Statistical analysis of correlations between climate changes and infectious disease

This is an ongoing research effort looking at correlations between climate parameters like precipitation and the incidence of leptospirosis in Brazil. Future work might expand the number of examined parameters.

Included are data sets in this area, currently organized as an ad-hoc in-memory database in the Rust programming language. Work is ongoing to add more data sets and do statistical analysis.

Also included is a database of the Brazilian regions, states, state capitals and some other municipalities.

This re-evaluates and expands upon the work of de Queiroz, S. J., & de Andrade, M. (2021), Distribuição temporal da leptospirose nas macrorregiões brasileiras e sua relação com a pluviosidade, Saúde Coletiva (Barueri), 8(45), 893–898. (Translated title: "Temporal distribution of leptospirosis in the Brazilian macro-regions and its relationship with rainfall".)

The Rust programming language is used in an experiment to informally evaluate its suitability for this kind of data research. Anticipated advantages are preventing mistakes thanks to the ease of liberal use of explicit data typing, and speed of evaluation which may favor advanced analysis techniques. Also, this work may be partially reused in or grown into a more general simulation application around climate data, infectious diseases, and perhaps other uses, for which Rust as a systems programming language has additional advantages (e.g. running directly in the Web browser). Let us know if you are a researcher or data analyst and interested in an experience exchange.

Data sources

• Data on notifications of confirmed cases of leptospirosis from 2005 to 2014 were obtained from SINAN (Brazil's notifiable diseases information system).
• Data on the population and characteristics of Brazilian capitals were obtained from the website of the Brazilian Institute of Geography and Statistics, 2010 census (IBGE, 2016).
• The accumulated precipitation data per Brazilian capital were obtained from the website of the national institute of meteorology (INMET).
• Data on environmental and socioeconomic factors were obtained from Atlas Brasil (2010).

Authors / contact

This is an independent collaboration with the following contributors:

Programming and statistic evaluation: Christian Jaeger [email protected]

Scientific guidance and support: Monica de Andrade [email protected], UNIPAM.