# Collection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde

## TL;DR

This paper collects entomological, demographic, water, sanitation, and climate data in Praia, Cabo Verde, to support surveillance and prediction of mosquito-borne disease outbreaks.

## Contribution

The study provides a comprehensive dataset from Praia for modeling arbovirus outbreak risks using spatial and non-spatial indicators.

## Key findings

- Data were collected from 40 sentinel points in Praia from June to November 2022.
- The dataset supports predictive modeling of arbovirus outbreak risks using statistical models.
- The study highlights the utility of GBIF in transforming occurrence data into actionable surveillance information.

## Abstract

Vector-borne diseases, primarily those transmitted by mosquitoes, are a serious public health problem. Some, such as dengue, put half of the world’s population at risk. Combating these diseases requires multifaceted strategies, with vector surveillance and control playing key roles. Robust and predictive surveillance systems for vector-borne diseases, based on risk stratification, enable the implementation of appropriate interventions across time and space. Here, we present a collection of entomological, demographic, water and sanitation, and climatic data from Praia (Cabo Verde), a hotspot for mosquito-borne diseases. These data were collected from June to November 2022, at 40 sentinel points scattered across the urban area of Praia. They constitute a valuable source of information for developing predictive scenarios of arbovirus outbreak risk using statistical models applied to spatial and non-spatial indicators. These data demonstrate the utility of GBIF in transforming large volumes of occurrence data into valuable information for arbovirus surveillance and vector control.