{"id":48,"date":"2015-05-27T15:51:16","date_gmt":"2015-05-27T15:51:16","guid":{"rendered":"http:\/\/magazine.hmc.edu\/spring-2015\/?p=48"},"modified":"2015-08-28T10:34:43","modified_gmt":"2015-08-28T17:34:43","slug":"virtually-viral","status":"publish","type":"post","link":"https:\/\/magazine.hmc.edu\/spring-2015\/virtually-viral\/","title":{"rendered":"Virtually Viral"},"content":{"rendered":"

The growing urgency to learn more<\/strong>\u00a0about how epidemics spread among large populations has inspired epidemiologists to construct a world of their own\u2014virtual populations of fictional citizens calibrated to mimic the traits of real people in a selected area.<\/p>\n

Nadia Abuelezam \u201909 is at work on the HIV Calibrated Dynamic Model (HIV-CDM), a simulation tool that uses sophisticated statistical methods to recreate, with startling accuracy, the dynamics of a pathogen on the move.<\/p>\n

\"feature-5-body-2\"
Nadia Abuelezam \u201909<\/figcaption><\/figure>\n

A joint mathematics and biology major, Abuelezam went on to study epidemiology at the Harvard T. H. Chan School of Public Health, where she earned her doctorate and is now a postdoctoral research fellow. \u201cAt Harvey Mudd, I got this rigorous quantitative education, and the math faculty members were spectacular. I was drawn into public health because I could apply the math and influence people\u2019s lives.\u201d<\/p>\n

The HIV-CDM simulates reality in South Africa. \u201cEach person in the simulation model has various characteristics: gender, sexual tendencies and health profiles. They interact with others in this simulated world. We have set up rules for behavior\u2014for instance, someone with high-risk behavior would have multiple partners and likely one with HIV. When we run the model, it allows us to see the spread of HIV in the network, and we can also follow the health progression of individuals once they have HIV. We do everything from simulating initial transmission to simulating treatment and prevention on the far end.\u201d<\/p>\n

The broader benefits of models like the HIV-CDM become clear as one considers their operating scale and flexibility. \u201cWhat\u2019s unique about simulation models in an epidemiologic context is that they take data from other epidemiological studies and provide a framework for prediction and simulation that other methods don\u2019t. We can test the impact of prevention methods or ask hypothetical questions. This is something that is difficult to do with observational data.\u201d<\/p>\n

\"feature-5-body-3\"<\/figure>\n

The model is protean in its complexity and beholden to the accuracy of the input data and the modeler\u2019s assumptions. \u201cThis is something we struggle with,\u201d Abuelezam acknowledges. \u201cAll the values for our parameters come from published reports on South Africa, and there are also assumptions we make. Some are stronger than others.\u201d
\n<\/a> Certain fundamental data points for the HIV-CDM relate to stigmatized human activities and conditions or other personal or social characteristics that are difficult to ascertain from traditional epidemiologic methods. Other times, information is simply not fully understood. A calibration technique known as Bayesian melding allows the model to bypass these apparent roadblocks.<\/p>\n

In simple terms, Bayesian statistical analysis computes and takes into account comprehensively all the possibilities that could play a factor in a particular outcome to determine how likely that particular outcome is. Bayesian melding goes one step further. In the face of incomplete information and the added possibility of unforeseen events taking place within the system, the technique narrows the output range of a model and casts aside outliers.<\/p>\n

Where data are less than robust, researchers assign credible ranges for given parameters, whether it be the potential number of sexual partners or the prevalence of HIV at a particular point in time. \u201cThese ranges form the upper and lower bounds for that parameter. We then examine the parameter space defined by those ranges to fit the model\u2019s outcomes to existing data,\u201d Abuelezam explains.<\/p>\n

\"feature-5-body-4\"<\/figure>\n

To complicate matters, the HIV-CDM is dealing with a dozen or more such parameters simultaneously. Think of each parameter as a slider on a sound-mixing console, for example. Using complex calculations and running the model thousands of times to determine the most prevalent results, you can adjust each slider until the final outputs of the model are in tune with the observed state of affairs of the population you are recreating. You then have an operational engine. To game out different containment strategies for a disease, try different inputs.<\/p>\n

\u201cThat\u2019s the next step,\u201d Abuelezam says. \u201cIt\u2019s great that we can simulate a population, but what can we say about the future? What will it take to eliminate HIV in South Africa? What combination of treatment and prevention programs is most effective? When we answer these questions, we can make policy recommendations to try to ensure people live long, healthy, uncomplicated lives.\u201d<\/p>\n

Epidemiology is two things. One is the analysis of a disease or social problem. The second part is taking what you have learned and trying to influence people\u2019s behavior to improve health.<\/p>\n

\u2013 Nadia Abuelezam \u201909<\/cite><\/p><\/blockquote>\n

With epidemics so much in the news, Abuelezam is used to fielding questions about them. \u201cOf course, Ebola comes up frequently, and we have multiple teams here working on it. Simulation modeling is universal to epidemic disease, so Ebola is prime for such treatment, but it\u2019s a different beast than HIV. It\u2019s easier to acquire, for obvious reasons. We don\u2019t have the same amount of historical data that we have for HIV, but what we do have is intense information about Ebola\u2019s geospatial spread. People get treatment quickly, and so you can pinpoint with GPS places where people have it and subsequent cases that come up.\u201d<\/p>\n

The mention of the current resurgence of measles, a disease for which there is a safe and readily available vaccine, elicits polite exasperation from Abuelezam. \u201cEpidemiology is two things. One is the analysis of a disease or social problem. The second part is taking what you have learned and trying to influence people\u2019s behavior to improve health.\u201d<\/p>\n

A new project that Abuelezam is working on involves wading into social networks. \u201cWe want to learn more about hidden and at-risk populations in the Middle East and North Africa, where HIV transmission is on the rise. We plan to use existing social networking platforms to infer information about the sexual network in locations where traditional epidemiologic studies are impossible to conduct. It\u2019s a very touchy project, and scary for a lot of people, because there is hostile legislation toward these groups in the region. Obviously, we don\u2019t want to do anything that would put people in harm\u2019s way. We do want to understand whether they are at risk and if they have access to prevention
\nand treatment.\u201d<\/p>\n

\n

The Facts on HIV\/AIDS<\/h2>\n

11.7 MILLION<\/h3>\n

At the end of 2013, 11.7 million people had access to antiretroviral therapy in low- and middle-income countries.<\/p>\n

35 MILLION<\/h3>\n

At the end of 2013, 35 million people were living with HIV.<\/p>\n

28 MILLION<\/h3>\n

Over 28 million people are eligible for antiretroviral therapy under WHO 2013 consolidated antiretroviral guidelines.<\/p>\n

Source: who.int\/hiv\/en<\/cite><\/p>\n<\/div>\n

Abuelezam has worked in Africa before and has been fascinated by it since she was a child. \u201cThe continent, the geography, but also the people in need.\u201d She took a class on HIV at Harvey Mudd with Karl Haushalter, associate professor of chemistry and biology. Subsequently, through the class, she was able to arrange work in Uganda with The AIDS Support Organization. The trip was transformative.<\/p>\n

\u201cIt was my first trip abroad. I had to learn to get myself where I needed to be, how to handle myself and communicate. It pushed me past a lot of my comfort points,\u201d Abuelezam says. The trip had profound professional ramifications as well.<\/p>\n

\u201cThere is no way I would be able to put what I am working on fully into context had I not gone. When you talk about age-disparate relations, rural areas with little access to health care, I saw all that firsthand. When I think about different aspects of the model, there is always a flashback in my mind:
\nYou were there, you saw this happening. It\u2019s improved my ability to model those situations
\nmore accurately.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

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