />condition of a place.<br />
  9. 9.
  10. 10.
  11. 11.
  12. 12. The spatial and temporal scales of various weather phenomena <br />Characteristic length L – defines the spatial range for a particular event <br />Characteristic time T – defines the time interval for a particular event to occur <br />Ratios = L / Lc or T / Tc<br />When numerical values of ratios are becoming large <br />enough, then processes occurring at scales of the <br />order of Lc (Tc) are averaged and appear as fixed for<br />scales larger than those previously analyzed.<br />“Long”<br />Waves<br />weeks<br />to<br />months<br />Temporal Scales<br />High / Low <br />Pressure<br />“Short”<br />Waves<br />Hurricanes<br />Tropical <br />Storms<br />Mesoscale<br />Convective<br />Systems<br />days<br />to<br />weeks<br />hours<br />to<br />days<br />Land / Sea<br />Breezes<br />Thunderstorms<br />minutes<br />to<br />hours<br />Tornadoes<br />Small – Scale<br />Motions<br />(Turbulence)<br />seconds<br />to<br />minutes<br />0.000001 km<br />1 km<br />10 km<br />100 km<br />1000 km<br />10000 km<br />Microscale<br />Mesoscale<br />Synoptic Scale<br />
  13. 13. Forms of Energy Transfer<br />Conduction:Particle by particle transfer of <br />thermal and electric energy. Heat transferred<br />in this fashion always flows from warmer to <br />colder regions. Generally, the greater the <br />Temperature difference, the more rapid the<br /> heat transfer.<br />Convection:Transfer of thermal energy by <br />mass movement of a fluid. In a convective <br />circulation the warm, rising air cools. In our<br />atmosphere, any air that rises will expand and <br />cool, and any air that sinks is compressed and <br />warm.<br />Advection:The horizontally moving part of <br />The circulation (called winds) carries properties <br />Of the air in that particular area with it.<br />Radiation:Transfer of<br />Electromagnetic energy <br />through empty space in <br />form of waves, traveling <br />at a constant speed – c.<br />
  14. 14. Composition of the Atmosphere<br />
  15. 15. World Physical Geography and Climatic Zones<br />UTC or Z – time = Universal standard Time = It is the time measured at Royal Observatory in Greenwich.<br />EDT = Eastern Day Time = UTC - 5 hr (4 hr during time adjustment) <br />
  16. 16. Slopes, Trigonometric Functions, Average Values, and Global Warming<br />Range of <br />variation<br />Cloudiness and Random Fluctuations in the weather are responsible for theseirregularities<br />It is worth to notice the periodicity (24 hrs) of these peaks; however it<br />is clear the irregular shape of all these peaks too – Why?<br />
  17. 17. Eagle Valley HS, Eagle Bend, MN<br />Boyd Buchanan, Chattanooga, TN<br />St. Thomas University, Miami Gardens, FL<br />
  18. 18. Periodic Patterns in Nature and its Graphical Representation<br />Time Series Analysis<br />Daily variations – Days and Nights <br />Period = T = 24 hr<br />Daily, monthly, and yearly variations - three periods T1= 24 hr, T2= 90 days, T3= 365 days<br />Maximum <br />Mean or Average<br />Minimum<br />Range<br />More complicated behaviors are indicators of <br />hidden dynamical processes to be studied<br />
  19. 19. Slopes, Trigonometric Functions, Average Values, and Global Warming<br />Trigonometric Interpolation<br />Case 1: The free term To is <br /> a constant<br />Case 2: The free term To is <br /> a linear function of <br /> time<br />Case 3: The free term To is <br /> a quadratic function <br /> of time<br />Weather is all about the values of these<br />Functions at some moments of time, known as the time series<br />Climate is all about the value of this Integral, known as the average value<br />
  20. 20. Slopes, Trigonometric Functions, Average Values, and Global Warming<br />It is worth to notice how the trigonometric <br />function oscillates around the main value<br />function To(t).<br />A minimum of 30 years it is needed to<br />make a conclusion about a warming <br />Climate. It is worth to notice also how ,<br />short Cold intervals may coexist with a<br />warming trend. <br />
  21. 21. Why to study Asthma? <br />Asthma Statistics Worldwide<br />Number of people diagnosed: more than 150 M<br />Europe: the # of cases has doubled<br />USA: the number of cases has increased more <br />than 60%<br />India: between 15 and 20 M<br />Africa: between 11 and 18% population<br />Number of deaths yearly: around 180,000<br />Miami Dade County , Florida<br />7.1% Middle and HS children were reported with <br />asthma<br />The number of hospitalizations due to asthma <br />has doubled.<br />The number 1 cause of school absences and <br />35 % of parents missed work<br />
  22. 22. Why to study Asthma? How far Bio-Meteorology may help with?<br />
  23. 23. Why to study Asthma? How far Bio-Meteorology may help with?<br />
  24. 24. Why to study Asthma? How far Bio-Meteorology may help with?<br />
  25. 25. Seasonal Variations ofAsthma Hospital Admissions in the United States<br /><ul><li>Asthma seasonal variations confirmed
  26. 26. Larger seasonal variation associated with a decrease in age. </li></ul>Aichatou Hassane, UNH; Robert Woodward, PhD, UNH; Ross Gittell, PhD, UNH - May 27, 2004<br />
  27. 27. Seasonal Variations ofAsthma Admissions in the United States<br />Regional seasonal variation exists: <br /><ul><li> Midwest has the largest rate of Asthma - East North Central division: Illinois and Wisconsin
  28. 28. West region has the lowest rate of Asthma - Mountain division: Arizona and Colorado</li></li></ul><li>Miami Dade Asthma Snapshot<br />Areas of major incidence<br />
  29. 29. Create a database of weather parameters and environmental triggers for asthma ( WeatherBug & WeatherBug Achieve)<br />
  30. 30. Zip codes patients came from<br /> WeatherBug Mesonet stations<br /> NWS stations, MIA & Tamiami<br />
  31. 31. Seasonal Variations of Asthma diagnosed cases <br />Kendall Medical Group in Miami Dade, FL<br />
  32. 32. Seasonal Variations of Asthma diagnosed cases in standard units <br />Z = (N – Nave)/S, Kendall Medical Group in Miami Dade, FL<br />
  33. 33. Tmax<br />Tmin<br />Tmean=(Tmax+Tmin)/2<br />
  34. 34. ΔT=Tmax-Tmin<br />ΔT/Tmean<br />
  35. 35. Pmean<br />Pmax<br />Pmin<br />dPmean/dt<br />
  36. 36. Hmax<br />Hmin<br />Hmean<br />dHmean/dt = H[i+1] - H[i]<br />
  37. 37.
  38. 38. Correlations between the number of cases and the given set of variables <br />(IBM-SPSS-19)<br />
  39. 39.
  40. 40.
  41. 41.
  42. 42. 90th Annual Meeting of AMS, Atlanta 2010<br />
  43. 43.
  44. 44. 91thAnnual Meeting <br />of AMS, Seattle 2011<br />
  45. 45. Future Directions<br />Statistical Correlations<br />--- Asthma Index ---<br />Urban Biometeorology<br />--- Experiment and Modeling ---<br />Lung Dynamics and Immune Response<br /> --- Modeling ---<br />Intelligent Expert System for Asthma Risk Analysis --- IESARA --- <br />Online Weather Center<br /> --- Live Weather & Biometeorology ---<br />
  46. 46. Statistical Analysis<br />
  47. 47. Urban fluid dynamics and weather<br />Weather Research and Forecast - WRF<br />Microscale and Mesoscale meteorology<br />
  48. 48. Air Quality Modeling and Human Health<br />
  49. 49. Mathematical modeling of the episodes of <br />Asthma by using dynamical systems and<br />Complexity theory<br />Macroscopic<br />Dynamics of<br />breathing<br />Theory of Systems and System Biology<br />Mesoscopic<br />Immune <br />cells population<br />dynamics<br />Microscopic<br />Genes<br />Cooperative effect – Emergent properties<br />
  50. 50. Mathematical Biology: Mathematical Modeling in Physiology and Anatomy<br />The dark outline on the left is an actual tracing of human bronchi, the schematic on the right is a computer-generated fractal representation. <br />Measured across mammalian diversity, lung surface tends to scale to the 3/4 power of mass. Systems and networks that grow by iterative fractal branching exhibit developmental plasticity that allometrically scaling structures lack, and allow tissues and organs to respond adaptively to unusual circumstances.<br />
  51. 51. Computational Fluid Dynamics and<br />the dynamics of breathing<br />Fractal branching along with the finite element method to re-create the airway mesh<br />
  52. 52.
  53. 53. Mesoscopic immune description of an asthma episode<br />A system of differential equations describes the population dynamics of each one of the cells involved in an asthma episode.<br />In asthmatic individuals, antigen presentation is thought to results in the polarization of T-cells towards a Th2 patterns whereas T cells from non atopic, non-asthmatic individuals show the opposing Th1 (interferon-γ and IL2) pattern of cytokine secretion<br />A very complicated Network of cells (IL4, IL3, IL5, IL13- Cytokines, IgE – Immunoglobuline)Interacting and Competing.<br />
  54. 54. Minimal model describing the functioning of the Immune system<br />Logistic growth terms<br />Non – linear dynamics<br />
  55. 55. Intelligence Expert System for Asthma Risk Analysis - IESARA<br />Geographic Information<br />Systems -- GIS<br />Artificial Intelligence<br />Optimization<br />
  56. 56. Conclusions<br /><ul><li>African Americans and Non White Hispanics seem to be more affected by asthma.
  57. 57. Zip codes from Miami Dade with the major incidence seem to be related with</li></ul>socio-economic background rather than particular microclimatic conditions.<br /><ul><li> Among weather variables, Tmean, ΔT/Tmean, Tmin, and ΔH/Hmean appear to</li></ul>correlate better with the number of asthma cases.<br /><ul><li> The observed patterns seem to be originated in the thermoregulation response</li></ul>to cold weather (homeostasis), rather than in allergic pathways. However, <br />environmental triggers are not excluded as an additional possibility for stress.<br /><ul><li> More statistical work is needed in order to establish an Asthma Index for </li></ul>Bio-Meteorological applications. Urban weather and Air Quality modeling.<br /><ul><li> Mathematical modeling of the processes taking place during an asthma episode</li></ul>is a must for a better and full understanding of this disease.<br />Acknowledgments<br /><ul><li>Oscar Hernandez M.D. and Elizabeth Fontora, Medical Group, Miami Dade, FL
  58. 58. School of Science, St. Thomas University</li>

×