From ec203f9fd59a007cb0ebc23336c3d0ccf1bac923 Mon Sep 17 00:00:00 2001 From: Samantha Hamilton <67113216+samanthahamilton@users.noreply.github.com> Date: Mon, 3 Aug 2020 11:14:46 -0400 Subject: [PATCH] Update faq.mdx Issue #687, cotninued: Improve styling of pages, optimize readability on /faq page --- src/assets/text/faq.mdx | 85 +++++++++++++++++++++-------------------- 1 file changed, 43 insertions(+), 42 deletions(-) diff --git a/src/assets/text/faq.mdx b/src/assets/text/faq.mdx index 267ea05ef..9344bd46b 100644 --- a/src/assets/text/faq.mdx +++ b/src/assets/text/faq.mdx @@ -6,9 +6,9 @@ import youtubeTutorialThumbnail from '../img/youtube-tutorial-thumbnail.jpg' -**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:_ @@ -17,61 +17,62 @@ import youtubeTutorialThumbnail from '../img/youtube-tutorial-thumbnail.jpg' + --- **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._ @@ -81,19 +82,19 @@ 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._ --- @@ -101,31 +102,31 @@ 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._