Are epidemics a "Computer Science" problem?

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?

CA - real ones…

our one!(?)and

http://tones.wolfram.com/

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

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?

http://www.ndemiccreations.com/en/22-plague-inc

http://www.simulation-argument.com/

http://www.simulation-argument.com/

“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