Malaria, a protozoan infection of the red blood cells, remains one of the greatest global health challenges (World malaria report, 2019). Infection with malaria parasites results in a wide range of clinical disease presentations, from severe to uncomplicated; in addition, in hyperendemic areas, asymptomatic infections are common (Bousema et al., 2014). It is well established that chronic asymptomatic infection with Plasmodium falciparum, the most common and fatal malaria parasite, can lead to morbidity for those infected and contribute to ongoing transmission (Bousema et al., 2014; Okell et al., 2012; Tadesse et al., 2018; Slater et al., 2019). Characterization of these asymptomatic infections is paramount as they represent a major obstacle for malaria elimination efforts. Therefore, an understanding of how host immunity and parasite factors interact to cause disease tolerance is required. While age-specific immunity to malaria in hyperendemic areas is well-characterized, less attention has been paid to the possibility of a sex bias in malarial susceptibility despite evidence for a male bias in malaria infections in non-human animals and a male bias in the prevalence of other human parasitic infections (Zuk and McKean, 1996; Klein, 2004; Roberts et al., 2001).

The clearest evidence for sexual dimorphism in malaria susceptibility is in pregnant women, who are at greater risk of malaria infection and also experience more severe disease and higher mortality (Desai et al., 2007). However, multiple studies performed in different contexts have demonstrated a male bias in incidence and/or prevalence of malaria infection in school-aged children and adults (Molineaux et al., 1980; Pathak et al., 2012; Landgraf et al., 1994; Camargo et al., 1996; Abdalla et al., 2007); this bias is more well established in hypoendemic areas, but has also been observed in hyperendemic regions (Houngbedji et al., 2015; Mulu et al., 2013). Where there is a male bias in malaria infection, it has often been postulated that these differences in malaria incidence or prevalence stem from an increased risk of males acquiring infection due to socio-behavioral factors (Pathak et al., 2012; Camargo et al., 1996; Moon and Cho, 2001; Finda et al., 2019). However, because biological sex itself has been demonstrated to affect responses to other pathogens, an alternative hypothesis is that the sexes may have different responses to the malaria parasite once infected (Nhamoyebonde and Leslie, 2014; Bernin and Lotter, 2014; Fischer et al., 2015; Fish, 2008).

Estimating the host response to P. falciparum infection requires close follow-up of infected individuals, sensitive detection of parasites, and the ability to distinguish superinfection, which is common in endemic areas, from persistent infection. To test the hypothesis that sex-based differences in host–parasite interactions affect the epidemiology of malaria, we intensively followed a representative cohort of individuals living in a malaria endemic area of eastern Uganda. Using frequent sampling, ultrasensitive quantitative PCR (qPCR), and amplicon deep-sequencing to genotype parasite clones, we were able to accurately detect the onset of new infections and follow all infections over time to estimate their duration. Using these data, we show that females cleared their asymptomatic infections more rapidly than males, implicating biological sex-based differences as important in the host response to this globally important pathogen.

Previous studies have reported a higher prevalence of malaria infection in males compared to females, with the difference often ascribed to differences in exposure (Molineaux et al., 1980; Pathak et al., 2012; Landgraf et al., 1994; Camargo et al., 1996; Abdalla et al., 2007; Houngbedji et al., 2015; Mulu et al., 2013). In a cohort study in eastern Uganda, we noted higher prevalence of malaria infection in males compared to females. By closely following a cohort of children and adults and genotyping every detected infection with sensitive amplicon deep-sequencing, we were able to estimate both the rate of infection (FOI) and duration of infection and to compare these measures by sex. We found that lower prevalence in females did not appear to be due to lower rates of infection but rather due to faster clearance of asymptomatic infections. To our knowledge, this is the first study to report a sex-based difference in the duration of malaria infection.

Though there are some conflicting reports in the literature, the majority of studies of malaria incidence and/or prevalence that evaluated associations with sex in late childhood, adolescence and adulthood have found a male bias in the observed measure of burden (Molineaux et al., 1980; Pathak et al., 2012; Landgraf et al., 1994; Camargo et al., 1996; Abdalla et al., 2007; Houngbedji et al., 2015; Mulu et al., 2013). We note that this is more often observed in hypoendemic settings and may be confounded by factors such as treatment-seeking behavior; however, this male bias has been reported in studies of both P. vivax and P. falciparum. Overall, these studies consistently suggest that males exhibit higher incidence and/or prevalence of malaria that begins during late childhood, persisting through puberty and the majority of adulthood (excepting the years when pregnancy puts women at higher risk). One possible explanation put forward for the sex-specific difference in burden has been that males are more frequently bitten by malaria-carrying mosquitos due to behavioral differences such as working outside, not sleeping under a net, or traveling for work (Pathak et al., 2012; Camargo et al., 1996; Moon and Cho, 2001; Finda et al., 2019). In our study, however, there were no statistically significant differences in malaria incidence or FOI by sex, though there was a trend toward higher FOI in males. We also saw no evidence of behavioral trends that would result in more infections in males; in fact, older women in our study did most of the traveling outside the study area (an area of low transmission compared to surrounding areas). We did not assess work habits as part of our study questionnaire, but the fact that we observe similar patterns in prevalence by sex in all age categories makes this a less likely explanation. Therefore, in this particular cohort, it appears that the observed male bias in parasite prevalence is best explained by slower clearance of infection in males. We cannot rule out that males were differentially exposed in the recent past (e.g., had a higher force of infection when transmission was higher, which could have affected immunity), and we plan to genotype samples from a prior cohort from 2011 to 2017 to test this hypothesis.

Very few studies have been conducted to explore immunological differences between males and females in their response to the malaria parasite. RTS,S vaccination is associated with higher all-cause mortality in girls compared to boys, and a trend toward higher risk of fatal malaria has been noted in vaccinated girls compared to boys, suggesting possible sexual dimorphism in immunological responses to malaria (Klein et al., 2016). The comprehensive Garki project found a male bias in the prevalence of P. falciparum infection after the age of 5 years that was noted to increase after control measures including IRS and mass drug administration. They also found that females had higher levels of certain antibodies against P. falciparum compared to males, but saw no difference in these levels after control measures (Molineaux et al., 1980). Hormonal differences have been posited to play a role in these sex-based immunological differences to malaria infection; for example, studies in mice show that testosterone appears to downregulate the immune response to malaria (Delić et al., 2011; Wunderlich et al., 1991). One study of irradiated sporozoite vaccination in mice showed that female mice vaccinated after puberty were better protected than males following parasite challenge, but this sex difference was not seen if the mice were vaccinated prior to puberty, suggesting a role for sex hormones in the development of immunity (Vom Steeg et al., 2019). Testosterone was again associated with decreased protection from malaria in that study (Vom Steeg et al., 2019). In addition, in Kenya, two studies showed that dehydroepiandrosterone sulfate (DHEAS) levels were significantly associated with decreased parasite density in both males and females, even after adjustment for age, but neither study directly compared males to females (Kurtis et al., 2001; Leenstra et al., 2003). Given that levels and types of sex hormones differ between males and females, as does the age of onset of puberty, the interaction between these hormones, age, and protection against malaria is likely to be complicated. We did not detect an interaction between age and sex in our model. This could be due to a lack of statistical power or imperfect detectability, as adults have lower density infections than children due to improved anti-parasite immunity (Bousema et al., 2014; Okell et al., 2012). In addition, the adult age group was comprised of a wide age range, which included post-menopausal women and might have blunted the effect of sex in that age group if there is a hormonal basis for some of this effect. Repeating our analysis using different age categories (<8 years, 8–13 years, and 13+ years) did not significantly change our findings. It is also possible that sex-based differences are explained by a different mechanism, such as differences in innate immunity, Y-linked chromosomal factors in males, increased X-linked gene expression in females, or by a combination of factors. More studies are needed to elucidate the relationship between sex-based biological differences between males and females and their impact on the development of effective antimalarial immunity in humans.

Of the variables we evaluated in addition to sex, baseline infection status and parasite density were most strongly associated with the rate of clearance of infection. Infections that were already present at the beginning of the study and persisted past the left censoring date may have been a non-random selection of well-established asymptomatic infections that were present at baseline at a higher frequency than average because they had a fundamentally different trajectory than newly established asymptomatic infections. Higher parasite densities were also associated with slower clearance of infection in both adjusted models, but the effect was most pronounced when the data were analyzed by infection event. This may be because low-density infections that we were unable to successfully genotype were only included in the infection event analysis, and these events tended to have short durations. The inclusion of parasite density in our multivariate models did not meaningfully alter associations between sex and duration of infection, providing evidence that the sex-based differences in duration were not mediated primarily by differences in parasite density in our cohort.

A limitation of our study is the statistical model’s assumption that all infections clear at the same rate, which was necessary because we did not observe the beginning of most infections. To rely less on this assumption, we adjusted for baseline infections, allowing them to have a different rate of clearance than new infections; this did not change our primary finding of a difference in rate of clearance by sex. We also acknowledge there may be other unmeasured confounders, such as genetic hemoglobinopathies or sex-based differences in unreported outside antimalarial use, that could affect our results. Another caveat is that our findings may not be generalizable to areas with different transmission intensity given that our study was conducted in a very specific setting: an area with previously very high transmission intensity that has been greatly reduced in recent years by repeated rounds of IRS.

Additionally, because it is difficult to genotype low-density infections and because parasite densities fluctuate over time, we allowed several ‘skips’ in detection before declaring an infection cleared or the same clone in an individual a new infection with the same clone. This requires the assumption that re-infection with the same clone is relatively rare, which is reasonable given the high genetic diversity and low force of infection seen in this setting; this assumption has been made in other longitudinal genotyping studies (Felger et al., 2012; Smith et al., 1999). Our method may have biased us toward longer durations of infection and fewer new infections overall, but this is unlikely to have introduced any significant bias in the observed associations by sex. We performed a sensitivity analysis to ensure that changing the number of skips allowed did not significantly change the main finding regarding longer duration of infection in males and our findings were consistent. Given decreasing parasite densities over time and a setting with low force of infection and low EIR, assuming perfect recovery of clones is likely to artificially inflate the force of infection in this cohort. There were not enough infections to perform a rigorous analysis of the distribution of clones within and between households, but given that the overall force of infection was quite low, the probability of re-infection with the same clone already present in a participant from another member in the household (which could bias toward longer duration of infection) was low and unlikely to have introduced any significant bias by sex.

In summary, we estimated the clearance of asymptomatic P. falciparum infections by genotyping longitudinal samples from a cohort in Nagongera, Uganda, using sensitive amplicon deep-sequencing and found that females cleared their infections at a faster rate than males; this finding remained consistent when adjusting for age, baseline infection status, and parasite density. Furthermore, we found no conclusive evidence for a sex-based difference in exposure to infection, either behaviorally or by FOI. Though there have long been observed differences in malaria burden between the sexes, there is still little known about sex-based biological differences that may mediate immunity to malaria. Unfortunately, much reported malaria data is still sex-disaggregated. Our findings should encourage epidemiologists to better characterize sexual dimorphism in malaria and motivate increased research into biological explanations for sex-based differences in the host response to the malaria parasite.

Data from the PRISM2 cohort study is available through a novel open-access clinical epidemiology database resource here: https://clinepidb.org/ce/app/record/dataset/DS_51b40fe2e2. Sequencing data is available on Github at https://github.com/EPPIcenter/sex_based_differences as referenced in the paper. In addition. all sequences of haplotypes are included in Supplementary file 1.

1. 1. Dorsey G

   (2020) ClinEpiDB

   ID DS_51b40fe2e2. PRISM2 ICEMR Cohort.

   https://clinepidb.org/ce/app/record/dataset/DS_51b40fe2e2

1. 1. Abdalla SI
   2. Malik EM
   3. Ali KM

   (2007) The burden of malaria in Sudan: incidence, mortality and disability--adjusted life--years

   Malaria Journal 6:97.

   https://doi.org/10.1186/1475-2875-6-97

   • PubMed
   • Google Scholar
2. 1. Bernin H
   2. Lotter H

   (2014) Sex Bias in the outcome of human tropical infectious diseases: influence of steroid hormones

   Journal of Infectious Diseases 209 Suppl 3:S107–S113.

   https://doi.org/10.1093/infdis/jit610

   • PubMed
   • Google Scholar
3. 1. Bousema T
   2. Okell L
   3. Felger I
   4. Drakeley C

   (2014) Asymptomatic malaria infections: detectability, transmissibility and public health relevance

   Nature Reviews Microbiology 12:833–840.

   https://doi.org/10.1038/nrmicro3364

   • PubMed
   • Google Scholar
4. Software

   1. Briggs J

   (2020) sex_based_differences, version jbriggs7

   Github.

   https://github.com/EPPIcenter/sex_based_differences
5. 1. Camargo LM
   2. dal Colletto GM
   3. Ferreira MU
   4. Gurgel SM
   5. Escobar AL
   6. Marques A
   7. Krieger H
   8. Camargo EP
   9. da Silva LH

   (1996) Hypoendemic malaria in Rondonia (Brazil, western Amazon region): seasonal variation and risk groups in an urban locality

   The American Journal of Tropical Medicine and Hygiene 55:32–38.

   https://doi.org/10.4269/ajtmh.1996.55.32

   • PubMed
   • Google Scholar
6. 1. Delić D
   2. Ellinger-Ziegelbauer H
   3. Vohr HW
   4. Dkhil M
   5. Al-Quraishy S
   6. Wunderlich F

   (2011) Testosterone response of hepatic gene expression in female mice having acquired testosterone-unresponsive immunity to Plasmodium chabaudi malaria

   Steroids 76:1204–1212.

   https://doi.org/10.1016/j.steroids.2011.05.013

   • Google Scholar
7. 1. Desai M
   2. ter Kuile FO
   3. Nosten F
   4. McGready R
   5. Asamoa K
   6. Brabin B
   7. Newman RD

   (2007) Epidemiology and burden of malaria in pregnancy

   The Lancet Infectious Diseases 7:93–104.

   https://doi.org/10.1016/S1473-3099(07)70021-X

   • PubMed
   • Google Scholar
8. 1. Felger I
   2. Maire M
   3. Bretscher MT
   4. Falk N
   5. Tiaden A
   6. Sama W
   7. Beck HP
   8. Owusu-Agyei S
   9. Smith TA

   (2012) The dynamics of natural Plasmodium falciparum infections

   PLOS ONE 7:e45542.

   https://doi.org/10.1371/journal.pone.0045542

   • PubMed
   • Google Scholar
9. 1. Finda MF
   2. Moshi IR
   3. Monroe A
   4. Limwagu AJ
   5. Nyoni AP
   6. Swai JK
   7. Ngowo HS
   8. Minja EG
   9. Toe LP
   10. Kaindoa EW
   11. Coetzee M
   12. Manderson L
   13. Okumu FO

   (2019) Linking human behaviours and malaria vector biting risk in south-eastern Tanzania

   PLOS ONE 14:e0217414.

   https://doi.org/10.1371/journal.pone.0217414

   • PubMed
   • Google Scholar
10. 1. Fischer J
    2. Jung N
    3. Robinson N
    4. Lehmann C

    (2015) Sex differences in immune responses to infectious diseases

    Infection 43:399–403.

    https://doi.org/10.1007/s15010-015-0791-9

    • PubMed
    • Google Scholar
11. 1. Fish EN

    (2008) The X-files in immunity: sex-based differences predispose immune responses

    Nature Reviews Immunology 8:737–744.

    https://doi.org/10.1038/nri2394

    • PubMed
    • Google Scholar
12. 1. Hathaway NJ
    2. Parobek CM
    3. Juliano JJ
    4. Bailey JA

    (2018) SeekDeep: single-base resolution de novo clustering for amplicon deep sequencing

    Nucleic Acids Research 46:e21.

    https://doi.org/10.1093/nar/gkx1201

    • PubMed
    • Google Scholar
13. 1. Hofmann N
    2. Mwingira F
    3. Shekalaghe S
    4. Robinson LJ
    5. Mueller I
    6. Felger I

    (2015) Ultra-sensitive detection of Plasmodium falciparum by amplification of multi-copy subtelomeric targets

    PLOS Medicine 12:e1001788.

    https://doi.org/10.1371/journal.pmed.1001788

    • PubMed
    • Google Scholar
14. 1. Houngbedji CA
    2. N'Dri PB
    3. Hürlimann E
    4. Yapi RB
    5. Silué KD
    6. Soro G
    7. Koudou BG
    8. Acka CA
    9. Assi SB
    10. Vounatsou P
    11. N'Goran EK
    12. Fantodji A
    13. Utzinger J
    14. Raso G

    (2015) Disparities of Plasmodium falciparum infection, malaria-related morbidity and access to malaria prevention and treatment among school-aged children: a national cross-sectional survey in côte d'ivoire

    Malaria Journal 14:7.

    https://doi.org/10.1186/1475-2875-14-7

    • PubMed
    • Google Scholar
15. 1. Klein SL

    (2004) Hormonal and immunological mechanisms mediating sex differences in parasite infection

    Parasite Immunology 26:247–264.

    https://doi.org/10.1111/j.0141-9838.2004.00710.x

    • PubMed
    • Google Scholar
16. 1. Klein SL
    2. Shann F
    3. Moss WJ
    4. Benn CS
    5. Aaby P

    (2016) RTS,S malaria vaccine and increased mortality in girls

    mBio 7:e00514-16.

    https://doi.org/10.1128/mBio.00514-16

    • PubMed
    • Google Scholar
17. 1. Koepfli C
    2. Schoepflin S
    3. Bretscher M
    4. Lin E
    5. Kiniboro B
    6. Zimmerman PA
    7. Siba P
    8. Smith TA
    9. Mueller I
    10. Felger I

    (2011) How much remains undetected? probability of molecular detection of human plasmodia in the field

    PLOS ONE 6:e19010.

    https://doi.org/10.1371/journal.pone.0019010

    • PubMed
    • Google Scholar
18. 1. Koepfli C
    2. Mueller I

    (2017) Malaria epidemiology at the clone level

    Trends in Parasitology 33:974–985.

    https://doi.org/10.1016/j.pt.2017.08.013

    • PubMed
    • Google Scholar
19. 1. Kurtis JD
    2. Mtalib R
    3. Onyango FK
    4. Duffy PE

    (2001) Human resistance to Plasmodium falciparum increases during puberty and is predicted by dehydroepiandrosterone sulfate levels

    Infection and Immunity 69:123–128.

    https://doi.org/10.1128/IAI.69.1.123-128.2001

    • PubMed
    • Google Scholar
20. 1. Landgraf B
    2. Kollaritsch H
    3. Wiedermann G
    4. Wernsdorfer WH

    (1994) Parasite density of Plasmodium falciparum malaria in Ghanaian schoolchildren: evidence for influence of sex hormones?

    Transactions of the Royal Society of Tropical Medicine and Hygiene 88:73–74.

    https://doi.org/10.1016/0035-9203(94)90505-3

    • PubMed
    • Google Scholar
21. 1. Leenstra T
    2. ter Kuile FO
    3. Kariuki SK
    4. Nixon CP
    5. Oloo AJ
    6. Kager PA
    7. Kurtis JD

    (2003) Dehydroepiandrosterone sulfate levels associated with decreased malaria parasite density and increased hemoglobin concentration in pubertal girls from western Kenya

    The Journal of Infectious Diseases 188:297–304.

    https://doi.org/10.1086/376508

    • PubMed
    • Google Scholar
22. 1. Lerch A
    2. Koepfli C
    3. Hofmann NE
    4. Messerli C
    5. Wilcox S
    6. Kattenberg JH
    7. Betuela I
    8. O'Connor L
    9. Mueller I
    10. Felger I

    (2017) Development of amplicon deep sequencing markers and data analysis pipeline for genotyping multi-clonal malaria infections

    BMC Genomics 18:864.

    https://doi.org/10.1186/s12864-017-4260-y

    • PubMed
    • Google Scholar
23. 1. Lerch A
    2. Koepfli C
    3. Hofmann NE
    4. Kattenberg JH
    5. Rosanas-Urgell A
    6. Betuela I
    7. Mueller I
    8. Felger I

    (2019) Longitudinal tracking and quantification of individual Plasmodium falciparum clones in complex infections

    Scientific Reports 9:7.

    https://doi.org/10.1038/s41598-019-39656-7

    • Google Scholar
24. 1. Miller RH
    2. Hathaway NJ
    3. Kharabora O
    4. Mwandagalirwa K
    5. Tshefu A
    6. Meshnick SR
    7. Taylor SM
    8. Juliano JJ
    9. Stewart VA
    10. Bailey JA

    (2017) A deep sequencing approach to estimate Plasmodium falciparum complexity of infection (COI) and explore apical membrane antigen 1 diversity

    Malaria Journal 16:490.

    https://doi.org/10.1186/s12936-017-2137-9

    • PubMed
    • Google Scholar
25. Website

    1. Molineaux L
    2. Gramiccia G
    3. Organization WH

    (1980) The garki project : research on the epidemiology and control of malaria in the Sudan savanna of west africa

    World Health Organization. Accessed February 18, 2020.

    https://apps.who.int/iris/handle/10665/40316
26. 1. Moon JJ
    2. Cho SY

    (2001) Incidence patterns of vivax malaria in civilians residing in a high-risk county of Kyonggi-do (province), Republic of korea

    The Korean Journal of Parasitology 39:293–299.

    https://doi.org/10.3347/kjp.2001.39.4.293

    • PubMed
    • Google Scholar
27. 1. Mulu A
    2. Legesse M
    3. Erko B
    4. Belyhun Y
    5. Nugussie D
    6. Shimelis T
    7. Kassu A
    8. Elias D
    9. Moges B

    (2013) Epidemiological and clinical correlates of malaria-helminth co-infections in southern Ethiopia

    Malaria Journal 12:227.

    https://doi.org/10.1186/1475-2875-12-227

    • PubMed
    • Google Scholar
28. 1. Nankabirwa JI
    2. Briggs J
    3. Rek J
    4. Arinaitwe E
    5. Nayebare P
    6. Katrak S
    7. Staedke SG
    8. Rosenthal PJ
    9. Rodriguez-Barraquer I
    10. Kamya MR
    11. Dorsey G
    12. Greenhouse B

    (2019) Persistent parasitemia despite dramatic reduction in malaria incidence after 3 rounds of indoor residual spraying in Tororo, Uganda

    The Journal of Infectious Diseases 219:1104–1111.

    https://doi.org/10.1093/infdis/jiy628

    • PubMed
    • Google Scholar
29. 1. Nankabirwa JI
    2. Arinaitwe E
    3. Rek J
    4. Kilama M
    5. Kizza T
    6. Staedke SG
    7. Rosenthal PJ
    8. Rodriguez-Barraquer I
    9. Briggs J
    10. Greenhouse B
    11. Bousema T
    12. Drakeley C
    13. Roos DS
    14. Tomko SS
    15. Smith DL
    16. Kamya MR
    17. Dorsey G

    (2020) Malaria transmission, infection, and disease following sustained indoor residual spraying of insecticide in Tororo, Uganda

    The American Journal of Tropical Medicine and Hygiene 103:1525–1533.

    https://doi.org/10.4269/ajtmh.20-0250

    • Google Scholar
30. 1. Nguyen TN
    2. von Seidlein L
    3. Nguyen TV
    4. Truong PN
    5. Hung SD
    6. Pham HT
    7. Nguyen TU
    8. Le TD
    9. Dao VH
    10. Mukaka M
    11. Day NP
    12. White NJ
    13. Dondorp AM
    14. Thwaites GE
    15. Hien TT

    (2018) The persistence and oscillations of submicroscopic Plasmodium falciparum and Plasmodium vivax infections over time in Vietnam: an open cohort study

    The Lancet Infectious Diseases 18:565–572.

    https://doi.org/10.1016/S1473-3099(18)30046-X

    • PubMed
    • Google Scholar
31. 1. Nhamoyebonde S
    2. Leslie A

    (2014) Biological differences between the sexes and susceptibility to tuberculosis

    Journal of Infectious Diseases 209 Suppl 3:S100–S106.

    https://doi.org/10.1093/infdis/jiu147

    • PubMed
    • Google Scholar
32. 1. Okell LC
    2. Bousema T
    3. Griffin JT
    4. Ouédraogo AL
    5. Ghani AC
    6. Drakeley CJ

    (2012) Factors determining the occurrence of submicroscopic malaria infections and their relevance for control

    Nature Communications 3:1–9.

    https://doi.org/10.1038/ncomms2241

    • Google Scholar
33. 1. Pathak S
    2. Rege M
    3. Gogtay NJ
    4. Aigal U
    5. Sharma SK
    6. Valecha N
    7. Bhanot G
    8. Kshirsagar NA
    9. Sharma S

    (2012) Age-dependent sex Bias in clinical malarial disease in Hypoendemic regions

    PLOS ONE 7:e35592.

    https://doi.org/10.1371/journal.pone.0035592

    • PubMed
    • Google Scholar
34. Software

    1. Python Language Reference

    (2020) Python, version 3.7.4

    Python Software Foundation.

    http://www.python.org
35. Software

    1. R Development Core Team

    (2019) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
36. 1. Roberts CW
    2. Walker W
    3. Alexander J

    (2001) Sex-associated hormones and immunity to protozoan parasites

    Clinical Microbiology Reviews 14:476–488.

    https://doi.org/10.1128/CMR.14.3.476-488.2001

    • PubMed
    • Google Scholar
37. 1. Rondeau V
    2. Mazroui Y
    3. Gonzalez JR

    (2012) Frailtypack : An R Package for the Analysis of Correlated Survival Data with Frailty Models Using Penalized Likelihood Estimation or Parametrical Estimation

    Journal of Statistical Software 47:1–28.

    https://doi.org/10.18637/jss.v047.i04

    • Google Scholar
38. 1. Rondeau V
    2. Gonzalez JR

    (2005) Frailtypack: a computer program for the analysis of correlated failure time data using penalized likelihood estimation

    Computer Methods and Programs in Biomedicine 80:154–164.

    https://doi.org/10.1016/j.cmpb.2005.06.010

    • PubMed
    • Google Scholar
39. 1. Ross A
    2. Koepfli C
    3. Li X
    4. Schoepflin S
    5. Siba P
    6. Mueller I
    7. Felger I
    8. Smith T

    (2012) Estimating the numbers of malaria infections in blood samples using high-resolution genotyping data

    PLOS ONE 7:e42496.

    https://doi.org/10.1371/journal.pone.0042496

    • PubMed
    • Google Scholar
40. 1. Slater HC
    2. Ross A
    3. Felger I
    4. Hofmann NE
    5. Robinson L
    6. Cook J
    7. Gonçalves BP
    8. Björkman A
    9. Ouedraogo AL
    10. Morris U
    11. Msellem M
    12. Koepfli C
    13. Mueller I
    14. Tadesse F
    15. Gadisa E
    16. Das S
    17. Domingo G
    18. Kapulu M
    19. Midega J
    20. Owusu-Agyei S
    21. Nabet C
    22. Piarroux R
    23. Doumbo O
    24. Doumbo SN
    25. Koram K
    26. Lucchi N
    27. Udhayakumar V
    28. Mosha J
    29. Tiono A
    30. Chandramohan D
    31. Gosling R
    32. Mwingira F
    33. Sauerwein R
    34. Paul R
    35. Riley EM
    36. White NJ
    37. Nosten F
    38. Imwong M
    39. Bousema T
    40. Drakeley C
    41. Okell LC

    (2019) The temporal dynamics and infectiousness of subpatent Plasmodium falciparum infections in relation to parasite density

    Nature Communications 10:1433.

    https://doi.org/10.1038/s41467-019-09441-1

    • PubMed
    • Google Scholar
41. 1. Smith T
    2. Felger I
    3. Fraser-Hurt N
    4. Beck HP

    (1999) Effect of insecticide-treated bed nets on the dynamics of multiple Plasmodium falciparum infections

    Transactions of the Royal Society of Tropical Medicine and Hygiene 93 Suppl 1:53–57.

    https://doi.org/10.1016/S0035-9203(99)90328-0

    • PubMed
    • Google Scholar
42. 1. Snounou G
    2. Viriyakosol S
    3. Zhu XP
    4. Jarra W
    5. Pinheiro L
    6. do Rosario VE
    7. Thaithong S
    8. Brown KN

    (1993) High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction

    Molecular and Biochemical Parasitology 61:315–320.

    https://doi.org/10.1016/0166-6851(93)90077-B

    • PubMed
    • Google Scholar
43. 1. Tadesse FG
    2. Slater HC
    3. Chali W
    4. Teelen K
    5. Lanke K
    6. Belachew M
    7. Menberu T
    8. Shumie G
    9. Shitaye G
    10. Okell LC
    11. Graumans W
    12. van Gemert GJ
    13. Kedir S
    14. Tesfaye A
    15. Belachew F
    16. Abebe W
    17. Mamo H
    18. Sauerwein R
    19. Balcha T
    20. Aseffa A
    21. Yewhalaw D
    22. Gadisa E
    23. Drakeley C
    24. Bousema T

    (2018) The relative contribution of symptomatic and asymptomatic plasmodium vivax and Plasmodium falciparum infections to the infectious reservoir in a Low-Endemic setting in Ethiopia

    Clinical Infectious Diseases 66:1883–1891.

    https://doi.org/10.1093/cid/cix1123

    • PubMed
    • Google Scholar
44. 1. Vom Steeg LG
    2. Flores-Garcia Y
    3. Zavala F
    4. Klein SL

    (2019) Irradiated sporozoite vaccination induces sex-specific immune responses and protection against malaria in mice

    Vaccine 37:4468–4476.

    https://doi.org/10.1016/j.vaccine.2019.06.075

    • PubMed
    • Google Scholar
45. Report

    1. World malaria report

    (2019) World Malaria Report 2019

    WHO.

    https://www.who.int/publications-detail-redirect/9789241565721

    • Google Scholar
46. 1. Wunderlich F
    2. Marinovski P
    3. Benten WP
    4. Schmitt-Wrede HP
    5. Mossmann H

    (1991) Testosterone and other gonadal factor(s) restrict the efficacy of genes controlling resistance to Plasmodium chabaudi malaria

    Parasite Immunology 13:357–367.

    https://doi.org/10.1111/j.1365-3024.1991.tb00289.x

    • PubMed
    • Google Scholar
47. 1. Zuk M
    2. McKean KA

    (1996) Sex differences in parasite infections: patterns and processes

    International Journal for Parasitology 26:1009–1024.

    https://doi.org/10.1016/S0020-7519(96)80001-4

    • PubMed
    • Google Scholar

Author details

1. Jessica Briggs

   Department of Medicine, University of California San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   Jessica.Briggs@ucsf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8078-3898

2. Noam Teyssier

   Department of Medicine, University of California San Francisco, San Francisco, United States

   Contribution

   Formal analysis, Visualization, Methodology

   Competing interests

   No competing interests declared

3. Joaniter I Nankabirwa

   1. Infectious Diseases Research Collaboration, Kampala, Uganda
   2. Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda

   Contribution

   Resources, Supervision, Project administration

   Competing interests

   No competing interests declared

4. John Rek

   Infectious Diseases Research Collaboration, Kampala, Uganda

   Contribution

   Supervision, Project administration

   Competing interests

   No competing interests declared

5. Prasanna Jagannathan

   Department of Medicine, Stanford University, Palo Alto, United States

   Contribution

   Conceptualization, Supervision, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6305-758X

6. Emmanuel Arinaitwe

   1. Infectious Diseases Research Collaboration, Kampala, Uganda
   2. Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Supervision, Project administration

   Competing interests

   No competing interests declared

7. Teun Bousema

   1. Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
   2. Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2666-094X

8. Chris Drakeley

   Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   Resources, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4863-075X

9. Margaret Murray

   Department of Medicine, Stanford University, Palo Alto, United States

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   No competing interests declared

10. Emily Crawford

    Chan-Zuckerberg Biohub, San Francisco, United States

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

11. Nicholas Hathaway

    Department of Medicine, University of Massachusetts, Amherst, United States

    Contribution

    Software, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

12. Sarah G Staedke

    Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    Resources, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

13. David Smith

    Institute for Health Metrics & Evaluation, University of Washington, Seattle, United States

    Contribution

    Conceptualization, Writing - review and editing

    Competing interests

    No competing interests declared

14. Phillip J Rosenthal

    Department of Medicine, University of California San Francisco, San Francisco, United States

    Contribution

    Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

15. Moses Kamya

    1. Infectious Diseases Research Collaboration, Kampala, Uganda
    2. Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda

    Contribution

    Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

16. Grant Dorsey

    Department of Medicine, University of California San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

17. Isabel Rodriguez-Barraquer

    Department of Medicine, University of California San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Validation, Methodology, Writing - review and editing

    Contributed equally with

    Bryan Greenhouse

    Competing interests

    Reviewing editor, eLife

    "This ORCID iD identifies the author of this article:" 0000-0001-6784-1021

18. Bryan Greenhouse

    Department of Medicine, University of California San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Isabel Rodriguez-Barraquer

    Competing interests

    No competing interests declared

• 2,581

  views

• 290

  downloads

• 55

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.