{"paper_id":"5e998ea4-310a-4c70-a369-8478e0ab3502","body_text":"Over-dispersion in Malaria–Schistosoma Co-infection:  \nInsights from a Meta-analytical Approach and Systematic Review \n \nCélia Koellsch–Amet1, Jérôme Boissier1, Ronaldo de Carvalho Augusto1 \n \n \n1IHPE, Université de Perpignan Via Domitia, CNRS, Ifremer, Université de Montpellier, \nPerpignan, France \n \n \n \nCorresponding author: ronaldo.augusto@univ-perp.fr \n \n \n \nORCID \nCK, 0009-0003-7227-2590 \nJB, 0000-0002-0793-3108 \nRCA, 0000-0003-1147-0043 \n \n \nAbstract \nMalaria and schistosomiasis are two major parasitic diseases that are co -endemic in many \nregions of sub -Saharan Africa. Despite their frequent overlap, the potential epidemiological \ninteractions in cases of co -infection remain poorly understood. We conduct ed a systematic \nreview and meta -analysis in accordance with the PRISMA -ScR guidelines. A total of 192 \nstudies published between 1996 and 2023 were reviewed, of which 59 studies involving 73,383 \nindividuals were included in the meta -analysis. Pooled analysi s showed that Plasmodium–\nSchistosoma co-infection occurs more often than expected by chance (overall odds ratio [OR] \n= 1.20, 95% CI: 1.02 –1.40), despite substantial heterogeneity (I² = 89%). Co -infection \nprevalence ranged from 1.1% (Benin) to 36.6% (Mali), with school -age children and pregnant \nwomen disproportionately affected. Subgroup analyses revealed no consistent differences by \nsex or by Schistosoma or Plasmodium species. Observational and experimental evidence \nsuggests that co -infection may exacerbate  anemia and modulate host immunity, but \nmechanistic pathways remain poorly defined. The high co -endemicity of these parasites \nunderscores the need for integrated surveillance and control programs and highlights the \npotential interactions between the two pa rasites. Understanding these mechanisms is \nessential for designing integrated control strategies and highlights the need to take \npolyparasitism into account in public health policies, particularly in endemic regions. \n \n \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nIntroduction \nMalaria and schistosomiasis are among the \nmost widespread and debiliting parasitic \ndiseases affecting 85 and 78 countries, \nrespectively1,2. On a global scale, it is \nestimated that 46.4 million DALYs \n(Disability-Adjusted Life Years) was lost \ndue to malaria 3, compared with 1.6 million \nfor schistosomiasis 4, making these two \nparasitic diseases the two most important \nin the world; More than a third of the world's \npopulation is thought to be infected by \nhelminths or Plasmodium 5 with Africa \nremaining the hardest -hit continent. Sub-\nSaharan Africa bears the greatest burden \nof both infections, where overlapping \necological and socio -economic conditions \ncreate extensive zones of co -endemicity6–\n10. Moreover, the epidemiology of co -\ninfection is influenced by a variety of factors \nincluding population dynamics, \nbehavioural, genetic, host physio -\nimmunology, population size, and parasite \ndispersal patterns6. These include the high \nfrequency of the two parasites in the same \npopulation, the similar geographical \ndistribution of the vectors and the need for \nan aquatic environment to complete both \nparasite life cycles. Whether plasmodium \nor schistosome, the two parasites have \ncomplex life cycles , dependent on \nenvironmental conditions that allow other \nhost species - mosquitoes for Plasmodium, \nfreshwater snails for Schistosoma - to \nthrive. Furthermore, spatial statistical \nmodels highlight the geographical overlap \nand thus support the co -endemicity of \nmalaria and schistosomiasis infections 11. \nEnvironmental sensitivity and \nunderprivileged socio-economic conditions \nhave repeatedly been reported to influence \nthe survival of free-living invertebrate hosts \nand helminths, thereby governing the \nspatial distribution of diseases and \nfavouring the geographical overlap of \nPlasmodium and Schistosoma 7,12,13. \nSince the last major pandemic of COVID -\n19 that recently affected the world, the \nquestion of an interaction between climate \nchange and infectious diseases has \nbecome increasingly worrying 14. As the \neffects of these changes are still poorly \nunderstood, climate change as a whole is \ngenerating major uncertainties about the \nepidemiology of many diseases, and is \nwidening the gap between current \nhealthcare techniques and the evolution of \nthe epidemiology of infectious diseases. To \ndate, it is esti mated that 58% of infectious \ndiseases in humans have at some time \nbeen intensified by climatic events \n15.  Climatic factors can also lead to the \nemergence of new pathogens and increase \nthe risk of transmission of existing diseases \nsuch as malaria  and other neglected \ntropical and subtropical diseases such as \nschistosomiasis 16–18. In Africa, malaria \nepisodes are impacted by environmental \nvariability and often occur after climatic \nanomalies such as periods of drought or \nextreme rainfall 19,20.  However, falciparum \nmalaria remains confined to tropical and \nsubtropical regions in the various predictive \nmodels 21, while Plasmodium vivax , \nformerly the dominant species in Europe, \ncould re -emerge 22,23. An outbreak of \nschistosomiasis has also recently been \nidentified in southern Europe (Corsica, \nFrance), with infected patients originated \nfrom France, Germany and Italy 24,25, \nresulting in both of an increase in the \nhuman migration and of the rise in the \ntemperature of fresh water ecosystems, \nwhich favours the establishment of the snail \nvector in southern Europe 26. Identifying \ncurrent hotspots of co -endemicity and \nunderstanding the ecological drivers of \noverlap will be critical for anticipating future \nshifts in disease distribution. Although the \nepidemiology and immunology of major \nparasitic diseases such as malaria and \nschistosomiasis are relatively well \ndocumented, the mechanisms and \nimplications of concomitant infections \nremain largely unexplored. However, co -\ninfection with Plasmodium spp. and \nSchistosoma spp.  is common in many \nendemic regions and could be modulat ed \nby global warming, prompting growing \ninterest in understanding the extent to \nwhich these parasites can interact in the \nsame host 18,27. In particular, several \nstudies suggest that Schistosoma \ninfections may modulate susceptibility or \nimmune response to Plasmodium infection, \nraising key questions about the potential \ninteractions between these pathogens, \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nboth clinically and immunologically. \nInteractions between Plasmodium and \nhelminths have already been demonstrated \nto influence the host immune response to \nmalaria infection 28,29. In the specific case of \nschistosomiasis, several studies also \nsuggest the existence of cross -effects with \nmalaria, although the exact nature of these \ninteractions - protective or deleterious - \nremains controversial 30–39. Some studies \nreport that co -infection with schistosomes \ncould worsen Plasmodium infection, by \nincreasing the intensity, prevalence or \nclinical impact of Plasmodium spp . \n30,33,37,40,41. Conversely, other studies \nindicate a mitigating or even protective \neffect of schistosomiasis on the severity of \nmalaria 31,36,39,41. At the same time, some \nstudies have found no significant \nassociation between these two infections \n42–45. These controversies may stem from \nthe fact that sampling protocols vary \nbetween studies. The strains of parasites \nstudied may also differ from one study to \nanother, as may the sampling region. The \ntiming of infection could influence the \noutcome of co -infection, whether \nsimultaneous or sequential and this still is \nan aspect neglected in epidemiological \nanalyses. \nObservational and experimental studies, in \nboth animals and humans, have begun to \nreveal the mechanisms of these \ninteractions, whether direct (through \ncompetition for resources) or indirect (via \nmodulation of the immune response). \nNevertheless, the complexity of \ninteractions between Plasmodium spp. and \nSchistosoma spp.  continues to be \ninvestigated, with the aim of better defining \nthe synergistic or antagonistic effects of \nthese co-infections in human populations46–\n48. To date, the appearance of \nsimultaneous infections by these two \nparasites in humans seems to be at the \norigin of the emergence of several studies \naimed at elucidating aspects related to the \nsynergistic and antagonistic interactions of \nPlasmodium spp. and Schistosoma spp.46–\n48. \nDespite the reported effects of malaria and \nschistosomiasis infections, the nature of \nthe interactions between the two parasites \nremains unclear. It is possible that this is \ndue to the general complexity of the \ninteractions between parasites and of the \npathways involved in these interactions \nduring co -infection. Thus, a clear \nunderstanding of the epidemiology of \nmalaria during co -infection with \nschistosomiasis is essential to inform \ndecisions on appropriate control strategies \nagainst these two diseases . Thi s \nsystematic review and meta -analysis \nprovide, a review of the epidemiological \nand experimental studies identified on \nSchistosoma and Plasmodium interactions \nand interprets the plausibility of these \ninteractions. Consistent with the studies \nconducted, we hypothesize that \nPlasmodium and Schistosoma interact \npositively during co-infection and generate \nboth an impact in their host.  \nMethod \nProtocol and registration \nThis systematic review and meta -analysis \nwas conducted in accordance with the \nrecommendations of the PRISMA -ScR \n2020 guidelines (Preferred Reporting Items \nfor Systematic Reviews and Meta-Analyses \nextension for Scoping Reviews), as \ndetailed in appendix S1. The aim of the \napproach was to explore and synthesise \nthe available data on interactions between \nPlasmodium spp. and Schistosoma spp. in \ncontexts of human co-infection. \nInformation sources and search \nstrategy \nA systematic search was conducted in two \nbibliographic databases: BibCNRS  and \nPubMed, on 24 January 2025. The search \nstrategy combined the terms \"Plasmodium\" \nOR “malaria” AND \"Schistosoma\" OR \n“schistosomiasis” AND \"co -infection\" OR \n\"coinfection\". This search was completed \nby a citation search of articles included \nbetween 28 January and 6 March 2025. \nWhere article abstracts were not available, \na selection was made from the tables of \ncontents, followed by a full-text analysis. In \naddition, a manual grey literature search \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nwas conducted and managed using \nMicrosoft Excel and Zotero software. \nEligibility criteria \nThe studies included in this literature review \nmet the following criteria: Epidemiological, \nexperimental or functional studies reporting \ndata on the prevalence or incidence of co -\ninfection with P. falciparum or P. vivax, and \nS. haematobium or S. mansoni; Studies on \ngeneral human populations, regardless of \nage or sex; Articles written in French or \nEnglish and published in accessible \nscientific journals. Studies were excluded \non the basis of the following criteria: \nStudies focusing solely on the treatment of \ninfections; Unpublished studies, \nconference abstracts, protocols, non -\naccessible grey literature and articles in a \nlanguage other than French or English; \nStudies mentioning co -infections with \ngeohelminths without specific data on \nSchistosoma; Studies reporting no figures \n(prevalence, incidence, numbers, etc.) or \nnot allowing a statistical effect to be \ncalculated. \nThis analysis did not distinguish between \nasymptomatic and uncomplicated malaria, \nnor between the degrees of severity of the \ntwo parasitic diseases, in order to estimate \nthe nature of the association between co -\ninfection with Schistosoma and \nPlasmodium.  \nInformation and data extraction \nThe data were extracted into a dedicated \nExcel sheet, including the following \ninformation for each study: References \n(author, year); Period and location of study; \nGender and age range of the population; \nTotal number of participants and number of \ncases for each group (co -infected, mono -\ninfected, uninfected); Prevalence rates for \nPlasmodium spp ., Schistosoma spp . and \nco-infection; Additional information: \nanaemia, malnutrition, other associated \npathologies. \nThese data were then imported into R via \nCSV files. The articles were classified by \ntype: epidemiological surveys, functional \nstudies and experimental studies. \nEpidemiological studies are subjected to a \nmeta-analysis, while other studies are \nsubjected to a written synthesis. Using this \nspreadsheet, a preliminary summary of the \nresults of the included studies was \ndeveloped. Relationships within and \nbetween studies were also examined, as \nwas the robustness of the synthesis for \neach study. The articles were gr ouped by \ntype of study under three headings: \nepidemiological investigations, functional \ninvestigations and experimental studies. \nEach of these headings represents a \nparagraph of this review.  \nStatistical analysis \nConcerning epidemiological studies, in  the \nExcel sheet of the included studies, the \npercentages of expected and observed co-\ninfection were calculated (Appendix S2). \n• Observed co -infection (actual co -\ninfection measured in the study): \nThis proportion is calculated from the \nnumber of people co-infected (infected with \nboth Plasmodium and Schistosoma) in \nrelation to the total sample size. \n \n• Expected co -infection (if the \ninfections were independent): \nThis is a theoretical estimate of the co -\ninfection that would occur if Plasmodium \nand Schistosoma infections were \nindependently distributed in the population. \nIt is calculated by multiplying the individual \nprevalence of each parasite: \n \nA Fisher's test was performed to compare \nthe expected and observed co -infection \nrates.The statistical analysis was then \ncarried out in R using the metafor and meta \npackages. Meta -analyses were performed \non the odds ratios (OR) using the Mantel -\nHaenszel method, with fixed -effects and \nrandom-effects models. Several sub -\nanalyses were conducted: By type of co -\ninfection (S. mansoni vs. S. haematobium / \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nP. falciparum vs. P.vivax) ; By sex (men vs. \nwomen) ; By overall prevalence. An OR > 1 \nsuggests that co-infection is more frequent \nthan expected by chance and suggests a \npositive association and an OR < 1 \nsuggests that co -infection is less frequent \nthan expected, so a potential pr otective \neffect. Heterogeneity between studies was \nassessed using the I² and Tau² indicators. \nForest plots were generated to visualise the \nresults, and the presence of publication \nbias was analysed by funnel plot. This bias \nwas als o tested more formally using the \nEgger asymmetry test (metabias). A \nFisher's exact test was used to assess the \nassociation between certain categorical \nvariables on a point-by-point basis. \nCo-occurrence map \nTo depict global patterns of malaria –\nschistosomiasis overlap, we compiled \ncountry-level data on malaria incidence and \nschistosomiasis occurrence from the World \nHealth Organization Global Health \nObservatory and the Preventive \nChemotherapy and Transmission C ontrol \n(PCT) Databank (data accessed January \n2025). Annual records from 2017 –2023 \nwere aggregated to classify each country \nas malaria only, schistosomiasis only, co -\noccurrence, or none. Maps were generated \nin R (v4.3) using the ggplot2 and \nchoroplethr packages. \nResults \nSearch results \nDatabase searches identified 2,064 articles \n(Figure 1). After removing duplicates, the \ntitles and abstracts of 1 ,897 articles were \nreviewed, of which 105 articles were \neligible for full review. A further 116 articles \nwere identified by citation search, of which \n112 were eligible. Of these 217 eligible \narticles, 25 were excluded for the following \nreasons (Figure 1): 18 did not evaluate the \nSchistosoma species that we were \nincluding in this review, 3 focused on drug \ntreatments and 4 were neither in French nor \nin English. A total of 192 articles were \nincluded in the systematic review to \nconstruct the qualitative part of this review, \nof which 59 were used for the quantitative \nsynthesis (meta -analysis) only. \nInterestingly, 34 articles were split between \nquantitative and qualitative analysis. As a \nresult, the qualitative analysis (systematic \nreview) alone included 167 articles. The \ncharacteristics of the articles included in the \nmeta-analysis are summarised in Appendix \nS3.  \nFigure 1. PRISMA 2020 flowchart of the \nprocess undertaken for the inclusion or \nexclusion of studies in the systematic \nreview.PRISMA (Preferred Reporting Items for \nSystematic Reviews and Meta -Analyses) \nflowchart showing how 167 studies were \nobtained for the review. Of the 167 reports, 59 \nwere included in the meta-analysis. \nCharacteristics of the included studies \nThe characteristics of the 59 studies \nincluded in the meta -analysis are \nsummarised in Appendix S3. All the studies \nwere published between 1996 and 2023 \nand covered 14 countries - all on the \nAfrican continent. Most studies were \nconducted in Nigeria (11/59, 18.64%). The \nselected studies included children and \nadults (both men and women). Co-infection \nbetween S. haematobium  and P. \nfalciparum were reported in 41 studies, 21 \nstudies reported co -infection between S. \nmansoni and P. falciparum , 3 studies \nreported co -infection between S. \nhaematobium, S. mansoni and P. \nfalciparum, 1 study reported co -infection \nbetween S. mansoni and P. vivax and no \nstudy reported co -infection between S. \nhaematobium and P. vivax.  \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\n1. Epidemiology of co-infection \nSpatial distribution of Plasmodium and \nSchistosoma \nThe epidemiological data (59 articles in \ntotal) covered the prevalence of co -\ninfection and the morbidity associated with \ndual infection. Helminthic and malarial \ninfections spread when climatic and \nenvironmental conditions are favourable to \ntheir development, particularly in low -\nincome communities that require the \ninstallation of essential elements such as \ndrinking water, water sanitation or \nimproved hygiene of premises 49,50. In \nTanzania, studies suggests linking the \ncontrol of malaria -schistosomiasis co -\ninfection to types of agro -ecosystem after \ndemonstrating that children in rice-irrigated \necosystems were more likely to be co -\ninfected than in other measured \necosystems 9. In countries such as Nigeria, \nUganda, Ivory Coast and Ethiopia, helminth \nand malaria infections are highly prevalent \nand represent a persistent public health \nproblem 51–54. More than a third of the \nworld's population, particularly those living \nin tropical and subtropical regions, are \nthought to have problems with concomitant \ninfection by various species of Plasmodium \nand soil helminths 6.  \nFigure 2. Global distribution of malaria, \nschistosomiasis, and their co -occurrence \n(2017–2022). Country shading indicates \nreported occurrence based on WHO Global \nHealth Observatory data assessed in 2025: \ngreen – malaria only, blue – schistosomiasis \nonly, red – co-occurrence of both infections, and \ngrey – no reported occurrence. \nBoth malaria and schistosomiasis are \nwidespread in similar tropical and \nsubtropical areas, particularly in sub -\nSaharan Africa (Figure 2). Approximately \n70% of the global burden of malaria is \nconcentrated in sub -Saharan Africa 55 and \n90% for schistosomiasis 56. These co -\ninfections are particularly well studied in \nNigeria and Uganda, where 11 and 9 \nstudies respectively have investigated the \nprevalence of Plasmodium and \nSchistosoma co-infection (Supp.Fig.1). \nSimilarly, the prevalence of co -infection \nwas recorded for Ethiopia, Ghana and Ivory \nCoast in 6 studies each, for Mali in 5 \nstudies, for Tanzania, Senegal, Kenya and \nGabon in 4 papers, for Cameroon in 3 \npapers, for Zimbabwe in 2  papers and for \nthe Democratic Republic of Congo and \nBenin in 1 paper each (Supp.Fig.1). Of the \n59 articles considered for the \nepidemiological data, some analysed \nseveral countries. All the geographical \nareas listed in the studies analysed for this \nreview are malaria - and schistosomiasis -\nendemic areas in sub-Saharan Africa.  \nDescriptive prevalence of malaria and \nschistosomiasis single and co -\ninfections \nMalaria is currently naturally caused by 9 \nspecies of Plasmodium: P. falciparum, P. \nvivax, P. malariae, P. ovale curtisi, P. ovale \nwallikeri found only in humans, as well as \nP. knowlesi, P. simium, P. brasilianum  and \nP. cynomolgi which, although mainly found \nin monkeys, is also a pathogenic agent in \nhumans 57–59. As far as schistosomiasis is \nconcerned, there are currently more than \n21 known species of Schistosoma, with 6 \nmain species infecting Human ( S. \nhaematobium, S. mansoni, S. intercalatum, \nS. japonicum , S. mekongi  and S. \nmalayensis) 60,61. \nFor this meta-analysis, we have chosen to \nfocus exclusively on infections with P. \nfalciparum and P. vivax as well as S. \nmansoni and S. haematobium  - the most \nwidespread representative species from \nboth parasites in Africa 57,61. The \nprevalence of P. vivax  infection remains \npoorly studied in Africa despite consistent \nevidence of the emergence of this species \non the continent (Supp.Fig.2A )62,63. The \nresults of the studies clearly show the \nresearch interest in the infection \nprevalence of the other three species, with \na maximum of 106 analyses carried out on \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nthe infection prevalence for P. falciparum in \nthe 59 studies, compared with 86, 41 and 6 \nprevalence recorded for S. haematobium, \nS. mansoni and P. vivax respectively. The \nprevalence rates assessed in the various \nstudies included in the meta -analysis were \nalso higher for S. mansoni  and P. \nfalciparum species, with peaks of 92% and \n100% respectively. The maximum \nprevalence analysed for S. haematobium  \nand P. vivax  were 78% and 22% \nrespectively. Furthermore, unlike the other \nthree species, the lowest prevalence of P. \nvivax was 13% (0% was observed for other \nparasites). The average prevalence of S. \nmansoni, S. haematobium , P. falciparum  \nand P. vivax  in these studies was 21%, \n30%, 40% and 17% respectively on the \nAfrican continent (Supp.Fig.2A). \nIn the present study, several types of co -\ninfection were analysed, ranging from an \nassociation between two parasites to three. \nThe most common and most studied co -\ninfection was that between P. falciparum  \nand S. haematobium , with 76 prevalence \nrecorded and an average prevalence of \n17.3%. This was followed by co -infections \nbetween P. falciparum and S. mansoni with \n30 recorded prevalence and an average of \n9.1%, between P. falciparum, S. \nhaematobium and S. mansoni  with 6 \nrecorded prevalence and an average of \n4.6%, between P. vivax  and S. mansoni  \nwith 1 census and 0.9% prevalence and \nbetween P. vivax, P. falciparum  and S. \nmansoni with 1 census and 11.2% \nprevalence (Supp.Fig.2B). \nDifferences in the prevalence of co -\ninfections were found between the different \nAfrican countries sampled in the meta -\nanalysis studies. At the top of the list were \nMali, with a prevalence of all co -infections \ncombined of 36.6%, Nigeria with 25.8% \nand Gabon with 23.6%. Conversely, Benin, \nGhana and Ethiopia had co-infections rates \nof less than 5% (Supp.Fig.3). However, \nthere has only been one study of the \nprevalence of co-infection in Benin.  \nMeta-analysis of Plasmodium spp.  and \nSchistosoma spp.  Co-infection over -\ndispersion \nMeta-analysis of the prevalence of co -\ninfection in all studies combined, in cohorts \ncomposed of men, women and children, \nrevealed statistical heterogeneity (I 2: 89%; \np-value < 0.0001) between studies (Figure \n3). Overall, the prevalence of co -infections \nappeared to be over -dispersed, with a \nrandom effect not including the 1 (95% CI: \n1.02; 1.40) (Figure 3). The most \noverdispersed estimated prevalences were \nfound i n a cohort from Senegal with an \nodds ratio of 39.86 (95% CI: 2.40; 661.19) \nand in second place in Kenya with an odds \nratio of 15.40 (95% CI: 2.03; 117.04). The \nhighest estimated prevalence of co -\ninfection was 74%, found in a very small \npopulation of 54 individuals, while the \nlowest estimated prevalence was 0%, \nfound in two studies of 404 and 6681 \npersons (Table S1). Two studies by Bassa \net al. 2022 and Lyke e t al. 2012 did not \nreport the number of mono -infected \npatients who developed malaria (Table S1). \nHowever, one of the studies may be subject \nto sampling bias since the patients selected \nwere all positive for malaria. (Table S1). \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nFigure 3. Forest diagram illustrating the \nresults of the network meta-analysis for the \noverall prevalence of co -infections in \nstudies that assessed the prevalence of \nmalaria-schistosomiasis co -infections. \nSensitive analysis comparing the difference \nbetween observed and expected co -infection. \nBlocks in the figure represent odds ratios, with \nerror bars indicating 95% confidence intervals. \nReports have been arranged alphabetically \naccording to the first author. OR: odds ratio, CI: \nconfidence interval, I²: hete rogeneity \ncoefficient, t²: inter-study variance.  \nSex Stratification Reveals Consistent \nOver-Dispersion \nA total of 73,383 men and women were \nsampled for the prevalence of co -infection \nacross the 59 studies listed. Some studies \ndistinguished between men and women in \ntheir sampling cohorts, while others did not. \nA sub-group analysis was therefore carried \nout, with women and men on one side \n(Figure 4). This highlighted the fact that, \nalthough a distinction is made between the \ntwo sexes in certain studies, t here is no \nsignificant difference in the dispersion of \nthe prevalence of co -infections between \nmen and women (x²1 = 0.15; p -value = \n0.70). There was statistical heterogeneity \n(I2: 48%; p -value = 0.0046), suggesting a \nmoderate level of heterogeneity among the \nincluded studies for women, and statistical \nheterogeneity (I2: 27%; p-value = 0. 1430), \nsuggesting a low level of heterogeneity \namong the included studies for men, \nalthough the Egger's regression tests did \nnot reach statistical significance (for \nwomen: Egger's test = 1.83, p -value = \n0.0805; for men: Egger's test = 1 .21, p -\nvalue = 0.2435). Overall, the meta-analysis \nby sex showed over -dispersion of co -\ninfection in both 9726 women and 4593 \nmen (Figure 4) in 22 and 15 studies \nrespectively. This corroborates  the results \nof the overall meta-analysis (Figure 3). \n \nFigure 4. Forest diagram illustrating the \nresults of the network meta-analysis for the \noverall prevalence of co -infections in \nwomen and men in studies that assessed \nthe prevalence of malaria -schistosomiasis \nco-infections. Sensitive analysis comparing \nthe difference between observed and expected \nco-infection in women and men. Blocks in the \nfigure represent odds ratios, with error bars \nindicating 95% confidence intervals. Reports \nhave been arranged alphabetically according to \nthe first author. OR: odds ratio,  CI: confidence \ninterval, I²: heterogeneity coefficient, t²: inter -\nstudy variance. \nWe may envisage that the distinction \nbetween men and women, in the cohorts, \nwas decided on the basis of socio -\neconomic status and behaviour, which \ndiffer between communities but also \nbetween the two sexes. Indeed, \ndemography and climatic factors, social \nand behavioural factors are associated with \nthe risk of infection by the two parasites 51. \nMen are thought to be more exposed to the \nrisk of schistosomiasis infection through \nlong-term activities linked to water sources \nsuch as fishing or rice -growing 64,65 or \nsimply bathing or washing in natural water \nsources 66. Moreover, in certain regions, for \nreligious and socio -cultural reasons, \nwomen are sometimes forbidden to take \npart in activities such as swimming and \nfishing, which makes them less vulnerable \nto infection 67,68. Conversely, with regard to \nmalaria infections, women would be more \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nlikely to be exposed to infections than men \ndue to their prolonged exposure to \nmosquitoes during the most dangerous \nhours associated with outdoor domestic \nchores 69. However, this remains \ncontroversial, with several studies pointing \nto a greater risk of malaria infection in men \n70,71. \nCo-infection rates of 46.8% were found \namong pregnant women in Cameroon, \nwhich can be explained mainly by the lack \nof water sources other than natural springs \n72. In contrast, co -infection prevalences of \n23.8% and 20.8% were reported among \npregnant women in Nigeria 73,74. This \ndifference is mainly explained by the fact \nthat pregnant women in some communities \nare denied access to natural water sources \n73.  \nGeneral clinical and immunological \nfeatures of malaria and schistosomiasis \nco-infection \nA major consequence of co -infection \nbetween malaria and schistosomiasis is \nanaemia 29. It is now known that these \ninfections are both responsible for anaemia \nproblems in humans, which could be \nexacerbated by the simultaneous presence \nof both parasites in the same host. For \nexample, it has been shown that anaemia \nin Nigerien schoolchildren can be strongly \ninfluenced by a single infection with \nPlasmodium spp. or S. haematobium, but \nalso by co -infection 10. Other studies have \nalso found higher anaemia in co -infected \npatients compared to Plasmodium infection \n33,40,54,72,75–78 (Table S2). It has also been \nshown that anaemia is greater in cases of \nco-infection than in cases of Schistosoma \ninfection 79. However, other studies have \nreported contrasting results, showing that \nco-infection results in lower anemia levels \ncompared to mono -infection with \nPlasmodium or Schistosoma 10,45,80–82 \n(Table S2). These discrepancies are \nhypothesized to arise from factors such as \nthe genetic background of the human hosts \nand/or parasites, which may influence \nparasite-parasite and parasite -host \ninteractions. In addition to the \naforementioned data, co -infection with \nmalaria and schistosomes is known to \nmodify the immune profile of co -infected \nindividuals, notably by altering the balance \nbetween the immune responses of TH1 \nresponse cells (helper T lymphocytes) and \nthe TH2 response. Since Schistosoma \ninduce a Th2 response in a chronic phase \nand a protozoan induces a Th1 response, \nco-infection of the two severely alters the \ndevelopment of an immune response and \nalso affects already established responses \n83. Co -infection could also lead to a \nreduction in the immunological control of \nPlasmodium. Studies in humans and mice \nhave shown that specific humoral and cell -\nmediated immune responses are essential \nfor resistance to infection by both parasites \n83. Furthermore, murine malaria infection \nappears to significantly affect antibody \nlevels in vivo and the cytokine response in \nvitro to S. mansoni antigen 83. Interestingly, \nP. yoelii  infections reduced granuloma \nformation in the lungs of mice injected with \nS. mansoni  eggs, indicating that malaria \ninfection may influence granuloma \nformation in vivo 84. \nMoreover, increasing evidence highlight \nthe pathology seen in schistosomiasis and \nother parasitic diseases arises not only \ndirectly from parasite products but also \nfrom normal com ponents of the immune \nresponse 85, in particular polymorphisms in \ncytokines such as IFN -γ, TNF -α, IL -4, IL -\n10, IL -13 and STAT -6 (Table S3 ). Some \npolymorphisms have been associated with \nsusceptibility or resistance to Schistosoma \ninfection 86. While other cytokine \npolymorphisms may be affiliated with the \nactivity, amount and timing of cytokine \nproduction, influencing malar ia \nsusceptibility and severity 87. Given that \nPlasmodium spp. and Schistosoma spp . \ninfections often coincide geographically in \nthe same regions, it is crucial to know \nwhether schistosomiasis infections \nmodulate immune responses against the \nmalaria parasite and affect its evolution. \n2. Coinfection of Species of \nPlasmodium and Schistosoma in \nexperimental models \nSeveral cases of co -infection with \nPlasmodium and Schistosoma in the \nmurine model have already been tried, and \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nnumerous combinations have been tested: \nS. mansoni  and P. berghei  84,88–91, S. \nmansoni and P. yoelii  88,92–94, S. mansoni  \nand P. chabaudi 83,88,95 and S. japonicum \nand P. berghei 96,97. In addition, several of \nthese studies have shown an effect of \nSchistosoma infection on the development \nof malaria, mainly reflected by an increase \nin parasitaemia 83,93 as well as an increase \nin the duration of malaria infection, resulting \nin the death of the mice. Conversely, the \nstudy by Waknine -Grinberg et al (2010) \ndemonstrated that co -infection with S. \nmansoni and P. berghei in mice (not inbred) \nled to a change in Plasmodium infection \nand, in particular, increased survival in \ninfected models. A change in cytokine \nexpression due to the presence of \nSchistosoma was associated with a \nreduction in cerebral malaria. \nHowever, co -infection with Schistosoma \nand Plasmodium is controversial in terms of \nits effects on either parasite. Indeed, some \nstudies report a negative interaction \nbetween Plasmodium and Schistosoma \n39,47,96–98 in which simultaneous infection \nwith schistosomiasis and malaria resulted \nin lower densities of Plasmodium than in \npopulations not infected with Schistosoma, \nsuggesting that co -infection with \nschistosomiasis may have a pro tective \neffect against malaria 99. Although some \nstudies demonstrate the possibility of a \nnegative association resulting from \ncoinfection, other studies have \ndemonstrated an additive or syn ergistic \neffect of coinfection 48,100. In an earlier study \nin animal models, co -infection with \nschistosomiasis prolonged the time to low -\ndensity malaria parasitaemia and induced \nanaemia compared with the group infected \nwith malaria alone 101. In many cases, \nhowever, it has been shown that previous \ninfection with Schistosoma often has an \neffect on subsequent infection with a \nprotozoan such as Plasmodium 99.  \nDiscussion \nThis systematic review with meta -analysis \nof 59 studies involving 73,383 individuals in \n14 endemic African countries showed an \noverall mean prevalence of malaria -\nschistosomiasis co -infection of 13.41%, \nvarying from 1.1% in Benin to an average \nof 36.6% in Mali. Helminth -malaria co -\ninfection seems to be the subject of recent \nstudies 17,76,102,103 and although \npolyparasitism is widespread in tropical and \nsubtropical regions, its impact on public \nhealth has not been sufficiently studied to \nhighlight the mechanisms involved. Our \nanalysis, combining a co -endemicity map \nand the meta -analysis, suggests that co -\ninfection between Plasmodium and \nSchistosoma is widespread in sub-Saharan \nAfrica.  \nThe overdispersion observed in \nPlasmodium–Schistosoma co-infections is \nprobably explained by a complex set of \nbiological, ecological and /or behavioural \nmechanisms. From an immunological and \nbiological perspective, several hypotheses \ncan be put forward. Co-infection may result \nfrom direct facilitation: a previous infection \nmay alter the host's immune response and \nfacilitate the establishment o f a second \nparasite, for example through \nimmunomodulation or inappropriate \npolarisation of the response 6,29. Similarly, \nthe cumulative effect of physiological stress \ncaused by a first infection can weaken the \nhost (loss of resources, alteration of the \nimmune barrier), increasing the likelihood \nthat another parasite will establish itself 104–\n106. This weakening could explain why \nsome individuals infected with one parasite \nare more susceptible to a second infection. \nFrom an ecological point of view, the \noverdispersion of co -infection is largely \ninfluenced by the co -endemicity of the two \nparasites. Traditionally, co-endemicity and \nthe overlap of the ecological niches of the \ntwo parasites are invoked to explain co -\ninfection 7,11. Although this argument is \nmore often used to justify coexistence \nrather than overdispersion, it can \nnevertheless contribute to it: indeed, \nrepeated co-exposure linked to the ecology \nof vectors (mosquitoes and molluscs \nsharing the same habitats) can increas e \nthe probability of encountering both \nparasites simultaneously and reinforce \ntheir association within a part of the \npopulation. Climatic factors, such as soil \nmoisture, rainfall and periods of drought, \ndirectly influence the survival and \ndevelopment of larval stages, thus \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\ndetermining the seasonality of transmission \n29. In a world subject to climate change, \nwhich is expected to increase habitat \ninstability, parasites may face shorter \nperiods of activity, which may force them to \noverlap in time.  Finally, from the \nperspective of host -related factors, human \nbehaviour is certainly a major determinant. \nSchool-age children are a particularly \nvulnerable population because they are \nmore active, spend more time outdoors and \nare more likely to frequent contaminated \nwater sources, which increases their \nlikelihood of being infected by multiple \nparasites 11,29. Daily habits (swimming, \nwashing clothes, fishing, irrigated \nagriculture) and the type of habitat \n(proximity to mosquito breeding areas or \nmollusc habitats) contribute to increasing \nthis exposure. Beyond these behaviours, \nthere is also intrinsic heterogen eity in host \nsusceptibility: some individuals, known as \n‘super-recipients’, are more susceptible to \nvarious infectious agents, either due to \ngenetic characteristics, physiological or \nimmune differences 103,107,108, or under the \ninfluence of gene-parasite interactions and \nenvironmental factors 109,110. These hosts \naccount for a disproportionate number of \nco-infections and contribute significantly to \nthe observed patterns of overdispersion 111. \nGenetic or physiological differences \nbetween individuals may also explain \nparticular susceptibility profiles. Some \nhosts have genotypes that make their \nimmune systems less effective against \nseveral infectious agents, contributing to a \nconcentration of co -infections in a small \nfraction of the population 107,112,113. Finally, \nco-adaptation mechanisms between \nparasites have been suggested, whereby \nniche sharing could reflect an evolutionary \nstrategy that limits direct competition in the \nlong term 108,111. \nIn short, the over -dispersion of \nPlasmodium–Schistosoma co-infection \ncannot be attributed to a single factor. It \nresults from a complex interaction between \nbiological mechanisms \n(immunomodulation, fragility, genetic \nsusceptibility), ecological factors (niche \noverlap, climatic influence, vector ecology) \nand host-related factors (risky behaviours, \nsuper-receptors). Understanding these \ninteractions is essential for better predicting \nthe dynamics of co -infections and refining \ncontrol strategies in co-endemic areas. \nIs important to highlight among the \nindividuals analysed that it was common \ndetect pregnant women co -infected by \nPlasmodium and schistosomes. It has been \nrepeatedly demonstrated that pregnant \nwomen and young children are more likely \nto suffer high morbidity and mortality when \ninfected with either malaria or \nschistosomiasis parasites 73,105,114. The \nimportance of including pregnant women \nand young children in sampling cohorts \ntherefore becomes clear when we \nunderstand that they are more likely to \nsuffer the consequences of infection. \nMalaria-schistosomiasis co -infections are \nall the more important because of the more \nsevere clinical symptoms and pathology \nthan in the case of a mono-infection 48,100,105 \nand the possibility of modulation of the \nimmune response 106,115 by the presence or \ninteraction of the two parasites.  \nSchool-age children are also widely \nconsidered in co-infection studies because \nof their immune fragility and higher infection \nintensities compared with older individuals, \nthe possibility of primary contact with in -\nutero infections that have made them \nfragile, and high -risk social behaviours \nsuch as bathing or washing in natural water \nsources 43,116. Indeed, in addition to the risk \nposed by the water feature in the \ntransmission model for the two infections, \nchildren may be exposed to different points \nof infection when they go there - such as \nopen defecation areas 117. Co -infections \nare also particularly common among \nschool-age children in Nigeria, Uganda and \nGhana 44,50,53,75,76,118,119. \nIn order to study the pathological and \nimmunological mechanisms underlying this \nco-infection, the experimental model offers \na number of advantages, providing access \nto more appropriate designs for the \ninteraction and an ability to take advantage \nof the immune system of a predefined host \n- as in this case the mouse model, which \nbenefits from a very well -characterised \nimmune system. Although a few studies \nhave used the simian model to study \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nPlasmodium and Schistosoma \ninfection/coinfection 98,120, the current \nunderstanding of the immunological basis \nof many disease processes was initially \nelucidated using mouse models, with \nstudies of experimental S. mansoni \ninfections in mice having been published \nsince 1915 121–123. All these denials \ndemonstrate the importance of the \nexperimental model for understanding the \nmany aspects of coinfection on the \nvirulence, compatibility and fitness of the \nparasite on several scales. \nDespite the limited data available, which \nvaries from one population to another, \nthere is still no systematic and comparable \ninformation on the impact of polyparasitism \namong different age groups and in different \nepidemiological and nutritional contexts. \nFurthermore, much of the morbidity \nassociated with polyparasitism remains \nunclear and poorly understood, such as the \nnon-health-related societal consequenc es \nof cognitive impairment caused by \nhelminthic and malaria co -infections 124. \nAnaemia is also a burden associated with \nschistosomiasis and malaria, and various \nstudies have shown that the prevalence of \nanaemia can be significantly incr eased in \ncases of co-infection 54,76,78. \nResearch carried out in various \nepidemiological contexts has shown that \npolyparasitism occurs at intervals that are \nvariable and dissimilar to those expected in \nthe hypotheses of independence 125–127. \nOur meta -analysis confirmed an over -\ndispersion in the prevalence of co-infection \ncompared with the expected results at the \nglobal level of the studies and between the \nsexes in the human host. There is \nincreasing evidence that schistosomal \ninfections are capable of altering \nsusceptibility to clinical malaria 31,35 and \nthere are now studies focusing on the \nmechanisms of co-infection, although these \nremain rare  47,81. In animal models, it has \nbeen suggested that these co -infections \nhave both synerg istic and antagonistic \neffects 39,83,90,93,95. Several hypotheses have \ntherefore emerged to explain the \ninteractions between Schistosoma and \nPlasmodium. Most studies on the \ninteractions between helminths and malaria \nseem to indicate that helminth infection has \na negative effect on the acquisiti on of \nimmunity against malaria 80,101,128. It has \nbeen proven on several occasions that \nhelminthic infection modulates the immune \nsystem of their host with the aim of survival \n106. One hypothesis is that helminths \npromote the production of non -cytophilic \nantibodies via the cytokine milieu, resulting \nin individuals who are more susceptible to \nclinical malaria infections 29. The presence \nof regulatory T cells intensifies during \nhelminth infection, which can induce non -\nspecific suppression by creating an  anti-\ninflammatory environment 129. This non -\nspecific suppression could extend to the \nimmune response of other pathogens such \nas Plasmodium 129. Added to this is \nexposure to various environmental factors \nwhich, according to Smolen et al. 2014, \ncould have an effect on the immune \nresponse of individuals. Indeed, this study \ncompared the immune responses of \nchildren encountered on four different \ncontinents and demonstrated significant \nheterogeneity in innate cytokine responses \nin the different geographical areas sampled \n110. The differences in innate immune \nresponses assessed were attributed to \nvariations in environmental exposures such \nas feeding patterns, past infections, \nvaccination status, mode of delivery, region \nof residence, resource availability etc 130. \nIt has also been shown that the genetics of \nindividuals represent a factor that can lead \nnot only to infectivity but also to the severity \nof the disease in patients exposed to the \nsame rates of infection 112. On the one \nhand, it seems that children born to \nmothers co-infected with Schistosoma and \nPlasmodium have a highe r risk of malaria \nparasitaemia 131. Some studies have also \nshown that a fetus that has been exposed \nto maternal infections can react with \nhyporeactivity of the T lymphocytes that \nsubsequently induce a reduction in \nimmunity to these two parasites 106,132. On \nthe other hand, the study by Oboh-Imafidon \net al. 2023 revealed that people carrying a \nheterozygous (CT) or mutant (TT) CD14 \n(Cluster of Differentiation 14) genetic \nvariant were up to 58% more likely to be co-\ninfected than to be infected by Schistosoma \nalone. CD14 is expressed by the majority of \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nimmune response cells - neutrophils, \nmonocytes, macrophages - during the pro-\ninflammatory response, thus playing a key \nrole in innate immunity and offering, in this \ncase, protection against the severity of the \ndisease relative to the egg load. Indeed, \nthis study also revealed that Schistosoma \negg counts were higher in individuals \nmono-infected with Schistosoma than in \nco-infected individuals c arrying the mutant \nallele (TT) 113. Although the results do not \nappear to be significant, other studies have \nshown a link between the number of eggs \nproduced by schistosomes and the \nintensity of malaria infection during co -\ninfection 30,31,47,133. Tokplonou et al. 2023 \nsuggest that eggs may act to activate, \nsuppress or regulate immune pathways.  \nOther, less obvious factors, such as \npathogen species, microgeographic al \nvariations 29 or the age of individuals may \nalso come into play to explain the high rates \nof co-infection found in the meta -analysis. \nAge, in particular, is a recurring factor, with \nchildren being more affected by \npolyparasitism in gen eral 75,134 and by \nschistosomiasis-malaria co -infection \n40,51,102,135.  The meta -analysis carried out \nbetween the sexes showed that there were \nno differences in co-infection rates between \nmen and women, even though many \nstudies report a different prevalence of \ninfection between the two sexes for the two \nparasites 64–66,69,71.  \nLimitations \nThis systematic review did not escape the \nlimitations identified in the studies \nconsidered in the meta -analysis. Most of \nthese studies were cross -sectional, which \nmade it difficult to establish conclusively the \nprevalence of malaria -schistosomiasis co-\ninfections over time. Some studies had \ndeliberately selected infected or co-infected \ncandidates, causing selection bias, as did \nthe refusal of a proportion of the population \nto participate in certain studies. The \npotential for co -infection could also vary \nfrom one study to another depending on \nseasonality. Biases may have been \nintroduced because the studies were \nconducted in very diverse populations, with \nsignificant variations in design and \nimplementation methodologies. The \nsample size was sometimes too small to \nobtain a significant effect on the prevalence \nof co -infection and the effects on the \nimmune system, for example. Some papers \nfocused only on asymptomatic or \nsymptomatic forms of malar ia, preventing \nthe detection of possible associations with \nthe severit y of infection and masking the \nburden of co -infection.  There is no \nabsolute method for detecting Schistosoma \nor Plasmodium infections, which explains \nthe variations in diagnostic methods used \nfrom one study to another. Some of these \nmethods, although considered standard \n(such as PCR for Plasmodium, Kato -Katz \nor urine filtration for Schistosoma), may \nunderestimate the prevalence of infection \ndue to their lack of accuracy. However, this \nsystematic review, accompanied by a \nmeta-analysis, has enabled us to upd ate \nour knowledge on Schistosoma -\nPlasmodium co -infection and explore the \npotential interactions between these two \nparasites, thanks to a comprehensive \nsynthesis of the data collected. \nConclusion \nIn conclusion, we have shown that the \nprevalence of malaria -schistosomiasis co-\ninfection is higher than expected in \nendemic countries in sub -Saharan Africa. \nThe nature of the interactions between \nhelminths, and more specifically \nSchistosoma and Plasmodium, during co -\ninfection is still poorly understood, but \nseveral relevant avenues are being \nexplored. Knowledge of genetic variation \nand diversity, parasite biology, population \ndynamics and parasite transmission and \nmolecular evolution is particularly important \nfor understanding the physiological and \nimmune mechanisms that govern \ninteractions between these two parasites \nand their hosts. In addition, studies of \ndisease phenotypes in a mouse model or \nother animal models of co -infection under \ncontrolled laboratory conditions, combined \nwith parasite genetics and genomics, can \nshed light on the pathogenesis of these two \nparasites and provide new insights into the \ncontrol and management of disease in \nhumans. \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nData availability  \nThe data adopted in this meta-analysis are \navailable from the corresponding author \non reasonable request. \nFundings \nFunding was provided by the French \nNational Research Agency (ANR) under \ngrant agreement ANR -22-CPJ1-0056-01, \nwithin the framework of the project “Tropical \ndiseases of today, European diseases of \ntomorrow: a systems biology approach to \nunderstand, predict and control their \nemergence”. \n \nAcknowledgements \nWe would like to thank all the authors of the \nstudies included in this systematic review \nand meta -analysis for their valuable \ncontributions in this field. We are also \ngrateful to our colleagues at the IHPE \nlaboratory in Perpignan for their insightful \ndiscussions and technical support during \nthe preparation of this manuscript.  \nContributions \nCélia Koellsch, Jérôme Boissier, and \nRonaldo de Carvalho Augusto designed \nthe study. Célia Koellsch conducted the \ndatabase searches, data extraction, and \nstudy quality assessment. Célia Koellsch, \nJérôme Boissier, and Ronaldo de Carvalho \nAugusto performed the statistical analyses \nand interpreted the results. Célia Koellsch \nwrote the manuscript. Jérôme Boissier and \nRonaldo de Carvalho Augusto reviewed the \nmanuscript. All authors approved the \nsubmission of the manuscript. \nCorresponding authors \nCorrespondence to Ronaldo de Carvalho \nAugusto \nCompeting interests \nThe authors declare no competing \ninterests. \n \n \nReferences \n1. World schistosomiasis report 2023. \nhttps://www.who.int/news-room/fact-\nsheets/detail/schistosomiasis. \n2. 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No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\n \nAppendixes \nAppendix S1  \nChecklist for PRISMA 2020 \n \n \nFrom: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guide line for \nreporting systematic reviews. BMJ 2021;372:n71. doi: https://doi.org/10.1136/bmj.n71. This work is licensed under CC BY 4.0. To view a copy of \nthis license, visit https://creativecommons.org/licenses/by/4.0/  \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nAppendix S2 \n \nAppendix S3 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nSupplementary data \nFigure S1 \nSupplementary Figure 1. Geographical areas most frequently sampled for the prevalence of co -infection. This \nbar chart illustrates the number of studies included in the systematic review and meta -analysis according to the \nsampled countries. Nigeria reported the highest number of studies (n = 11), followed by Uganda (n = 9), Ethiopia \n(n = 6), Ghana (n = 6), and Ivory Coast (n = 6). Mali (n = 5), Tanzania (n = 4), Senegal (n = 4), Kenya (n = 4), Gabon \n(n = 4), and Cameroon (n = 3) follow. Zimbabwe (n = 2), Benin (n = 1), and the Democratic Republic of Congo (n = \n1) contributed the fewest studies. Overall, the figure highlights the geographical heterogeneity of the studies, \nwith a predominance in West and East African countries where co-infections of Plasmodium spp. and Schistosoma \nspp. are most frequently investigated \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nFigure S2  \nSupplementary Figure 2. A. Prevalences of single infections and B. co-infections of the two species of Schistosoma \n(S. mansoni and S. haematobium) and the two species of Plasmodium (P. falciparum and P. vivax) in integrated \nstudies for epidemiological data and meta-analysis. The averages for each prevalence - single and co-infections - \nare shown in the colour associated with the legend. The y-axis shows the percentage prevalence of single and co-\ninfections, while the x -axis shows the number of prevalences found in the total of 59 studies analys ed (several \nprevalences can be found in the same article). \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint \n\nFigure S3  \n \nSupplementary Figure 3. Average prevalence of co -infection for each country listed in the studies included in \nthe meta -analysis. This figure illustrates the average prevalence of Plasmodium –Schistosoma co -infections \nreported in the studies included in the meta -analysis, showing marked geographical variation across African \ncountries. The highest prevalence rates were observed in Mali (36.6%), Nigeria (25.8%), and Gabon  (23.6%), \nsuggesting possible hotspots of co -infection. Intermediate prevalence levels were found in Uganda (15.5%), \nTanzania (15.5%), Senegal (9.2%), Kenya (8.2%), and Ivory Coast (8.1%). In contrast, lower co-infection rates were \nreported in Ethiopia (4. 1%), Ghana (2.8%), and Benin (1.1%). These findings highlight substantial geographical \nheterogeneity in the burden of Plasmodium–Schistosoma co-infections across sub-Saharan Africa. \n \nSupplementary Table 1 . Prevalence and distribution of co -infections between schistosomiasis and malaria \nreported in various African studies. This table summarises data from several epidemiological studies conducted \nin Africa and included in the meta -analysis on the prevalence of infections with Schistosoma mansoni, \nSchistosoma haematobium, Plasmodium falciparum and Plasmodium vivax, as well as  their co-infections. The \ninformation includes study periods, geographical areas, demographic characteristics of th e populations studied, \ninfection rates, types of co-infection observed, and associated statistical results (Fisher's exact test, dispersion). \n \nSupplementary Table 2. Epidemiological surveys on the co-occurrence of schistosomiasis and malaria infections \nin Africa.  This table presents a summary of cross -sectional studies reporting the prevalence of Schistosoma \nmansoni, Schistosoma haematobium, Plasmodium falciparum and Plasmodium vivax infections, as well as their \nco-infections. The data include the characteristics of the populations studied (sample size, age, sex, geographical \narea), the presence of anaemia or malnutrition, and the main methodological limitations reported by the authors. \nTaken together, the data highlight the epidemiological variability and contextual factors influencing the \nassociation between these parasitic diseases. \n \nSupplementary Table 3. Functional studies on the immunological and biochemical impact of schistosomiasis –\nmalaria co-infections. This table summarises functional studies conducted in Africa exploring the immunological, \nbiochemical and clinical consequences of co-infections between Schistosoma spp. and Plasmodium spp. The data \ninclude the characteristics of the populations studied, t he prevalence of single and combined infections, and the \nobserved effects on immune responses (cytokines, antibodies, regulator y cells), biochemical parameters (ALT, \nAST, bilirubin, glucose, proteins), and clinical markers (anaemia, malnutrition). The reported studies highlight \ncomplex interactions between the two parasitic diseases, which may modulate susceptibility, vaccine resp onse, \nand the clinical severity of malaria. \n \n \nAll rights reserved. No reuse allowed without permission. \nperpetuity. \npreprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 2, 2025. ; https://doi.org/10.1101/2025.10.31.25339164doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}