| Full text | |
| Author(s): |
Lacerda, Alec Brian
[1]
;
del Castillo Saad, Leila
[2]
;
Ikefuti, Priscilla Venancio
[1]
;
Pinter, Adriano
[3]
;
Chiaravalloti-Neto, Francisco
[1]
Total Authors: 5
|
| Affiliation: | [1] Univ Sao Paulo, Sch Publ Hlth, Dept Epidemiol, Av Dr Arnaldo 715, Sao Paulo, SP - Brazil
[2] Prof Alexandre Vranjac Hlth Secretariat State Sa, Epidemiol Surveillance Ctr, Sao Paulo, SP - Brazil
[3] SUCEN, Endem Control Superintendence, Sao Paulo, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 11, n. 1 AUG 11 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
We investigated the sylvatic yellow fever (SYF) diffusion process in Sao Paulo (SP) between 2016 and 2019. We developed an ecological study of SYF through autochthonous human cases and epizootics of non-human primates (NHPs) that were spatiotemporally evaluated. We used kriging to obtain maps with isochrones representative of the evolution of the outbreak and characterized its diffusion pattern. We confirmed 648 human cases of SYF in SP, with 230 deaths and 843 NHP epizootics. Two outbreak waves were identified: one from West to East (2016 and 2017), and another from the Campinas region to the municipalities bordering Rio de Janeiro, Minas Gerais, and Parana and those of the SP coast (2017-2019). The SYF outbreak diffusion process was by contagion. The disease did not exhibit jumps between municipalities, indicating that the mosquitoes and NHPs were responsible for transmitting the virus. There were not enough vaccines to meet the population at risk; hence, health authorities used information about the epizootic occurrence in NHPs in forest fragments to identify priority populations for vaccination. (AU) | |
| FAPESP's process: | 20/01596-8 - Use of remote sensing and artificial intelligence to predict high risk areas for Aedes aegypti and Arbovirus infestation |
| Grantee: | Francisco Chiaravalloti Neto |
| Support Opportunities: | Regular Research Grants |