Please use this identifier to cite or link to this item: http://cris.unibe.edu.do/handle/123456789/204
Title: Malaria: bad air ─ Is climate a reliable predictor for malaria case distributions in the Dominican Republic?
Autores: Tapia-Barredo, Leandro
Arredondo-Abreu, César Alberto
Ruíz-Matuk, Carlos B.
Paulino-Ramírez, Robert
Researchers (UNIBE): Tapia-Barredo, Leandro 
Arredondo-Abreu, César Alberto 
Ruíz-Matuk, Carlos B. 
Paulino-Ramírez, Robert 
Affiliations: Instituto de Medicina Tropical y Salud Global (IMTSAG) 
Decanato de Investigación e Innovación (DII) 
Decanato de Investigación e Innovación (DII) 
Instituto de Medicina Tropical y Salud Global (IMTSAG) 
Research area: Ciencias de la Salud
Issue Date: 2019
Publisher: Royal Society of Tropical Medicine and Hygiene
Source: Abstracts from the 11th European Congress on Tropical Medicine and International Health, Royal Society of Tropical Medicine and Hygiene, 113 (Suppl. 1), S19
Journal: Transactions of the Royal Society of Tropical Medicine and Hygiene 
Volume: 113
Issue: Suppl 1
Start page: S19
Conference: 11th European Congress on Tropical Medicine and International Health, 16-20 September, Liverpool, United Kingdom
Abstract: 
Introduction: Malaria, caused by the Plasmodium spp parasite, has established endemicity in the Dominican Republic. Malaria cases reports follow a seasonality pattern throughout the tropical regions. With the United Nations’ goal to eradicate Malaria, understanding it’s infectious dynamics has taken a front stage in the fight against this disease. Aim: The aim of this study is to describe the relationship between climatologic factors and Malaria cases in Santo Domingo. Methods: Weekly malaria reports from January 2013 to December 2017 were extracted from the Ministry of Health database. Meanwhile, Maximum, Minimum and Mean Temperature, Rainfall and Relative Humidity were obtained from to the National Meteorological Office. Correlation of individual factors was calculated using 1 week, 4 weeks, and 24 weeks lag time to establish a relationship between disease and climate. Regressions for these lags were conducted to explain the combined variance explained by the climatologic factors. Results: At 1-week lag time, rain correlates with Malaria cases (tau = 0.10, p < 0.05). At a 4-week lag time, a negative correlation (tau= - 0.09, p < 0.05) exists between Humidity and Malaria cases reported. At 24 weeks lag, correlations between malaria cases and Mean Temperature (tau= -.10, p < 0.05) and maximum temperature (tau= - 0.10, p < 0.05) were found. Regression models carried out with each lag were not significant. Conclusion: Climatologic factors correlate with reported Malaria cases but fail to function as a predictive model for future disease. Strongest correlation occurs at a 24 week lag between malaria cases and temperature measures. Temperature proves to be a determining condition for Anopheles mosquito metabolic demand. Behavior factors related to malaria prevention is recommended to have a more comprehensive model.
URI: http://cris.unibe.edu.do/handle/123456789/204
DOI: 10.1093/trstmh/trz094
Appears in Collections:Publicaciones del IMTSAG-UNIBE

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