Fix typos

posts/mpox-preparedness/index.qmd

@@ -22,12 +22,12 @@ The Epiverse-TRACE team is developing a set of tools to help provide analytics s
 
 Outbreak analytics in the context of the ongoing mpox outbreak involves several tasks that can be handled by existing and emerging R tools. Some of the tasks include estimating the transmission potential, forecasting infection dynamics, estimating severity, and assessing the impact of interventions.
 
-Here, we briefly describe the data required some common tasks, and the ready tools developed by the Epiverse-TRACE team and the wider community.
+Here, we briefly describe the data required for some common tasks, and the ready tools developed by the Epiverse-TRACE team and the wider community.
 
 ### Cleaning and validating data
 
 ::: {.callout .callout-info}
-Data cleaning is often the first task in outbreak analytics. This usually involves identifying and correcting errors in the data, standardizing the format of key variables, and ensuring that the data is in a format that can be used for analysis. Data validation is also important to ensure that the data is accurate.
+Data cleaning is often the first task in outbreak analytics. This usually involves identifying and correcting errors in the data, standardizing the format of key variables, and ensuring that the data is in a format that is fit for analysis using R. Data validation is also important to ensure that the data is accurate.
 :::
 
 [`{cleanepi}`](https://epiverse-trace.github.io/cleanepi/)} is useful for cleaning individual-level datasets, and [`{listlist}`](https://epiverse-trace.github.io/linelist/)} can be used to tag and validate key variables in datasets that might change overtime. The [`{numberize}`](https://epiverse-trace.github.io/numberize/) package can also be used to convert numbers written as text. It currently has functionality for English, Spanish, and French.
@@ -44,11 +44,11 @@ The basic reproduction number, $R0$, is the average number of secondary cases pr
 
 If data is available on the daily number of reported cases, [`{EpiNow2}`](https://epiforecasts.io/EpiNow2/) and  [`{EpiEstim}`](https://mrc-ide.github.io/EpiEstim/) can be used to estimate the time varying reproduction number, $Rt$. These packages require data on the time scale of transmission (i.e. the generation time, or the serial interval, which is [commonly used as a proxy](https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0756) for this). While [`{EpiEstim}`] focuses on retrospective estimation of $Rt$, `{EpiNow2}` is designed for both retrospective and real-time estimation.
 
-In estimating $Rt$, one practical consideration is the impact of various delays (biological and reporting) on the estimates [@charniga2024best;@park2024estimating;@gostic2020practical]. `{EpiNow2}` adjusts for these delays in various ways. For example, it accounts for the symptom onset and reporting delays by taking the incubation period and reporting delay as inputs. Moreover, `{EpiNow2}` can estimate estimate the reporting delay from the data if data on incidence by date of onset and report are available.
+In estimating $Rt$, one practical consideration is the impact of various delays (biological and reporting) on the estimates [@charniga2024best;@park2024estimating;@gostic2020practical]. `{EpiNow2}` adjusts for these delays in various ways. For example, it accounts for the symptom onset and reporting delays by taking the incubation period and reporting delay as inputs. Moreover, `{EpiNow2}` can estimate the reporting delay from the data if data on incidence by date of onset and report are available.
 
 Furthermore, dedicated packages have emerged for estimating epidemiological delays from data using best practices. [`{epidist}`](https://epidist.epinowcast.org/articles/epidist.html) offers the ability to estimate delay distributions, accounting for issues such as truncation (i.e., not all disease outcomes will yet be known in real-time).
 
-If delay data are not available, published estimates of the incubation period and serial interval can be used. The [`{epiparameter}`](https://epiverse-trace.github.io/epiparameter/) package collates a database of epidemiological distributions from the literature and provides functions for interacting with the database. See the [database here](https://epiverse-trace.github.io/epiparameter/dev/articles/database) for currently available parameters (more entries are planned). Additionally, if only summary statistics are available (e.g. range and median), `{epiparameter}` can be used to extract the distribution parameters.
+If delay data are not available, published estimates of the incubation period and serial interval can be used. The [`{epiparameter}`](https://epiverse-trace.github.io/epiparameter/) package collates a database of epidemiological distributions from the literature and provides functions for interacting with the database. See the [database](https://epiverse-trace.github.io/epiparameter/dev/articles/database) for currently available parameters (more entries are planned). Additionally, if only summary statistics are available (e.g. range and median), `{epiparameter}` can be used to extract the distribution parameters.
 
 #### Individual level (superspreading)