Background
Malaria transmission is spatially heterogeneous over all geographical scales. At a global level, countries or regions experience varying levels of transmission [
1]. Within these regions, transmission is clustered into foci. While the size may vary, the term “focus” is typically used to describe an area of several square kilometres that supports malaria transmission. Within foci, transmission is found to be heterogeneous across smaller units, termed hotspots, which may be a single household or group of households that experience higher than average exposure to infectious mosquitoes [
2‐
4]. While large- and medium-scale patterns of transmission are driven by variations in climate and ecology, the increased risk of exposure observed in hotspots is likely caused by factors such as the proportion of children present, host-genetic polymorphisms, socio-economic status, use of vector control measures, type of housing and micro-environmental factors [
5‐
11]. Evidence suggests that targeting malaria control interventions to hotspots can have a more dramatic impact on transmission than untargeted introduction of control efforts [
4,
12‐
14]. Active case detection (ACD), in the form of mass screen and treat (mass blood surveys) campaigns, may be an effective method to detect and treat individuals in hotspots and is being (re)-explored for malaria control and elimination [
15‐
20].
ACD currently relies on rapid diagnostic tests (RDT) or microscopy to identify infected individuals. There is, however, a growing body of evidence that shows that these diagnostic tests miss a substantial proportion of malaria infections in endemic areas compared to PCR[
21,
22], primarily due to the difficulty of detecting low parasite densities [
23‐
27]. A recent study estimated that in very low prevalence settings, subpatent infections comprise 70-80% of all malaria infections and are responsible for 20-50% of all human-to-mosquito infections [
22]. Without treatment, these highly prevalent, low density infections are likely to sustain malaria transmission.
Using aggregated survey prevalence estimates, Okell
et al. found a positive relationship between transmission and parasite density, with the proportion of infections that are subpatent being highest in low transmission settings [
22]. This suggests that at a medium-scale and in high transmission areas, microscopy and RDTs will display adequate sensitivity for the targeting of interventions. It is however, not clear whether this relationship between transmission and parasite density seen in larger geographical areas exists over smaller scales, such as within villages. A better understanding of this issue is important for the detection, and subsequent management, of malaria hotspots.
This study has used intensive cross-sectional sampling in north-western Tanzania to examine household-level heterogeneity in parasite exposure and density. The data obtained were used for simulations of different screen and treat strategies to maximize impact on subpatent malaria infections.
Discussion
This paper investigates the micro-epidemiology of P. falciparum infection in a rural community in north-western Tanzania, exploring the relationship between parasite density and exposure (distance-weighted parasite prevalence), using a novel application of qPCR on DNA from filter-paper blood-spots. In contrast to studies conducted over larger geographical areas, this study found that in a moderate transmission setting, at the household level, the proportion of infections defined as subpatent is positively associated with exposure. If similar findings are found in other settings, this suggests that microscopy and RDTs are unlikely to have adequate sensitivity to identify transmission hotspots where acquired immunity allows people to control infections to very low densities; yet these are the exact places one needs to detect cases during a mass screen and treat campaign. Simulations showed that the proportion of infections that are correctly identified and treated can be increased using tMDA. However, this approach still misses a large proportion of infections, suggesting that MDA of entire foci may be required for interruption of transmission.
Results of qPCR analysis showed that 56.2% of infections were of a density <100 parasites/μl, the density typically quoted as the limit of detection for routine microscopy and RDTs. This finding fits with the analysis of Okell
et al. who estimated that where prevalence of infection is 35% by PCR, around 60% of infections are missed by microscopy [
22]. This study found that, controlling for age, parasite density showed a negative relationship with exposure. Such a finding is supported by Clarke
et al., who showed that with increasing proximity to the River Gambia, the prevalence of infection increased but the density of infection decreased [
35]. These micro-epide-miological patterns are markedly different from patterns between endemic regions where lower endemicity is related to lower average parasite density and a larger proportion of infections that are below the microscopic threshold for detection [
22].
Multiple factors can influence the relationship between parasite exposure and the density of blood stage infection, and these factors may be more or less apparent at different spatial scales. Potential explanations for a negative association between exposure and parasite density within a focus, seen in this study at a microscale, is that highly exposed individuals acquire greater blood stage immunity, controlling parasite densities more effectively [
36]. On the other hand, an explanation for a positive association between exposure and parasite density across different foci, as simulated by Arnot
et al. [
30] is that differences in the age of infection may over-ride the influence of exposure-related immunity. In addition, it is likely that parasite diversity is lower in foci of lower transmission, potentially enhancing the acquisition of immunity to circulating strains [
30,
37].
If, over small geographical regions, density of infection is indeed lowest among those at highest exposure, there are substantial implications for mass screen and treat campaigns that plan to rely on RDTs for diagnosis. As this data suggest, due to a positive association between RDT sensitivity and parasite density, RDTs are likely to display lowest sensitivity in transmission hotspots. Failure to properly target individuals in hotspots may allow transmission to persist. Use of more sensitive diagnostics might be able to circumvent this problem. However, those currently available, such as PCR and LAMP, are not yet practical for widespread routine field use (Gadalla and Mosha, in prep), and point of care serological tests, that may be able to differentiate hotspot households, remain in development. An alternative approach, extrapolating from qPCR data of this study, is to target MDA to those households with highest prevalence of infection by RDT. The simulations show that such an approach increases the proportions of true positives who would be treated. However, even with a threshold of 10% prevalence of RDT positives in a household, where nearly half the population would be treated, an estimated 36.5% of infections would still go untreated.
The importance of the subpatent asymptomatic parasite pool rests on the understanding that subpatent infection can transmit malaria. While subpatent infections are known to transmit infection [
38,
39], the absolute density of infection necessary to transmit is unknown. nPCR is typically able to detect infections down to approximately one parasites/μl using dried blood spots, which equates to approximately 5,000,000 parasites in an adult. Presumably there are many infections below this threshold that were not detected. Whether to screen and treat using a more sensitive tool or to institute MDA without targeting requires a better understanding of the minimum density of malaria parasites that result in human-mosquito transmission, as well as the comparative costs and operational ease of different approaches.
This study has several limitations. Firstly, the qPCR method applied here is novel, as this is the first study to use qPCR on filter-paper blood-spot DNA to provide an estimate of parasite density in a cross-sectional survey. This was achieved without having to measure the volume of blood used in the assay. This method is yet to be fully validated on filter-paper samples from the field; further, the parasite target used is pangenus, and some contribution from
Plasmodium malariae and
Plasmodium ovale spp. to the density estimates cannot be ruled out [
24,
30]. Secondly, RDTs do not have a clear-cut detection limit but show a smoother decline in sensitivity as parasite density drops. Reliable, randomly sampled RDT data were, however, not available, as RDTs were only used on individuals with reported recent fever. The use of a simple RDT detection limit (100mL
-1) may overestimate the numbers of infections that would be missed, but would not change the overall finding that RDT sensitivity is likely to be least adequate in hotspot households. Thirdly, it is possible that PCR produced some false negatives and false positives which could affect conclusions. That said, false negatives would presumably be due to very low density infections which likely play a minor role in transmission. False positives are unlikely, but may have arisen due to contamination, although all steps were taken to minimize this possibility. Lastly, assumptions were made that, distance-weighted PCR prevalence provides a suitable estimate of exposure and location of hotspots. This assumption is based on the study by Olotu
et al. [
32], which showed that distance-weighted prevalence was a good predictor of household infection. It would be interesting to explore whether these findings hold using alternative methods to measure exposure, such as Entomological Inoculation Rate (EIR), but it was not possible to make these measurements in this study. Assuming transmission hotspots are stable over time, serology may also help to identify hotspot households.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JFM was involved in the study design, supervised the implementation of the study and data collection, analysed data, drafted and revised the manuscript. HJWS and BG were involved in data analysis, interpretation of the data, drafted and revised the manuscript. DC and RG were involved in overall study design and supervision, interpretation of the data and revisions of the manuscript. TB, CS and CD were involved in supervision of laboratory work, interpretation of the data and revision of the manuscript. BG and GK were involved in interpretation of the data and revisions of the manuscript. NG and SA performed the real-time PCR testing and revised the manuscript. All authors have read and approved the final version of the manuscript.