QUANTITATIVE RESEARCH

 

In science, the majority of impactful advances are made through quantitative research.

Quantitative research leverages different methods and datasets that are then formally integrated to address the most interesting questions.

 

Disease ecology is one of the fields known for its innovative use of quantitative research, as it builds on field observations, field experiments, laboratory assays, statistics and mathematical modelling.

 

This kind of science is exhilarating, and is moving this field forward at a high pace!

 

A great example of this is the seminal 2012 Ecology Letters paper (one of my favorites) by Olivier Restif and colleagues, which nicely outlines how model-guided field work can lead to the best insights. 

(The idea is that the design of field work should be informed by models, even very simple ones, so that the most important aspects of the model can be addressed.  New insights from field data can then lead to better models, which can lead to new model-guided field work, etc.)

 

The most exciting aspect of research, to me, is this idea of quantitative research.  It forces one to keep learning, keep looking for new methods that can lead to deeper insights.  Sometimes that means setting up and conducting a large field experiment in Tanzania, while other times it could mean developing new laboratory methods to improve assays, or develop new statistical methods.  

An example of quantitative research are the different steps of a project we conducted during my PhD, in order to find out how rodent population density affects virus transmisison:

1

Develop simple mathematical model of virus transmission based on existing field data.

This identified the contact-density relationship as the most important missing parameter.

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Multimammate mouse, host of pathogens such as the plague-causing Y. pestisLeptospira, and African arenaviruses such as Lassa virus.

2

Design and perform field experiment in Tanzania, where contacts between rodents were measured at a range of densities.

This provided the missing contact-density relationship, which turned out to have a sigmoidal shape.

3

Develop a more complex mathematical model of virus transmission that incorporates density-dependent transmission.

This model was calibrated using existing long-term field data.

4

Perform model simulations in which long-term persistence of the virus in a fluctuating rodent population is simulated for a range of different shapes of the contact-density relationship. 

We learned that the exact shape can be crucial for virus persistence and transmission.

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Field enclosures in Tanzania