GCI2016: Mini-course 5: Analysis of Infectious Diseases Risk... - Lecture 2: Cory Morin

Mini-course 5: Analysis of Infectious Diseases Risk Using Weather, Climate, and
Climate Change Data
Cory Morin (NASA Marshall Space Flight Center)
Lecture 1: Weather/Climate Sensitive Infectious Diseases
Weather and climate can impact the transmission risk of water, food, vector, rodent, and
airborne pathogens through their sensitivities to temperature, precipitation, humidity,
wind, and other atmospheric variables. These drivers of disease transmission can also
interact, resulting in unexpected outcomes. Local, short-term atmospheric conditions,
“weather”, are constantly varying and thus can quickly and considerably change risk of
pathogen transmission. For example, heavy rains can cause flooding and contamination of
drinking water by waterborne pathogens such as V. cholerae. Longer term atmospheric
conditions, “climate”, can dictate the seasonality of diseases. For instance, pathogen vectors
(mosquitoes, flies, ect.) may propagate during specific seasons when optimal temperatures
or precipitation prevail. Furthermore, large scale variations in climate and climate change
can create novel conditions that expand or contract the range of a pathogen or disease. This
lecture will provide a basic survey of atmospherically sensitive infectious diseases and
discuss the mechanisms through which weather and climate influence and/or regulate their
ecology.
Lecture 2: Availability and Use of Weather, Climate, and Climate Change Data
There exist various sources and methods for collecting and using atmospheric data. The
choice of variables and datasets will depend on many factors including the application and
their availability. Weather/climate data can be recorded from weather stations and satellites
or calculated using algorithms or models. There are inherent challenges associated with all
sources of atmospheric data: data may not exist or it may be inaccessible, there may be
holes or recording interruptions in the data, and there is often a tradeoff between spatial
and temporal resolution. Application of climate change projections for use in disease
analysis can be especially difficult because of the inherent uncertainty associated in estimating future behavior. This lecture will introduce the many types of atmospheric data
including their pros and cons, sources for obtainment, and important considerations during
their application. Lastly, an overview of methods used by the Intergovernmental Panel on
Climate Change to generate climate change projections will be provided.
Lecture 3: Group Exercise with An Introduction to Systems Modeling
Systems modeling is a mechanistic (versus empirical) approach to modeling disease
transmission risk. Simulations provide a means of exploring sensitivities and thresholds of a
disease system when exposed to hypothetical situations such as varying weather/climate
conditions or increased/decreased transmission control scenarios. They can also be used as
forecasting tools when provided with weather/climate forecast/projection data. Building
these models also forces the researcher to reflect on the structure of the disease system and
its most important components. Unlike empirical models, they can reveal deeper
understanding of the disease’s ecology. During this exercise the group will work together
with the instructor to create a dynamic model for a theoretical disease. A brief overview of
systems modeling and its components will be provided. The exercise will be performed with
a user-friendly dynamic modeling program called Stella (© isee System). The group will
work together to build the model, run simulations using weather/climate input, and explore
the sensitivities of the system.