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Am. J. Trop. Med. Hyg., 75(2 suppl), 2006, pp. 38-45
Copyright © 2006 by The American Society of Tropical Medicine and Hygiene

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INFECTIOUSNESS OF MALARIA-ENDEMIC HUMAN POPULATIONS TO VECTORS

GERRY F. KILLEEN, AMANDA ROSS, AND THOMAS SMITH*
Ifakara Health Research and Development Center, Ifakara, Tanzania; Swiss Tropical Institute, Basel, Switzerland


ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Despite its key role in determining the stability and intensity of malaria transmission, the infectiousness of human populations to mosquitoes has rarely been estimated. Field-based analyses of malaria transmission have frequently relied on the prevalence of asexual parasites or gametocytes as proxies for infectiousness. We now summarize empirical data on human infectiousness from Africa and Papua New Guinea. Over a wide range of transmission intensities there is little relationship between the infectiousness of human populations to vector mosquitoes and mosquito-to-human transmission intensity. We compare these data with the predictions of a stochastic simulation model of Plasmodium falciparum epidemiology. This model predicted little variation in the infectiousness of the human population for entomologic inoculation rates (EIRs) greater than approximately 10 infectious bites per year, demonstrating that the lack of relationship between the EIR and the infectious reservoir can be explained without invoking any effects of acquired transmission-blocking immunity. The near absence of field data from areas with an EIR < 10 per year precluded validation of the model predictions for low EIR values. These results suggest that interventions reducing mosquito-to-human transmission will have little or no effect on human infectiousness at the levels of transmission found in most rural areas of sub-Saharan Africa. Unless very large reductions in transmission can be achieved, measures to prevent mosquito-to-human transmission need to be complemented with interventions that reduce the density or infectiousness of blood stage parasites.


INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The endemicity of Plasmodium falciparum malaria is extremely stable in the most affected regions of Africa.1,2 One reason for this is probably that the infectiousness of the human population is very insensitive to anti-malaria interventions. In spite of its crucial role in malaria transmission, human infectiousness has only been estimated at population level for a handful of locations and has never been related systematically to potential underlying determinants. Most malaria transmission models have not used direct field estimates of infectiousness because of the practical difficulties of obtaining them, relying instead on the assumption that infectiousness is directly related to prevalence of blood-stage parasites or gametocytes.

The burden of morbidity and mortality can be alleviated by vector control.3–8 Although it can take years for parasite prevalence to subside, large reductions of transmission intensity can be achieved and these dramatically reduce the burden of P. falciparum in afflicted communities.8,9 Vector control relies to a large extent on community effects, most obviously in the case of larval control, but also with indoor residual spraying10 or insecticide- impregnated nets.11–13

A number of vaccines against P. falciparum are about to enter field trials.14 An important element of the impact of a malaria vaccine will be the extent to which it has a community effect,15 but the assessment of effects on infectiousness is not part of usual trial designs. To go beyond protecting direct recipients, vaccination must reduce the infectiousness of the human host.1,16 Transmission-blocking vaccines aim to reduce infectiousness directly. Other malaria vaccines, targeting pre-erythrocytic or asexual blood stages of the parasite will affect infectiousness only indirectly.

We have now compiled all the field based estimates of the infectiousness to vector mosquitoes of malaria-afflicted human populations that we could assemble from Africa and Papua New Guinea. To predict the effects of interventions on infectiousness, we consider the relationship between infectiousness of the human populations and the mosquito-to-human transmission intensity they experience. We use these data to validate a stochastic simulation model of P. falciparum epidemiology that we have developed.17–19 The comparison between the field estimates and model predictions enables us to provide an explanation for patterns of infectiousness recorded in the field, and to consider how these are likely to be modified by vaccination and by other interventions.


MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Field estimates of population level infectiousness of humans to mosquitoes. We assembled as many studies as we could find in the literature that either reported or enabled the calculation of transmission intensity, expressed as annual entomologic inoculation rate (EIR), and the mean infectiousness of the human population to mosquitoes ({kappa}) (Table 1Go).


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TABLE 1
Field-based estimates of human infectiousness and its underlying parameters*
 
The combined EIR resulting from all vector species at each site was estimated using standard approaches.20,21 Infectiousness ({kappa}) was estimated by a variety of methods. Infectiousness can be estimated directly ({kappa}d) by feeding laboratory-reared mosquitoes on either randomly sampled members of the human population22–27 or deliberately selected gametocyte carriers.22,28,29 However, such studies are extremely laborious and often difficult to justify or poorly accepted because of the need to feed mosquitoes on blood from large numbers of infected volunteers. The remaining approaches for measuring human infectiousness rely on measuring infection rates in the vector population. Although some very direct methods can use early sporogonic stages of the parasite such as ookinetes,30 most rely on later stages such as oocysts and sporozoites, requiring correspondingly more complex biodemographic models of the vector population to calculate {kappa}.23,31–33 Here we apply two approaches to estimating human infectiousness to malaria, based on oocyst (o) or sporozoite (s) prevalence, using models adapted from previous publications.31 The original equations assumed a completely anthropophilic vector population. We have now added the human blood index (Q) of the vector as an additional parameter to estimate infectiousness based on oocyst ({kappa}o) and sporozoite ({kappa}s) prevalence.31 Both models rely on field estimates of vector survival per feeding cycle (M) and the sporozoite-dependent model also requires the duration of the vector feeding cycle (u) and the temperature (T)–dependent extrinsic incubation period of the parasite (E) as input parameters


Formula 1


Formula 2

where p = M1/u and E = 111/(T – 16) where T is the temperature in °C.

Only four sites could be identified from the literature with reasonable estimates of all three values, M, Q and o, and of the EIR. {kappa}o, the more direct and reliable measure, could only be estimated for these four sites. In contrast, estimates of M, T, Q, and s were available for at least one major vector species at 32 sites for which the EIR is known. This is presumably because sporozoite rates are essential to calculate EIR20,21 and are of more direct relevance to transmission than oocyst rates. Although estimates of u were available for only nine sites because this is a difficult parameter to measure in the field, it is typically between two and three days for the major vector species of Africa34 and Papua New Guinea,35 the regions from which all these data are drawn. A median value of u = 2.5 days was therefore used for all sites for which this value is unknown.

Additional estimates of human infectiousness to mosquitoes were obtained through alternative approaches ({kappa}a) from a small number of innovative studies that used ookinete infection rates,30 age-specific sporozoite prevalence curves,36,37 or sporozoite accumulation rates in aging mosquito populations.38

Study sites were considered as being independent if the sampling centers were more than five kilometers or five years apart. Some sites, Muheza and the Kilombero Valley in Tanzania, were studied a decade or more apart, during which time conditions in both sites appear to have changed appreciably (Table 1Go). Where more than one estimate of infectiousness was possible with any given vector species at a given site, the geometric mean of the estimated of infectiousness ({kappa}g) to that vector was calculated.

Model-based prediction of the infectious reservoir {kappa}. The individual-based stochastic simulation model predicts the distribution of parasite densities as a function of the seasonal pattern of the EIR and is described in the accompanying papers.39 We have now fitted a statistical model to data from neurosyphilis patients treated by malariatherapy40 to obtain estimates of Im(i,t), the probability that a mosquito feeding on malariatherapy patient i at time t, becomes infected as a function of the recent history of asexual parasite densities.18 To make predictions of {kappa} from this model, we extracted EIR seasonality patterns corresponding to those measured in field sites across Africa (Table 1Go). We then implemented our stochastic simulation model to predict the parasite density distributions for populations exposed since birth to this annual cycle of inoculations. We applied the model for Im(i,t) to the predicted densities from these simulations.

There is evidence that the risk of being bitten by an anopheline is approximately proportional to body size.41 To allow for this, we convert the estimates of individual specific probability of infecting a mosquito, Im(i,t), to model predictions of the overall proportion of mosquitoes that get infected, {kappa}m (t), by calculating a weighted average of Im(i,t) over the whole simulated population (consisting of at least 10,000 simulated individuals), where the weights are equal to the estimated (age-specific average) body surface areas, A(a(i)).19


Formula 3

For comparison with field data, we use the mean value of {kappa}m (t) over the year, Formula 3m.

The malariatherapy infectiousness data were accrued from artificial feeding of Anopheles quadrimaculatus and An. albimanus. The overall propensity to produce viable gametocytes is likely to have been different from that found in the field with the African vectors An. gambiae or An. funestus. We assume that the ratio of {kappa}g to Formula 3m is a constant that arises because of the differences between the vectors and in the conditions in which they took their feeds. We compute a parameter {eta}, equal to the ratio


Formula 4

(where ns is the number of sites) and propose to use {kappa}u(t) = {eta};{kappa}m(t) as our estimate of the infectious reservoir in dynamic models of the effects of interventions.


RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Field estimates of {kappa} were available from 37 different field sites across Africa and Papua New Guinea (Table 1Go). For most of these sites the estimates referred only to An. gambiae or to its sibling species An. arabiensis. Where estimates of {kappa}s or {kappa}g for more than one vector species are available at any site, these were averaged using the geometric mean because they were largely concordant and the literature suggests little evidence for major differences in susceptibility between sympatric wild populations of major vector species.31,33,34,42,43

Potential outlying estimates that were identified a priori and excluded from our calculations of averages were from Kilifi in Kenya and the bed net trial areas of The Gambia. In both these cases the parameters had been assembled from quite a wide area over long, non-concurrent periods. Further data that were regarded a priori as being less reliable were from Bo in Sierra Leone because sampling for mosquito survival estimation does not appear to have been extensive or systematic and Muheza in the 1960s because the lack of a population census in sampled houses necessitated some guesswork for EIR estimation from indoor resting catches. Additional suspect sites were identified on the basis of improbable (Kou Valley, Burkina Faso) or inconsistent (Kilombero Valley, Tanzania) levels of infectiousness. The remarkably high infectiousness levels estimated at Muheza in the 1990s appear to be reasonably accurate because all the estimates are relatively consistent with the most direct approach reported, namely the determination of age-specific sporozoite rates ({kappa}a). This large increase relative to the same site in the1960s also appears consistent with longitudinal trends in sporozoite prevalence that have been related to the history of chloroquine availability and efficacy in the area.44

In the complete field dataset, {kappa}g had a geometric mean of 0.027 and {kappa}s had a geometric mean of 0.030. The associations between these measures and the EIR were weak and none were statistically significant (Table 2Go and Figure 1Go, top panel).


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TABLE 2
Correlation* between human infectiousness and entomologic inoculation rate
 

Figure 1
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  		    FIGURE 1. Empirical relationship between estimates of human infectiousness to mosquitoes and entomologic inoculation rate (EIR). Top panel, Site-specific means of estimates for infectiousness as determined by all methods for which data are available ({kappa}g). Bottom panel, Site-specific estimates for infectiousness as determined using sporozoite prevalence ({kappa}s). Filled circles represent outlying data points that were excluded from the calculation of means and identified as described in the text.

 
The major obvious gap in the field estimates of {kappa} arises because only areas with high EIR are included. This is a result of the historical focus of medical entomology on areas of high transmission20,21,45,46 with all the approaches except for {kappa}d rely on catching significant numbers of infected mosquitoes. Only one estimate came from a site with an EIR estimate of less than 10 infectious bites per year.

The dataset was tested for publication or study site-selection bias using a funnel plot of the inverse of the variances of the infection prevalence used to estimate {kappa}, versus the {kappa} estimate itself. The limited available data suggest that estimates of {kappa}o might be biased towards areas of high infectiousness; this appears to be more clearly the case for {kappa}d. This is particularly understandable in the latter case where investigators generally carry out considerable feasibility studies before undertaking such challenging and laborious surveys using large numbers of human subjects. In contrast, estimates of {kappa}s and {kappa}a exhibit no obvious biases.

In the case of {kappa}a, this may be because of the limited number of reports and the diverse methodology applied. However, estimates of {kappa}s are by far the most abundant in the dataset and it may be an advantage that the parameters we have used to estimate {kappa}s were generally not collected for that explicit purpose.

The simulated values of Formula 4m were positively correlated with the EIR where this was less than approximately 10 infectious bites per person per year (Figure 2Go), with some variation between sites. At higher levels of transmission there was little relationship between Formula 4m and the EIR, Formula 4m for each site taking a value of approximately 0.048, with a slight tendency to decrease with increasing EIR, giving an estimate of {eta} of 0.56. Where the EIR is low, the model also predicted some variability in Formula 4m between sites with similar overall EIR but different seasonal patterns, but this was not the case at high EIR. For those sites where both Formula 4m with {kappa}g could be determined, a paired comparison was carried out (Figure 3Go). This served to emphasize the lack of variation in the values of Formula 4m in all the high transmission field sites. Unsurprisingly, there was no evidence of a correlation between the two measures.


Figure 2
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  		    FIGURE 2. Modeled relationship between estimates of human infectiousness to mosquitoes and entomologic inoculation rate (EIR). Estimates of {kappa}m were made using the stochastic simulation model.18,39

 

Figure 3
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  		    FIGURE 3. Paired comparison of values of Formula 4m with {kappa}g for sites where both quantities were estimated.

 

DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The analysis of field data suggests that moderate reductions in malaria transmission intensity in highly endemic areas for P. falciparum will have little long term impact on human population infectiousness. The relationship between transmission intensity and {kappa} is weak and in field estimates is largely overshadowed by measurement error and some apparently genuine natural variations. The relationship between EIR and {kappa} is even more equivocal than are those between EIR and infection prevalence, morbidity, and mortality.4,21,47–51

A good example of natural variation that seems unrelated to EIR is the contrast between the quite reliable but very different estimates of infectiousness from Namawala and Muheza in Tanzania during the 1990s. Although their EIR values are in approximately the same range, all estimates of {kappa} indicate that the human population in Muheza was least 10 times more infectious than that in Namawala. In addition to the less direct but methodologically similar estimates of {kappa}s, more direct and plausible estimates of {kappa}a using age-specific sporozoite prevalence or rate of change also demonstrate a consistent difference of an order of magnitude for the same vector species in two holoendemic parts of the same country at approximately the same time. In comparison to these variations, those associated with transmission intensity appear relatively modest.

Our mathematical model predicted very little variation between sites in {kappa}, which confirmed that most of the variation seen in the field data represents imprecision in the measurements or natural variation due to factors not taken into account in the modeling. In contrast to levels of human infectiousness assumed in many other mathematical models of malaria, our model gives predictions of {kappa} of similar magnitude to the average of those from the field. The estimate of 56% for {eta} is a plausible value for the reduction in infection probability for An. gambiae feeding on natural infections compared with An. quadrimaculatus and An. albimanus imbibing selected P. falciparum strains in a semi-optimized setting.

The model agreed with the field data in predicting only a weak relationship between transmission intensity and {kappa} at EIR values >10 per year. This endorses other theoretical projections1,2,52 that interventions to prevent mosquito-to-human transmission are unlikely to greatly reduce the effective infectiousness of the parasite reservoir in the human population at initial levels of transmission typical of rural sub-Saharan Africa.

It is tempting to try to explain the weak relationship between {kappa} and EIR as an effect of acquisition of transmission-blocking immunity. Acquired transmission-blocking immunity would certainly lead to a value of {eta} less than unity. However, although there is strong evidence for natural transmission-blocking immunity,53–55 the little evidence that exists suggests that this varies little or not at all with exposure to P. falciparum.56 Our simulation model does not invoke any acquisition of transmission-blocking immunity, but predicts infectiousness as a function of blood-stage parasite densities.18 Acquired immunity to blood stages reduces infectiousness of the host because of the reduction in asexual parasite densities and consequently gametocytemia. In this model, changes in EIR modify the age distribution of parasitemia, but this mainly modifies the contributions to transmission of different ages of host, rather than impacting on overall infectiousness.

Effective interventions are needed if epidemiologically significant reductions of infectiousness of malaria-endemic human populations are to be achieved. Transmission-blocking vaccines targeting the mosquito stages of the parasite represent a direct approach to reduce {kappa}. Our model predicts that asexual blood stage vaccines should also have an effect. Pre-erythrocytic vaccines will also prevent mosquito-to-human transmission,14 but this is achieved via a reduction in the number of effective inoculations and so will only be apparent where transmission is brought down to low levels. Very high levels of efficacy and coverage would be required from any such vaccine to substantially reduce infectiousness.

At least one-third of the infectious individuals in human populations are the adolescents and adults18,28 who provide most blood meals to vector mosquitoes because of their greater attractiveness.41,57 Thus, to achieve large-scale community effects on malaria transmission burden, malaria vaccines need to achieve extensive coverage of adults through delivery mechanisms that are as yet undeveloped in the poorest endemic nations.

The development of sustainable vaccination schemes that achieve high coverage of the whole population rather than just infants are difficult to envisage in the near future. To optimize community effects, vector control measures and vaccines against pre-erythrocytic antigens need to be complemented with measures that directly block transmission or target blood stage parasites.

Fortunately, vector control approaches can deliver community-wide protection without the necessity to lower human infectiousness because these approaches suppress the transmission capacity of vector populations. Radical reductions of malaria transmission intensity and corresponding suppression of overall burden have been achieved in even the most challenging settings by indoor residual spraying, treated bed nets, and larval control.4–8,58 The analysis presented here should not discourage support for such programs.

Received September 18, 2005. Accepted for publication November 6, 2005.

Acknowledgments: We thank the members of the Technical Advisory Group (Michael Alpers, Paul Coleman, David Evans, Brian Greenwood, Carol Levin, Kevin Marsh, F. Ellis McKenzie, Mark Miller, and Brian Sharp), the Project Management Team at the Program for Appropriate Technology in Health (PATH) Malaria Vaccine Initiative, and GlaxoSmithKline Biologicals S.A. for their assistance.

Financial support: The mathematical modeling study was supported by the PATH Malaria Vaccine Initiative and GlaxoSmithKline Biologicals S.A.

Disclaimer: Publication of this report and the contents hereof do not necessarily reflect the endorsement, opinion, or viewpoints of the PATH Malaria Vaccine Initiative or GlaxoSmithKline Biologicals S.A.

* Address correspondence to Thomas Smith, Swiss Tropical Institute, PO Box, CH-4002, Basel, Switzerland. E-mail: Thomas-A.Smith{at}unibas.ch Back

Authors’ addresses: Gerry F. Killeen, Ifakara Health Research and Development Center, Ifakara, Kilombero District, Tanzania, Telephone: 255-748-477-118, Fax: 255-23-262-5312, E-mail: gkilleen{at}ihrdc.or.tz. Amanda Ross and Thomas Smith, Swiss Tropical Institute, PO Box, CH-4002, Basel, Switzerland, Telephone: 41-61-284-8273, Fax: 41-61-284-8105, E-mails: amanda.ross{at}unibas.ch and Thomas-A.Smith{at}unibas.ch.

Reprint requests: Thomas Smith, Swiss Tropical Institute, PO Box, CH-4002, Basel, Switzerland.


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 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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