| Full text | |
| Author(s): Show less - |
Musah, Anwar
;
Dutra, Livia Marcia Mosso
;
Aldosery, Aisha
;
Browning, Ella
;
Ambrizzi, Tercio
;
Borges, Iuri Valerio Graciano
;
Tunali, Merve
;
Basibuyuk, Selma
;
Yenigun, Orhan
;
Moreno, Giselle Machado Magalhaes
;
da Silva, Ana Clara Gomes
;
dos Santos, Wellington Pinheiro
;
de Lima, Clarisse Lins
;
Massoni, Tiago
;
Jones, Kate Elizabeth
;
Campos, Luiza Cintra
;
Kostkova, Patty
Total Authors: 17
|
| Document type: | Journal article |
| Source: | DATA; v. 7, n. 8, p. 13-pg., 2022-08-01. |
| Abstract | |
Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction. (AU) | |
| FAPESP's process: | 20/11567-5 - Data driven early warning system and community engagement for better control mosquito populations in Brazil |
| Grantee: | Giselle Machado Magalhães Moreno |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 19/23553-1 - Mosquitoes populations modelling for early warning system and rapid response public by health authorities correlating climate, weather and spatial-temporal mobile surveillance data |
| Grantee: | Tercio Ambrizzi |
| Support Opportunities: | Regular Research Grants |