are epidemics a "computer science" problem?
TRANSCRIPT
É realmente verdade que o controle de fenômenos epidêmicos nas
cidades do futuro será realizado por software?
Are epidemics a “Computer Science” problem?
#CPRecife4
[slides in english]
Epidemiology
• Epidemiology is the study of the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. http://www.who.int/topics/epidemiology/en/
Historical perspectiveshttp://ocp.hul.harvard.edu/contagion/
DDDs?
DDD - Digital Disease Detection
http://www.www2015.it/3rd-international-digital-disease-detection-conference-ddd-2015/(take a look at THE sponsors)
and
DDD - Detection of Digital Disease
(1) Digital Disease is a term created by The Institute for Responsible Online and Cell-Phone Communication for any current or future malicious, harmful, or socially negative action or trend utilizing digital technologies. SPAM, virus, …(www.iroc2.org)
(2) The emersion of the Internet did not only change human communication and information seeking, it also contributed to manifold alterations in the manifestation, perception and treatment of mental disorders. Pandora's digital box: mental disorders in cyberspace (http://www.ncbi.nlm.nih.gov/pubmed/22136939)
A CA CODE… EPITRACK - ONÍCIO
DDDs?
DDD - Digital Disease Detectionhttp://www.www2015.it/3rd-international-digital-disease-detection-conference-ddd-2015/ (take a look at THE sponsors)
andDDD - Detection of Digital Disease
(1) Digital Disease is a term created by The Institute for Responsible Online and Cell-Phone Communication for any current or future malicious, harmful, or socially negative action or trend utilizing digital technologies. SPAM, virus, …(www.iroc2.org)
(2) The emersion of the Internet did not only change human communication and information seeking, it also contributed to manifold alterations in the manifestation, perception and treatment of mental disorders. Pandora's digital box: mental disorders in cyberspace(http://www.ncbi.nlm.nih.gov/pubmed/22136939)
Is this a Computer ScienceProblem?
Fundamentals
• Epidemiology is the study of the distributionand determinants of health-related states orevents (including disease), and theapplication of this study to the control ofdiseases and other health problems.
http://jech.bmj.com/
• The Tipping Point, Epidemics are a function ofthe people who transmit infectious agents,the infectious agent itself, and theenvironment in which the infectious agent isoperating. And when an epidemic tips, whenit is jolted out of equilibrium, it tips becausesomething has happened, some change hasoccurred in one (or two or three) of thoseareas.
Fundamentals
An inflection point is a point on a curve at which the sign of
the curvature (i.e., the concavity) changes. Inflection points may
be stationary points, but are not local maxima or local minima.
The first derivative test can sometimes distinguish inflection points
from extrema for differentiable functions
The second derivative test is also useful. A necessary condition
for to be an inflection point is
A sufficient condition requires and to have opposite signs in
the neighborhood of (Bronshtein and Semendyayev 2004, p. 231).
a scientific question…
?is it another buffon needle problem?
Take a look at youtube:
“Buffon´s Needle Animated in 3D”
Is it possible?
no money for study this, then…
www.pmt.eswww.upc.edu
epischisto.orghttp://200.17.137.109:8081/xiscanoe/people
www.ufrpe.br
www.cesar.org.br
www.fiocruz.br
www.ines.org.br
Epidemiologia Computacional para Esquistossomose
Scientific Computingon epischisto.org
• Scientific computing (or computational science) is the field ofstudy concerned to the construction of mathematical modelsand techniques of numerical solutions using computers toanalyze and solve scientific and engineering problems.
• Typically, such models require a large amount of calculation,and usually run on computers with great power scalability(parallel and distributed machines)
• Scientific computing is currently regarded as a third way forscience complementing experimentation (observation) andtheory.
http://www.springer.com/mathematics/computational+science+%26+engineering/journal/10915
Fundamentals – MathematicalEpidemiology
Fundamentals – how to solve these Differential Equation systems? Some are unsolved in analytic form, but in numerical one…
Adams-Bashforth-Moulton Method
Adams' Method
Collocation Method
Courant-Friedrichs-Lewy Condition
Euler Backward Method
Euler Forward Method
Galerkin Method
Gauss-Jackson Method
Gill's Method
Isocline
Kaps-Rentrop Methods
Milne's Method
Predictor-Corrector Methods
Relaxation Methods
RK2
RK4
Rosenbrock Methods
Runge-Kutta Method
and for neglected diseases?
with sparse
data
Aitken Interpolation Chebyshev Approximatio... Moving Median
B-Spline Cubic Spline Muller's Method
Berlekamp-Massey Algor... Gauss's Interpolation... Neville's Algorithm
Bernstein-Bézier Curve Hermite's Interpolatin... Newton's Divided Diffe...
Bézier Curve Internal Knot NURBS Curve
Bézier Spline Interpolation NURBS Surface
Bezigon Lagrange Interpolating... Richardson Extrapolation
Bicubic Spline Lagrange Interpolation Spline
Bulirsch-Stoer Algorithm Lagrangian Coefficient Thiele's Interpolation...
C-Determinant Lebesgue Constants Thin Plate Spline
Cardinal Function Moving Average
Solving sparse systems
Interpolation?
and some hidden scenarios…
A New Kind of Science (2002)...
Three centuries ago science was transformedby the dramatic new idea that rules based onmathematical equations could be used todescribe the natural world.
My purpose in this book is to initiate anothersuch transformation... Cellular Automata
our CA… how is it going?
2006 starts a new monitoring
Praia Carne de Vaca
Praia Enseada dosGolfinhos
Praia do Forte
Praia Pau Amarelo
Praia do Janga
Lagoa do Náutico
Praia Porto de Galinhas
BRAZIL
a REALLY neglected disease in Brazil...
No dataNo case reportsNo statistical seriesNo reliable dataOnly poor comunities
Fiocruz (Schistosomiasis Laboratory) works to discover, to control and to report
Fiocruz starts a new study in 2006...http://200.17.137.109:8081/xiscanoe/infra-estrutura
2006 – 2007, data collect in-loco
2006 – 2007, data collect in-loco
http://200.17.137.109:8081/xiscanoe/infra-estrutura/expedicoes
Figure 1.
Adjusted Prelavence
0to 10 (3)10to
20 (32)
20to 30 (11)30to 50 (3)
Stream
Prevalence per 100 hab
0 to 1 (15)1 20 (17)
20 60 (14)60 80 (2)80 100 (1)
Breeding sites
to
to
to
to
water-collecting tank
Riacho Doce
1a. Prevalence 1b. Adjusted Prevalence
Male Female Total
Age group Pop1 Posit
2 Prev
3 Pop Posit Prev Pop Posit Prev
up to 9 99 7 7.1 100 3 3.0 199 10 5.0
10 to 19 109 26 23.9 99 24 24.2 208 50 24.0
20 to 29 76 31 40.8 90 21 23.3 166 52 31.3
30 to 39 88 18 20.5 103 23 22.3 191 41 21.5
>= 40* 141 14 9.9 168 18 10.7 310 32 10.3
unreported 16 3 18.8 10 2 20.0 26 5 19.2
Total 529 99 18.71 570 91 15.96 1100 190 17.3
* No information on sex for one individual. 1 population. 2 Number of positives. 3 Prevalence
per 100 inhabitants.
Spatial pattern, water use and risk levels associated with the transmission of schistosomiasis on the north coast of Pernambuco, Brazil. Cad. Saúde Pública vol.26 no.5 Rio de Janeiro May 2010.
http://dx.doi.org/10.1590/S0102-311X2010000500023
2008 – 2009, data analysis and reports...Parasitological exams on 1100 residents
2008 and 2009 data analysis and reports...Summary data for molluscs collected...
Ecological aspects and malacological survey to identification of transmission risk' sites for schistosomiasis in Pernambuco North Coast, Brazil. Iheringia, Sér. Zool. 2010, vol.100, n.1, pp. 19-24.
http://dx.doi.org/10.1590/S0073-47212010000100003
Collecting
Sites
Alive Dead Positive to
S. mansoni
% de
infection
I 0 0
II 1707 129 4 0,23
III 297 198 0 0
IV 0 0
V 0 0
VI 0 0
VII 2355 322 37 1,57
VIII 76 125 3 3,95
IX 0 0
Total 4435 774 44 0,99
2009-2010, modelling with 15 real parameters (?)
Paremeter Ranges (avg) How were obtained?
Susceptible human population 0-23 social inquires (Paredes et al, 2010)
Infected human population 0-23 croposcological inquires (Paredes et al, 2010)
Recovered population of humans 0-23 social inquires (Paredes et al, 2010)
Rate of mobility of humans 0-26% social inquires (Paredes et al, 2010)
Rate of mobility of molluscs 0-2% malacological research (Souza et al, 2010)
Population of healthy molluscs 0-1302 malacological research (Souza et al, 2010)
Population of infected molluscs 0-11 malacological research (Souza et al, 2010)
Area susceptible to flooding 0-45%
LAMEPE - Meteorological Laboratory of Pernambuco (lamepe, 2008)
and environmental inquires (Souza et al, 2010)
Connection to other cells 0-100%
LAMEPE - Meteorological Laboratory of Pernambuco (lamepe, 2008)
and environmental inquires (Souza et al, 2010)
Rate of human infection 0-100% croposcological inquires and social inquires (Paredes et al, 2010)
Rate of human re-infection 0-100% croposcological inquires and social inquires (Paredes et al, 2010)
Recovery rate 0-100% croposcological inquires and social inquires (Paredes et al, 2010)
Mollusc infection rate 0-100% malacological research (Souza et al, 2010)
Rate of sanitation 0-93% social and environmental inquires (Souza et al, 2010)
Rainfall of the area 39-389mm LAMEPE - Meteorological Laboratory of Pernambuco (Lamepe, 2008)
From one year (population 1 snapshot, molluscs 12 snapshots)without previous historical...
Mechanistic epidemic models
Two alternative approaches
Top-down Population-based Models (PbMs)
Bottom-up Agent-based Models (AbMs)
PbM AbM
one proposal: a top-down approach using a cellular automaton
a b
1 km
a ba b
1 km
simulation space, a 10x10 square grid
remembering...a cellular automaton
Cellular automaton A is a 4-upla A = <G, Z, N, f>, where
• G – set of cells• Z – set of possible cells states• N – set, which describes cells neighborhood• f – transition function, rules of the automaton:
– Z|N|+1Z (for automaton, which has cells “withmemory”)
– Z|N|Z (for automaton, which has “memoryless” cells)
Statistical mechanics of cellular automataRev. Mod. Phys. 55, 601 – Published 1 July 1983
Simple initial conditions: Homogeneous states orSelf-similar patterns
Random initial conditions:
Self-organization phenomena
Moore Neighbourhood (in grey) of the cell marked with a dot in a 2D square grid
Rule 30 - 1000 iterações
a cellular automatagrammar?
natural biotic types
Patterns of some seashells, like the ones in Conus and Cymbiolagenus, are generated by natural CA.
http://www.answers.com/topic/cellular-automaton
arts
CA - real ones…
and Schistosomiasis (?)
the dynamics
Mollusk population dynamicsa growth model for the number of individuals (N) that
considers the intrinsic growth rate (r) and the maximum
sustainable yield or carrying capacity (C) defined at each
site (Verhulst, 1838):
)1(C
NrN
dt
dN
Human infection dynamics (SIR - SI)
This model splits the human population into three compartments: S (for susceptible), I (for infectious) and R (for recovered and not susceptible to infection) and the snail population into
two compartments: MS (for susceptible mollusk) and MI
(for infectious mollusk).
Socioeconomic and environmental factors
environmental quality of the nine collection sites in Carne de Vaca, according to the criteria of Callisto et al (Souza et al, 2010).
rteN
NC
CtN
0
01
)(
the model calculates the local increase of population using equation 1 and calculating N(t+1)out from N(t). The values for r and C are set at each site and each time step, using monthly meteorological inputs and considering the ecological quality of the habitat
(1)
αRχI=dt
dR
χI·S·Mp=dt
dI
αR+p·S·M=dt
dS
IH
I
ISMI
SSMS
rM·I·Mp=dt
dM
rM·I·Mp=dt
dM
(3a)
(3b)
Cells and infection forces
statesblack: rate of human infection = 100%;red: 80% ≤ rate of human infection < 100%;light red: 60% ≤ rate of human infection < 80%;yellow: 40% ≤ rate of human infection < 60%;light yellow: 20% ≤ rate of human infection < 40%;cyan: 0% ≤ rate of human infection < 20%.
Infection forcesHuman
S -> I (infected molluscs contact, pH)
I -> R (if treated (1-α), χ)
Molluscs
S -> I (infected human contact, pM)
the algorithm
1. Choose a cell in the world;
2. For each human in the cell perform a random walk weighted by the “probability of movement" defined
at each site.
Repeat these steps for every cell in the world. Then update data.
3. Choose a cell in the world;
4. Call the “Events” process;
5. Return the individual to his original cell after the infection phase;
6. Choose a cell in the world;
7. For the mollusk population in that cell, perform a diffusion process weighted by the “rate of movement"
defined at each site;
Repeat these steps for every cell in the world. Then update data.
1. Increase the population of mollusks using the growth model described in Section 3.1;
2. Compute the transition between population compartments of humans using the set of equations (3b)
defined in Section 3.2;
3. Compute the transition between population compartments of humans using the set of equations (3a)
defined in Section 3.2;
Update local data of the spatial cell.
Events process
Main
sumulations
Mathematica 7.0 (Mathematica, 2011) with a processor Intel i5 3GHz, 4MB Cache, 8GB RAM.
Computational costs of a complete simulation when assuming a fixed world size (10x10 cells) and extent (365 time steps) and an increasing number of parameters being swept for rejection sampling (from 1 to 15)
Computational vs Statistical modelsDay 26 Day 43 Day 88
Day 106 Day 132 Day 365Color Legend
I = 100%80% ≤ I < 100%
60% ≤ I < 80%
40% ≤ I < 60%
20% ≤ I < 40%
0% ≤ I < 20%
(I = percentage of
infected humans)
Temporal
evolution
Day 26Day 26 Day 43Day 43 Day 88Day 88
Day 106Day 106 Day 132Day 132 Day 365Day 365Color Legend
I = 100%80% ≤ I < 100%
60% ≤ I < 80%
40% ≤ I < 60%
20% ≤ I < 40%
0% ≤ I < 20%
(I = percentage of
infected humans)
Temporal
evolution “according to the risk indicator, in the scattering diagram of Moran represented in the Box Map (Figure 2), indicated 18 areas of highest risk for the schistosomiasis, all located in the central sector of the village. Areas with lower risk and areas of intermediate risk for occurrence of the disease were located in the north and central portions with some irregularity in the distribution”
Predictive scenarios
2012 2017 2022 2027Color legend
I = 100%
80% ≤ I < 100%60% ≤ I < 80%
40% ≤ I < 60%
20% ≤ I < 40%
0% ≤ I < 20%
Predictive scenarios generated with the parameter calibration of the year 2007 that show endemic schistosomiasis. I stands for the average percentage of infected humans per spatial cell predicted by the model
Prêmio Pirajá da Silva do MS em 2008, 1º lugar(bianual)
CA - real ones…
our onea detailed view
Remembering our scientific question…
?Is it possible?
INNOVATION on collecting DATA:an integrated plataform www.ankos.com.br
http://ankos.sourceforge.net/
INNOVATION on collecting DATA:an integrated plataform SchistoTrack (patent in progress)
We are Health Map in PE-Brazil!http://healthmap.org/
by Conway, Cellular Automata are “not just a game”, 1970
by epischisto.org ,Schistosomiasis by mobile phones and social machines and simulatorsbased on Cellular Automata, 2011
and...
Epitrack.com.br
flunearyou.orgsaludboricua.orgsaudenacopa
• 2 prêmios do MCTI• 2 prêmios do MS
• 1 prêmio internacional
and with other sensors…
INNOVATION on collecting DATA: automatic proposal for diagnosis of schistosomiasis...
SEE PROJECTShttp://200.17.137.109:8081/xiscanoe/projeto/graduate-projects/automatic-diagnosis-methods-and-tools/andre-caetano
Prêmio Pirajá da Silvado MS em 2010, 1º lugar(bianual)
Atas da Conferência IADIS Ibero-Americana Computação Aplicada, 2013. v. 1. p. 87-94.
Automatic Diagnostic - Malaria
Image acquisition
PreprocessingTraining and classification
2014
http://200.17.137.109:8081/xiscanoe/projeto/graduate-projects/automatic-diagnosis-methods-and-tools/automatic-diagnosis-for-malaria
VIDEO
other areas?…
• Epidemics in Software Engineering
• Epidemics in Energy
• Drones as Automata Computer Science Machines
• Automata Drones as environmental
sensors
and genetic sensors…
What is iGEM-LIKA-CESAR?
we are trying to build some genetic codes… and some grammars
and, who knows, machines…
with genetic engineering, robotics, some computer science
theory and Innovation, a lot of it!
http://2014.igem.org/Team:LIKA-CESAR-Brasil
Synthetic Biology and Robotics - The Integration that can Save Lives
The LIKA-CESAR BRASIL proposes the development of a biosensor for the
detection of breast cancer with the help of synthetic biology and robotics. The idea
was to build a robotic system linked to genetic engineering capable of processing
and prepare small samples of blood in an automated manner.
For this our team, created the Coli Alert for the BreastBotSensor. This system is
one robot to DNA/RNA extraction coupled by an electrochemical biosensor and
linked to one quality control, the ColiAlert, responsible to confirm the process of
nucleic acid extraction.
The team believes that the best way to solve problems is joining technologies. For
this, we think that synthetic biology should go hand in hand with robotics and
information technology. Then our project was to join the synthetic biology with the
robots, aiming to fight against to the one of biggest health problems: The Breast
Cancer.
http://2014.igem.org/Team:LIKA-CESAR-Brasil
so…
• Genetic engineering
• Genetic engineering 2.0 = synthetic biology
• Synthetic biology = molecular biology like computer science– Programming DNA like software!
– Binary code versus dna code
– We have 2 worlds today: a real one and a virtual one by software…
–Synthetic biology will generate a NEW world!
– Preparing the world for synthetic biology: http://www.technologyreview.com/article/403544/preparing-the-world-for-synthetic-biology/
in the future…
new bio machines(?) with new sensors computing CAs!(?)
and “into” codes, languages and
machines… machines that recognize languages and rules!
Lets see THE new machines (2015)https://www.youtube.com/watch?v=IhVu2hxm07E
and JIBOhttp://www.fastcompany.com/3033167/most-creative-people/how-star-wars-influenced-jibo-the-first-robot-for-families
here, Recife-PE, in www.epischisto.org we have HEALTHDRONES by EPITRACK/ISI-TICs/INES/CESAR
what about the future? “a revolução pode ser antecipada?”by Silvio Meira (in Portuguese)http://terramagazine.terra.com.br/silviomeira/blog/2013/10/04/a-revoluo-pode-ser-antecipada/
/
http://medimoon.com/2012/07/genetically-modified-mosquitoes-a-new-hope-for-dengue-fever/
and the Smart cities!
flunearyou.orgsaludboricua.org
saudenacopa
Epitrack.com.br
and our scientific question…
?is it another buffon needle problem?See youtube: “Buffon´s Needle Animated in 3D”
Is it possible?
no money for study this, then…
and…
a big game?
“everything is software”by Silvio Meira
Back to…
Is the tipping point of an epidemic a Computer Science Problem? now…
what do you think about?
tks!
jones.albuquerque