Influenza hit early and hard this year. Emergency professionals were, as always, the first to feel the effects as infection rates rose significantly with increased virulence over last season. Influenza Type A (H3N2) was the predominant culprit, and the vaccine was well matched with a 62 percent effectiveness rate, according to the Centers for Disease Control and Prevention. But pandemic spread has always been more of a math problem than a biological one. Like Vegas, sometimes the numbers seem to work against our favor, despite the odds.
Static constants were applied to early biological models in attempts to understand how epidemics spread. Kermack and McKendrick were the first to describe the mathematical relationship between infection rates, recovery rates, and the rate of death in a 1927 paper. (Proc Roy Soc Lond 1927;115:700.) This was groundbreaking methodology. Epidemiologists utilized this model of the susceptible, the infected and the recovered (SIR model) to describe the spread of disease for decades; it was an effective tool.
A certain critical mass occurs to initiate and terminate an epidemic. The model concludes that epidemics tend to stop when a sufficient number of susceptible individuals in a population is no longer available to infect. It wasn't until a 1982 paper by Anderson and May that the SIR model's simplicity was challenged by no longer treating population size as a constant. They were correct; it isn't. It was just easier math pretending it was.
The success of an infectious disease is proportional to complex interactions between its transmission rate within a population and its lethality and recovery rate, according to Anderson and May. They also included another step that takes into consideration death rates from other causes. The bottom line was that they built upon the work of many other theorists and demonstrated the correlation between the rate of transmission and recovery with virulence and unrelated mortalities. Meaning x rate of transmission at y recovery rate and z unrelated mortalities equal a given virulence.
Between these two hallmark mathematical events was the work of George MacDonald who worked on malaria spread in the 1950s. He developed the basic reproduction rate equation (referred to as R0, pronounced R-naught), which provided an index of transmissibility. It was based on how many secondary infections were produced when one infectious agent enters a population. A low reproduction rate less than 1.0 goes away. If the rate is greater than 1.0, it spreads; the higher the reproduction rate, the greater the epidemic.
Transmissibility and virulence are engines that drive the equations. Imagine a mosquito biting and infecting thousands of people with a disease during its life cycle, as has been posited in the literature. With a basic reproduction rate in the thousands, it is understandable how some diseases are more difficult to suppress than others.
McDonald became director of the Ross Institute and Hospital for Tropical Diseases in London. He demonstrated that epidemics could be triggered by small changes in transmission factors. This is why we worry so much about RNA viruses, which can be highly virulent and easily transmitted because of their high rate of mutation and replication. Combine those factors evading efforts to combat them, like developing vaccines, with high lethality, and we have the next big epidemic.
If this were not complicated or scary enough, we also have to contend with the concept of a “superspreader.” This is a person who spreads the disease greater than the typical infected individual. Typhoid Mary comes to mind. It is the renegade variable that confounds the basic reproduction rate. Hundreds of infected patients can be isolated, but you fail to stop the epidemic if one superspreader is missed.
We will not even get into the details of the concept of number needed to treat (NNT), which is an epidemiological measure of effectiveness and yet another mathematical model. As emergency professionals know, the higher the NNT, the less effective the treatment. Clearly, the measures are complex, but getting better all the time. The formulas watch for the next big one.
Imagine the perfect storm of a highly adaptable RNA virus that mutates rapidly and avoids attempts at vaccine production. The virus has a high level of lethality typical of Ebola, which kills nearly everyone it infects. Virulence is quite prolific, and so is transmissibility if it is airborne like an avian flu. The combination could make the 50 million deaths during the 1918-1919 flu epidemic a mere footnote in medical history.
The point is that there is no magic bullet. There is no perfect vaccine and no universal defense. Too many random variables exist to predict effectively when and where the next big epidemic will occur, although we do understand that the tipping points can be discrete. So what do we do?
The two biggest threats are bioterrorism and zoonotic disease. Combating both requires vigilant surveillance and rapid response to pathogen emergence. These are the two keys to success to the two biggest threats. Scientists at the Centers for Disease Control and Prevention are among the many storm troopers plugged into a network of civilian, military, and international teams fighting on the front lines to mount such responses.
Many factors are beyond our control, and we must act in comprehensive ways to control the variables we can. Epidemics spread through complex mechanisms, and we must be sophisticated enough to mount intelligent and comprehensive responses in multilateral fashion.
Emergency physicians are also among the scientists serving on the front line, and EDs are expected to respond with rational methodology in concert with epidemiologists. This requires vigilance to pathogenic spread, which is especially important in emergency medicine because emergency medicine is always the first to feel it, and, we should ensure we understand it.