Update Readme with new title

README.Rmd

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-# {{ packagename }}: A library of compartmental epidemic scenario models <img src="man/figures/logo.svg" align="right" width="130"/>
+# {{ packagename }}: Composable epidemic scenario modelling <img src="man/figures/logo.svg" align="right" width="130"/>
 
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 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/license/mit/)
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 [![CRAN status](https://www.r-pkg.org/badges/version/{{ packagename }})](https://CRAN.R-project.org/package={{ packagename }})
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-_{{ packagename }}_ is an R package that provides a convenient interface to a library of compartmental models that can help to model epidemic scenarios for directly transmitted respiratory infections such as influenza or Covid-19 as well haemorrhagic fevers such as Ebola virus disease.
+_{{ packagename }}_ is an R package that provides modular representations of populations and public health response measures, allowing them to be combined with epidemiological model structures curated from the published literature, to conveniently compose and compare epidemic scenario models.
 
-The models in _{{ packagename }}_ implement methods outlined in @bjornstad2020a and @bjornstad2020.
+The models in _{{ packagename }}_ focus on directly transmitted infections, and implement methods outlined in @bjornstad2020a and @bjornstad2020.
 The models in _{{ packagename }}_ can help provide rough estimates of the course of epidemics, and the effectiveness of pharmaceutical and non-pharmaceutical interventions.
 
 _{{ packagename }}_ relies on [Eigen](https://gitlab.com/libeigen/eigen) via [{RcppEigen}](https://cran.r-project.org/package=RcppEigen), and on [Boost Odeint](https://www.boost.org/doc/libs/1_82_0/libs/numeric/odeint/doc/html/index.html) via [{BH}](https://cran.r-project.org/package=BH), and is developed at the [Centre for the Mathematical Modelling of Infectious Diseases](https://www.lshtm.ac.uk/research/centres/centre-mathematical-modelling-infectious-diseases) at the London School of Hygiene and Tropical Medicine as part of the [Epiverse-TRACE initiative](https://data.org/initiatives/epiverse/).
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 ## Package models
 
-_{{ packagename }}_ currently provides three models:
+_{{ packagename }}_ provides a convenient interface to a library of compartmental models that can help to model epidemic scenarios for directly transmitted respiratory infections such as influenza or Covid-19 as well haemorrhagic fevers such as Ebola virus disease:
 
-1. A deterministic SEIR-V model with susceptible, exposed, infectious, recovered, and vaccinated compartments (SEIR-V), allowing for heterogeneity in social contacts, the implementation of a group-specific non-pharmaceutical intervention that reduces social contacts, and a vaccination regime with group-specific start and end dates,
+1. A deterministic SEIR-V model with susceptible, exposed, infectious, recovered, and vaccinated compartments (SEIR-V), allowing for heterogeneity in social contacts, the implementation of a group-specific non-pharmaceutical intervention that reduces social contacts, and a vaccination regime with group-specific start and end dates;
 
-2. The deterministic Vacamole model developed at [RIVM, the Dutch Public Health Institute](https://www.rivm.nl/) for the Covid-19 pandemic, with a focus on scenario modelling for hospitalisation and vaccination [@ainslie2022],
+2. The deterministic Vacamole model developed at [RIVM, the Dutch Public Health Institute](https://www.rivm.nl/) for the Covid-19 pandemic, with a focus on scenario modelling for hospitalisation and vaccination [@ainslie2022];
 
-3. A stochastic, discrete-time, compartmental SEIR model suitable for modelling haemorrhagic fevers such as Ebola Virus Disease, including hospitalisation and hospital and funeral transmissions, adapted from @li2019 and @getz2018,
+3. A stochastic, discrete-time, compartmental SEIR model suitable for modelling haemorrhagic fevers such as Ebola Virus Disease, including hospitalisation and hospital and funeral transmissions, adapted from @li2019 and @getz2018;
 
 4. An initial implementation of a compartmental model for diphtheria in the context of internally displaced persons camps, including a reporting rate, hospitalisation rate, and delays in entering and leaving hospital, taken from @finger2019.