I want to briefly jot down my reflections surrounding the day:
The day began relatively late, as is expected in Namibia. It is rare for programs to start on time, even when the “08h00 for 08h30” rule applies. That worked in my favour, because I had enough time to show up.
As I arrived a student spoke about a mathematical model of food and mouth disease. As I entered, he was speaking about the basic reproductive number – R nought. I did not see the start of his talk, where he elaborated on the states and structure. Yet I understood his model was rather complex as his R naught was a function of the transmission probability -Beta, divided by several other parameters. If you consult a basic mathematical modelling textbook, you will find that R naught there is defined as Beta over Alpha. Beta is a quantity modelers use to estimate the force of infection in these Susceptible Infectious Removed models. The force of infection – the average rate at which susceptible individuals acquire the infection – is proportional to this Beta. Alpha is now the average rate at which infected individuals move to the removed state. I think Wikipedia and other blogs will help clarify this. I did point out his definition of R naught must state that it is the expected number of new infections that happen when the population is totally free of infected individuals and one of these arrives.
What I asked him at the end of the talk was an eye opener for me. I posed the following question : Your talk is rather mathematically intense, so how do you propose to communicate the importance of modelling to our policy makers,. some of whom only have basic education? His reply was really something else. Basically, he said he as a mathematician. He told me that he would work with epidemiologists and they would then explain it to the policy makers! Is that not a worthy response for an undergraduate? Did he just sum up my whole search for meaning in public health? I guess epidemiology is the career choice for me then. For many people in the audience, this might have been the first time they heard the word “epidemiologist”. By fashioning the meaning of epidemiologist as a person who communicates the usefulness of an infectious disease model to policy makers, what meaning does epidemiology take on? How does it relate to the large, global, profession of epidemiology in academia and the development agency world?
The other talk I really enjoyed was a review of the Demographic Health Survey data of 2013 in Namibia. I am glad the student who presented accurately described the goal of her survey to understand socio-economic determinants of HIV infection. She also explained the logistic regression technique well, though she could have explained what a covariate was. Most of us in the audience though were taken aback with her humor. For example, she found that relative to single people, widowed and separated people “had a higher risk of HIV infection”. She then added “yes we don’t know why those people are widowed” with a slight laugh. I am not sure if this was inappropriate, but she could have gone further to point out this was a survey. Hence, the implication that being widowed leads to HIV infection is not the most plausible one – rather that widowed individuals lost their partners, in all likelihood, to AIDS. In this talk, widowed referred to people of either gender. However women had significantly greater HIV prevalence.
I did wish to ask her several questions – such as who does she think the survey left out? In particular, did the survey manage to identify transgender people who have the largest likelihood of being HIV positive of any at risk population known? But I did not, for the simple reason that her talk was replete with misguided interpretations. For instance, people who use condoms are at higher risk, according to her, of HIV infection. This was one of the findings. But this could have been avoided with the mere use of the term “likelihood of HIV infection” instead of risk. I let her know after the talk. One lady in the audience though correctly pointed out that “condom use” by itself is meaningless, unless it is consistent and correct condom use. I was thinking of the simpler possibility that condom users knew they were HIV positive and purposefully wished to protect their partners from infection. But of course, I wonder which explanation is more popular in the literature.
Time to go to Mass.
And then I have to finish my mini-dissertation corrections!