2. Module II: Integrating Epidemiological Data with Climate and Environmental Data

2.1. Lecture 1: From Data to Decision–making - and back again

2.1.1. Instructional Goal

Provide an overview of basic concepts that connect data to decision-making.

2.1.2. Learning Objectives

  • Understand the relationship between data and decision-making.
  • Explore how this understanding can help create evidence for policy and practice.
  • Learn how these perspectives have been developed in the context of malaria prevention and control.

2.1.3. Summary

It is widely assumed (and there is some supporting evidence) that more data and better information can result in better decisions but there is an increasing understanding that there are limitations to this rational view – mainly because decisions are made by people.

In this lecture we explore the implicit and explicit assumptions of the relationships between identification of data, data collection, analysis, interpretation, communication, decision-making and implementation. In addition we will consider what influences the use of data to make decisions rather than judgment or intuition. Using practical examples we will discuss the use of data in the creation of policy relevant evidence for improved control of climate sensitive vector-borne diseases.

2.1.6. Quiz

Quiz 1

2.2. Lecture 2: Introduction to Remote Sensing and Satellite Imagery

2.2.1. Instructional Goal

Introduce the concepts of remote sensing and provide information on how to retrieve environmental factors using remotely-sensed products.

2.2.2. Learning Objectives

  • Understand remote sensing as a tool to monitor environmental data.

  • Know the remote sensing products available to monitor climate and environmental data.

  • Understand the methodology to retrieve:
    • Rainfall
    • Air Temperature
    • Vegetation
    • Water bodies
  • Learn how to use the Map Room to:
    • Visualize data on rainfall, temperature, vegetation and water bodies
    • Extract time series
    • Extract anomalies
    • Download data
    • Download images
    • Integrate images into ArcView®

2.2.3. Summary

Remote sensing is the science of obtaining information about an object through the analysis of data acquired by a device (sensor) that is not in contact with the object (remote). As you read these words, you are employing remote sensing. Your eyes are acting as sensors which analyze the electromagnetic waves (visible light) reflected from this page. The light your eyes acquire is analyzed in your mental computer to enable you to explain the words. Apart from the eyes, more sophisticated sensors have been developed to measure the electromagnetic waves in domains outside the visible. By measuring the electromagnetic waves in domains from Gamma rays to Microwaves, we can retrieve information on objects we want to study.

2.2.4. Recommended readings

  • Ceccato P, Dinku T. Introduction to Remote Sensing for Monitoring rainfall, temperature, vegetation and water bodies: Introduction to remote sensing
  • Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk. Parassitologia 2005;47(1):81-96: Application of GIS and remote sensing

2.2.5. Presentation

2.2.6. Quiz

Quiz 2

2.3. Lecture 3: Introduction to the IRI Data Library

2.3.1. Instructional Goal

Participants will be introduced to the IRI Data Library and gain an initial understanding of its contents, structure, and capabilities, and how it may be applied as a useful tool for analyzing climate and health data.

2.3.2. Learning Objectives

  • Become familiar with the organization of the Data Library and its data sets.
  • Learn how to find data sets and select spatial and temporal domains.
  • Learn how to download data and images.
  • Learn how the Data Library is related to the IRI Map Rooms.

2.3.3. Summary

The IRI Data Library is a powerful online resource for accessing, analyzing, visualizing, and downloading climate-related data sets. It is capable of relating different types of data sets (e.g. gridded data, station data, geographic shapes) in a common data model such that relationships between gridded climate data and health data collected by geographic region, for example, can be analyzed. Specialized map and analysis tools in the IRI Map Rooms have been developed using Data Library functionality to meet specific needs in the health community and other sectors. This session provides an introduction to the IRI Data Library.

2.3.4. Recommended readings

The IRI Data Library: A Tutorial: http://iridl.ldeo.columbia.edu/dochelp/Tutorial/

2.3.5. Presentation

2.3.6. Quiz

Quiz 3

2.4. Malaria Control in Eritrea: Case study

Eritrea is a malaria epidemic-prone country in the horn of Africa. To improve epidemic control in climate sensitive regions, the World Health Organization (WHO) has proposed a framework for the development of integrated malaria early warning systems (MEWS) based on vulnerability monitoring, seasonal climate forecasting, environmental and meteorological monitoring, and epidemiological surveillance. This case study exercise will examine the contents of this framework: