Dengue incidence and climatic variables in Cali from 2015 to 2021

In this work we studied the relationship between dengue incidence in Cali and the climatic variables that are known to have an impact on the mosquito and were available (precipitation, relative humidity, minimum, mean, and maximum temperature). Since the natural processes of the mosquito imply that any changes on climatic variables need some time to be visible on the dengue incidence, a lagged correlation analysis was done in order to choose the predictor variables of count regression models. A Principal Component Analysis was done to reduce dimensionality and study the correlation among the climatic variables. Finally, aiming to predict the monthly dengue incidence, three different regression models were constructed and compared using de Akaike information criterion. The best model was the negative binomial regression model, and the predictor variables were mean temperature with a 3-month lag and mean temperature with a 5-month lag as well as their interaction. The other variables were..., Monthly dengue incidence data was provided by the Public Health Department of Cali. The climatic data was collected from the Hydrology, Meteorology and Environmental Studies Institute (IDEAM) webpage. Missing data was imputed using a random forest regression model., , # Dengue incidence and climatic variables in Cali from 2015 to 2021

[https://doi.org/10.5061/dryad.0zpc8675h](https://doi.org/10.5061/dryad.0zpc8675h)

Data reporting contains monthly dengue incidence in Cali from 2015 to 2021 and measurements of climatic variables related to the vectors biological processes.

The following are the climatic variables present in the data set:

Precipitation (january 2015 - december 2021) in mm^3

Mean temperature (january 2005 - february 2023) in °C

Maximum temperature (january 2005 - febreuary 2023) in °C

Minimum temperature (january 2005 - february 2023) in °C

Relative humidity (january 2010 - january 2021) in %

RandomForestRegression_Mean_temp is a Python notebook that was used for the data imputation.

## Description of the data and file structure

*Data reporting* is an excel file where all the data can be found. Each variable has its own sheet along with the date of recording for each observation. Missing values are coded with #N/A. The last ...

本研究探讨了卡利市登革热发病率与已知对蚊子有影响且可获取的气候变量（降水量、相对湿度、最低气温、平均气温及最高气温）之间的关系。由于蚊子的自然生命周期过程意味着气候变量的变化需经过一段时间才能在登革热发病率中体现，因此我们开展了滞后相关分析（lagged correlation analysis）以筛选计数回归模型（count regression models）的预测变量。此外，我们还进行了主成分分析（Principal Component Analysis）以降维并研究气候变量间的相关性。最后，为预测月度登革热发病率，我们构建了三种不同的回归模型，并利用赤池信息准则（Akaike information criterion）对其进行比较。其中最优模型为负二项回归模型（negative binomial regression model），其预测变量包括滞后3个月的平均气温、滞后5个月的平均气温及其交互作用。其他变量为...
卡利市登革热月度发病率数据由卡利公共卫生部门（Public Health Department of Cali）提供；气候数据来源于水文、气象与环境研究所（IDEAM）官网；缺失数据通过随机森林回归模型（random forest regression model）进行插补。

# 2015-2021年卡利市登革热发病率与气候变量

[https://doi.org/10.5061/dryad.0zpc8675h]

Data reporting包含2015-2021年卡利市登革热月度发病率数据，以及与媒介生物过程相关的气候变量观测值。

数据集中包含的气候变量如下：

降水量（2015年1月-2021年12月），单位：立方毫米（mm³）
平均气温（2005年1月-2023年2月），单位：摄氏度（℃）
最高气温（2005年1月-2023年2月），单位：摄氏度（℃）
最低气温（2005年1月-2023年2月），单位：摄氏度（℃）
相对湿度（2010年1月-2021年1月），单位：百分比（%）

RandomForestRegression_Mean_temp是一个用于数据插补的Python笔记本。

## 数据与文件结构描述

*Data reporting*是一个包含所有数据的Excel文件；每个变量均有独立工作表（sheet），且附有各观测值的记录日期；缺失值以#N/A编码；最后...