A new paper published in Nature indicates that scientists now know more about Plasmodium falciparum, the deadliest species of malaria, than ever before. Dominic Kwiatkowski and co-authors succeeded in deep sequencing the malaria genome, a process that charts how genes interact with surrounding proteins to determine what traits are expressed and turned on or off. The hope is that this new information will allow health officials to foresee malaria trends based on biological information instead of mathematical models. At a fundamental level, surveillance of biological information is the most up-to-date evidence of a pathogen or parasite in population, while a mathematical model is a forecast based on predetermined metrics.
One of the reasons that biological models are favored over mathematical models is that they account for a dynamic factor that mathematical models can’t: different strains of malaria. There are five different malaria-causing parasites: Plasmodium vivax, Plasmodium ovale, Plasmodium falciparum, Plasmodium malariae, and Plasmodium knowlesi. Once people acquire a strain of malaria they do not develop complete immunity. Symptoms become less severe, but someone can relapse on the same strain, or get infected by a new strain. As a result, a person may end up harboring several different malarial strains from their various illnesses. With deep sequencing, scientists may be able to understand how strains develop drug-resistance and which areas are most vulnerable by mapping malarial strains, something impossible to grasp with a mathematical model. After officials have a strong grasp of what genetic strains exist in different locations, Kwiatkowski, head of the Wellcome Trust Sanger Institute’s Malaria Programme, told NPR that he hopes the gene sequencing can be used to create efficacious treatments: drugs whose efficacy will not fade through resistance from the malaria parasite. Given recent evidence of drug resistant malaria in Southeast Asia, the potential is tantalizing. Malaria continues to kill thousands of people every year, most of them children. In 2010, the World Health Organization attributed 655,000 deaths to malaria, but the Institute for Health Metrics and Evaluation estimated the real number that year to exceed 1.2 million.
Kwiatkowsi’s new gene sequencing has not been applied to surveillance systems yet, so we don’t yet know how successful it will be. But this new discovery does map just how frustrating it has been to fight malaria in the past. If history is an indicator, specifically our past hopes for vaccines and plans for eradication, it is evident that, most likely, no single intervention will successfully control the tiny blood-borne parasite.
P. falciparum is a complicated little creature. The parasite is transmitted in an indirect cycle, meaning that it requires two hosts – humans and mosquitoes. The fact the parasite has to survive transmission between two hosts obscures how well it survives in different populations. The simplest mathematical model to predict how P. falciparum will thrive in a population, the Ross Macdonald model, typically contains seven variables. What this means, is that there are a lot of factors to keep in mind. For example, how malaria spreads is impacted by mosquito-to-human as well as human-to-mosquito transmission rates, the number of female mosquitoes, and how often they are able to bite a human. There even exist two different versions of the same model: deterministic and stochastic. A deterministic model will show how badly malaria will affect a population in a vacuum; you chose numbers for each variable and plug-and-chug. A stochastic model accounts for chance and provides a series of possible outcomes. Needless to say, the situation only gets more complicated with population dynamics, drug resistance, and abundant strains of the malaria parasite. With so many variables, it is not surprising we have not found a single way to eradicate malaria. On the other hand, the eternal optimist can find a silver lining: the complicated life cycle of P. falciparum also begets opportunity.
We try to prevent malaria through a variety of methods, each one focused on different variables in the Ross Macdonald model. Much like how we combine treatments to fight HIV, the various stages of the parasite’s life provide different points of attack. Bed nets limit the number of bites a person receives; insecticides reduce mosquitoes in the environment; environmental and population factors can change the number of people a mosquito will bite. All these interventions need to be integrated, because each individually has its own pitfalls in application.
For example, for a long time, spraying houses with insecticides such as DDT was associated with almost negligible incidence of malaria. We stopped using DDT (for all the right reasons) and malaria skyrocketed on a nearly exponential curve for years. On a more sinister level, fake or sub-standard drugs provide the opportunity for parasites to encounter and survive medical treatments, increasing the chances of drug-resistance. Even when the drugs do work, there are issues of cost and access.
Any one intervention cannot be a panacea, and to simplify the issue only ensures that programs and donor funding will be misguided. To argue that there are simple and implicitly definitive solutions, such as a quick diversion of funds or a new drug, is at best misleading. At worst it is irresponsible. The worst case would be for programs and money spent to fall victim to the same pitfalls as before. P. falciparum is indeed a complicated creature and the solution that saves millions of lives will most likely not be simple, but an integration of several ideas. As a result, it is undoubtedly an accomplishment to perform a deep-sequence of malaria’s genome, and the new information will find a place in disease surveillance, but to expect an end to malaria would risk repeating history. After all, haven’t we been down this road before, specifically, ten years ago when scientists performed another analysis of the malaria genome?
