biometeorology asthma stu april2011
DESCRIPTION
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.TRANSCRIPT
Bio-meteorology of AsthmaDavid Quesada
School of Science, Technology and Engineering Management,St. Thomas University, Miami Gardens FL 33054
How far weather variability influences seasonal asthma episodes?
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. Tentative Outlook• What Biometeorology is? Weather & Climate.• 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 chemistry modeling, modeling asthma – Students are invited to participate !!!
WEATHER AND CLIMATE
Weather is defined as the state of the atmosphere at a given time at a given place. Weather is described by:1. Temperature2. Air pressure3. Humidity4. Cloudiness5. Wind speed and direction6. VisibilityWeather is going to be defined as the intersection of above Six sets of physical parameters.
Weather is a short term event, whereas Climate is a long-term one. Weather Can change over a short time span. Climate, on the other hand, must be Measured over periods of years, because climate Is the average weathercondition of a place.
Hurricanes
Tropical Storms
MesoscaleConvective
Systems
“Long”Waves
Small – ScaleMotions
(Turbulence)
Land / SeaBreezes
Thunderstorms
High / Low Pressure
“Short”Waves
Tornadoes
secondsto
minutes
minutesto
hours
hoursto
days
daysto
weeks
weeksto
months
0.000001 km 1 km 10 km 100 km 1000 km 10000 km
Microscale Mesoscale Synoptic Scale
Tem
por
al S
cale
s
The spatial and temporal scales of various weather phenomena Characteristic length L – defines the spatial range for a particular event Characteristic time T – defines the time interval for a particular event to occur
Ratios = L / Lc or T / Tc
When numerical values of ratios are becoming large enough, then processes occurring at scales of the order of Lc (Tc) are averaged and appear as fixed forscales larger than those previously analyzed.
Forms of Energy Transfer
Conduction: Particle by particle transfer of thermal and electric energy. Heat transferredin this fashion always flows from warmer to colder regions. Generally, the greater the Temperature difference, the more rapid the heat transfer.
Radiation: Transfer ofElectromagnetic energy through empty space in form of waves, traveling at a constant speed – c.
Convection: Transfer of thermal energy by mass movement of a fluid. In a convective circulation the warm, rising air cools. In ouratmosphere, any air that rises will expand and cool, and any air that sinks is compressed and warm.
Advection: The horizontally moving part of The circulation (called winds) carries properties Of the air in that particular area with it.
4TF
T
c
Composition of the Atmosphere
World Physical Geography and Climatic Zones
UTC or Z – time = Universal standard Time = It is the time measured at Royal Observatory in Greenwich.EDT = Eastern Day Time = UTC - 5 hr (4 hr during time adjustment)
Slopes, Trigonometric Functions, Average Values, and Global Warming
It is worth to notice the periodicity (24 hrs) of these peaks; however itis clear the irregular shape of all these peaks too – Why?
Range of variation
Cloudiness and Random Fluctuations in the weather are responsible for these irregularities
St. Thomas University, Miami Gardens, FL
Boyd Buchanan, Chattanooga, TN
Eagle Valley HS, Eagle Bend, MN
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Periodic Patterns in Nature and its Graphical Representation
DCBxAy )sin(
Daily variations – Days and Nights Period = T = 24 hr
Daily, monthly, and yearly variations - three periods T1= 24 hr, T2= 90 days, T3= 365 days
Time Series Analysis
Maximum
Minimum
Mean or Average
Range
More complicated behaviors are indicators of hidden dynamical processes to be studied
Slopes, Trigonometric Functions, Average Values, and Global Warming
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atTtT
atTtT
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tTtSinTtT M
Trigonometric Interpolation
Case 1: The free term To is a constant
Case 2: The free term To is a linear function of time
Case 3: The free term To is a quadratic function of time
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1 Climate is all about the value of this Integral, known as the average value
Weather is all about the values of theseFunctions at some moments of time, known as the time series
Slopes, Trigonometric Functions, Average Values, and Global Warming
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It is worth to notice how the trigonometric function oscillates around the main value
function To(t).
A minimum of 30 years it is needed tomake a conclusion about a warming Climate. It is worth to notice also how ,short Cold intervals may coexist with awarming trend.
Asthma Statistics Worldwide
Number of people diagnosed: more than 150 MEurope: the # of cases has doubledUSA: the number of cases has increased more than 60%India: between 15 and 20 MAfrica: between 11 and 18% populationNumber of deaths yearly: around 180,000
Miami Dade County , Florida
7.1% Middle and HS children were reported with asthmaThe number of hospitalizations due to asthma has doubled.The number 1 cause of school absences and 35 % of parents missed work
Why to study Asthma?
Why to study Asthma? How far Bio-Meteorology may help with?
Why to study Asthma? How far Bio-Meteorology may help with?
Why to study Asthma? How far Bio-Meteorology may help with?
Asthma admission by year
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Admission month
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ate
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000 2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1989
1988
Source:Nationwide Inpatient Sample and US Census
• Asthma seasonal variations confirmed
• Larger seasonal variation associated with a decrease in age.
Seasonal Variations of Asthma Hospital Admissions in the United States
Aichatou Hassane, UNH; Robert Woodward, PhD, UNH; Ross Gittell, PhD, UNH - May 27, 2004
2000 Asthma Admission by US region
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Admission month
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nu
aliz
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ate
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000
Northeast
Midwest
South
West
Source:Nationw ide Inpatient Sample and US Census
Seasonal Variations of Asthma Admissions in the United States
Regional seasonal variation exists: • Midwest has the largest rate of Asthma - East North
Central division: Illinois and Wisconsin• West region has the lowest rate of Asthma -
Mountain division: Arizona and Colorado
2001 2002 2003 2004 2005 2006 2007 2008130
135
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Rat
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Miami Dade Asthma Snapshot
Areas of major incidence
Feature Range (English)
Accuracy (English)
Range(Metric)
Accuracy(Metric)
Temperature -55F – 150F +/- 1F -45C – 60C +/- 0.5C
Relative Humidity 0 – 100% +/- 2% 0 – 100% +/- 2%
Wind Speed 0 – 125 mph +/- 2 mph 0 – 275 kph +/- 4 kph
Wind Direction 0 – 360 deg +/- 3 deg 0 – 360 deg +/- 3 deg
Barometric Pressure 28 – 32” Hg +/- 0.05”Hg 900 – 1100 mbar +/- 5 mbar
Rainfall Unlimited +/- 2% Unlimited +/- 2%
Light Intensity 0 – 100% N/A 0 – 100% N/A
Create a database of weather parameters and environmental triggers for asthma ( WeatherBug & WeatherBug Achieve)
Zip codes patients came from
WeatherBug Mesonet stations
NWS stations, MIA & Tamiami
Year Total Patients
Total Respiratory
Total Asthma
% of asthma
2008 5172 2950 2222 43
2009 6981 4301 2680 38
2010 7813 4960 2853 37
Year White White Hispanic
Non White Hispanic
African American
2008 490 505 820 510
2009 350 256 650 525
2010 528 495 605 657
Seasonal Variations of Asthma diagnosed cases Kendall Medical Group in Miami Dade, FL
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Seasonal Variations of Asthma diagnosed cases in standard units Z = (N – Nave)/S, Kendall Medical Group in Miami Dade, FL
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dTmean/dt = T[i+1] - T[i]
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dHmean/dt = H[i+1] - H[i]
Correlations between the number of cases and the given set of variables (IBM-SPSS-19)
Tmax Tmin ΔT Tmean dT/dt ΔT/Tmean
Pearson (r) - 0.524 - 0.529 0.357 - 0.531 - 0.122 0.487
P - value 0.000 0.000 0.002 0.000 0.306 0.000
Kendall - τ - 0.325 - 0.301 0.159 - 0.311 - 0.122 0.264
P - value 0.000 0.000 0.048 0.000 0.132 0.002
Spearman - ρ - 0.485 - 0.463 0.224 - 0.475 - 0.148 0.375
P - value 0.000 0.000 0.059 0.000 0.215 0.001
ΔP Pmean dP/dt ΔP/Pmean ΔH Hmean dH/dt ΔH/Hmean
Pearson (r) 0.367 - 0.021 0.082 0.42 0.452 - 0.213 - 0.015 0.445
P - value 0.002 0.862 0.491 0.000 0.000 0.073 0.899 0.000
Kendall - τ 0.269 0.008 0.045 0.291 0.282 - 0.052 0.006 0.264
P - value 0.001 0.922 0.579 0.000 0.000 0.521 0.938 0.001
Spearman - ρ 0.388 0.001 0.063 0.415 0.402 -0.091 0.003 0.373
P - value 0.001 0.996 0.600 0.000 0.000 0.445 0.979 0.001
90th Annual Meeting of AMS, Atlanta 2010
91th Annual Meeting of AMS, Seattle 2011
Lung Dynamics and Immune Response
--- Modeling ---
Urban Biometeorology--- Experiment and
Modeling ---
Statistical Correlations--- Asthma Index ---
Intelligent Expert System for Asthma Risk Analysis
--- IESARA ---
Future Directions
Online Weather Center --- Live Weather & Biometeorology ---
Statistical Analysis
Urban fluid dynamics and weather Weather Research and Forecast - WRF
Microscale and Mesoscale meteorology
Air Quality Modeling and Human Health
Theory of Systems and System Biology MacroscopicDynamics ofbreathing
MesoscopicImmune cells populationdynamics
Microscopic
Genes
Cooperative effect – Emergent properties
Mathematical modeling of the episodes of Asthma by using dynamical systems andComplexity theory
Mathematical Biology: Mathematical Modeling in Physiology and Anatomy
The dark outline on the left is an actual tracing of human bronchi, the schematic on the right is a computer-generated fractal representation. 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.
Computational Fluid Dynamics andthe dynamics of breathing
Fractal branching along with the finite element method to re-create the airway mesh
Mesoscopic immune description of an asthma episodeA system of differential equations describes the population dynamics of each one of the cells involved in an asthma episode.
A very complicated Network of cells (IL4, IL3, IL5, IL13- Cytokines, IgE – Immunoglobuline) Interacting and Competing.
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
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Intelligence Expert System for Asthma Risk Analysis - IESARA
Artificial Intelligence
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Conclusions• African Americans and Non White Hispanics seem to be more affected by asthma.
• Zip codes from Miami Dade with the major incidence seem to be related withsocio-economic background rather than particular microclimatic conditions.
• Among weather variables, Tmean, ΔT/Tmean, Tmin, and ΔH/Hmean appear tocorrelate better with the number of asthma cases.
• The observed patterns seem to be originated in the thermoregulation responseto cold weather (homeostasis), rather than in allergic pathways. However, environmental triggers are not excluded as an additional possibility for stress.
• More statistical work is needed in order to establish an Asthma Index for Bio-Meteorological applications. Urban weather and Air Quality modeling.
• Mathematical modeling of the processes taking place during an asthma episodeis a must for a better and full understanding of this disease.
Acknowledgments
• Oscar Hernandez M.D. and Elizabeth Fontora, Medical Group, Miami Dade, FL • School of Science, St. Thomas University