**7. References**

226 Epidemiology Insights

The risk of HIV infection was assessed with a major focus on unprotected anal intercourse (UAI). Two respondents out of ten reported having had UAI with an occasional partner the last time they had sex, while four out of ten reported having had UAI in the last 6 months with this kind of partner or partners. The percentages of UAI with an occasional partner were highest in Bucharest, Prague and Bratislava both for the last sexual encounter and for encounters over the previous 6 months. In line with this finding, in these cities MSM reported less use of a condom for anal intercourse the last time they had anal sex. Young MSM exhibited the riskiest behaviour, as the highest rates of UAI with occasional partners, both last time and in the last 6 months, were found amongst young people under 25 years old. As expected, UAI with a steady partner was more frequent than with an occasional

Finally, the association between HIV risk and alcohol and drug use was confirmed. At least one third of respondents had used drugs before or during sex over the last six months and half the respondents had used alcohol. This proportion was above half in Ljubljana,

This data suggests the (i) the need for health promotion and prevention messages particularly focused on sexual behaviour and alcohol and drug use (ii) the need for prevention and information programmes for STIs given that the presence of an STIs increases the risk of HIV infection (iii) the need for policies and strategies promoting VCT

The Capacity building in HIV/Syphilis prevalence estimation using non-invasive methods among MSM in Southern and Eastern Europe – SIALON project was funded by the European Commission under the European Commission Public Health Programme 2003-

The sole responsibility for this article lies with the authors and the European Commission is

Yuri Amirkhanian (CAIR – Medical College of Wisconsin, USA) Eva Baldassari (Mental Health Department, ULSS20, Verona) Luigi Bertinato (Service for International Social and Health Relations, ULSS20, Veneto Region, Verona) Chrisoula Botsi (HCDCP, Athens) Michele Breveglieri (Regional Centre for Health Promotion, Verona) Enrica Castellani (Regional Centre for Health Promotion, Verona) Paola Coato (Azienda Ospedaliera Universitaria Integrata Verona, Department of Pathology, Section of Microbiology) Jonathan Elford (City University London) Cinta Folch (CEEISCAT Badalona Barcelona) Roberta Fontana (Microbiology – Verona University Hospital) Mauro Fornasiero (University of Bristol, England, UK) Jean-Pierre Foschia (Regional Centre for Health Promotion, Verona) Martina Furegato (Regional Centre for Health Promotion, Verona) Lorenzo Gios (Azienda Ospedaliera Universitaria Integrata Verona, Verona) Jaroslav Gyurik (Association of AIDS Help, Bratislava) Victoria Gonzalez (CEEISCAT Hospital Universitari Germans Trias i PUjol, Badalona) Valentina Guarnieri (Microbiology – Azienda Ospedaliera Universitaria

not responsible for any use that might be made of the information contained therein.

Barcelona and Prague for drugs, and higher than 80% in Prague and Bratislava.

among hard to reach populations such as MSM, especially young MSM.

partner, in the overall sample and in all the cities.

**6. Acknowledgements** 

The SIALON Network is composed by:

Financial support:

2008.


http://www.who.int/hiv/pub/surveillance/guidelines/en/index.html

**0**

**12**

<sup>1</sup>*Portugal*

<sup>2</sup>*The Netherlands*

**Modeling Infectious Diseases Dynamics:**

**Dengue Fever, a Case Study**

Maíra Aguiar1, Nico Stollenwerk1 and Bob W. Kooi2

<sup>1</sup>*Centro de Matemática e Aplicações Fundamentais, Lisbon University*

<sup>2</sup>*Faculty of Earth and Life Sciences, Department of Theoretical Biology, Vrije Universiteit*

Throughout human history, infectious diseases have caused debilitation and premature death to large portions of the human population, leading to serious social-economic concerns. Many factors have contributed to the persistence and increase in the occurrence of infectious disease (demographic factors, political, social and economic changes, environmental change, public health care and infrastructure, microbial adaptation, etc.), which according to the World Health Organization (WHO), are the second leading cause of death globally (≈ 23 % of deaths)

Research on basic and applied aspects of host, pathogen, and environmental factors that influence disease emergence, transmission and spread have been supported so far, and the development of diagnostics, vaccines, and therapeutics has been greatly increased. In recent years, mathematical modeling became an interesting tool for the understanding of infectious diseases epidemiology and dynamics, leading to great advances in providing tools for identifying possible approaches to control, including vaccination programs, and for

Epidemiological models are a formal framework to convey ideas about the components of a host-parasite interaction and can act as a tool to predict, understand and develop strategies to control the spread of infectious diseases by helping to understand the behaviour of the system under various conditions. They can also aid data collection, data and interpretation and parameter estimation. The purpose of epidemiological models is to take different aspects of the disease as inputs and to make predictions about the numbers of infected and susceptible

In the early 20*th* century, mathematical models were introduced into infectious disease epidemiology, and a series of deterministic compartment models such as SI (susceptible-infected), SIS (susceptible-infected-susceptible), and e.g SIR (susceptibleinfected-recovered) have been proposed based on the flow patterns between compartments of hosts. In our days, most of the models developed try to incorporate other factors focusing on several different aspects of the disease, which can imply rich dynamic behaviour even in the most basic dynamical models. Factors that can go into the models include the duration

**1. Introduction**

after cardiovascular diseases (WHO, 2010).

people over time as output.

assessing the potential impact of different intervention measures.

