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. 2017 Nov 14;12(11):e0187897.
doi: 10.1371/journal.pone.0187897. eCollection 2017.

Emerging and reemerging arboviruses: A new threat in Eastern Peru

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Emerging and reemerging arboviruses: A new threat in Eastern Peru

Carlos Alva-Urcia et al. PLoS One. .

Abstract

Background: Arboviral diseases are one of the most common causes of acute febrile illness (AFI) and a significant health problem in South America. In Peru, laboratory etiologic identification of these infections occurs in less than 50% of cases, leading to underdiagnoses of important emerging arboviruses.

Aim: To assess the prevalence of the Dengue (DENV), Oropouche (OROV), Chikungunya (CHIKV), Mayaro (MAYV) and Zika (ZIKV) viruses in patients with acute febrile illness from Puerto Maldonado (Peru).

Methodology: Serum samples were obtained from patients with AFI during January 2016 to March 2016. A total of 139 specimens were analyzed for the presence of DENV, OROV, CHIKV, MAYV, and ZIKV using polymerase chain reaction (PCR).

Results: CHIKV in 9.4% and OROV in 8.6% were the most prevalent arboviruses, followed by DENV and ZIKV, with a prevalence of 6.5% and 5%, respectively. Among all patients, the most common symptoms accompanying fever were headaches 79.9%, muscle pain 65.5% and joint pain 63.3%.

Conclusions: During this short 3-month period, 4 arboviruses were detected by PCR, CHIKV and OROV being the most common arboviruses in Puerto Maldonado (Peru). Thus, it is crucial to include OROV detection in the national health surveillance. Furthermore, the etiologic clinical diagnosis of arboviral infections is not possible due to the low specificity of symptoms; therefore an increase of cases confirmed by molecular diagnostic methods will enhance arboviral surveillance in Peru.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of clinical symptoms in the AFI and infections caused by arboviruses DENV, OROV, ZIKV and CHIKV.
Fig 2
Fig 2. Scatter matrix and Pearson coefficient of correlation for the clinical symptoms.
Fig 3
Fig 3. Scatter matrix and Pearson coefficient of correlation for the odd-ratios of clinical symptoms.

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