Nine challenges in modelling the emergence of novel pathogens

James O Lloyd-Smith,Sebastian Funk,Angela R Mclean,Steven Riley,James L.N Wood

doi:10.1016/j.epidem.2014.09.002

Abstract

Studying the emergence of novel infectious agents involves many processes spanning host species, spatial scales, and scientific disciplines. Mathematical models play an essential role in combining insights from these investigations and drawing robust inferences from field and experimental data. We describe nine challenges in modelling the emergence of novel pathogens, emphasizing the interface between models and data.

Highlights

While humankind continues to battle ancient adversaries such as tuberculosis and malaria, there is constant concern about the emergence of new human pathogens from sources in non-human animals (Jones et al, 2008)
We describe nine challenges in modelling the emergence of novel pathogens, emphasizing the interface between models and data
At the time of writing, influenza A H7N9 in China (Centers for Disease Control and Prevention, 2013) and MERS-Coronavirus in the Saudi Arabian peninsula (Penttinen et al, 2013) are both causing substantial numbers of cases, and deaths, and health authorities are searching for effective responses

Summary

Introduction

While humankind continues to battle ancient adversaries such as tuberculosis and malaria, there is constant concern about the emergence of new human pathogens from sources in non-human animals (Jones et al, 2008). A crucial distinction exists between pathogens that are capable of sustained human-to-human transmission in some settings (i.e. R0 > 1 in humans), and those that exhibit inefficient spread, with subcritical dynamics (i.e. 0 < R0 < 1) This latter group includes many pathogens viewed as significant future threats, such as influenza A H5N1, influenza A H7N9, MERS-CoV and monkeypox virus. Another group includes microbes detected by ‘pathogen discovery’ in various non-human animal populations (Lipkin and Firth, 2013), including many that are previously unknown to science (e.g. Anthony et al, 2013), the relevance of which is often unknown

Better capture the disease dynamics in proximal non-human species

Expand models for emerging infections to account for host immunity

Devise approaches to measuring infection fatality rates

Summary