MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies
MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies
MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies
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PERCENTAGE OF <strong>IN</strong>DIVIDUALS <strong>IN</strong>FECTED<br />
but rather to create an environment in which any sporadic<br />
transmission that does occur is immediately halted.<br />
Uncertainty about the cause of the decline in malaria endemicity<br />
in the years leading up to 2003 remains the biggest cause for<br />
concern in considering the potential for Zanzibar to have a<br />
resurgence of malaria following elimination. If these declines<br />
were caused by a drought, a change in weather could bring<br />
malaria transmission back to much higher levels. If the declines<br />
were caused by development, absent significant socioeconomic<br />
shifts, the innate risk of malaria will not change following<br />
elimination. In any case, vectors will remain prevalent and able<br />
to rapidly restart transmission. At the same time, importation<br />
risk will continue as well, with a significant number of infected<br />
individuals traveling to the islands. Thus, if control measures are<br />
inappropriately relaxed, malaria will resurge. The same model<br />
used to predict the time required to reach zero transmission can<br />
be used to estimate what will happen in the absence of sufficient<br />
levels of IRS/ITN coverage and surveillance.<br />
FIGURE 16: ESTIMATED RESURGENCE OF <strong>MALARIA</strong> FOLLOW<strong>IN</strong>G<br />
RELAXATION OF VECTOR CONTROL MEASURES<br />
20%<br />
18%<br />
16%<br />
14%<br />
12%<br />
10%<br />
8%<br />
6%<br />
4%<br />
2%<br />
0%<br />
1 51 101 151 201 251 301 351<br />
ABOUT 35% COVERAGE (RC=3.4)<br />
ABOUT 50% COVERAGE (RC=1.5)<br />
ABOUT 55% COVERAGE (RC=1.3)<br />
DAYS<br />
As Figure 16 shows, even modest reductions in IRS or ITN<br />
coverage can lead to dramatic resurgence of malaria. As a result, it<br />
is imperative that Zanzibar use caution in withdrawing or altering<br />
interventions even once transmission has been interrupted. This<br />
is further reinforced by the uncertainty associated with these<br />
models: changes due to the current R 0 and the importation risk,<br />
among others, could mean that the risk of resurgence is greater<br />
than currently shown.<br />
There are two primary means through which Zanzibar could<br />
prevent the re-emergence of malaria following elimination:<br />
�� Permanently maintain effective coverage of interventions at<br />
a level sufficient to keep R C below one across all of Zanzibar;<br />
�� Improve surveillance and response capacity to the point that<br />
all imported cases will be identified and treated before local<br />
transmission can occur.<br />
5 R Development Core Team (2009). R: A Language and Environment<br />
for Statistical Computing. http://www.R-project.org. R Foundation for<br />
Statistical Computing: Vienna, Austria.<br />
1 | Technical Feasibility<br />
These options are not mutually exclusive, and it may be necessary<br />
for Zanzibar to do both in order to prevent reintroduction. The<br />
safest course would be both to develop strong surveillance and<br />
response capacity to identify cases and treat them rapidly before an<br />
opportunity to transmit occurs, as well as maintaining high levels<br />
of interventions to make such onwards transmission unlikely.<br />
However, the realities of maintaining political will, financial<br />
resources, and community engagement present challenges to such<br />
a comprehensive malaria control program after malaria is no longer<br />
perceived to be a public health threat. As such, a new simulation<br />
model was constructed to examine the potential for preventing<br />
resurgence of malaria in Zanzibar under different conditions and<br />
using different long-term control strategies.<br />
SIMULAT<strong>IN</strong>G RESURGENCE RISK POST-<strong>ELIM<strong>IN</strong>ATION</strong><br />
Determining the feasibility of preventing resurgence of malaria<br />
following elimination required using mathematical models<br />
that could test out different control strategies. To do so, we<br />
simulated outbreak control in a stochastic, spatial, individualbased<br />
simulator in a representative population of around 6,000<br />
individuals living in over 1,000 houses. The time course of each<br />
individual infection was determined by simulating infections to<br />
determine when a person first displayed symptoms, when the<br />
infection became patent, and when mature gametocytes first<br />
reached densities high enough to be infectious. The location of<br />
new infections was determined by an individual-based description<br />
of mosquito movement, and active case detection occurred in<br />
the houses that were nearest to the index case. The models were<br />
developed in R 5 and are available on request. The simulator<br />
allows specification of key parameters of interest, including the<br />
importation rate, the fraction of infected individuals who seek<br />
treatment at health facilities, and the fraction of individuals<br />
protected by protective measures like nets or IRS. Different<br />
antimalarial strategies can then be implemented in the simulated<br />
community to evaluate their effect on transmission.<br />
The simulator was used to examine the potential for different<br />
control strategies to prevent malaria reemergence under different<br />
scenarios. A wide range of strategies and scenarios were examined<br />
to understand approaches that might be feasible. Here, we<br />
describe a few key scenarios that best illustrate requirements for<br />
maintaining elimination over many years.<br />
Simulating Post-elimination with High ITN Coverage<br />
Reaching zero will require high coverage by nets and/or IRS. In<br />
this first scenario, we assume an effective ITN coverage of 75% is<br />
maintained following elimination, giving an R C of about 0.5. We<br />
must further define the fraction of infections that are identified<br />
and treated at health facilities. This fraction will be affected by:<br />
�� The fraction of infected individuals with clinical disease<br />
�� The fraction of individuals with clinical disease who seek<br />
treatment at health facilities<br />
�� The fraction of those seeking treatment who are tested<br />
�� The sensitivity of the diagnostic test<br />
�� The fraction of those with a positive test that are treated and<br />
adhere to treatment<br />
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