Epidemiology of Relapsing and Falciparum Malaria in the Highlands of Cameroon: An Integrated Community Survey of Human Infection and Vector Abundance

Epidemiology of Relapsing and Falciparum Malaria in the Highlands of Cameroon: An Integrated Community Survey of Human Infection and Vector Abundance | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Epidemiology of Relapsing and Falciparum Malaria in the Highlands of Cameroon: An Integrated Community Survey of Human Infection and Vector Abundance View ORCID Profile Samuel J. White , View ORCID Profile Valery P. K. Tchuenkam , Mariama Mbouh , Claudia Gaither , Aline Gaelle Bouopda-Tuedom , Belinda Kiam , View ORCID Profile Zachary R. Popkin-Hall , View ORCID Profile Jacob M. Sadler , View ORCID Profile Kelly Carey-Ewend , Emily Hand , Miriam N. Ngum , Yannick N. Ngomsi , View ORCID Profile Jeffrey A Bailey , Darlin B. Kaunda , Lethicia K. Mafo , Giresse N. Lemogo , Clifford L. Dinka , Clifford A. Nsani , Michel Noubom , View ORCID Profile Varun Goel , Ibrahima Ibrahima , Janvier C. Onguene , View ORCID Profile Feng-Chang Lin , View ORCID Profile Jessica T. Lin , View ORCID Profile Sandrine E. Nsango , View ORCID Profile Rhoel R. Dinglasan , View ORCID Profile Jonathan J. Juliano , View ORCID Profile Innocent M. Ali doi: https://doi.org/10.1101/2025.04.28.25326551 Samuel J. White 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Samuel J. White Valery P. K. Tchuenkam 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valery P. K. Tchuenkam Mariama Mbouh 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claudia Gaither 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 3 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aline Gaelle Bouopda-Tuedom 4 Centre du Pasteur Cameroon , Yaounde, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Belinda Kiam 4 Centre du Pasteur Cameroon , Yaounde, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zachary R. Popkin-Hall 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zachary R. Popkin-Hall Jacob M. Sadler 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 5 Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob M. Sadler Kelly Carey-Ewend 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 3 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kelly Carey-Ewend Emily Hand 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miriam N. Ngum 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yannick N. Ngomsi 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeffrey A Bailey 6 Department of Pathology and Laboratory Medicine, Brown University , Providence, RI, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jeffrey A Bailey Darlin B. Kaunda 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lethicia K. Mafo 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Giresse N. Lemogo 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clifford L. Dinka 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clifford A. Nsani 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michel Noubom 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Varun Goel 7 Department of Geography, University of South Carolina , Columbia, South Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Varun Goel Ibrahima Ibrahima 4 Centre du Pasteur Cameroon , Yaounde, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Janvier C. Onguene 4 Centre du Pasteur Cameroon , Yaounde, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site Feng-Chang Lin 9 University of Douala, Faculty of Medicine and Pharmaceutical Sciences Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Feng-Chang Lin Jessica T. Lin 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 5 Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica T. Lin Sandrine E. Nsango 4 Centre du Pasteur Cameroon , Yaounde, Cameroon 9 University of Douala, Faculty of Medicine and Pharmaceutical Sciences 10 University of Bertoua, Faculty of Sciences Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sandrine E. Nsango Rhoel R. Dinglasan 11 Department of Infectious Diseases & Immunology and the Emerging Pathogens Institute, College of Veterinary Medicine, University of Florida , Gainesville, Florida, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rhoel R. Dinglasan Jonathan J. Juliano 1 Institute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 3 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America 5 Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill , Chapel Hill, North Carolina, United States of America Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan J. Juliano For correspondence: jjuliano{at}med.unc.edu dr.alinn{at}gmail.com Innocent M. Ali 2 Department of Biochemistry, Faculty of Science, Université de Dschang , Dschang, West Region, Cameroon Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Innocent M. Ali For correspondence: jjuliano{at}med.unc.edu dr.alinn{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Despite global malaria control efforts, the disease caused 263 million cases and 597,000 deaths in 2023. While Plasmodium falciparum accounts for most cases in Africa, non-falciparum species, such as P. ovale spp. and P. vivax , can cause relapse infections and are increasingly recognized as significant contributors to human disease. In particular, the highlands of West Cameroon have previously been reported to have high P. vivax infection rates. This study presents preliminary results from the Relapsing Malaria in Africa (ReMA) study, conducted in Dschang, Cameroon, to assess the prevalence and epidemiology of P. vivax and P. ovale . A cross-sectional survey of 3,661 participants from 871 households across seven health areas identified a low prevalence of P. vivax (0.1%) and P. ovale spp. (0.64%) using quantitative real time PCR (qPCR), while P. falciparum remained prevalent at 8.1%. Co-infections of P. ovale spp. with P. falciparum were observed in 23.1% of P. ovale spp. cases. While gametocytemia was common among falciparum infections, leveraging a new ovale gametocyte assay, we found that gametocytemia was uncommon among the qPCR-positive ovale infections. Spatial analysis found P. vivax and P. ovale spp. infections concentrated in Penka-Michel and Baleveng, the former having higher Anopheles spp. abundance than other areas assessed. Risk factor analysis revealed children and those with recent domestic travel had higher odds of P. falciparum infection, but no significant associations were found for P. ovale spp. infections. Entomological surveys confirmed high densities of Anopheles gambiae sensu lato ( s.l. ) and An. funestus (s.l.), with significantly higher human landing capture rates for An. gambiae s.l compared to other mosquito species. While these findings suggest that the relapsing malarias are not as widespread as previously thought in West Cameroon, understanding factors driving their persistent transmission, especially without prevalent gametocytemia, will be important for disease control. AUTHOR SUMMARY Malaria caused 263 million cases and 597,000 deaths in 2023, with most cases in Africa due to Plasmodium falciparum . However, the dangers of other species like P. ovale and P. vivax , which can cause relapsing infections, are becoming more clear in Africa. The Relapsing Malaria in Africa (ReMA) study in Dschang, Cameroon, surveyed 3,661 people from 871 households to better understand these infections. Results showed low rates of P. vivax (0.1%) and P. ovale (0.64%) compared to P. falciparum (8.1%). Infections were more common in areas with high mosquito abundance and biting activity. While the relapsing malarias were less common than expected, understanding factors driving their persistent transmission, especially without prevalent gametocytemia, will be important for disease control. INTRODUCTION Despite global control efforts, there were 263 million malaria cases and 597,000 deaths from malaria in 2023 ( 1 ). While most of the burden is caused by Plasmodium falciparum in Africa, there is growing evidence that non-falciparum malaria is becoming more common ( 2 – 4 ). Three species, P. ovale spp., consisting of P. ovale curtisi and P. ovale wallikeri , as well as P. vivax cause relapsing malaria, which further complicate control by requiring radical cure for dormant hypnozoites. The assumption that P. vivax and P. ovale spp. results in only benign infections have also been challenged by increased reports of severe disease, particularly in young children and pregnant women ( 5 – 8 ). P. ovale infections are known to be endemic to Africa, but an increase in prevalence has been observed in some countries in the face of declining P. falciparum infections ( 4 , 9 – 11 ). Historically, P. vivax has been assumed to be largely absent from Sub-Saharan Africa due to the predominance of the Duffy-negative phenotype in Sub-Saharan African populations ( 12 ). However, cases of P. vivax have been repeatedly documented in studies showing P. vivax infection in Duffy-negative populations across Africa ( 13 – 17 ). P. ovale spp. and P. vivax pose unique challenges to elimination efforts in Africa. Due to distinct biological characteristics, including early development of gametocytes and liver dormancy as hypnozoite stages, risk factors for these species typically do not match those for P. falciparum in Africa ( 18 ). They also tend to occur in lower transmission settings in Africa ( 9 , 19 – 21 ). The undetectable hypnozoite stage and the typically low asexual parasitemia of P. ovale spp. and P. vivax infections in Africa relative to P. falciparum make diagnosis by microscopy difficult even among trained microscopists ( 22 ). Molecular methods such as polymerase chain reaction (PCR) are more sensitive, particularly for asymptomatic infections, and allow for the detection of mixed infections. Relapsing malarias have been detected in Cameroon, but with inconsistent findings surrounding their epidemiology ( Table 1 ). Almost all previous studies of the relapsing malaria in Cameroon have been in febrile clinical populations. Outside of the West province, overall prevalence of P. ovale spp. and P. vivax has been low, never encompassing more than 5% of screened individuals ( 23 – 29 ). These studies were done in geographically diverse regions of Cameroon, and represented each of the five distinct ecological zones of Cameroon between them (Sahelian, Soudanian, Sahelo-Guinean, Humid Savannah and Forest) ( 30 ). Recent studies in the West region, around Dschang, have identified these species more commonly in febrile clinical patients. Russo et al. found P. vivax infection in 5.6% of febrile outpatients in 2017, while Dongho et al. reported that 35.2% of febrile patients in the hospital were PCR positive for P. vivax during the rainy season in 2021 ( 14 , 31 ). Previous work by our group in 2020 found 7% of malaria positive individuals who presented to clinics had P. ovale spp. infection ( 29 ). Given the significant number of P. ovale spp. and P. vivax infections detected in clinics in the region, a deeper understanding of relapsing malaria epidemiology is needed in the Western highlands; yet no comprehensive community survey has been completed. View this table: View inline View popup Download powerpoint Table 1: Previous studies of relapsing malaria in Cameroon Plasmodium spp. are transmitted by anopheline vectors, which are highly diverse in Cameroon with at least 60 known species ( 32 ). Currently, long-lasting insecticidal nets (LLIN) are the only vector control strategy used by the National Malaria Control Program of Cameroon, but insecticide resistance has been observed in An. gambiae sensu lato ( s.l. ) and An. funestus ( s.l. ), primarily to dichlorodiphenyltrichloroethane (DDT), permethrin, deltamethrin and bendiocarb ( 33 ). Previous entomological studies in the West Region of Cameroon have found An. gambiae ( s.l. ) and An. funestus ( s.l. ) to be the primary P. falciparum malaria vectors, but An. nili ( s.l. ), An. ziemanni, and An. rufipes have also been reported ( 34 , 35 ). Despite these efforts, the primary vectors for P. ovale and P. vivax, which included An. coluzzii previously in Cameroon remain unclear ( 36 ). To provide integrated community-based surveys of malaria and vectors in the Western highlands, the Relapsing Malaria in Africa (ReMA) study was launched in 2023 to better describe the epidemiology and transmission of relapsing malaria and falciparum malaria in Dschang and the surrounding area in the West Region, Cameroon. This report details the results of the preliminary cross-sectional community survey, which aimed to: 1) estimate P. ovale spp. and P. vivax prevalence in the community, 2) determine the rate of P. ovale and P. vivax gametocytemia, 3) determine geographical distribution of different Plasmodium species, 4) define risk factors associated with P. ovale spp. and P. vivax infection in the region, and 5) determine vector species and abundance in the communities relative to household collection of asymptomatic infections. METHODS Ethics Statement Signed written consent was obtained by all adult participants (>20 years of age) enrolled in the study. Written, signed parental consent and assent from participants 20 years of age or younger was also provided. Children under the age of 6 years old were not eligible for enrollment, as components of the overall project not described here involved the need to give meaningful assent, which the IRBs deemed not possible for those under 6 years of age. The study protocol was approved by the Cameroon National Ethics Committee for Human Health Research (N°2022/12/1507/CE/CNERSH/SP) and University of North Carolina at Chapel Hill (22–1445). Study site A cross-sectional community survey was performed in the city of Dschang, in the Fialah Foréké, Siteu, Fometa, and Doumbouo neighborhoods, and surrounding areas [Baleveng, Mboua and Penka-Michel (Centre Urbain)], located in the West Region of Cameroon ( Figure 1 ). These sites were identified as having higher malaria transmission using data from the 2022 Dschang Health District annual malaria report. Specific characteristics about malaria and environmental characteristics that impact malaria transmission for each health area are shown in Table 2 , including rapid test positivity rate from health center data, estimates of vegetation coverage, and percentage of built space (as a proxy of urban development). Malaria positivity by RDT in health center data ranged from 39.7% in Siteu to 72.2% in Doumbouo, with built-up land percentage ranging from 3.0% in Mboua and 23.9% in Fialah Foréké. Download figure Open in new tab Figure 1. Map of study area and distribution of households sampled and mosquito collections In Panel A, light grey areas represent administrative boundaries. Household and mosquito sampling locations are each randoml jittered a distance of 450m. The region sampled is highlighted by the red box in Panel B. View this table: View inline View popup Download powerpoint Table 2. Study Sites Characteristics (May-Sep 2023) Dschang, the capital of the Menoua division of the West Region, has a population of roughly 130,000. It is in a humid savannah approximately 1380 meters above sea level and receives 2000mm annual rainfall. Temperatures range from a low of 13.6°C to a high of 25.3°C throughout the year (mean 20-23°C) and the region has two distinct seasons: 1) the main rainy season (mid-March to early October), which is punctuated by a short dry season in July, and 2) the main dry season (November to early March). The area is considered hypoendemic for malaria and has seasonal malaria transmission lower than much of the rest of the country ( 30 ). The primary industry in Dschang is agriculture but there is a large student population (14,000 full-time students) that attend the University of Dschang. The peri-urban neighborhoods of Dschang are mostly made up of permanent housing and are dominated by hills with an average elevation of 1450m above sea level. Dschang also includes multiple small lakes in the urban centre and marshy lowlands with creeks and areas around watercourses used for market gardening and backyard pig rearing. Most of the sampled neighborhoods/villages outside of Dschang consist of an urban center surrounded by rural areas with a primarily agricultural population. The landscape is further characterized by a succession of high peaks and deep valleys (Doumbouo) or an alternate series of undulating landforms. Baleveng shares similar features and lies along the regional road linking Bafoussam, the regional capital, and Dschang. Penka-Michel on the other hand has a hilly relief punctuated with savannas and forests, and small streams that traverse the neighbourhood of Centre Urbain, a semi-rural health area in the Penka-Michel health district. The altitude of these neighbourhoods vary between 1300m-1450m. Household sampling and enrollment Sampling occurred during the latter part of the main rainy season from May 10th to June 19th, and August 4th to September 18th, 2023. Within the selected health areas, a random selection of clusters, representing neighborhoods in urban areas and villages in rural areas, were identified. Sixty households were targeted to be sampled per cluster. When possible, the first household visited in each neighborhood was the local chief’s and then a bottle was spun to pick a direction to continue sampling of further households in that direction. Sampling weights were calculated for each cluster by taking the inverse of the product of the probability of a cluster being selected and the probability of a household in that cluster being selected ( Supplemental Table 1 ). If a sampled household was not available to be surveyed, the neighboring household was visited. Population and household distribution estimates used to construct sampling weights were taken from the 2022 National Onchocerciasis Control Program (PNLO) ( 37 ). The distribution of households and vector sampling sites is shown in Figure 1 . Survey and sample collection Each participant was interviewed with responses recorded on a tablet using REDCap (Research Electronic Data Capture) ( 38 , 39 ). Demographic information, education level, occupation, travel history, and malaria diagnosis and treatment history were recorded for each participant. The temperature of each participant was recorded. If febrile, they were tested for malaria with a P.f/Pan malaria rapid diagnostic test (RDT) (Abbott Laboratories, Green Oaks, Illinois). For each participant, dry and liquid blood spots were collected from a finger-prick, and hemoglobin concentration was measured (Hemocue 801, Ängelholm, Sweden). Dried blood spots (DBS) were collected on Whatman 3MM filter paper, dried, and stored in packets with desiccant (Cytiva, Marlborough, Massechussetts). Whole blood was collected into microfuge tubes containing 2X DNA/RNA shield (Zymo Research, Irvine, CA) ( 40 ). The head of each household was given an additional interview on family size, bed net use, previous malaria treatment and its monetary cost, livestock ownership, household water source, and housing material. Detection of human Plasmodium sp. infection Parasite DNA was extracted from DBS using Extracta DBS (QuantaBio, Beverly, MA) following the manufacturer’s protocol. Parasitemia was determined by semi-quantitative real-time PCR (qPCR) for P. falciparum , P. vivax and P. ovale . using species-specific 18S rRNA assays run to 45 cycles ( Supplemental Table 2 ) ( 9 , 15 , 41 ). For quantification, known dilutions of plasmids (MRA-177, 178, and 180; BEI Resources, Manassas, VA) were used as previously described ( 3 ). Six copies of the species specific plasmid were used to estimate a genomic equivalent for all species ( 42 ). Each PCR plate also contained two negative controls (water). All qPCR was done at University of Dschang using a Biorad CFX96 real time PCR machine. Determination of gametocyte carriage RNA from samples that were qPCR positive for P. falciparum and P. ovale spp. were extracted from up to 75 microliters of whole blood stored in DNA/RNA shield (Zymo Research) using a magnetic bead based protocol (Quick-RNA MagBead, Zymo Research). Successful RNA extraction was confirmed by detecting human-beta tubulin RNA in the first few samples using a reverse-transcriptase quantitative PCR (rt-qPCR) assay ( 43 ). P. falciparum gametocytes were detected as previously described using primers specific to P. falciparum surface protein 25 ( pfs25 ) and P. falciparum male gametocyte-enriched transcript ( pfmget) targets for female and male gametocytes, respectively ( Supplemental Table 3 ) ( 44 , 45 ). Control reactions containing no reverse transcriptase were run for all pfs25 positive samples, to ensure positivity was not due to genomic DNA amplification. This was not needed for the pfmget assay, as primers were designed to span an intron. Positive controls for falciparum gametocyte assays were generated by PCR amplification of malaria genomic DNA using pfs25 and pfmget primers followed by cloning into plasmid vectors and transformation into E. coli using LB agar plates and competent cells (TOPO™ TA Cloning™ Kit, with pCR™2.1-TOPO™, Invitrogen, Waltham, Massachusetts). A conversion factor of 87.5 and 12.5 for pfs25 and pfmget was used to go from transcript copies to gametocyte count, respectively. P. ovale spp. female gametocytes were detected using the previously described pos25 target ( Supplemental Table 4 ), orthologous to the pfs25 target ( 46 ). No reverse transcriptase controls have been previously described for the pos25 target, and were not run in this analysis. Positive controls for the assays were generated using plasmid containing the pos25 100 bp target sequence. Transcript copies are reported for P. ovale assays as no conversion factor is available. A new P. ovale male gametocyte assay was developed to: 1) have a single assay to detect both species and 2) utilize a gene with an intron to reduce false positive detection. The target was identified by searching for P. ovale primer sequences targeting the ortholog to the falciparum gametocyte target pfmget . Primers and probes were designed to be approximately 20 bp and intron-spanning using Primer3 ( Supplemental Table 4 ) ( 47 ). Cycling conditions are described in Supplemental Table 4 . A positive control for the ovale assay was synthesized using species-specific sequences (GeneWiz, South Planefield, NJ) to produce control plasmids. To determine the limit of detection (LOD) for the P. ovale spp. gametocyte assay, probit analysis was conducted on replicate rt-qPCR runs using templates with known copy number concentrations ( 48 ). Positivity was based on a cycle threshold <45 cycles. Given that the number of transcripts for each gene per gametocyte is not known, we report on transcript copies, rather than gametocytemia. All positive samples were confirmed by manually reviewing the amplification curves using machine software. Standard curves generated from plasmid positive controls were required to have a minimum r-squared value of 0.95 for each run. Analysis of spatial clustering of malaria infection To investigate the spatial distribution of malaria across the study area we estimated kernel-smoothed spatial relative risk surfaces for P. falciparum and P. ovale spp. ( 49 ). Spatial relative risk is calculated as a ratio of the kernel-estimated density of PCR positive cases and PCR negative cases, using an adaptive kernel bandwidth-estimation. It describes the risk of malaria at a particular location compared to the expected risk across the study area after taking the background population in account ( 50 , 51 ). We also identified clusters of malaria prevalence, defined as areas with significantly elevated malaria risk, by calculating adaptive tolerance contours at a significance level of 0.05 using 999 monte-carlo simulations ( 52 ). All spatial clustering analysis was conducted using the ‘sparr’ package in R version 4.2.2. ( 53 ). Mosquito abundance data To evaluate the relationship between vector abundance and malaria cases, mosquito abundance data was determined from collections performed in these areas. Details on mosquito capture and entomological indicators are described previously ( 54 ). Here, we provide a subset analysis of these data for only those areas where community collections from participants occurred. We included these data as the abundance of mosquitoes and the rate of infection are closely linked, as larger, competent mosquito vector populations may contribute to a rise in parasite transmission. Statistical analysis For categorical variables, frequencies and percentages were calculated. Continuous variables were summarized with median (IQR) unless otherwise specified. Weighted prevalences and 95% Confidence Intervals for each study site were calculated from weights derived from the PNLO census. Sampling weights were calculated by multiplying the probability of selection at three levels: health area, cluster, and household. Crude and adjusted odds ratios with 95% Confidence Intervals for PCR positivity for P. falciparum, P. ovale spp., and P. vivax were calculated with sampling weights using multivariate logistic regression with sampling weights in the PROC SURVEYLOGISTIC procedure in SAS 9.4 (SAS Institute Inc., 2024). Multivariate analysis was adjusted for each of the presented covariates. Descriptive statistical analyses and visualizations were done in R version 4.2.2 (R Foundation for Statistical Computing, 2022). RESULTS Household and participant characteristics A total of 871 households were visited across 7 health areas with 3,661 participants enrolled from May-September 2023, the latter part of the main rainy season. Patient and household characteristics are described in Table 3 . Most participants were female (n=2113/3661, 57.7%), and the median age of participants was 22 years old (IQR 13-39), with just over one third of participants under the age of 18 (n=1376/3661, 37.6%). Roughly half (n=1745/3661, 47.7%) of participants reported sleeping under a bednet the night before, and 469 (12.8%) participants had undergone treatment for malaria in the last 30 days. Travel in the 28 days prior to enrollment was uncommon for international destinations with only 13 out of 3661 (0.4%) individuals traveling outside of Cameroon, but 709 (19.4%) participants had recently traveled within the country. The median household size was 6 persons (IQR 4-9). Almost half (n=418/871, 48.0%) of households owned livestock, with chickens (73.9%) and pigs (36.6%) as the most common animals raised. View this table: View inline View popup Table 3. Community survey population and household characteristics Community parasite prevalence Overall, community carriage of malaria parasites was low when determined by qPCR ( Table 4 ). The relapsing malarias were rare across all sites, with only 2 (0.1%; [0%-0.29%]) cases of P. vivax and 26 (0.64%; [0.3%-1.0%]) cases of P. ovale spp. identified by qPCR. Falciparum malaria was more common with the weighted prevalence of P. falciparum at 8.1%; [7.0%-9.2%]. This prevalence was variable across sites with Penka-Michel (19.7%; [15.6%-23.7%), Baleveng (10.0%; [7.4%-12.6%]), and Mboua (8.6%; [4.6%-12.5%]) having the highest weighted prevalence of P. falciparum. Both participants with a P. vivax infection, and 6/26 (23.1%) of the P. ovale spp. cases were coinfections with P. falciparum. Fever was reported for 241/3661 participants (weighted proportion 6.3%; 95% CI: [5.2%-7.4%]) with 11.2% (n=27/241) of those with fever positive for P. falciparum by RDT. Only 4/26 (15.3%) of the P. ovale spp. (including 6 with co-infecting P. falciparum ) and 36/302 (11.9%) of the P. falciparum cases were febrile at the time of sample collection. View this table: View inline View popup Download powerpoint Table 4. Weighted prevalence and 95% CI of P. falciparum, P. ovale and P. vivax by site Clustering analyses found a statistically significant difference in risk of PCR-positivity for P. ovale spp. in Penka-Michel and Baleveng compared to the rest of the study area. Similarly, Penka-Michel, Baleveng, and Mboua had a statistically significant difference in risk of PCR-positivity for P. falciparum compared to the rest of the study area ( Figure 2 ). Download figure Open in new tab Figure 2. Spatial distribution of (A) P. ovale spp. and (B) P. falciparum Black dotted line shows adaptive tolerance contours indicating areas of significantly increased risk of infection. Parasite density, gametocytemia and gametocyte assay LOD Overall, the median parasitemia was 10 (IQR 3.9-64) genome equivalents/µL for P. ovale spp. and 16 genome equivalents/µL (IQR: 2.8-138) for P. falciparum ( Supplemental Figure 1 ). Parasitemia was significantly higher in febrile participants who were PCR-positive for P. falciparum , but not for P. ovale spp. ( Figure 3 ). Gametocytemia was measured in 22/26 P. ovale spp. infected individuals. Female gametocytes ( pos25 ) for P. ovale spp. were detected in 3/22 (14%) samples with a range of 45-228 transcript copies/µL. Male ovale gametocytes ( pomget ) were also detected in 2/22 (9%) samples with 12.0 and 6.4 transcript copies/µL. Gametocytemia was measured in 300/302 samples that were positive for P. falciparum . Female gametocytes for P. falciparum were detected in 115/300 (38%) with a median gametocytemia of 1.4 gametocytes/µL (IQR: 0.4-5.4 gametocytes/µL). Male gametocytes for P. falciparum were detected in 28/300 (9.3%) samples with a median gametocytemia of 2.1 (IQR: 0.5-8.8 gametocytes/µL). There was no correlation between P. falciparum female gametocytemia and parasitemia (r = 0.089, Supplemental Figure 2 ). A LOD determined by probit analysis for the pomget gametocyte target was estimated at 281 transcript copies per µl (95% CI: 162, 1,310) using 12 replicates across a range of target concentrations. (Supplemental Figure 3, Supplemental Table 5) . The interassay coefficient of variation, a measure of precision and repeatability, was determined for the pomget assay ( Supplemental Table 6 ). Replicates of control plasmids for pos25 , showed a detection level similar to the previously published LOD, which detected 1 transcript copy per µl in at least 50% of replicates ( Supplementary Table 5 ) ( 46 ). No false positive results were observed across our non-template controls for all gametocyte detection assays. Download figure Open in new tab Figure 3. Parasitemia by fever status for P. falciparum and P. ovale spp Kruskal-Wallis chi-squared = 9.48, p=0.002 for P. falciparum and Kruskal-Wallis chi-squared = 0.45, p-value = 0.50 for P. ovale spp. *: significant difference Factors associated with P. ovale spp. and P. falciparum infection Multivariate logistic regression with sampling weights were used to assess for associations between infection with each species and household and individual characteristics ( Figure 4 , Supplemental Table 7 ). Children (14 years and younger) had 1.8 (95% CI: [1.3-2.6]) times the adjusted odds of PCR positivity for P. falciparum when compared to adults. Participants who had traveled within Cameroon (aOR=2.4; [1.7-3.4]), and those who had received antimalarial treatment in the last 28 days (aOR= 2.1; [1.3-3.4]) had increased adjusted odds of PCR positivity for P. falciparum . Over 60% of the travel within Cameroon was to either Bafoussam (159/709, 22%), Douala (149/709, 21%), or Yaoundé (124/709, 18%). No factors associated with an increased odds of PCR positivity for P. ovale spp. were identified. Download figure Open in new tab Figure 4. Adjusted odds ratios of infection and 95% confidence intervals for individual and household characteristics CMR: Cameroon; Tx: treatment Mosquito abundance in relation to infection prevalence We analyzed the subset of mosquito data for areas where human sampling was previously done ( 54 ), which provided mosquito density data for Penka-Michel and Dschang, which included study sites Fialah Foréké, Fometa, and Doumbouo ( Figure 1 ). Of the 871 households where human sampling occurred, 660 (75.8%) were within 2km of a household where mosquito collections occurred. Due to low mosquito abundance in the Dschang neighborhoods, data from these sites were combined for analysis. Individual neighborhood data in Dschang were similar and are shown in Supplemental Table 8 . Overall mosquito abundance and human-biting rates (estimated by human landing catches) were higher in Penka-Michel, which had high P. ovale spp. and P. falciparum infection rates compared to Dschang ( Table 5 ). An. gambiae ( s.l. ) was the most abundant vector collected at all study sites, accounting for 82.9% (2619/3157) of all mosquitoes captured. Mosquito species composition varied across the sites, with Penka-Michel having significantly greater abundance of all three species identified ( An. gambiae ( s.l. ) , An. funestus ( s.l. ), and An. ziemanni ) compared to other collection sites ( Table 5 and Figure 5 ). We observed an outdoor shift toward earlier (before 10 PM) human host-seeking behavior for An. gambiae ( s.l. ) and a lower abundance of An. gambiae ( s.l .), An. funestus ( s.l .), and An. ziemanni, in Dschang compared to Penka-Michel ( Supplemental Table 8 and Supplemental Figure 4 ). Download figure Open in new tab Figure 5. Anopheles spp. abundance per night and household by collection View this table: View inline View popup Download powerpoint Table 5. Relationship between mosquito abundance, biting rates, and malaria infections DISCUSSION This study provides high-quality, community-based molecular survey data of relapsing malaria and falciparum malaria in the Western highlands of Cameroon, centered around Dschang. Overall, relapsing malaria was uncommon in the community. P. ovale spp. was found to have an overall prevalence of 0.64% (95% CI: [0.2%-1.0%]), while only two cases of P. vivax were detected. P. falciparum remained the most common species with an overall prevalence of 8.1% [7.0%-9.2%]. Spatial clustering analysis showed significant differences between health zones, with higher prevalence in Penka-Michel ( Figure 2 and Table 4 ). This was also associated with higher Anopheles spp. abundance and higher biting rates ( Table 5 and Figure 5 ). Located roughly 20 kilometers east of Dschang, Penka-Michel is more rural and less densely populated. Baleveng, an outlying village near Dschang, showed the second highest prevalence for both P. ovale spp. and P. falciparum , with statistically significant clustering ( Figure 2 ). Mboua showed an increased risk of P. falciparum ; however, no cases of P. ovale spp. were detected in Mboua. Our results are different from previous studies of relapsing malaria that have focused on clinic populations in Dschang. Russo et al found P. vivax infection in 5.6% of febrile outpatients, while Djeunang Dongho et al . reported that 35.2% of febrile patients in the hospital were PCR-positive for P. vivax during the rainy season ( 14 , 31 ). This high rate of vivax positivity in the clinic is very distinct from the negligible number of cases we identified in this predominantly asymptomatic community survey. Based on these previous studies, the near absence of P. vivax from the study population was unexpected. Potential explanations for the lack of P. vivax cases in our study population include the resolution of a previous outbreak captured in previous studies, overall low malaria transmission during the study period, inaccessible sources of previous vivax outbreaks like short-term economic migrants to Dschang, or the initial reports represented initial outbreaks, which resulted in population immunity. These possibilities would need to be assessed by different studies. As this was a community survey, most infections were asymptomatic, with only 6.6% of participants febrile at the time of sample collection. A substantial proportion of ovale infections (23%) were mixed with P. falciparum . In general, low case counts for P. ovale resulted in wide confidence intervals in risk factor assessment and limited our ability to find significant associations. Despite other evidence that school-aged children (6-18 years old) act as a significant asymptomatic reservoir for non-falciparum malaria ( 55 ), we did not find increased odds of PCR positivity for P. ovale in this age group compared to adults. This was true for both mono-species P. ovale or for all P. ovale infections. This is consistent with findings in Tanzania, where risk of infection was not different for school-aged children compared to adults ( 21 ). However for P. falciparum , being school-aged was a significant predictor of PCR positivity (aOR = 1.6; [1.2-2.2]) consistent with previous studies ( 56 , 57 ). Recent travel within Cameroon was also associated with over twice the odds of PCR positivity for P. falciparum , which may indicate that these cases were contracted while traveling to or through higher transmission areas. Three destinations accounted for over 60% of travel within Cameroon: Bafoussam (n=159/709), Yaoundé (n=124/709), and Douala (n=149/709). Bafoussam is the capital and economic center for the West Region of Cameroon and is located approximately 50 km from Dschang with an estimated population of 480,000 ( 58 ). It is known to be endemic for malaria, with the vast majority being P. falciparum , but P. vivax has been detected ( 28 , 59 ). Yaoundé is the capital of Cameroon, with a population exceeding 4.5 million people, and is located 238 km away from Dschang. Malaria remains common in the urban and peri-urban areas of Yaoundé, with asymptomatic carriage rates in children reaching over 70% ( 60 ). Asymptomatic carriage in adults is also common in the surrounding areas of Yaoundé, with 81% of P. falciparum positive adults in a longitudinal household without symptoms ( 61 ). Douala is the major port and economic capital for Cameroon, with a population of over 3.5 million people, and is 159 km from Dschang. P. falciparum infections are common and P. ovale has also been reported in this region ( 62 , 63 ). A previous study of P. ovale spp. gametocytemia in Papua New Guinea reported a low gametocyte carriage rate (2.2%, 11/505) in the population, with 64.7% of P. ovale infected individuals (11/17) being gametocytemic ( 46 ). This study leveraged a female-specific assay targeting the orthologue of pfs25 with a LOD estimated at 1 copy/μL ( 46 ). We found a significantly lower rate of detectable gametocytes [8.7% (3/22)] among P. ovale spp. qPCR-positive samples using this assay despite a similar LOD. Our new P. ovale spp. male-specific assay ( pomget ) detected the same proportion of gametocytemia as the pos25 assay [14% (3/22)], but with a higher assay LOD. While sample quality and RNA extraction efficiency could cause underdetection, P. falciparum gametocytemia was relatively high in our cohort, with 38% (115/302) of qPCR-positive samples also having detectable levels of gametocytes. This rate is higher to a previous study in the Center Region of Cameroon that found a gametocyte positivity rate of 10% (32/317) using the pfs25 assay among individuals who were infected with P. falciparum, as determined by RDT or LAMP ( 64 ). It is intriguing that such low gametocytemia rates were detected given human skin feeding studies in Tanzania, which did not quantify P. ovale spp. gametocytemia burden, have shown high infectivity ( 65 ). It suggests that there may be undetermined biological factors (highly competent malaria vectors, contribution of hypnozoite-induced relapses, frequent but transient parasitemia/gametocytemia) that help support community transmission of P. ovale spp. As expected, malaria rates were higher in areas with greater Anopheles vector abundance and elevated biting rates ( Table 5 ). Consistent with this, previous analyses revealed that mosquito infection rates were higher in Penka-Michel compared to Dschang, with the majority of infected mosquitoes in Penka-Michel carrying P. falciparum ( 54 ). Penka-Michel is a more rural, less populated and more densely vegetated area compared to Dschang. In Penka-Michel, the overall infection rate was 3.2% (13/392), and three mosquito species tested positive for malaria: Anopheles gambiae , An. funestus , and An. ziemanni ( 54 ). Among the infected mosquitoes, P. ovale was detected in 7.7% (1/13), identified in a An. gambiae ( s.l. ) mosquito ( 54 ). Unfortunately, mosquito abundance in Dschang was too low to support meaningful analyses of associations with environmental variables ( Tables 2 and 3 ). While this study provides the most detailed study of relapsing and falciparum malaria in the communities around Dschang in the Western Highlands, there are several notable limitations. First, our sampling was confined to late in the malaria transmission season. Reports from Tanzania have suggested that P. ovale spp. infection peaks early in the transmission season ( 65 ). Second, while the associations between malaria prevalence, mosquito abundance, and biting rates are as expected, the geographic overlap of sampling of households and vectors is not one-to-one,with only 75.8% of human sampling households within 2km of a mosquito collection site. In addition, mosquito collection did not occur in two sites: Mboua had inadequate mosquito abundance, and collections in Baleveng are planned for a future study. This may introduce bias into these associations as mosquito exposure in the human sampling population is only approximated by the mosquito collections done. Lastly, children younger than 6 years of age were not sampled, excluding an age group expected to have lower immunity and higher risk of adverse health outcomes associated with exposure. CONCLUSION This is the first community molecular survey of falciparum and the relapsing malarias in and around Dschang, Cameroon, an area with previous evidence of waning P. falciparum predominance in clinic populations ( 14 , 24 , 27 , 31 ). Overall, prevalence of relapsing malaria was low among the 3,661 participants. Malaria was heterogeneously distributed across sampled communities, with prevalence ranging from 19.7% to 3.1% for P. falciparum and 1.5% to 0% for P. ovale spp.,and as expected, vector abundance was associated with higher malaria rates within the community. These data are at odds with previous reports of P. vivax infection being common among clinic patients in this community and raises questions about the dynamics of non-falciparum malaria within the region and its burden in the community. Furthermore, gametocytemia was low among those infected with P. ovale , in contrast to P. falciparum infections. The Relapsing Malaria in Africa (ReMA) study is continuing to gather data from both community surveys like the one described herein, as well as clinic based surveillance and paired vector surveillance, to better understand the potentially dynamic epidemiology of relapsing malaria in the region. Data Availability Metadata for this study is available at UNCs Dataverse at doi: https://doi.org/10.15139/S3/JQXDZ0 , except for geographic location which is available from Dr. Ali upon reasonable request. Mosquito data is available from Kiam et al. 2025 on request, doi: https://doi.org/10.21203/rs.3.rs-5558659/v1 . https://doi.org/10.15139/S3/JQXDZ0 https://doi.org/10.21203/rs.3.rs-5558659/v1 Author Contributions Data Curation: SJW, VPKT, MM, AGBT and BK Formal Analysis: SJW, VPKT, and VG Funding Acquisition: JJJ, RRD, IMA and SEN Investigation: VPKT, MM, CG, LKM, ZPH, KCE, EH, JMS, AGBT, BK, MNN, DBK, GNL, CLD, CAN, MN, II, JCO FCL and YNN Methodology: JJJ, SEN, RRD, JTL, JAB and IMA. Project Administration: RRR, SEN, RRD, JTL and IMA. Resources: JJJ, RRD, IMA and SEN Software: SJW and VG Supervision: RRR, SEN, RRD, JTL and IMA. Validation: SJW, VG and VPKT Visualization: SJW and VG Writing – Original Draft Preparation: SJW, JJJ, JTL Writing – Review & Editing: All authors Conflicts of Interest All authors declare no competing interests. Generative AI was used in the development of this manuscript. The authors take full responsibility for the content. Data Availability Metadata for this study is available at UNC’s Dataverse at doi: https://doi.org/10.15139/S3/JQXDZ0 , except for geographic location which is available from Dr. Ali upon reasonable request. Mosquito data is available from Kiam et al. 2025 on request, doi: https://doi.org/10.21203/rs.3.rs-5558659/v1 . Funding This study was funded by the National Institute for Allergy and Infectious Diseases, National Institutes of Health (R01AI165537 to JJJ, RRD, and SN; 1U19AI181594 to RRD and JJJ; K24AI134990 to JJJ; R21AI152260 to JTL; F30AI179111 to KCE). Acknowledgements We thank the participants of the study. We thank the members of the ReMA team for the work that went into conducting the community survey. We thank Oksana Kharabora and Catherine Gorman for assistance in the lab. The following reagent was obtained through BEI Resources, NIAID, NIH: Diagnostic Plasmid Containing the Small Subunit Ribosomal RNA Gene (18S) from Plasmodium falciparum , MRA-177, contributed by Peter A. Zimmerman. The following reagent was obtained through BEI Resources, NIAID, NIH: Diagnostic Plasmid Containing the Small Subunit Ribosomal RNA Gene (18S) from Plasmodium vivax , MRA-178, contributed by Peter A. Zimmerman. 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