Today epidemiologists have released four maps showing the way a flu pandemic will spread, depending on how nations respond with anti-viral drugs. Two scenarios demonstrate how to stop the spread of swine flu, fast.
Led by Hong Kong medical researcher Jospeh Wu, the team used mathematical modeling to look at how a closed city like Hong Kong might respond to the outbreak of an epidemic with drug treatments. In particular, they were interested in what happens if the city responds by treating everybody with one drug, usually Tamiflu, vs. if they treat everybody with two drugs, Tamiflu and Relenza. Because flu viruses evolve resistance to drugs so quickly, their question was how best to knock the virus out quickly without it having a chance to evolve a resistance.
What they learned was that the best possible way to stop a pandemic from spreading was for every country to have stockpiles of two different antiviral drugs to treat flu: A primary stockpile and a secondary one. The first wave of cases should be treated with the secondary drug until it runs out, then the next wave should be treated with the primary drug. This knocks out the first wave of virus, and then just as it begins to evolve resistance hits it with a new drug. Apparently in scenarios where public health officials respond like this, using what's called "sequential drug multitherapy" or SMC, the spread of the virus is reduced significantly. "Monotherapy," or the treatment with just one drug, created a lot of drug-resistant flu strains and did not significantly impair the spread of the epidemic.
According to New Scientist:
The two strategies that used more than one drug decreased the number of people who finally became infected from 68 to 58 per cent. It also reduced the chance of resistance emerging from 38 to just two per cent, which would translate into a significant number of lives saved, says Wu.
Right now, swine flu is not resistant to either Tamiflu or Relenza, so their scenario is perfect for our current epidemic. Unfortunately, most nations only have a stockpile of one drug, generally Tamiflu. Let's take a look at all four scenarios.
According to the study Wu and colleagues published today in PLoS Medicine:
[Here are scenarios for] sequential multidrug chemotherapy in a global network of 105 cities. Hong Kong (HK) is the source of infection in the network with 30 wild-type seeds on day 0. Twenty-eight cities implement large-scale antiviral intervention: Hong Kong, London, New York, Geneva, and 24 other cities (randomly chosen for each stochastic realization). Cities that implemented SMC had a drug B stockpile coverage of 1%.Four scenarios are shown.
Here's what to look for in these maps. All of them show the virus starting in Hong Kong, and the blue spreading dots are attack rates from the flu (AR). As the blue slowly changes color through the rainbow, what you're seeing is a drug-resistant virus evolving. Red dots are 100% drug-resistant attack rates (RAR). Colors between show the attacks slowly developing higher percentages of drug-resistant strains.
(A) HK and all 27 cities implemented monotherapy.
(B) HK and all 27 cities implemented sequential multidrug chemotherapy (SMC).
(C) HK, New York. Geneva and 11 other randomly chosen cities implemented SMC; London and 13 other randomly chosen cities implemented monotherapy.
(D) Same as (C) except that HK did not implement SMC.
Bottom line: Based on these mathematical models, SMC is the best way to prevent the spread of flu, especially drug-resistant flu. As long as the country that is the source of the flu uses SMC, the treatment is effective even if many other countries still use monotherapy. Monotherapy will stop the spread of flu initially but then as drug-resistant strains evolve it will again explode into epidemic proportions. And the flu that spreads will be resistant to the single drug that most countries have stockpiled.
Here is the authors' full explanation, which is slightly technical but worth checking out:
If only monotherapy was used, the importation of resistance promoted the spread of the resistant strain and downstream populations had higher attack rates and drug-resistant attack rates, e.g., New York had a higher attack rate [AR] and drug-resistant attack rate [RAR] than London because the pandemic reached New York later, with a higher proportion of introduced infections being resistant. Population size also played a role. The small population of Geneva had a smaller RAR than London even though the two were hit at approximately the same time: smaller populations were less vulnerable to the local emergence of antiviral resistance because fewer cases were treated with drug A. We note that our city population sizes are only proxy measures for entire local populations which feed into major airports.
If all 28 populations that had stockpiles of antivirals implemented sequential multidrug chemotherapy (SMC) [giving first a secondary drug, then giving out a primary drug when the first runs out] rather than monotherapy, reductions in AR and RAR in these populations were similar to those in a single source population (scenario B). Therefore, the connectedness of cities had little impact on the effectiveness of SMC if all populations that implemented large-scale antiviral interventions adopted SMC. The effectiveness of SMC was attenuated (but was still significant) if only half of these 28 selected populations
adopted SMC (Scenario C). Interestingly, in this scenario, those populations that implemented only monotherapy (e.g., London) still benefited from the implementation of SMC in the other populations because fewer resistant cases were circulating within the network.
The source population was the key to the robustness of SMC as a resistance-limiting strategy at the global scale. If the source population implemented only monotherapy, then SMC had little benefit in any downstream population.
Image by Cynthia Goldsmith