Update faq.mdx

Issue neherlab#687, cotninued: Improve styling of pages, optimize readability on /faq page

src/assets/text/faq.mdx

@@ -6,9 +6,9 @@ import youtubeTutorialThumbnail from '../img/youtube-tutorial-thumbnail.jpg'
 
 <Container>
 
-**Q:** How to use this tool? Is there a tutorial?
+**Q:** How do I use this tool? Is there a tutorial?
 
-**A:** _Tutorial video is available on YouTube:_
+**A:** _Our tutorial video is available on YouTube:_
 
 <Row>
   <Col className="d-flex">
@@ -17,61 +17,62 @@ import youtubeTutorialThumbnail from '../img/youtube-tutorial-thumbnail.jpg'
     </LinkExternal>
   </Col>
 </Row>
+
 ---
 
 **Q:** How do I refer to the tool in publications? Is there a paper?
 
-**A:** _We have posted a [preprint](https://www.medrxiv.org/content/10.1101/2020.05.05.20091363v2) describing covid19-scenarios.org
- on medrxiv. Please use this as a reference until our work appears in a journal.
- [doi: 10.1101/2020.05.05.20091363](https://doi.org/10.1101/2020.05.05.20091363 )_
+**A:** _We have posted a preprint describing covid19-scenarios.org on medRxiv. Please use this as a reference until our 
+work appears in a journal. [doi: 10.1101/2020.05.05.20091363](https://doi.org/10.1101/2020.05.05.20091363 )_
 
 ---
 
 **Q:** Why does the outbreak grow more slowly when I increase the infectious period?
 
-**A:** _The number of secondary cases a particular case causes is specified by R0. If you increase the infectious
-period, the same number of infections happen over a longer period. Hence the outbreak grows more slowly._
+**A:** _The number of secondary cases resulting from a particular case is specified by $R_0$. If you increase the infectious
+period, the same number of infections occur, but over a longer time period. Hence the outbreak grows more slowly._
 
 ---
 
 **Q:** Why is the number of severe cases lower than the number of critical cases?
 
-**A:** _COVID-19 cases in critical condition need intensive care for a long time. Our model assumes that they spend on
-average 14 days in the ICU. The severely ill (our proxy for those in need of a regular hospital bed) either deteriorate
-fast or recover (default in our model is four days). Hence at any given point in time, the number of critically ill
-people might exceed the number of severely ill._
+**A:** _Critical condition COVID-19 cases need intensive care for a long time. Our model assumes that they spend an
+average of 14 days in the ICU. Severely ill is our proxy for those in need of a regular hospital bed. These individuals will either deteriorate
+rapidly or recover (default recovery time in our model is 4 days). Therefore, at any given point in time, the number of critically ill
+people can exceed the number of severely ill._
 
 ---
 
 **Q:** Is the model fit to observations?
 
-**A:** _Yes, provided we have a good source of COVID-19 cases, we fit a few model parameters to observations.
-Specifically, we estimate R0, the initial size and date of the epidemic, and the case underreporting fraction. For case
-severity information, we utilize estimate from case outcome data from China._ Currently mitigation efforts, both the
-timing and the efficacy are _not_ estimated from the data. We are actively looking for user-provided dates for
-mitigation efforts for your regions of interest.
+**A:** _Yes, provided we have a good source of COVID-19 cases, we fit several model parameters to observations.
+Specifically, we estimate $R_0$, the initial size and date of the epidemic, and the case underreporting fraction. For case
+severity information we utilize an estimate from case outcome data from China._ 
+
+Note: Currently mitigation efforts, both the timing and the efficacy, are _not_ estimated from the data. 
+We are actively looking for user-provided dates of mitigation efforts for your region(s) of interest.
 
 ---
 
-**Q:** My country/region/town is missing!
+**Q:** My town/region/country is missing!
 
-**A:** _If you have suggestions on additional regions that should be covered, head over to
-[our data directory](https://github.com/neherlab/covid19_scenarios/tree/master/data) and make a pull request!_
+**A:** _If you have suggestions on additional regions that should be covered, head over to our 
+[GitHub data directory](https://github.com/neherlab/covid19_scenarios/tree/master/data) and make a pull request!_
 
 ---
 
-**Q:** What is ICU overflow?
+**Q:** What is "ICU overflow"?
 
-**A:** _In places that have seen severe COVID-19 outbreak, the capacity of intensive care facilities is quickly
-exhausted. Patients that need ventilation but can't get ventilation due to shortage will die faster. "ICU overflow" is
-our label for critically ill patients that should be ventilated but are not since no ventilators are available. These
-patients will die faster. The degree to which they die faster is specified by the `Severity of ICU overflow` parameter._
+**A:** _In places that have seen a severe COVID-19 outbreak, the capacity of intensive care facilities is quickly
+exhausted. Due to the resulting resource shortage, patients that need ventilation cannot get access. "ICU overflow" is
+our label for critically ill patients that should be ventilated, but are NOT, since no ventilators are available. These
+patients will die faster; the degree to which is specified by the `Severity of ICU overflow` parameter._
 
 ---
 
-**Q:** Wouldn't it be a good idea to model isolation of specific age-groups?
+**Q:** Wouldn't it be a good idea to model the isolation of specific age-groups?
 
-**A:** _Yes! This is indeed possible on covid-scenarios. Expand the card
+**A:** _Yes! This is indeed possible on [covid-scenarios](https://covid19-scenarios.org/). Expand the card
 `Severity assumptions and age-specific isolation`. The last column allows you to specify to what extent individual age
 groups are isolated from the rest of the population._
 
@@ -81,51 +82,51 @@ groups are isolated from the rest of the population._
 
 **A:** _You are probably referring to the
 [March 16 report by Neil Ferguson et al](https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf).
-Like us, Ferguson et al model the effect of interventions on the spread of COVID-19 using a computational model. Their
+Like us, Ferguson et al. use a computational model to investigate the effect of interventions on the spread of COVID-19. Their 
 model is individual based, meaning their program represents a large number of individuals among whom the virus is
-spreading. Our model breaks the population into age-groups and different categories corresponding to susceptible,
-infected, dead, recovered, etc. This allows for faster simulations, but loses some realism. The faster simulation allows
-exploration of parameters._
+spreading. Our model breaks the population into age-groups and different categories corresponding to those susceptible,
+infected, dead, recovered, etc. While this may lose some realism, our model type allows for faster simulations and
+exploration of various parameters._
 
 ---
 
 **Q:** What do the parameter ranges correspond to?
 
-**A:** _Parameter ranges allow the user to specify the distribution of possible values. We assume a uniform prior, i.e.
-any value within the range has an equal probability of being chosen. The model is then run from a Monte Carlo sampling from
-all specified parameter ranges for user tunable number of samples. The median trajectory, as well as the 20% and 80% percentiles
+**A:** _Parameter ranges allow the user to specify the distribution of possible values. We assume a uniform prior, i.e.,
+any value within the range has an equal probability of being chosen. The model is then run with a Monte Carlo sampling from
+all specified parameter ranges for user-specified number of samples. The median trajectory, as well as the 20th and 80th percentiles,
 are displayed as the shaded uncertainty region._
 
 ---
 
 **Q:** Why do the curves sometimes have strange kinks?
 
 **A:** _The curves show the median of a number simulations sampled from the parameter ranges. If the simulations
-corresponding to different parameter combinations intersect, the curve representing the median can change
+corresponding to different parameter combinations intersect, the curve representing the median can change,
 resulting in a kink._
 
 ---
 
 **Q:** What is a good number of simulations to run?
 
-**A:** _We chose 15 as the default as this is a good balance between sufficient sampling and maintaining interactivity.
-Once you find a set of parameters that you find reasonable, we suggest to increase the number to more accurately capture
+**A:** _We chose 15 as the default number of simulations to run. This is a good balance between sufficient sampling and maintaining interactivity.
+Once you have discovered a set of parameters that you find reasonable, we suggest increasing the number to more accurately capture
 the statistics._
 
 ---
 
 **Q:** Can I run a simulation with no uncertainty?
 
-**A:** _If you set the lower and upper bounds of each parameter to the same value, you'll only get one curve
-and no uncertainty bands. In this case the number of simulation runs is ignored._
+**A:** _If you set the lower and upper bounds of each parameter to the same value, your result will include only get one curve
+and no uncertainty bands. Note that in this case the number of simulation runs is ignored._
 
 ---
 
-**Q:** How to interpret the figures reported under "Proportions"? Is a fatal case also counted as critical or severe case? Is a critical case also counted as severe case?
+**Q:** How do I interpret the figures reported under "Proportions"? Is a fatal case also counted as critical or severe case? Is a critical case also counted as severe case?
 
-**A:** _A: These numbers sum to 100%. The number reported as "critical" is the fraction of infected people that fall critically ill but didn't die.
-Similarly, the reported fraction of "severe" cases is the fraction of cases that get severely ill but don't need critical care.
-In summary, these numbers report the most serious outcome for each case._
+**A:** _These numbers sum to 100%. The number reported as "critical" is the fraction of infected people that fall critically ill but did not die.
+Similarly, the reported fraction of "severe" cases is the proportion of cases that were severely ill but did not need critical care.
+In essence, these numbers report the most serious outcome for each case._
 
 
 </Container>