Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. It is transmitted by female mosquitoes mainly of the species Aedes aegypti and is widespread in the tropics, with local variations in risk influenced by rainfall, temperature, relative humidity and unplanned rapid urbanization. The incidence of dengue has grown dramatically around the world in recent decades. The number of dengue cases reported to WHO increased over 8 fold over the last two decades, from 505,430 cases in 2000, to over 2.4 million in 2010, and 5.2 million in 2019. About 3.5 billion people, 55% of the world’s population living in tropical and subtropical regions are at risk, with about 50 million dengue infections occurring annually and approximately 500,000 requiring hospitalization annually. The combined impact of COVID-19 and dengue epidemics can potentially result in devastating consequences for the populations at risk.
Using the environmental data collected by various Government agencies this study aims to predict the number of dengue fever cases reported each week in two cities-San Juan, Puerto Rico and Iquitos, Peru.
The main objective of this study is to predict the number of dengue fever cases reported each week in San Juan, Puerto Rico and Iquitos, Peru using environmental data collected by various U.S. Federal Government agencies.
To examine the yearly trend of dengue fever. To examine the monthly trend of dengue fever. Week of the year with the most cases of dengue fever. To determine the relationship between temperature and no of cases. To determine the relationship between precipitation and no of cases. To determine the correlation between humidity and the number of cases.
- Google colab
- Python language
- Statsmodels
- Matplotlib
- Pandas