Abstract
Background
Malaria remains a major public health challenge in Malawi, yet contemporary epidemiological data from northern Malawi are limited. This study aimed to characterize the prevalence, demographic distribution, temporal patterns, and parasite density of malaria using routine laboratory surveillance data from a tertiary referral hospital.
Methods
A retrospective cross-sectional study was conducted using malaria screening records from Mzuzu Central Hospital, northern Malawi, collected between January 2017 and August 2025. Laboratory-confirmed malaria cases and malaria-negative individuals were included to estimate malaria prevalence across demographic and temporal subgroups. Malaria prevalence was compared using the χ2 test or Fisher's exact test, with Bonferroni correction for multiple comparisons. Parasite density, available for a subset of confirmed cases from 2024–2025, was summarized as median (interquartile range [IQR]) and compared using the Mann–Whitney U test or Kruskal–Wallis test.
Results
A total of 31,253 individuals were screened, of whom 3405 were laboratory-confirmed malaria cases, yielding an overall prevalence of 10.89%. Plasmodium falciparum accounted for 86.23% of identified infections. Malaria prevalence was significantly higher among males than females (11.85% vs. 10.01%, P < 0.001). Children aged < 5 years (13.11%) and 5–14 years (13.31%) had significantly higher prevalence than adults (P < 0.001). Annual malaria prevalence ranged from 7.99 to 12.81% during 2018–2024. Clear seasonal variation was observed, with prevalence consistently peaking during the second quarter (April–June) and declining during the third quarter (July–September). Parasite density decreased significantly with age (p < 0.001), with children aged 5–14 years showing the highest median density (6,285 parasites/μL), but did not differ by gender or season.
Conclusions
Malaria remains highly endemic in northern Malawi, with substantial age- and season-related heterogeneity in transmission. Children younger than 15 years experienced both the highest malaria prevalence and parasite densities, while transmission increased during and immediately after the rainy season. These findings provide updated region‑specific evidence to support targeted, age‑ and season‑tailored malaria control strategies, and highlight the need for strengthened surveillance and preventive interventions in high‑risk populations.
Keywords: Malaria, Prevalence, Epidemiology, Plasmodium falciparum, Parasite density, Seasonality, Malawi
Introduction
Malaria remains a major global public health challenge, causing substantial morbidity and mortality, particularly in sub-Saharan Africa [1]. The disease is caused by protozoan parasites of the genus Plasmodium, including Plasmodium falciparum (P. falciparum), Plasmodium vivax (P. vivax), Plasmodium ovale (P. ovale), Plasmodium malariae (P. malariae), and Plasmodium knowlesi (P. knowlesi), and is transmitted primarily through the bite of infected female Anopheles mosquitoes [2]. Although substantial progress has been made in malaria control over recent decades, sub-Saharan Africa continues to bear a disproportionate share of the global malaria burden, with young children, pregnant women, and non-immune individuals remaining the most vulnerable populations [3].
Malaria remains endemic in 83 countries worldwide [4]. According to the latest estimates, global malaria cases increased from 229 million in 2019 to 282 million in 2024, with the African Region accounting for approximately 94% of all malaria cases and 95% of malaria-related deaths [5–7]. Malawi is among the 15 countries with the highest malaria burden globally [6]. Despite substantial expansion of malaria control interventions over the past decade, transmission remains intense and geographically heterogeneous, with district-level incidence ranging from 155 to 1,050 cases per 1000 population in 2020 [8]. An estimated 4.3 million malaria cases were reported nationally in 2022 [9]. Malawi accounts for approximately 1.8% of global malaria cases and 1.2% of malaria-related deaths, while contributing nearly 8% of all reported malaria cases in Eastern and Southern Africa [4]. Malaria transmission in Malawi is strongly seasonal, with peak incidence occurring during and immediately after the rainy season (November–April) [10]. Collectively, these observations highlight the substantial and persistent public health and socioeconomic burden of malaria in the country.
Several studies have investigated malaria epidemiology, associated risk factors, and the effectiveness of control interventions in Malawi; however, many were conducted several years ago, focused on specific population groups, or were based on relatively small sample sizes [11–13]. Moreover, most available evidence originates from central and southern Malawi, whereas contemporary epidemiological data from northern Malawi remain limited. Long-term studies describing demographic and temporal trends in malaria transmission in this region are particularly scarce. Given the considerable geographical heterogeneity in malaria transmission driven by differences in climate, population characteristics, and access to healthcare, updated region-specific epidemiological evidence is essential for informing targeted malaria control strategies.
To address this knowledge gap, we analyzed malaria screening records from Mzuzu Central Hospital, the principal public tertiary referral hospital in northern Malawi, collected between January 2017 and August 2025. Using a large hospital-based retrospective dataset, we aimed to estimate malaria prevalence, characterize demographic and temporal patterns of malaria, and identify population groups disproportionately affected by the disease. Our findings provide updated region-specific evidence to strengthen malaria surveillance and support evidence-based prevention and control strategies in northern Malawi.
Methods
Study design and population
This retrospective hospital-based study analysed routinely collected data from individuals screened for malaria at Mzuzu Central Hospital between January 2017 and August 2025. Mzuzu Central Hospital is a 610-bed government tertiary referral hospital serving a population of approximately three million people in northern Malawi.
Medical records containing complete demographic information (age, gender, and date of testing) and laboratory results were eligible for inclusion. Records with incomplete demographic, clinical, or laboratory information were excluded to ensure data quality and analytical rigor.
A total of 31,253 eligible individuals were included in the final analysis. Malaria-positive and malaria-negative individuals were compared according to demographic characteristics and temporal patterns to describe the epidemiology of malaria within the hospital catchment area. In addition, parasite density among laboratory-confirmed malaria cases was analysed according to demographic and temporal characteristics.
Blood collection and laboratory procedures
Approximately 3 mL of venous blood was collected from each participant by trained phlebotomists using standard venipuncture procedures and transferred into ethylenediaminetetraacetic acid (EDTA) vacuum tubes to prevent coagulation.
Thick and thin blood smears were prepared on clean glass slides. Thin smears were fixed with absolute methanol, whereas thick smears remained unfixed. All smears were stained with 3% Giemsa solution for 30 min and examined under oil immersion at × 1000 magnification using light microscopy.
Microscopic examination was performed to detect Plasmodium parasites and identify the infecting species. A blood smear was considered negative only after approximately 200 microscopic fields had been systematically examined without detecting parasites. Parasite density was quantified as the number of parasites per microlitre of blood using the standard World Health Organization (WHO) method, assuming a white blood cell count of 8,000 cells/μL.
Data collection
Demographic and laboratory data were retrospectively extracted from the laboratory information system of Mzuzu Central Hospital. The dataset included age, gender, date of testing, malaria microscopy results, Plasmodium species, and parasite density where available.
Malaria prevalence was defined as the proportion of laboratory-confirmed malaria cases among all individuals screened during the study period or within each demographic or temporal subgroup.
Participants were categorized into four age groups (< 5 years, 5–14 years, 15–49 years, and ≥ 50 years) based on malaria epidemiological characteristics.
Ethical approval
This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Mzuzu University Research Ethics Committee (Approval No. MZUNIERC/DOR/24/166). Owing to the retrospective nature of the study and the use of routinely collected anonymized data, the requirement for informed consent was waived by the Ethics Committee.
Statistical analyses
Statistical analyses were performed using SPSS version 30.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables are presented as medians with interquartile ranges (IQR), whereas categorical variables are presented as frequencies and percentages. Malaria prevalence was calculated as the proportion of laboratory-confirmed malaria cases among all individuals screened within each demographic or temporal subgroup and is presented with corresponding 95% confidence intervals (95% CI), where appropriate. Differences in categorical variables were assessed using Pearson's χ2 test or Fisher's exact test, as appropriate. When overall comparisons were statistically significant, pairwise comparisons were performed using the Bonferroni correction. Temporal trends in malaria prevalence were summarized by calendar year, quarter, and month. To minimize bias resulting from incomplete annual records, data from 2017 to 2025 were excluded from temporal trend analyses. Parasite density data were non-normally distributed and are therefore presented as medians with IQR. Comparisons between two groups were performed using the Mann–Whitney U test, whereas comparisons among three or more groups were conducted using the Kruskal–Wallis test. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant.
Results
General characteristics of the study population
A total of 31,253 individuals who underwent malaria screening at Mzuzu Central Hospital between 2017 and 2025 were included in this retrospective analysis. The overall malaria prevalence was 10.89% (3405/31,253). Individuals with malaria were significantly younger than those without malaria (median age: 11.0 years [IQR, 6.0–25.0] vs. 17.0 years [IQR, 7.0–34.0]; P < 0.001). Overall, 15,032 (48.10%) participants were male and 16,221 (51.90%) were female.
Among the 3405 laboratory-confirmed malaria cases, P. falciparum was the predominant species, accounting for 86.23% (2936/3405) of infections. Other identified species included P. malariae (0.56%, 19/3405), P. ovale (0.26%, 9/3405), and P. vivax (0.06%, 2/3405), whereas parasite species could not be determined in 12.89% (439/3405) of positive samples (Table 1). Overall, 67.55% (2300/3405) of malaria-positive cases were diagnosed between January and June, whereas 32.45% (1105/3405) occurred between July and December, suggesting a seasonal concentration of malaria cases during the first half of the year.
Table 1.
General information
| Variables | Total (n = 31253) | Malaria negative (n = 27,848) | Malaria positive (n = 3405) | Statistic | P |
|---|---|---|---|---|---|
| Age (years), M (Q1, Q3) | 16.00 (7.00,33.00) | 17.00 (7.00,34.00) | 11.00 (6.00,25.00) | Z = − 14.542 | < 0.001 |
| Gender, n (%) | |||||
| Female | 16,221 (51.90) | 14,597 (52.45) | 1624 (47.66) | χ2 = 27.101 | < 0.001 |
| Male | 15,032 (48.10) | 13,251 (47.55) | 1781 (52.34) | ||
| Malaria Species, n (%) | |||||
| P. falciparum | 2936 (9.39) | 0 (0.00) | 2936 (86.23) | ||
| Variables | Total (n = 31,253) | Malaria Negative (n = 27,848) | Malaria Positive (n = 3405) | Statistic | P |
|---|---|---|---|---|---|
| P. malariae | 19 (0.06) | 0 (0.00) | 19 (0.56) | ||
| P. ovale | 9 (0.03) | 0 (0.00) | 9 (0.26) | ||
| P. vivax | 2 (0.01) | 0 (0.00) | 2 (0.06) | ||
| Undetermined species | 439(1.40) | 0 (0.00) | 439 (12.89) | ||
| Year, n (%) | |||||
| 2017 | 770 (2.46) | 720 (2.59) | 50 (1.47) | χ2 = 195.886 | < 0.001 |
| 2018 | 3734 (11.95) | 3375 (12.12) | 359 (10.55) | ||
| 2019 | 3818 (12.22) | 3514 (12.62) | 304 (8.93) | ||
| Variables | Total (n = 31,253) | Malaria Negative (n = 27,848) | Malaria Positive (n = 3405) | Statistic | P |
|---|---|---|---|---|---|
| 2021 | 3590 (11.49) | 3284 (11.79) | 306 (8.99) | ||
| 2022 | 3560 (11.39) | 3160 (11.35) | 400 (11.75) | ||
| 2023 | 3961 (12.67) | 3576 (12.84) | 385 (11.31) | ||
| 2024 | 4953 (15.85) | 4334 (15.56) | 619 (18.19) | ||
| 2025 | 3004 (9.61) | 2518 (9.04) | 486 (14.25) |
Malaria prevalence according to gender
Malaria prevalence was significantly higher among males than females (11.85%, 1781/15,032 [95% CI 11.33–12.37%] vs. 10.01%, 1624/16,221 [95% CI 9.55–10.47%]; P < 0.001) (Fig. 1).
Fig. 1.
Malaria prevalence by gender
Malaria prevalence according to age
Malaria prevalence differed significantly across age groups (χ2 = 148.18, P < 0.001). The highest prevalence was observed among children aged 5–14 years (13.31%, 1429/10,740), followed by children aged < 5 years (13.11%, 545/4156), adults aged 15–49 years (9.52%, 1220/12,820), and adults aged ≥ 50 years (5.97%, 211/3537).
Post hoc pairwise comparisons with Bonferroni correction showed no significant difference between the two paediatric age groups (P = 0.757), whereas both groups had significantly higher prevalence than either adult age group (all P < 0.001). Malaria prevalence was also significantly lower among adults aged ≥ 50 years than among those aged 15–49 years (P < 0.001) (Fig. 2; Table 2).
Fig. 2.
Malaria prevalence by age group
Table 2.
Age‑specific malaria prevalence, 95% confidence intervals, and pairwise comparisons among study participants
| Age group | Positive cases/Total screened | Prevalence, % (95% CI) | Significance group† |
|---|---|---|---|
| < 5 years | 545/4156 | 13.11 (12.1–14.2) | a |
| 5–14 years | 1429/10740 | 13.31 (12.7–14.0) | a |
| 15–49 years | 1220/12820 | 9.52 (9.0–10.0) | b |
| ≥ 50 years | 211/3537 | 6.0 (5.2–6.8) | c |
† Groups sharing the same letter are not significantly different (Bonferroni-corrected pairwise comparison, < 5 vs. 5–14: χ2 = 0.10, p = 0.757). All other pairwise comparisons were significant at p < 0.001. Overall test for heterogeneity across the four groups: χ2 = 198.98, p = 0.000
Among all malaria-positive cases, 63.46% (2161/3405) occurred in children (< 18 years), whereas 36.54% (1244/3405) occurred in adults (≥ 18 years), indicating that the malaria burden was disproportionately concentrated among children (Fig. 3).
Fig. 3.
Distribution of laboratory-confirmed malaria cases by age category. Proportion of malaria-positive cases occurring among children (< 18 years) and adults (≥ 18 years). Percentages represent the distribution of confirmed malaria cases rather than malaria prevalence
Malaria prevalence among four age groups (< 5 years, 5–14 years, 15–49 years, and ≥ 50 years). Bars represent the proportion of laboratory-confirmed malaria cases among all individuals screened within each age group. Error bars indicate 95% confidence intervals.
Temporal and seasonal patterns of malaria prevalence
To minimize bias arising from incomplete annual records, data from 2017 to 2025 were excluded from temporal trend analyses. Annual malaria prevalence ranged from 7.99% (305/3818) to 12.81% (495/3863) during 2018–2024, with the highest prevalence observed in 2020 (12.81%, 495/3863) and the lowest in 2019 (7.99%) (Fig. 4A).
Fig. 4.
Temporal trends in malaria prevalence from 2018 to 2024. A Annual malaria prevalence. B Quarterly malaria prevalence by year. C Monthly malaria prevalence by year. Malaria prevalence was calculated as the proportion of laboratory-confirmed malaria cases among all individuals screened during each time period. Data from 2017 to 2025 were excluded because only partial-year data were available
Marked seasonal variation was observed across study years (Fig. 4B). In most years, malaria prevalence peaked during the second quarter (April–June), reaching 17.21% (163/947) in 2022, whereas the third quarter (July–September) generally showed the lowest prevalence. Monthly analyses demonstrated a comparable pattern, with malaria prevalence increasing during and immediately after the rainy season and declining during the dry season (Fig. 4C).
Parasite density according to age, gender, and season
Parasite density analysis was restricted to 670 laboratory-confirmed malaria cases from 2024–2025, when parasite density was routinely recorded.
Parasite density differed significantly across age groups (P < 0.001) (Fig. 5A). The highest median parasite density was observed among children aged 5–14 years (6285 parasites/μL; IQR, 1,000–23,000), followed by children aged < 5 years (6000 parasites/μL; IQR, 1500–23,555), adults aged 15–49 years (3544 parasites/μL; IQR, 710–15,467), and adults aged ≥ 50 years (2300 parasites/μL; IQR, 407–7600).
Fig. 5.
Parasite density according to age, gender, and calendar quarter. A Parasite density by age group. B Parasite density by gender. C Parasite density by quarter. Box plots show the median (horizontal line), interquartile range (box), and 1.5 × interquartile range (whiskers). Parasite density was expressed as parasites/μL
No significant difference in parasite density was observed between males and females (P = 0.596). The median parasite density was 5896 parasites/μL in males and 4702 parasites/μL in females (Fig. 5B). Likewise, parasite density did not differ significantly across calendar quarters (P = 0.076) (Fig. 5C).
Discussion
This study provides updated epidemiological evidence on malaria in northern Malawi based on a large retrospective hospital-based dataset collected over a nine-year period. By analysing more than 31,000 malaria screening records, we demonstrated that malaria transmission in the Mzuzu healthcare catchment area remains characterized by the predominance of P. falciparum, a disproportionately high burden among children, significant seasonal variation, and modest gender differences in malaria prevalence. These findings provide contemporary region-specific evidence to support malaria surveillance and guide targeted control strategies in northern Malawi.
The predominance of P. falciparum observed in this study is consistent with previous reports from Malawi and other malaria-endemic countries in sub-Saharan Africa, where this species accounts for the vast majority of malaria cases and malaria-related deaths [14]. This finding indicates that P. falciparum remains the principal target for malaria surveillance and control programmes in northern Malawi.
Children aged < 15 years experienced the highest malaria prevalence in our study, consistent with previous reports from Malawi and other malaria-endemic countries in sub-Saharan Africa, where children bear a disproportionate burden of malaria morbidity and mortality [4, 9]. This age-related pattern is likely explained by limited naturally acquired immunity during early childhood, whereas repeated exposure to Plasmodium parasites gradually leads to the development of partial protective immunity with increasing age [15, 16]. Consistent with this explanation, parasite density also differed significantly across age groups, with the highest median parasite densities observed among children younger than 15 years and progressively lower densities among adults. This finding further supports the role of age-dependent immunity in limiting parasite multiplication following infection [17, 18]. Higher parasite burdens in children may also increase the risk of symptomatic disease and contribute to ongoing malaria transmission, underscoring the importance of prioritizing early diagnosis, prompt treatment, and effective preventive interventions in this vulnerable population [9, 19].
The higher malaria prevalence observed among males is consistent with findings from Ethiopia, Nigeria, and other malaria-endemic settings [20–22]. This disparity is likely multifactorial and may reflect behavioural differences, including greater outdoor occupational exposure, increased nighttime activities, and differences in the use of personal protective measures, all of which may increase exposure to mosquito bites [23, 24]. Despite the higher prevalence among males, parasite density did not differ significantly between genders, suggesting that gender-related differences are more likely attributable to exposure than to biological differences in parasite multiplication or host immune responses. Similar findings have been reported elsewhere [25–27], although conflicting evidence exists [15, 28], indicating that further multicentre studies are warranted.
The marked seasonal variation observed in this study is consistent with the climatic characteristics of Malawi, where malaria transmission closely follows the rainy season (November–April) [29]. Malaria prevalence consistently increased during the second quarter (April–June), immediately following the rainy season, when abundant breeding sites and favourable environmental conditions promote mosquito survival and transmission. Similar seasonal patterns have been reported in Burkina Faso and other regions of sub-Saharan Africa, highlighting rainfall and temperature as major determinants of malaria transmission [30–32]. Identification of April as the peak transmission month and August as the seasonal minimum provides practical evidence for optimizing the timing of vector control interventions, including indoor residual spraying, insecticide-treated net distribution, and other seasonal prevention strategies.
The present findings have several implications for malaria control in northern Malawi. First, the substantial malaria burden among children, particularly those aged 5–14 years, suggests that current prevention programmes may insufficiently target this age group and supports expanding school-based malaria interventions. Second, the pronounced seasonal increase in malaria prevalence provides a clear opportunity for implementing intensified vector control and other preventive measures before and during the rainy season. Third, the higher malaria prevalence observed among males highlights the importance of behavioural interventions and health education aimed at reducing exposure associated with nighttime outdoor activities and occupational risks.
Several limitations should be acknowledged. As a single-centre hospital-based study, our findings may underestimate the true community burden of malaria because asymptomatic infections and cases managed at primary healthcare facilities were not captured. The retrospective study design may also have introduced selection bias. Furthermore, the absence of entomological data prevented assessment of vector species composition and seasonal vector dynamics, while information on important individual-level risk factors, including insecticide-treated net use, housing conditions, and socioeconomic status, was unavailable, limiting further investigation of determinants of malaria risk. Future multicentre studies integrating community-based surveillance with entomological investigations are needed to provide a more comprehensive understanding of malaria transmission dynamics in northern Malawi.
Despite these limitations, this study makes an important contribution to the understanding of malaria epidemiology in northern Malawi, where contemporary surveillance data remain limited. To our knowledge, it represents one of the largest laboratory-based investigations conducted in this region and provides a comprehensive analysis based on nine years of routinely collected laboratory-confirmed malaria data. The large sample size, extended observation period, and laboratory-confirmed diagnoses strengthen the robustness of the findings and provide valuable region-specific evidence on demographic and seasonal transmission patterns. These data complement national surveillance reports, address an important regional evidence gap, and establish a baseline for evaluating future malaria control interventions and informing evidence-based malaria control policies in northern Malawi.
Conclusion
Malaria remains a major public health challenge in northern Malawi, with substantial demographic and seasonal heterogeneity in transmission. Children, particularly those younger than 15 years, bear the greatest burden of malaria, while transmission intensifies during and immediately after the rainy season. These findings provide updated epidemiological evidence from northern Malawi based on a large nine-year laboratory surveillance dataset and highlight the need for age-targeted prevention strategies, seasonally optimized malaria control interventions, and strengthened surveillance systems. The results offer valuable region-specific evidence to support malaria control planning and contribute to ongoing malaria elimination efforts in Malawi.
Acknowledgements
Not applicable.
Author contributions
QW, QZ and XQW was involved in conception and design of the study. XXH, JH and YL conducted formal analysis. QW, YFM and SWL prepared all figures and tables. QW, TSM and DMN wrote the original draft. All authors reviewed, edited and provided approval of the final version.
Funding
This research was funded by grants from The Science and Technology Program of Xi’an (Grant Number: 24LLRHZDZX0028) and University-Industry-Research Innovation Fund of the Ministry of Education of the People's Republic of China (Grant Number: 2025MR003).
Data availability
All data generated or analysed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
This study was performed in compliance with the ethical principles set forth in the Declaration of Helsinki and received approval from the Research Ethics Committee of the Mzuzu University (Approval No. MZUNIERC/DOR/24/166). The requirement for written informed consent was waived by the Ethics Committee in accordance with national legislation and institutional guidelines.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qian Wu and Qi Zhang are co-first authors and have contributed equally to this work.
References
- 1.Millar SB, Cox-Singh J. Human infections with Plasmodium knowlesi--zoonotic malaria. Clin Microbiol Infect. 2015;21(7):640–8. [DOI] [PubMed] [Google Scholar]
- 2.Griffin JT, Hollingsworth TD, Okell LC, Churcher TS, White M, Hinsley W, et al. Reducing Plasmodium falciparum malaria transmission in Africa: a model-based evaluation of intervention strategies. PLoS Med. 2010;7(8):e1000324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lalloo DG, Hill DR. Preventing malaria in travellers. BMJ. 2008;336(7657):1362–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.World Health Organization. World malaria report 2024: addressing inequity in the global malaria response. Geneva: World Health Organization; 2025. [Google Scholar]
- 5.World Health Organization. 2020 World malaria report. vol. WHO/HTM/GM, World Health. 2020;238.
- 6.World Health Organization. World malaria report. Geneva: World Health Organization; 2023. [Google Scholar]
- 7.World Health Organization. World malaria report 2025. Geneva: World Health Organization; 2025. [Google Scholar]
- 8.Malawi health management information systems. https://dhis2.health.gov.mw/dhis-web-commons/security/login.action#/
- 9.National Malaria Control Programme (NMCP). The National Malaria Strategic Plan 2023–2030. Lilongwe: Ministry of Health, Malawi; 2023. [Google Scholar]
- 10.Mathanga DP, Walker ED, Wilson ML, Ali D, Taylor TE, Laufer MK. Malaria control in Malawi: current status and directions for the future. Acta Trop. 2012;121(3):212–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Kim B, Chang S, Shin MJ, Hong P, Lee G, Koh MJ, et al. Patterns of pediatric malaria incidence and outcomes in Kasungu, Malawi in 2016. Adv Infect Dis. 2016;9(4):277–84. [Google Scholar]
- 12.Mpanga V, Maluwa A, Kafulafula U, Pindani M, Bultemeier K. Comprehension of risk factors of malaria during pregnancy among pregnant women attending antenatal care in Malawi. Open J Nurs. 2014;4(12):897–905. [Google Scholar]
- 13.Chidzere N, Munthali C, Chapweteka G, Munthali T, Kumwenda P, Chikasema B, et al. Prevalence of anxiety and associated factors among guardians of children admitted with severe malaria at Thyolo District Hospital in Malawi. S Afr J Psychiatr. 2025;31:2591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Awosolu OB, Yahaya ZS, Farah Haziqah MT. Prevalence, Parasite Density and Determinants of Falciparum Malaria Among Febrile Children in Some Peri-Urban Communities in Southwestern Nigeria: A Cross-Sectional Study. Infect Drug Resist. 2021;14:3219–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Carshon-Marsh R, Bondy S, Witek T, Jha P. Validation of malaria-attributed deaths using verbal autopsy studies: a systematic review. Malar J. 2024;23:217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ogbuanu IU, Otieno K, Varo R, Sow SO, Ojulong J, Duduyemi B, et al. Burden of child mortality from malaria in high endemic areas: results from the CHAMPS network using minimally invasive tissue sampling. J Infect. 2024;88:106107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Felger I, Maire M, Bretscher MT, Falk N, Tiaden A, Sama W, et al. The dynamics of natural Plasmodium falciparum infections. PLoS ONE. 2012;7(9):e45542. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rodriguez-Barraquer I, Arinaitwe E, Jagannathan P, Kamya MR, Rosenthal PJ, Rek J, et al. Quantification of anti-parasite and anti-disease immunity to malaria as a function of age and exposure. Elife. 2018;7:e35832. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.White NJ, Turner GD, Day NP, Dondorp AM. Lethal malaria: Marchiafava and Bignami were right. J Infect Dis. 2013;208(2):192–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Awosolu OB, Yahaya ZS, Farah Haziqah MT, Simon-Oke IA, Fakunle C. A cross-sectional study of the prevalence, density, and risk factors associated with malaria transmission in urban communities of Ibadan, Southwestern Nigeria. Heliyon. 2021;7(1):e05975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gebretsadik D, Feleke DG, Fiseha M. Eight-year trend analysis of malaria prevalence in Kombolcha, South Wollo, north-central Ethiopia: a retrospective study. Parasit Vectors. 2018;11(1):55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Escobar DF, Lucchi NW, Abdallah R, Valenzuela MT, Udhayakumar V, Jercic MI, et al. Molecular and epidemiological characterization of imported malaria cases in Chile. Malar J. 2020;19(1):289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Stresman GH. Beyond temperature and precipitation: ecological risk factors that modify malaria transmission. Acta Trop. 2010;116(3):167–72. [DOI] [PubMed] [Google Scholar]
- 24.Pulford J, Hetzel MW, Bryant M, Siba PM, Mueller I. Reported reasons for not using a mosquito net when one is available: a review of the published literature. Malar J. 2011;10:83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ikeh EI, Teclaire NN. Prevalence of malaria parasitaemia and associated factors in febrile under-5 children seen in Primary Health Care Centres in Jos, North Central Nigeria. Niger Postgrad Med J. 2008;15(2):65–9. [PubMed] [Google Scholar]
- 26.Nwaorgu OC, Orajaka BN. Prevalence of malaria among children 1–10 years old in communities in awka north local government area, Anambra State South East Nigeria. African Res Rev. 2011;5(5):264–81. [Google Scholar]
- 27.Kimbi HK, Keka FC, Nyabeyeu HN, Ajeagah HU, Tonga CF. An update of asymptomatic falciparum malaria in school children in Muea. Southwest Cameroon J Bacteriol Parasitol. 2012;3:154. [Google Scholar]
- 28.Awosolu OB, Yahaya ZS, Farah Haziqah MT, Simon-Oke IA, Olanipekun IT, Oniya MO. Epidemiology of falciparum malaria among residents of some rural and periurban communities in Ekiti State. Southwestern Nigeria Trop Biomed. 2021;38(1):14–21. [DOI] [PubMed] [Google Scholar]
- 29.Semenza JC, Rocklöv J, Ebi KL. Climate change and cascading risks from infectious disease. Infect Dis Ther. 2022;11(4):1371–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Pascoe L, Clemen T, Bradshaw K, Nyambo D. Review of importance of weather and environmental variables in agent-based arbovirus models. Int J Environ Res Public Health. 2022;19(23):15578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Rouamba T, Nakanabo-Diallo S, Derra K, Rouamba E, Kazienga A, Inoue Y, et al. Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso. BMC Public Health. 2019;19(1):249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Yaro JB, Tiono AB, Ouedraogo A, Lambert B, Ouedraogo ZA, Diarra A, et al. Risk of Plasmodium falciparum infection in south-west Burkina Faso: potential impact of expanding eligibility for seasonal malaria chemoprevention. Sci Rep. 2022;12(1):1402. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
All data generated or analysed during this study are included in this published article.