IMSc Workshop on

Modeling Infectious Diseases

September 4-6, 2006

Venue: Ramanujan Auditorium, IMSc

Workshop Schedule

4/9/06 (9:15-9:45):  Registration

MONDAY (4/9/06)
TUESDAY (5/9/06)
WEDNESDAY (6/9/06)
 Inauguration & Opening Remarks

Niloy Ganguly

B Raveendran
Somdatta Sinha
Discussion Summaries by Group Members
Tea/Coffee Tea/Coffee Tea/Coffee
Vineeta Bal Mohan D Gupte Discussion Summaries by Group Members
Debashish Chowdhury
Sudeshna Sinha
Concluding Session
Bhaskar Saha
Aparup Das


Parongama Sen
S S Manna

Gautam Menon
Sitabhra Sinha
Sunita Gakkhar

Discussion, Informal Group formation
Discussion, Informal Group formation

List of Talks

B Raveendran
Hygine Hypothesis: Is the proof too far ?
Vineeta Bal Vaccines to prevent infectious diseases:
Why do we have very few success stories?
Debashish Chowdhury Modelling Immune Response: Discrete and Continuum Approaches
Bhaskar Saha Reciprocal signaling through CD40 induces counteractive effector functions
Parongama Sen Spatial Models of Epidemics
Gautam Menon
Using Agent-Based Models to study Epidemic Spreading
Sitabhra Sinha Excitable Media with long-range connections: Modeling waves of epidemic spreading
Niloy Ganguly Epidemic spreading on networks: A topographic view
Somdatta Sinha Parasite Invasion in Space: Modelling and Data Analysis
Mohan D Gupte Epidemiological Modelling of (a) Leprosy and (b) HIV
Sudeshna Sinha Dynamic Transitions in a Model of Infection Spreading
Aparup Das Evolutionary Genomic Perspective of Malaria
S S Manna Disease Spreading in a Diffusive System
Sunita Gakkhar Complex dynamical behavior in epidemiological systems


Vaccines to prevent infectious diseases:
Why do we have very few success stories?

Vineeta Bal
National Institute of Immunology, Aruna Asaf Ali Road, New Delhi 110067.

In today’s date vaccines are extremely fashionable. It is a general perception that vaccines provide the safest, most efficient and permanent way to prevent illnesses. Traditionally vaccines are used for preventing infectious diseases. In some cases vaccines are used for limiting further spread of an infectious disease as well. More recently, vaccines are being developed for prevention of cancer such as cervical cancer in women; or prevention of metastasis in cancers such as melanoma or prostate; or preventing worsening of neurodegenerative diseases such as Alzheimer’s disease. Since a significant role for vaccines in preventing infectious diseases is well-established and as the focus of the conference is infectious diseases, here an attempt will be made to understand how vaccines are supposed to prevent infectious diseases. With that other questions would automatically emerge - what is expected out of a ‘successful’ vaccination, are all vaccines ‘successful’, do successful vaccines provide uniformly good response in every recipient, is it necessary to trigger such a response in 100% of the individuals, does the same strategy of vaccine development work for every infectious disease, do vaccines generate very long lasting immune responses and so on. While small pox was the first viral disease which was eradicated from the world with the help of a vaccine, there is hardly any other success story which matches with small pox eradication. For some diseases many vaccines have been tried and failed e.g. malaria. Incidence of some infections has come down significantly with vaccination but eradication is still a dream e.g. polio. For a new world disease such as AIDS, it is still unclear what would be the best target for vaccine development! Many infections prominently found in the third world such as leshmaniasis [kala azaar], filariasis, infections leading to diarrhoeas and dysenteries are also the cases where no successful vaccine has been developed. Are the vaccine failure stories leading to failure in controlling infections only to do with our limited understanding of immunology of vaccine development or there are other factors too?!

Evolutionary Genomic Perspective of Malaria

Aparup Das
National Institute of Malaria Research (ICMR), 22 Sham Nath Marg, New Delhi 110 054

Malaria is a serious tropical infectious disease, causing high morbidity and mortality mainly in the developing countries, resulting in huge burden to the country of its endemicity and the society. Being a vector-borne disease caused by the protozoan parasite Plasmodium, malaria is a continuous threat to the human health. Even though India contributes only about 10% of the total malaria cases across the globe, the severity and relapses of the disease have far-reaching social implications. Researches from various angles are underway to understand the disease, its mechanism and to design and develop new therauptic measures, however, with feeble success. Application of different modern biological tools with interdisciplinary approaches in malaria research is the ultimate hope to tackle these scourges. In this respect, the availability of the whole genome sequence information of all the three taxa involved in malaria (Plasmodium, Anopheles and humans) at the public domain and rapid development of statistical-mathematical models have created an opportunity not only to understand the finer details of the blueprint of life of each individual taxa but also to unravel the mystery by which the three organisms interact to cause the disease. The newly emerging field of evolutionary genomics would help in understanding the genome in a evolutionary standpoint and infer how genes responded and evolved differentially in the changing environment each taxa had faced (e.g. drug pressure to the parasites, spraying of insecticides to the vectors and malaria burden to humans) in the long history of the disease. Considering the spatial position of India in the globe covering a wide latitudinal range with heterogeneous environment, genetic composition of species populations are expected to be highly diverse. The bright prospect that malaria research provides in India with evolutionary genomic and bioinformatic approaches would not only help mitigating this disease but could also serve as a model for other infectious diseases.

Complex dynamical behavior in epidemiological systems

Sunita Gakkhar
Department of Mathematics, I I T Roorkee, 247667

‘Pulse’ vaccination strategies in contrast to continuous vaccination are cost effective as they help in disease eradication at relatively low values of vaccination. A planned pulse vaccination regime reportedly gives a stable disease free solution in a SIR epidemic model. The introduction of seasonal variation into the basic SIR model leads to complex dynamical behavior which includes periodic, quasi periodic and chaotic dynamics for different choices of parameters.
The impulsive vaccination strategy applied to an epidemic model with non-linear incidence rate will be discussed. The model analysis will be presented. The complex dynamical behavior with pulse vaccination is expected. To study the influence of key parameters the results of numerical simulations will be discussed out.

Epidemic Spreading on Networks: A Topographic View

Niloy Ganguly

Department of Computer Science & Engineering, I I T Kharagpur, Kharagpur 721302

In this talk we give an overview of a new approach to understand epidemic spreading on networks. The ideas are basic, however they have ready application to a wide range of problems, including the control of the spread of data viruses and other harmful electronic information. A "topographic" picture of the network is built, in the sense that there are neighborhoods of high and low spreading power. Close link between eigenvector centrality (EVC) and spreading power is established. The topographic picture helps to develop a detailed understanding, at the level of neighborhoods, of epidemic spreading over the network. The analysis of spreading thus gives a finer resolution than typical whole-graph studies. The picture is strongly confirmed by a series of simulations on empirical social/information networks, and is also supported by some limited mathematical results. Finally, a set of design suggestions for both helping and hindering spreading is put forward, and some test results of these suggestions are presented. Finally we present how the idea can be used to enhance performance of search algorithms.

Disease Spreading in a Diffusive System

Subhrangshu Sekhar Manna
Satyendra Nath Bose National Centre for Basic Sciences (SNBNCBS), Block-JD, Sector - III, Salt Lake, Kolkata - 700098

The susceptible-infected-susceptible (SIS) model is discussed for the regular and scale-free graphs - known results are explained. The dynamics is modified by using a system of diffusing particles (random walkers) and studying the spreading of infectious diseases among them. In the context of SIS model, a density dependent critical point is observed. For Scale-free networks (SFNs) prevalence seems to saturate at small values at the low infection rates.

Parasite Invasion in Space: Modelling and Data Analysis

Somdatta Sinha
Centre for Cellular and Molecular Biology, Uppal Road, Hyderabad 500 007, India

Spread of infectious disease in space is due to invasion of parasites through infected/carrier hosts, vectors, or direct and indirect dispersal of the parasites among subpopulations of hosts. In nature, a host metapopulation consists of subpopulations having non-identical behavioural and demographic properties, occupying habitats of variable extent/quality and connectivity. Functional dynamics of those species, which have the capability to induce damage to other species (e.g.,parasite/pathogen), assume larger importance in the metapopulation context. Thus interaction of local host-parasite dynamics and migration in space is important for both ecologists and epidemiologists.
In this lecture, two approaches to study parasite invasion will be discussed. First, parasite invasion in a model lattice metapopulation of host-parasite species with spatial and demographic heterogeneity will be presented. The spatiotemporal dynamics of host and parasite with short and long range migration will be discussed. Secondly, spatiotemporal description of Malaria prevalence data in India will be shown, and how important information from these maps can be extracted will be discussed.

Dynamic Transitions in a Model of Infection Spreading

Sudeshna Sinha
The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai

We discuss the nature of dynamic transitions in a model of infection spreading. Specifically we examine the role of the underlying network structure on the temporal dynamics of the epidemic. We find that varying the degree of disorder in the underlying population network yields transitions from low quasi-fixed states (analogous to endemic infection) to self-sustained oscillations arising from synchronized periodic disease outbreaks. Further we show how the timescales of the local dynamics, in particular the length of the disease cycle, is also crucial in determining the dynamic transitions.