| Literature DB >> 32310065 |
Issa Diarra, Elif Nurtop, Abdoul Karim Sangaré, Issaka Sagara, Boris Pastorino, Souleymane Sacko, Amatigué Zeguimé, Drissa Coulibaly, Bakary Fofana, Pierre Gallian, Stephane Priet, Jan Felix Drexler, Anna-Bella Failloux, Abdoulaye Dabo, Mahamadou Ali Thera, Abdoulaye Djimdé, Bourèma Kouriba, Simon Cauchemez, Xavier de Lamballerie, Nathanaël Hozé, Ogobara K Doumbo.
Abstract
The circulation of Zika virus (ZIKV) in Mali has not been clearly characterized. Therefore, we conducted a serologic survey of 793 asymptomatic volunteers >15 years of age (2016), and 637 blood donors (2013) to assess the seroprevalence of ZIKV infection in 2 ecoclimatic regions of Mali, tropical savannah and warm semiarid region, using ELISA and seroneutralization assays. The overall seroprevalence was ≈12% and increased with age, with no statistical difference between male and female participants. In the warm semiarid study sites we detected immunological markers of an outbreak that occurred in the late 1990s in 18% (95% CI 13%-23%) of participants. In tropical savannah sites, we estimated a low rate of endemic transmission, with 2.5% (95% CI 2.0%-3.1%) of population infected by ZIKV annually. These data demonstrate the circulation of ZIKV in Mali and provide evidence of a previously unidentified outbreak that occurred in the late 1990s.Entities:
Keywords: Mali; Zika virus; arbovirus; mosquitoes; seroprevalence; vector-borne infections; viruses
Mesh:
Year: 2020 PMID: 32310065 PMCID: PMC7181926 DOI: 10.3201/eid2605.191383
Source DB: PubMed Journal: Emerg Infect Dis ISSN: 1080-6040 Impact factor: 6.883
Figure 1The main climatic zones of Mali by Köppen climate classification and sites of study of Zika virus seroprevalence.
Precision of seroprevalence determination according to prevalence estimates and sample size in study of Zika virus, Mali*
| Prevalence estimate | Precision for sample size | ||
|---|---|---|---|
| 100 | 150 | 800 | |
| 0.050 | 0.044 | 0.035 | 0.015 |
| 0.100 | 0.060 | 0.048 | 0.020 |
| 0.150 | 0.070 | 0.058 | 0.025 |
| 0.200 | 0.080 | 0.065 | 0.028 |
| 0.250 | 0.086 | 0.070 | 0.030 |
| 0.300 | 0.091 | 0.074 | 0.032 |
*α = 0.05.
Demographic characteristics of the population in study of Zika virus, Mali
| Characteristic | Niono 2016, n = 65 | Bamako | Kadiolo 2016, n = 136 | Bougouni 2016, n = 127 | Kita 2016, n = 40 | Bandiagara 2016, n = 187 | Diéma 2016, n = 109 | Total, n = 1,430 | |
|---|---|---|---|---|---|---|---|---|---|
| 2016, n = 129 | 2013,* n = 637 | ||||||||
| Sex | |||||||||
| M | 9 | 50 | 572 | 40 | 42 | 16 | 58 | 27 | 814 |
| F | 56 | 79 | 65 | 96 | 85 | 24 | 129 | 82 | 616 |
| M/F ratio | 0.2 | 0.6 | 8.8 | 0.4 | 0.5 | 0.7 | 0.4 | 0.3 | 1.3 |
| Median age, y | 35 | 32 | 28 | 30 | 45 | 23 | 35 | 28 | 30 |
*Volunteer blood donors.
Results of seroepidemiological investigations for Zika virus according to study sites and time of sampling, Mali
| Study site and year | Total no. | IgG* doubtful, no. (%) | IgG* positive, no. (%) | VNT† positive, no. (%) |
|---|---|---|---|---|
| Niono 2016 | 65 | 4 (6 0.2) | 4 (6.2) | 2 (3.1) |
| Bamako 2016 | 129 | 0 (0.0) | 11 (8.5) | 7 (5.4) |
| Bamako 2013‡ | 637 | 18 (2.8) | 67 (10.5) | 47 (7.4) |
| Kadiolo 2016 | 136 | 14 (10.3) | 22 (16.2) | 14 (10.3) |
| Bougouni 2016 | 127 | 3 (2.4) | 22 (17.3) | 15 (11.8) |
| Kita 2016 | 40 | 5 (12.5) | 7 (17.5) | 6 (15.0) |
| Bandiagara 2016 | 187 | 24 (12.8) | 81 (43.3) | 29 (15.5) |
| Diéma 2016 | 109 | 14 (12.8) | 39 (35.8) | 22 (20.2) |
| Total | 1,430 | 82 (5.7) | 253 (17.7) | 142 (9.9) |
*Result by Euroimmun IgG ELISA assay. †Result by cytopathic effect-based virus neutralization test. ‡Volunteer blood donors.
Figure 2Zika virus seroprevalence by age group, Mali, 2016.
Figure 3Observed and predicted profiles for Zika virus seroprevalence by age, climatic zone, and the assumed mode of transmission, Mali. Observed age-specific seroprevalence mean (black dots) and range (error bars) are compared with predictions (blue lines) of models; shading indicates 95% CI. Panels A–C show data for tropical savannah and D–F for semiarid regions. Predictions assume a constant force of infection over time (A, D) or a single epidemic in the past (B, E). Force of infection is shown over time by the best fitting model for each climatic region (C, F).