MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies

A series of standardized surveys at different times of the year
could easily be used to collect this information. Surveys might be
conducted on board the ferries during the trip from the mainland,
although sampling strategies will need to be evaluated to ensure
a representative cross-section of passengers are interviewed.
Informal boat traffic passes through a few known hubs in
Zanzibar, and similar surveys could be conducted in these areas. It
would also be very useful to ascertain the prevalence of infection
in individuals traveling by ferry or informal boat using RDTs.
However, in the absence of this information, infection prevalence
can be estimated as long as travelers’ origins are known. Data
over all months of a year from all mobile phone providers on
the mainland and Zanzibar (and surrounding countries) would
also provide a much more representative sample of movement
patterns to and from Zanzibar, enabling more sophisticated
analyses and robust conclusions to be drawn.
This analysis also emphasizes how Zanzibar’s prospects of malaria
elimination will be highly related to progress with malaria control on
the mainland. Importation risk will change in concert with malaria
transmission intensity on the mainland. If strong control measures
are put into place, prevalence of infection will decrease and will lead
to fewer infected travelers to Zanzibar. Figure 7 displays an estimate
of migrant-based ICR by mainland district. Improving coverage
by control measures in those districts with high ICR will have a
very large effect on decreasing the number of infected individuals
entering Zanzibar, improving the long-term outlook for Zanzibar’s
ability to reach and maintain malaria-free status.
CAN ELIMINATION BE ACHIEVED?
The previous sections have detailed the calculation of transmission
and importation risk in Zanzibar. Together, these two measures
indicate the amount of malaria parasites being transported to
the islands and the amount they will spread among them given
a particular level of interventions. In this section and the one
that follows, we use these estimates of the malariogenic potential
as inputs into mathematical transmission models to predict the
potential for reaching and staying at elimination. These models
are simplified representations of the world, but they provide
the best understanding of the potential and risks of malaria
elimination on Zanzibar under different scenarios.
MODELING MALARIA IN ZANZIBAR
The potential for Zanzibar to eliminate malaria was evaluated
using a published malaria transmission model (Smith and
Hay, 2009). The model incorporates a number of complexities
that make it more realistic than the models that were used for
planning during the GMEP. For example, classical mathematical
models assume that mosquitoes bite all individuals equally, but in
this model it is more realistically assumed that some individuals
are bitten more often than others. The model also incorporates
important concepts like immunity and superinfection, the ability
of individuals to harbor multiple infections at the same time. For
more specifics of the model, see the appendix in this report or
details in the publications in the literature.
24
An additional model was used in conjunction with the
transmission model to estimate the effect of different levels
of ITNs or IRS (Smith et al., 2009). This secondary model is
based on the mosquito feeding cycle; it describes changes in the
vectorial capacity, or the mosquito-related aspects of R 0 and R C
(Garrett-Jones, 1964) that are related to ITN use. The effect of
ITNs depends on the proportion of the whole community that
owns and uses a net, called the effective coverage (Le Menach et
al., 2007). Increased ITN use lowers the vectorial capacity and
therefore reduces R C. This lower transmission risk then feeds
back into the malaria transmission model. In this way, the impact
of ITN coverage on overall malaria incidence and the potential
to get to and remain at zero can be examined.
For Zanzibar, current control measures include both ITNs
and IRS. There is currently insufficient evidence to effectively
distinguish between the effects of the two interventions or to
determine their interaction when employed simultaneously.
As such, we make an assumption that the model can treat the
protection offered by having a house sprayed with IRS in the
same way as the protection offered by sleeping under an ITN.
For example, the model assumes that there is no difference
between having 60% of the population protected by IRS and
60% sleeping under ITNs. The model should be updated once
more evidence is available on this issue.
MODEL RESULTS
The results of these models indicate that elimination is possible
with current control tools, but that it is dependent on maintaining
high levels of effective coverage with ITNs and/or IRS. The
rate at which malaria is decreasing (or increasing) in Zanzibar
varies with R C, and thus with the amount of control. If control
measures are sufficient to keep R C less than one (meaning that
each case of malaria generates fewer than one additional cases),
malaria will eventually go to zero. If control measures are not
high enough to keep R C at or below one (meaning that each case
of malaria generates more than one additional cases), malaria will
increase.
Using the model, it is possible to estimate the coverage of LLINs
or IRS that equates to different values of R C in Zanzibar. We
estimate that achieving the “tipping point” R C value of one
should require, conservatively, coverage of about 60% of the
population. It should be noted that considerable uncertainty
surrounds this estimate because of the lack of detailed data
on parasite prevalence over time, the heterogeneity in current
transmission levels across Zanzibar, and the need to parameterize
the model with values from studies conducted elsewhere. There
is, however, an easy test. Surveillance of net usage, IRS coverage,
and PfPR over time will provide the evidence to evaluate whether
these levels are high enough to sustain elimination. This threshold
means that achieving effective coverage of bednets and/or IRS for
more than 60% of the population will likely result in decreases in
transmission and, eventually, elimination, while lower effective
coverage will mean that malaria will increase. The further the
actual coverage level (and therefore R C) is above or below this
threshold will determine the speed with which elimination is