| Summary: | <b>Background</b>: Co-infection with malaria and chikungunya could exert a significant public health impact with infection misdiagnosis. Therefore, this study aimed to collect qualitative and quantitative evidence of malaria and chikungunya co-infection among febrile patients. <b>Methods</b><b>:</b> Potentially relevant studies were identified using PubMed, Web of Science, and Scopus. The bias risk of the included studies was assessed using the checklist for analytical cross-sectional studies developed by the Joanna Briggs Institute. The pooled prevalence of malaria and chikungunya co-infection among febrile patients and the pooled prevalence of chikungunya virus (CHIKV) infection among malaria patients were estimated with the random effect model. The odds of malaria and chikungunya co-infection among febrile patients were also estimated using a random effect model that presumed the heterogeneity of the outcomes of the included studies. The heterogeneity among the included studies was assessed using the Cochran Q test and I<sup>2</sup> statistics. Publication bias was assessed using the funnel plot and Egger’s test. <b>Results</b><b>:</b> Of the 1924 studies that were identified from the three databases, 10 fulfilled the eligibility criteria and were included in our study. The pooled prevalence of malaria and chikungunya co-infection (182 cases) among febrile patients (16,787 cases), stratified by diagnostic tests for CHIKV infection, was 10% (95% confidence interval (CI): 8–11%, I<sup>2</sup>: 99.5%) using RDT (IgM), 7% (95% CI: 4–10%) using the plaque reduction neutralization test (PRNT), 1% (95% CI: 0–2%, I<sup>2</sup>: 41.5%) using IgM and IgG ELISA, and 4% (95% CI: 2–6%) using real-time RT-PCR. When the prevalence was stratified by country, the prevalence of co-infection was 7% (95% CI: 5–10%, I<sup>2</sup>: 99.5%) in Nigeria, 1% (95% CI: 0–2%, I<sup>2</sup>: 99.5%) in Tanzania, 10% (95% CI: 8–11%) in Sierra Leone, 1% (95% CI: 0–4%) in Mozambique, and 4% (95% CI: 2–6%) in Kenya. The pooled prevalence of CHIKV infection (182 cases) among malaria patients (8317 cases), stratified by diagnostic tests for CHIKV infection, was 39% (95% CI: 34–44%, I<sup>2</sup>: 99.7%) using RDT (IgM), 43% (95% CI: 30–57%) using PRNT, 5% (95% CI: 3–7%, I<sup>2</sup>: 5.18%) using IgM and IgG ELISA, and 9% (95% CI: 6–15%) using real-time RT-PCR. The meta-analysis showed that malaria and chikungunya co-infection occurred by chance (<i>p</i>: 0.59, OR: 0.32, 95% CI: 0.6–1.07, I<sup>2</sup>: 78.5%). <b>Conclusions</b><b>:</b> The prevalence of malaria and chikungunya co-infection varied from 0% to 10% as per the diagnostic test for CHIKV infection or the country where the co-infection was reported. Hence, the clinicians who diagnose patients with malaria infections in areas where two diseases are endemic should further investigate for chikungunya co-infection to prevent misdiagnosis or delayed treatment of concurrent infection.
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