Increasing risk behaviour can outweigh the benefits of antiretroviral drug treatment on the HIV incidence among men-having-sex-with-men in Amsterdam

Background: The transmission through contacts among MSM (men who have sex with men) is one of the dominating contributors to HIV prevalence in industrialized countries. In Amsterdam, the capital of the Netherlands, the MSM risk group has been traced for decades. This has motivated studies which provide detailed information about MSM's risk behavior statistically, psychologically and sociologically. Despite the era of potent antiretroviral therapy, the incidence of HIV among MSM increases. In the long term the contradictory effects of risk behavior and effective therapy are still poorly understood.Methods: Using a previously presented Complex Agent Network model, we describe steady and casual partnerships to predict the HIV spreading among MSM. Behavior-related parameters and values, inferred from studies on Amsterdam MSM, are fed into the model; we validate the model using historical yearly incidence data. Subsequently, we study scenarios to assess the contradictory effects of risk behavior and effective therapy, by varying corresponding values of parameters. Finally, we conduct quantitative analysis based on the resulting incidence data.R

Applications Of Operations Research/Statistics In Infection Outbreak Management

Operations Research (OR) can be identified as the discipline that uses statistics, mathematics, computer-modelling and similar science methodology for decision making (Luss, Rosenwein, 1997). OR, powered with statistics and models, is a high potential engine for use in many areas that require evidence-based or model-based decision making. One of the most promising areas is specifically the infection outbreak management. Surprisingly, very little OR/statistics research has been aimed at infection outbreak management; usually, other general epidemiology issues were tackled in models. However, OR/statistics models for use in the infection outbreak management exist and can be effectively used in public policy and outbreak management practice. Probably, key reasons for that little involvement of OR/statistics in the infection outbreaks management is low awareness among the specialist community of OR/statistics use and benefits for their decision making. Up to the moment, there is lack of contemporary review of OR/statistics-applied models used for the infection outbreak management decision making. The present paper aimed at filling that gap and providing two benefits to involved health care managers and academics: first, developing awareness on the use and benefits of OR/statistics models for the infection outbreak management decision making, and second, for plotting the current state of affairs to highlight research opportunities for developing the field by academics and epidemic control professionals

Simulating Individual-Based Models of Epidemics in Hierarchical Networks

Abstract. Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics operators, which are iteratively applied to the contact network. We reduce the network generator’s computational complexity, improve cache efficiency and parallelize the simulator. To evaluate its running time we experiment with an HIV epidemic model that incorporates up to one million homosexual men in a scale-free network, including hierarchical community structure, social dynamics and multi-stage intranode progression. We find that the running times are feasible, on the order of minutes, and argue that SEECN can be used to study realistic epidemics and its properties experimentally, in contrast to defining and solving ever more complicated mathematical models as is the current practice.