Estimating transmission intensity is an essential aspect of modeling malaria and the foundation for further analysis. It is therefore important to understand what is required to do this, and some of the difficulties involved in its measurement.
Characterizing and estimating transmission intensity
In most cases, the end goal of the MMC’s work is to run simulations of malaria transmission in specific locations, under various scenarios, in order to determine what will work to interrupt transmission and what will not. To do this, we must first understand the details of malaria transmission in the area(s) of interest. This characterization of transmission will determine what interventions will have the most impact, and is the starting point for any subsequent analyses.
Of course, malaria transmission is incredibly complex, depending on both physical and built environments as well as the interactions and behavior of human, mosquito, and parasite populations, and the history of transmission and interventions. Despite this complexity (or, perhaps, because of it) summary metrics are often used as a way to quickly convey the intensity of transmission. While these summary metrics are conceptually clear, they are not easy to compute. Data about the many different factors that affect transmission in a particular setting must be fit to clinical data about the incidence or prevalence of malaria there, in order to arrive at a metric of transmission intensity that can be used to parameterize malaria models.
There are several summary metrics of transmission, each of which differs in its formulation and is useful in different ways:
The Entomological Inoculation Rate (EIR) is the most basic summary metric for transmission intensity. EIR is the number of infectious bites per person in a given time frame (often one year). It is possible to estimate EIR (using one of several methodologies) by counting mosquitoes as they attempt to bite humans, or using models and prevalence or incidence data. However, this metric will be strongly affected by interventions limiting the ability of mosquitoes to reach humans, even if there is no impact on the mosquitoes’ potential ability to transmit malaria.
Vectorial Capacity (VC), which is the number of infectious bites resulting from a single infectious case, provides a way to measure the potential transmission intensity mosquitoes can sustain. This does not change as the prevalence of malaria changes, and can remain high even after elimination in an area. This would indicate that if malaria is reintroduced to the area, it will likely be sustained by endemic transmission.
Using vectorial capacity, and accounting for the length of infection and infectiousness of mosquitoes, we can calculate the number of new infections caused by a single infectious case, the reproductive number, R. There are calculations of R for different sets of circumstances. The basic reproductive number, R0 is a measure of transmission at baseline, before any interventions have been introduced. The reproductive number under control, Rc then adjusts for any interventions that have been implemented. These metrics are particularly useful for long-term planning, and to determine what effect size is necessary to eliminate malaria (that is, how much of an impact an intervention or set of interventions needs to have R in order to interrupt transmission).
A critical threshold is Rc<1, as this describes situation in which malaria infections will not replace themselves, and malaria will be eliminated if current conditions (including interventions) continue. However, it is also important to remember that R indicates whether or not elimination will occur if current conditions continue, and not how long it will take to eliminate malaria. This means some interventions may be useful even in areas where Rc<1 in order to accelerate elimination.
With an understanding of transmission intensity, it is possible to begin addressing more operationally relevant questions, such as what the optimal mix of interventions for a specific area is, or where a given intervention (for example, mass drug administration or community health workers) will have the most impact. This type of analysis can be useful not only for the scientific community, but also for national malaria programs and funders interested in malaria elimination or control.