4.1 Article

Climate influence the human leptospirosis cases in Brazil, 2007-2019: a time series analysis

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/trstmh/trab092

Keywords

ARMAX; epidemiology; modelling; time series analysis; zoonosis

Funding

  1. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)
  2. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
  3. Fundacao de Amparo a Pesquisa de Minas Gerais (Fapemig)

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This study analyzed human leptospirosis in different regions of Brazil and found heterogenous distributions with higher incidence during the rainy season and influenced by precipitation. A forecast model predicted the number of cases for 2020 and provided incidence, mortality, and case fatality rates for the period.
Background Human leptospirosis is responsible for great losses and deaths, especially in developing countries, which can be mitigated by knowing the correct health indicators and climate influence on the disease. Methods Leptospirosis cases and deaths, population and precipitation were recovered from different databases (2007-2019). Annual incidence, mortality and case fatality rates (CFRs) of human leptospirosis and average precipitation were calculated for Brazil and its regions. Time series analysis using an moving average with external variable (ARMAX) model was used to analyse the monthly contribution and precipitation influence over leptospirosis cases for each Brazilian region and for the whole country. A forecast model to predict cases for 2020 was created for Brazil. Results Human leptospirosis exhibited heterogeneous distribution among Brazilian regions, with most cases occurring during the rainy season and precipitation influenced the disease occurrence in all regions but the South. The forecast model predicted 3276.99 cases for 2020 (mean absolute percentage error 14.680 and root mean square error 53.013). Considering the annual average for the period, the leptospirosis incidence was 1913 cases per 100 000 inhabitants, mortality was 0.168 deaths per 100 000 inhabitants and the CFR was 8.83%. Conclusions The models built can be useful for planning leptospirosis surveillance and control actions for the whole country and its regions and, together with the health indicators, revealed no uniform epidemiological situation of leptospirosis in Brazil.

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