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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
<title><em>simex</em>: a disease modelling tool for decision makers</title>
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<body>
<div class="include-before">
</div>
<div class="frontmatter">
<div class="title"><h1><em>simex</em>: a disease modelling tool for decision makers</h1></div>
<div class="author"><h2></h2></div>
<div class="date"><h3></h3></div>
</div>
<div class="body">
<h2 id="simex-a-disease-modelling-tool-for-decision-makers"><em>simex</em>: a disease modelling tool for decision makers</h2>
<p><em>simex</em> is an R package for simulating the spread of infectious diseases, as
well as interventions such as social distancing measures, isolation and
vaccination. It uses an age-structured SEIR compartmental model with
country-specific age demographics and contact rates.</p>
<pre><code>## Error in eval(expr, envir, enclos): object 'opts_chunk' not found
</code></pre>
<h2 id="installation">Installation</h2>
<p>To install the development version from github:</p>
<pre><code class="language-r">remotes::install_github("finlaycampbell/simex", dependencies = TRUE, force = TRUE)
</code></pre>
<p>Load the package using:</p>
<pre><code class="language-r">library("simex")
</code></pre>
<h2 id="running-simex">Running <em>simex</em></h2>
<h3 id="parameters-and-settings">Parameters and settings</h3>
<p>Most settings are specified via the <code>get_parameters</code> function. The arguments and
their default values are given below:</p>
<table>
<thead>
<tr>
<th align="left">Argument</th>
<th align="left">Description</th>
<th align="left">Default value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">iso3</td>
<td align="left">The ISO3 code of the country used to draw age-distributions and contact rates from.</td>
<td align="left">“USA”</td>
</tr>
<tr>
<td align="left">R0</td>
<td align="left">The basic reproduction number.</td>
<td align="left">2.5</td>
</tr>
<tr>
<td align="left">generation_time</td>
<td align="left">The mean generation time in days.</td>
<td align="left">7</td>
</tr>
<tr>
<td align="left">incubation_period</td>
<td align="left">The mean incubation period in days.</td>
<td align="left">3</td>
</tr>
<tr>
<td align="left">infectiousness_presymp</td>
<td align="left">Relative infectiousness of presymptomatic cases to symptomatic cases.</td>
<td align="left">0.25</td>
</tr>
<tr>
<td align="left">frac_symp</td>
<td align="left">The proportion of cases that eventually develop symptoms.</td>
<td align="left">0.8</td>
</tr>
<tr>
<td align="left">ifr</td>
<td align="left">Infection fatality rate provided either as a single value or as a vector of the same length as the number of age categories. Use the function ‘age_to_ifr’ to calculate a COVID-like IFR from a vector of ages.</td>
<td align="left">age_to_ifr(get_age_median())</td>
</tr>
<tr>
<td align="left">hosp_mortality</td>
<td align="left">The probability of death given a case is admitted to hospital, either as a single value or as a vector of the same length as the number of age categories. The inverse of the number of cases admitted to hospital per death.</td>
<td align="left">1/seq(20, 5, length = 16)</td>
</tr>
<tr>
<td align="left">hosp_protection_death</td>
<td align="left">Given a case requires hospitalisation, the proportion of deaths admission to hospital averts.</td>
<td align="left">0.75</td>
</tr>
<tr>
<td align="left">hosp_duration</td>
<td align="left">The mean duration of stay in the hospital in days, either as a single value or as a vector of the same length as the number of age categories.</td>
<td align="left">seq(7, 21, length = 16)</td>
</tr>
<tr>
<td align="left">hosp_capacity</td>
<td align="left">Total hospital bed capacity given as a proportion of the population.</td>
<td align="left">0.0025</td>
</tr>
<tr>
<td align="left">comm_mortality</td>
<td align="left">The probability of death of cases that remain in the community, either as a single value or as a vector of the same length as the number of age categories.</td>
<td align="left">0</td>
</tr>
<tr>
<td align="left">vax_rate</td>
<td align="left">The daily rate of vaccination as a proportion of the population.</td>
<td align="left">0</td>
</tr>
<tr>
<td align="left">vax_infectiousness</td>
<td align="left">The reduction (as a proportion) in infectioussness of an individual due to vaccination.</td>
<td align="left">0.3</td>
</tr>
<tr>
<td align="left">vax_infection</td>
<td align="left">The protection (as a proportion) against infection provided by vaccination.</td>
<td align="left">0.5</td>
</tr>
<tr>
<td align="left">vax_hosp</td>
<td align="left">The protection (as a proportion) against hospitalisation provided by vaccination, given infection.</td>
<td align="left">0.5</td>
</tr>
<tr>
<td align="left">vax_death</td>
<td align="left">The protection (as a proportion) against death provided by vaccination, given hospitalisation.</td>
<td align="left">0.8</td>
</tr>
<tr>
<td align="left">isolation_adherence</td>
<td align="left">The proportion of symptomatic individuals that adhere to isolation measures.</td>
<td align="left">0</td>
</tr>
<tr>
<td align="left">isolation_effectiveness</td>
<td align="left">The reduction in daily transmission potential of a given individual due to adherence to isolation measures.</td>
<td align="left">0.8</td>
</tr>
<tr>
<td align="left">isolation_delay</td>
<td align="left">The mean delay from symptom onset to isolation in days.</td>
<td align="left">3</td>
</tr>
<tr>
<td align="left">social_distancing</td>
<td align="left">A named vector of length 4 containing the proportion reduction in contacts due to social distancing of ‘home’, ‘school’, ‘work’ and ‘other’.</td>
<td align="left">c(home = 0, school = 0, work = 0, other = 0)</td>
</tr>
<tr>
<td align="left">vax_prioritised</td>
<td align="left">A logical indicating whether older age groups are vaccinated first.</td>
<td align="left">TRUE</td>
</tr>
<tr>
<td align="left">hosp_prioritised</td>
<td align="left">A logical indicating whether older age groups are hospitalised first when hospital capacity is exceeded.</td>
<td align="left">TRUE</td>
</tr>
</tbody>
</table>
<h3 id="running-default-settings">Running default settings</h3>
<p>To run the model using default settings, specify a parameter object <code>par</code> and
feed this into the <code>run_model</code> function.</p>
<pre><code class="language-r">## set parameters using defaults
pars <- get_parameters()
## run model using default parameters
output <- run_model(pars)
## look at output
print(output)
</code></pre>
<pre><code>##
## [Simex Object]
## - Days: 1 to 365
## - Age Categories: age_1 to age_16
## - Compartments: S_u | E_u | C_u | H_u | R_u | D_u | S_v | E_v | C_v | H_v | R_v | D_v
</code></pre>
<h3 id="visualising-outputs">Visualising outputs</h3>
<p>To visualise the results, use the generic <code>plot</code> function defined for the
<code>simex</code> class. Below, we first visualise prevalence and then incidence,
specified using the <code>what</code> argument.</p>
<pre><code class="language-r">## visualise prevalence
plot(output, what = "prevalence")
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6" />
<p class="caption">plot of chunk unnamed-chunk-6</p>
</div>
<pre><code class="language-r">## visualise incidence
plot(output, what = "incidence")
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAH4CAMAAACVNaKBAAABNVBMVEUAAAAAADoAAGYAOmYAOpAAZmYAZrYZGUgZGXEZSJcZcboaGho6AAA6ADo6AGY6OgA6OmY6OpA6ZmY6ZrY6kNtIGRlISJdIcbpIl91NTU1NTW5NTY5NbqtNjshmAABmADpmAGZmOgBmOjpmOmZmOpBmtv9uTU1uTY5ubqtuq6tuq+RxGUhxGXFxuv+OTU2OTY6ObquOjsiOq+SOyP+QOgCQOjqQ2/+XSBmXSEiX3f+rbk2rbo6rjqur5P+2ZgC2Zjq2tma225C2/7a2//+6cRm6cUi6///Ijk3Ijm7IyP/I/+TI///bkDrb25Db/7bb/9vb///dl0jd///kq27kq47k5Kvk/8jk///r6+v/tmb/unH/urr/yI7/25D/3Zf/5Kv//7b//7r//8j//9v//93//+T////p6+mLAAAACXBIWXMAAAsSAAALEgHS3X78AAAd7UlEQVR4nO2dDX/dtnXGGc/ztjjLmsXtehPX8drZSbXWcWLHW5ta3aI0TmZlc6quljwrUq5rfP+PML7eSxBvBzggCZDP01+tCALAQ+LPQwAEDgsBQQwVcxsA5S0ABLEEgCCWABDEEgCCWAJAEEsACGIJAEEsASCIJQAEsQSAIJZmBOjL69ffnO/oYXpxvdSv57YiVN//OL7x8wH0oqTny9za4sVP57aAoR9+9ZX44V9/E7fS+QD6/id/nO3YwcoaoFGMn/ERVj4O/i43hqpH2N9+NbcVgfpuDH8/byf6u9xu6Pw90P99FbfS+QD67k0ANK2q/s9f/i2yG5p3FJblI+x6thAtaxQGLUIACGIJAEEsGQF6/dvNe4+bnz8TF3fff/r6d88ntAvKREaAtp80P18eiCcPTsr/vXwwlU1QRjICdHF3c7NyOX96XDJUeqD/2jmg0xRFO1s1m6YgKSleJroSsFVNMgJUep6LT8ufX1cAVb8PHNCoVoVmcml+C9cDkBANOH9qAHr9u/+9W/eJJrEKAOmUgK0eAJ08aJxO3QeqHFD5v4OprAJAOiVgqwdA9ehre/95/bP6j+09eKAxMtGVgK1+jzCrAFCsyslKwNbVAjT3IFErD/vpVwUAcTK5NL+FM3igAgCRM7kUcqSiVM4AVfYTygEgigCQsRwAoigUIEoLJApQobUfAOkzuRRwJH0DAKBd/WommgBQhMr9FP4IA0BzA0RogUQBggfyyeQSADKWA0AUrQ8gsvkTAjT3BK5WRNMJKYsEaGg/PJA+k0sAyFgOAFEEgIzlABBFAMhYDgBR5H8kQwMAoN0B1Ew0ASB+5Z4CQHHLASAAxCo3KUDuFogB0Em99aXe06nZywmA4pabDCDdiohRAHqyqQGq93Rq9nICoLjlFgfQ9pvGA9V7OiPt5ewAijdlu1N7gSwnZBUA4leuqAGo29MZtJcTHoiTyaU8ABKCsZcTAHEyuZQHQO2ezrC9nAbzC0um0MqbI6iZaAJA/MoVlehw93JqzD+dCaCLj6o+nCk+UJoA2cfB/kdqLv1kANmVGUCvf1uHdzHFB0oSIMc4GACZy8UH6OSX93OLDxR/HNyMgEcdB9OVF0AXH/25Biiv+ED2cTA8kLlcdIBONptNfvGB7ONgAGQuN0Inent/1wfKJj6QfRwMgMzlxgEou/hA9nEwADKXGwEgu9IEyCEAZCwHgCgCQMZyAIgiBkDOFkgRoMZuAETO5FIgQBoXBIC6Q6iZaAJA7Mp9BYDilgNAAIhVDgABIFY5AOQDUGHMFFp5ewg1E00AiF25r4IBUu0HQPpMLgEgYzkARBEAMpabEKARVsbwRTSdkNJP6i48ADImwQPZkgCQMwkA2ZL6ALlaAAD5aVUAqTMpAKg7hJqJJgDErdxDoT3Dyuz+z6hqL1HQCVEvDgAiJJEFDxS3HAACQKxyAMgLoMKUKbTy9hBqJpoAELdyb4UDpLggAKTP5BIAMpYDQBQBIGM5AGRV4Eh1N/wdcRzscRK+SS02AIicyaWVeaBZAWo25+UW3sUhAGQsFx2glwdVXIXMwru4BICM5UZ4hL3+/KnIK7yLUwDIWC4+QNt79SMsr/AuDgEgY7kxOtEv8wvv4hAAMpaLDlAVIqX2PFmFd3EIABnLRQeoHH3dzC+8i0MAyFhujEeYVesDyNECAMhP6wKI0AIAyE9rAGh32QGQMQkAWZIAkDsJAFmSAJA7CQBZkpYFUKHPFFp5dww1E00AiFm5vxgADe0HQPpMLgEgYzkARBEAMpYDQBQBIGM5AERR6gDtPncWvoYvA4DmXvqjFdF0QsqMALWfO2Ot4csAII/a4YEISTt1nzuLtoavMlr9z1hqr5HlhKwCQMzKdWoAYq3h05gPD0TO5FIeALHW8AEgTiZHLzQTgFhr+AAQJ5OjF5oDQBE/+w2AfDNF7oX2O54jdEK9H8EAKG458yPM1Av1PNL+oqsBUsbxQHYBoLjlzACZeqEMgNwtAID8lDJApl4oADKXA0C1on50FwARkhYGkEPrBqjQZQqtfHcQNRNNAIhXeYA4AAkARMnkEgAylosO0Pbe5v0qOseK4wMBIEKSEaCyO3qy2xu/zvhAAIiQZHuE1cisOD5Q5gD1XsSIWQAqh8Fi1fGBABAhyQzQ9uOqC7Tm+EAKQNYWAECyLj6s+VlzfCAJIGcLACBZTzabzcG64wMBIEIS5oHMSQCIkASAzEmLA6hQM4VWvhMAMictCiABgAiZXAJAxnIAiCIAZCwHgCgCQMZyAIgiryP1rjgAMiYBIGNSQgAFvR6sDDb/yld7lYJOiHpxABAhiSx4oLjlABAAYpUDQJkCFPVBGksj2Dx2H8KbpaUA5FH70jyQrQXggfy0OoBcLQCA/ASAWJWHCADFLQeAiOYDIHIml9YOUDHMFFr5XgDImLQwgAQAcmdyCQAZywEgigCQsRwAoggAGcsBIIoAkLEcAKIIABnLASCKAJCxHACiyOdIyvUGQJ4ARfj20OIAsrQAABooxreHFgWQowUAkKzo3x4aV6bTcJ0/AAqqfC/nI2y18YEWCVAxyBRa+V5OgFYbH2hxACnDSkblezkBWm18IAAUAaA1xwcCQFyA7AJAnMq1au7V+ud7j7lfbdbQAoCcmVxKG6C2tyC2n4jK//O+2gyAQjK5lDZA7XhFXNzd3HzOnTmpjHUncdReJssJWZUiQK5ngM+RNC8uxgZoN2NyIC4+FcyvNsMDBWRyPQMiAGRugXgeSIgIX202ADToVwdW3tOiAIr5DND5+1GeAXt1/JfD35fsrzbrYAdAjhTXM4DpgezPgEijsFhfbQZAAZlcz4DEAXIJAMUtZ+4DmZ4BAAgAWVNczwAABIDsmVxaD0DaKYdEAIo4DoknoumEFAdAxhYAQH5aoweytgAA8hMAYlQepigAFYZyAIiilQM0DBkUWnlf+4t0XNV+5UgtpBcAYlQeprQBuvzgkZrfIgDEqDxMiQN0m+x86LXnC5DEDwAyJ+2v0uEdNb9FACi88kClDdDlLfSBeklmgEwtsHaAfLVKgGwtsHqAKhdEd0AAiFF5oNIG6NXDsg90dvWZWkgvABReeaA8rqZ2DNlLH6MPVI3CPIZiACi88kClDRA8kJQEgLh9oN4OB218oCIRqSdpEQBq/zAGQAN1q/tM8YFmI2Ygk/1arR6g7g/RAbq8/cVgHqhbX2yKDzQXMEONsx6oqlmbq/sDAOoqVDM16nY4mOIDzYeMLJP9Wq0GIFFMDtBwFCZ5oNXFB8odoGJqgF49bO7n/ShM6gOtLj5QYgB5r/AtTDsgC+NfvNVekM7G4RRQlF1uyQDke3HM1zl6C3icBDlJ5n+iPhAWlPWSjB7I3AIpPcLMALV/GqMP9MGjwztn19QyBi0aoGHvPDeAxBwA3T46voFXGWIRAM3hgV599ujsGgASFIC0LZAYQKZMowEkzq7+z8PihlrGoLUCZGyBhAAy9+C6G2AMgDy1aIDEcgES4wDULGjFKKxNWrAHGgkgfy0dIGMu0zMgJ4CKEQDCovp9kvKOTe2GJg2QrRPd2D+WBzpDJ5oGkKYFEgLI6oFGBQjDeOF8hJlaIB2AbD040d+bFFL5UHJN528TAIr0Jiiu3Gbrzz/AA+UOkHp6PpUPNegD4RHWtoAtl/4WTgcg2yREJa39GIVRRDuSen01D4HQym3mORTTA40AUOWC6JsylgyQywORexHzAOR4AAu9/WyAsK1nl+T0QNR7eD4P5Mg0BkDYWNiJcHWTBsjtgXT28x9hh2UHGuuBRNMCrly0h8AsAJHYUO2PNAqjz0UvFiAaG6R7OHGArD1tahJGYUPRnk5qCyQCEPHppNgfZxSG8C7k7g3lHp4BoEIdQhp72lJOjMIoclaicSymckUxyJwCQFr79eUG9mMURpG9kkJhwlqu6Itq4YgAGc03lJPthwcS7ogi5P3Yw/yng981LeAl9fQo5pPjEQzNV38/9bkaVusX1QdyRRRxXTLjFdcDFKkJyObHBijUfPmS6s+l0kltvvF2UAvMDpArogi5BU4H+V2/+0q/goBlfv+PI5svLYAwvwt7sqkBMt4OYW08KkCuiCKjeSDfe1k9PYr5o3kgT18kX9LuP4Z9oO03jQcy3g5pSexsFeaIIokrS/Mto7AGIOPdnKBcEUUSV5bm69+FPdncfC56HiiT28H5zdS0laX5lndhJ/s+UC63AzS57KOw3G4HaHIFv0yFoEoACGKp2xt/+wvPD85BUKXOAx0Ou9AQRJE8DwRBnkIfCGIp+G08BFUKXg8EQZWCVyRy9f0/lf/84TdTHS6Ovv/x9evXfzq3FaGqrP+b2Fd8Ng+UJ0Cl0X/598yM3qmy/odffRW30tn6QABoctWX/Ltfx610tlFY/TSI7lBHVuaPsAqgF5HNnw+gTD3Q9z/549xWhGphHihTgMSLN+e2IlQj94EmVrYAiS9zfYaNOwrz/dwTBFXazwN98GhOO6BMhZepEEu9RfV3ZjQDylX41EG0g88dXIFnBju4Qk/SJv/yn/ceZ7K12an5LVwwQPtXGdIGt+0notqgkcXWZqfmt3C5APVepg42+W9uPs9ka7NTAIiXyQZQbzmHtMm//K+LT4Vmp/+YVsED6ZSArQEeSAj91mYAFKtyshKwNaAPdPKgdj7K1uY0AdrFNNJ2/BOwMAyguTsIWjWmGUdh3a5m09bm4CsY7SMTmkxtTCNTxz9bgHyqM5phD+rL9ECXt7+YaB5IE18pWvN0MY2id/wro6PctZLUk7SLBZAcGmqMR5inUgSoF1FE2/EPvm5qYK7sPNDoAE0U5lcTJC0+QMLQ8QdAjkwBSW2drx42LTt+mN8pADJ1/EOPpKE+N4AG4Q1H8EDnhd9yjmQ9kC2m0XoBEqMD5LucI02AHFrvI2x8D+S7nCPsCuoCxaYPkMZqANRq4uUc2lDDAIgmBkDD6z6GB/LUigDSGZ0hQP0ZlDEAmuRbGQDIZp5DaQM0zdd6AJDNPIfSBmiaiUQAZDPPobQBggcyJi0FoB5Bs/aBOO8Pu7aI9T5yJ4Ld2vP3Acj7G7WkTMMV6Mxl6PMB5KmVeaDhS+B4AEmrr/jL0LVmtMaP74E84ksBoDiVy+s/R1qG3jr9uL6/vTjdeUzSB9I1xdoBklag85ehz+aBJhmF7ZrC0SFdE0CqBxKcZejzPcL63wsjKBwgxQWtGyC1D8Rahu4CqODYavVAyvfC7FoPQFqjI4/CpBXorGXoNoDEmAD5ao0AFeZMoZX7KHGApngXljFAImOAdqaPCdB0ozAAFKi0AZpuFAaAApU2QPBApiQAZEuatA9Uz4QCoFClDdAUi+p7ANmHxABIJwDUAuSeUwFAOsUBqDBkCqx80l0ZAGhmgMSIAE2xKyNLgPRGA6BWuploaYmTEBcfPY8UZHM+gBirFmqjT8eI0OFhP/2qzAXQ+Vv7xUDS670So5vPIwXZhAcK9EBMUvfsx7wL2svT/Hj12SNxfKM1Vw5xd/LL+7FWN413MxPbJVeAfKqbxwNVY7Dzd9s0aYnTxUd/vl+hEyPIZvYeqDBlCq3cS8FtvDe8O4lxAHqnfYZJHuhks9nECrKZNUBiCQCJSQCS+0DVwpRIQTYB0HIB6i8nk5Y4NQDFCbIJgBYLkL8AUIzKvQSAABAA8qsdABGSyAJA+hExACIKAOnbAgARBYAAEADyq11OqY0HQMGKBVABgEyZXAJAAgDZMrkEgACQNZNLAUcyGB0PIDXAFGsJFgAKrJykFAFSgyuwlmABoMDKSWIDVOgzhVZeSRPeJeISrNPh6iv5N47a66OeI03rA0iMA5AaYIq1BEtNki82PJAtk0spAqR6INYSrAwACvZ5e+8Zz492IppOSJkaIE0fiLMEKwOAPGqHB3InaT5xzFmCBYACKycpRYDoAkAAKA2AikUAZJtTAUA6RQFILAIg16QcANIJAAEgAORX++QANZ/9rscympdJAEgAIGumJ5saoDZct/IyCQAJAGRL2X7TeKD2kyVR9/Ofxp/+VE/SrngAFQBIn6l9hHWvl4YOCB6o+x0A6TOJngfSvEwCQN3vWQLUnwMdF6D2jZLyMikCQIU2U2jlfgJAkwC029SvvkxiAiQAUFIAWR4HwQA5lCtAnK660vWPNRZor0nQCVEvjhEgR38iHYBMRufjgYYOaAIPpC7zjhJkEwDpk8jKBqDhEqevH0cJsgmA9ElkZQOQHGRTvP78aZRJOenZO8+kHAASUwAkLfMW23v184wfZBMeSJ9EVjYADTwQ4xPCAIiQRFZEgNRmH60PVH1C+CBKkM1lAFTwLEwAIE1KcOWWUVhvmffNOEE28wdIACAlBfNA9iQA5EgCQPYkAORIAkD2JADkSJoOoNZsABSsoDbW0AKAjJlcAkCGJABEU4oAqW8eWa8f1wSQeVIuGYBko0cBSA2uwHr9SARIaffcALLP6q4JIE2AqWivH0/1Lx11af5qL4l6jjQBIHbljdQAU6zXjwQPpE2DB6IoRYB0Hojx+hEABVVOVIoAqX0g1utHABRUOVEpAqQGmGK9fgRAQZUTFQOggmVhgNGe1QGgkMqJ4gIklgPQMHFCgAJHfYOxY5yh5E5E0wkpKwBIlwgPRBEAMiYCIKtCHdvQT0b1mx72068KAAqqnCh4IGMiAKIIABkTARBFAMiYmCVAxkk5AKQTANK3BQAiKqSN616/kgcAGTK5BID2qYOWTx6gzmQAFKx4AGlcEACiKFeAwueaDBNXMeaz2isSdELUi5M7QFqji0Gm0Mq9BQ8kJWUJkCYluHJvASApCQD5CgBJSQDIVwBISgJAvgJAUhIA8lVkgOSmjwmQtFdye2/z/tMIUVoBkCmJrIgAnY4K0DBC2clBhCitAMiURFY2ACkxEh9E2CapTF0VMSazdtKchkZxACoEANofQM00iNJaf8ScH6XVfDNn5YGUuenwyr2VDUCyB9p+/FREiNIKgExJZGUDkNQHuvjwaZ3GjdIKgExJZGUDkLRX8slmszmIEKUVAJmSyApo49rayQEiCQBxK/dWbICktgdAFKUIkBqhjDX1RgRImYvOFCDDnMqaANJG5wifelsqQDp3A4Aq6eMDxZh6O7UtHOPPwrXXQz1HmgAQt/JWmghlnKk3eKCAysnyPJLF6FE9EGfqzQegQkoJMx8AWZJMRhfj9oFYU29UgIYuCAAJaUDz3mPNMCYGQCI2QGqEMtbUGwAKqLxTdzNvPxHVJ+SVYUySANEFgAaZogPUdScu7m5uPh9jBUGc4Usn9STt8m9jG+wAyDygKf+5+FSow5jVeSAARKu8U+eBhCHQMgDqZQJA5j5QFWj5gWYYA4B6meYDKOh5r+s5FPG6E82JSQMazTAGAPUzyQQl7oH2xqrnF88DOQWAepkAkNPGsEpIRgOg7hBqJppWDZBpbsWj8gABoGGmLAGyTc55VB4gADTMBIC8BICGmQAQUaFjU/swlzkIbq9G0AlRLw4AIiSRFdkDyS4IHogiANTPBIC8BYD6mfIHyHiCSQNUp2cBkPEGbZP6BAEgigCQlAkA+QoASZkAkK8AkJQJAPkKAEmZ8gGoZ6naFqkCZDV6MQDpT5GeBICMScsHyHyK9CQdQNJ2/3p9HzfI5rIAGi7mC6k8RNkAJG11E082D3RbZAAQq/IQZQOQtNl2+03jgVhbZGqnP8L7vL00p6ERAJIzjQTQIMjmSe16WEE24YEsSWTFB8g40KQnOT1QCxAvyCYAMiepAaZY3c5eUmfm5ADJfaAGIF6QTXtbrBsgNbgCq9uZAkDy7pgaIGaQTQBkTlLDu0TpdhI7mIxOaHst1HOkac0A6QkKPA1dgClGt9PPA+3PJKYHIsmnjS1T5gBIF+KO0e0EQL6VkxTgoW3OPc7sQ2Oapg/E6XYGAOTMZEtKASDKzTy9B7IYrXdBnFGYFGCK1e30dS65A0R9GiwXILoAkC4TACILAOkyASCyRgPIMV1tTZodIGJbAKCRACK877AmASBDkmSW5rqpVmcMkHZZKAAKrGRlABnXFacDkL0tABBVqwVIRGiLWfpAllzpA7S3kAAQcattkh4oUYAcRquf/MsWINOamqQAspqQKkDWcgsDiLZLIA5Avq98avMcf2e/WSKaTkhpkgoKQPLFSwwgkSxAHrWT2kLjgub3QE6AVKsTA4jugQwvJPMCyNUW6wYoxCf7uPWgZ0BjWgJ9oNBvUM/bB1KxT88DkcvpXVA2HihFgNweSHFBaQFU+ADEeCM5PkAD20wAOdpiYoCcRp+qt21yAHmU07qgfAAi3MwzeCBXuaQBKvwA0hKUCEDOttD0J2YHiMTGwOqkAPL0QM25hIyFRweIxoazLaYFiNi9SRggXw+kc0FpACScbZEgQG6jdwBZ33dQLKDLAyCHYTpbAydTJvFAhHKutkjSA8lWpwSQ41Ww1lblKZYEQGSsHW0xKUDkOULpmicEUNDtWAwJyhAgj5mLJACSrE4MIM9yXT/Cu++kNZWg6A9We1tMCRDF6C6lZ3U6AAX3KGWEEgDIq2vfM35WgEhG71L21zwZgIL6Mv27uC0/N0BDj+gsV0jmO483CkBUo/cpFqvnAEhji9ftWEgiHE9jqBrySBPtqKBqkH9oaGcVuT599arhGpsjGj28YSMZbb3kp75HGB7OZL4Qvb63n/XuAFP1P5poR15nMhFAkuEamyMaHQ8g9WqbzPcASLnGVvOFiAuQGnBEE+2Ieir9JSeU333VX5wiGa6L0DSW0b7m9xfUqFd7BvOV3wnVmwHShTxSoh1RT2aY3+WBvCov5NthEB1UsXk0D9S0cgSjrZecfICh+ervdg/kOJ58SYUqXcgjJdpRihpEB83CZpMHysV8Wh9IE+0oRQ2ig2Zhs6kPlIv5xlGYHPJIjXaUouTooHnYrLnaWZkfPA8EQZUAEMQSAIJYAkAQS2EAddPvZp08kD7+oNX23ub9p65cw75luKIYPbHNgzrtmsX8MIC6oadR1dce+kNSrcoTPjlw5Sr/9PVjZ1WTGT2xzVKdjprmMT8MoG7yy6T6aw/9STGTXj5w53r9+VNKVZMZPaXNezlrmsn8MIC+dlZcnow0La/X9v5zZ67tvZ9RqprM6Elt3otQ0yzmj+OB6pNxM7z9+CnpnjmYxAPRjJ7W5r0INc1i/kh9oOpknE/Riw+fCmeuuqJp+kAUoye2eS9CTbOYP+co7MlmszkgDAluPk9nFDaxzVKdEUZh8c3HPBDEEgCCWAJAEEsACGIJAEEsASCIJQDkobOiKN54NLcVaQkAeejsmhDnbx/NbUZSAkAeqgASh3fEcVFcE4c32oR1CwB5qObl+Mb5O88ubx+VrujVwztzmzS7AJCHWoDE+VtlV6ikpyRpbpNmFwDyUPsIO7tydPnBo/K3kqXVCwB5qO1Elz/OysHY5Qc/Qn8aAPmoHcZf3ir++lbZ+zm+NrdBCQgABevVZ5gSAkDhOiuH8hAAgngCQBBLAAhiCQBBLAEgiCUABLEEgCCWABDEEgCCWAJAEEsACGIJAEEsASCIJQAEsQSAIJYAEMQSAIJYAkAQSwAIYgkAQSwBIIilFQD06mG9gfT4qroPWd6bfPkv+9+w7Z2oFQAkzip0CET0cdpn135w9vyt8r9LLg+vldXXfB5WETuqnYfF7ji6j9Re3ioTrvxnU/z83TLl94/kUplpDQDVOwArOuqwLFXjXzlq/j1/59t/vFWlVX959bC4+uysaH79q3+2A/Ru/ePyF784agCqdj0f3jmTNstrv3Jc+7mqeGlVB1DOW+zXAFC9B/nwhmjCslRxEY6vNf+WAL19VIVqaQK2vPOswuzwzvnbR+dvUQA6vnF8pwGo8V4AaJmqsLl9JJqwLE1Td/9++86zykF1f6mfJjeqqBuHzkfYlaOKgbJM3f5lydp/Vd5tUHJgTAWQ8gi7kmuchlUAVNJQOaEmLIsOoN1fmpBjEkBaNR6o4uCNvQM5vkHxJa0HqgyAB8pF5z/6+aO6n3L2xqM6tM/V/67//bYDqP5L9Qj7+zpwVPlcu0UAqILsrKGmAtQLoMqayi2W/wdAyevVw2og1oZlkTvRNUDNXy5v7TvRb/yDG6D6sXh5+z+KOmRi9wgrHDh0AFW9srN6+EUplazWARA0mgAQxBIAglgCQBBLAAhiCQBBLAEgiCUABLEEgCCWABDEEgCCWAJAEEsACGIJAEEsASCIpf8HauA8Vlo6oDoAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-6" />
<p class="caption">plot of chunk unnamed-chunk-6</p>
</div>
<p>Hospital capacity can be displayed by toggling the <code>show_hosp_capacity</code>
argument.</p>
<pre><code class="language-r">## visualise prevalence with hospital capaciy
plot(output, what = "prevalence", show_hosp_capacity = TRUE)
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-7" />
<p class="caption">plot of chunk unnamed-chunk-7</p>
</div>
<h3 id="accessing-outputs">Accessing outputs</h3>
<p>The <code>output</code> object is of class <code>simex</code> and is a list containing four items:</p>
<ul>
<li><code>prevalance</code> is an array with 3 dimensions: time (365 days) x age (16
categories) x state (12 compartments). The compartments are S (susceptible), E
(exposed, pre-symptomatic), C (symptomatic in community), H (sympomatic in
hospital), R (recovered), D (dead). Each comparment is split into unvaccinated
(with subscript _u) and vaccinated (with subscript _v). Each value in the
array represents the proportion of the population in that given age-disease
compartment on a given day.</li>
<li><code>deltas</code> is an array with the same dimensions as <code>prevalence</code>. Each value
represents the change in a given age-disease compartment from the previous
day.</li>
<li><code>incidence</code> is an array with the same dimensions as <code>prevalence</code>. Each value
represents the new additions to a given age-disease comparment on a given
day. For the exposed category E, this represents incidence of new infections,
for the hospitalised category H this represents new hospital admissions, and
for the dead category D this represents new deaths.</li>
<li><code>pars</code> contains the parameter set initially fed into <code>run_model</code>.</li>
</ul>
<p>Accessing the data is easiest using array indexing. Remember, the dimensions are
time, age and compartment. For example, accessing the prevalence on the 250th
day is done by:</p>
<pre><code class="language-r">## extract prevalence in percent on day 250
round(100*output$prevalence[250,,], 1)
</code></pre>
<pre><code>## state
## age S_u E_u C_u H_u R_u D_u S_v E_v C_v H_v R_v D_v
## age_1 1.7 0 0 0 3.8 0.0 0 0 0 0 0 0
## age_2 0.9 0 0 0 5.0 0.0 0 0 0 0 0 0
## age_3 0.5 0 0 0 5.6 0.0 0 0 0 0 0 0
## age_4 0.3 0 0 0 6.3 0.0 0 0 0 0 0 0
## age_5 0.9 0 0 0 5.7 0.0 0 0 0 0 0 0
## age_6 0.9 0 0 0 5.7 0.0 0 0 0 0 0 0
## age_7 1.1 0 0 0 5.9 0.0 0 0 0 0 0 0
## age_8 0.9 0 0 0 5.8 0.0 0 0 0 0 0 0
## age_9 0.9 0 0 0 5.6 0.0 0 0 0 0 0 0
## age_10 1.0 0 0 0 5.0 0.0 0 0 0 0 0 0
## age_11 1.1 0 0 0 5.0 0.1 0 0 0 0 0 0
## age_12 1.7 0 0 0 4.3 0.1 0 0 0 0 0 0
## age_13 3.0 0 0 0 3.1 0.1 0 0 0 0 0 0
## age_14 3.5 0 0 0 2.1 0.1 0 0 0 0 0 0
## age_15 3.0 0 0 0 1.4 0.1 0 0 0 0 0 0
## age_16 5.6 0 0 0 1.7 0.2 0 0 0 0 0 0
</code></pre>
<p>Accessing the prevalence of the 1st age compartment (0-4) and 1st infectious
compartment (unvaccinated susceptible) for days 30 to 35 is done by:</p>
<pre><code class="language-r">## extract in percent on day 130-135
round(100*output$prevalence[130:135,1,1], 1)
</code></pre>
<pre><code>## day_130 day_131 day_132 day_133 day_134 day_135
## 5.1 5.1 5.1 5.0 5.0 4.9
</code></pre>
<p>The outputs can also be extracted in <code>tibble</code> form using the <code>extract</code> function,
once again using the <code>what</code> argument to specify whether prevalence or incidence
is extracted.</p>
<pre><code class="language-r">## extract prevalence
extract(output, what = "prevalence")
</code></pre>
<pre><code>## # A tibble: 4,380 × 4
## day compartment vax value
## <dbl> <fct> <lgl> <dbl>
## 1 1 S FALSE 1.00
## 2 1 E FALSE 0
## 3 1 C FALSE 0.0000000469
## 4 1 H FALSE 0
## 5 1 R FALSE 0
## 6 1 D FALSE 0
## 7 1 S TRUE 0
## 8 1 E TRUE 0
## 9 1 C TRUE 0
## 10 1 H TRUE 0
## # ℹ 4,370 more rows
</code></pre>
<p>If we want to filter this list for a sequence of days, we can then do basic
data.frame manipulation:</p>
<pre><code class="language-r">## define start and end days
days_from <- 10
days_to <- 20
## extract prevalence
df <- extract(output, what = "prevalence")
df <- df[df$day %in% seq(days_from, days_to),]
df
</code></pre>
<pre><code>## # A tibble: 132 × 4
## day compartment vax value
## <dbl> <fct> <lgl> <dbl>
## 1 10 S FALSE 1.00e+ 0
## 2 10 E FALSE 4.30e- 8
## 3 10 C FALSE 6.82e- 8
## 4 10 H FALSE 1.33e- 9
## 5 10 R FALSE 5.26e- 8
## 6 10 D FALSE 4.17e-11
## 7 10 S TRUE 0
## 8 10 E TRUE 0
## 9 10 C TRUE 0
## 10 10 H TRUE 0
## # ℹ 122 more rows
</code></pre>
<h3 id="modelling-a-single-intervention">Modelling a single intervention</h3>
<p>We use vaccination as an example intervention. Referencing the table above, we
can see that vaccination rate is specified using the <code>vax_rate</code> argument and
set it to 0.5% of the population per day.</p>
<pre><code class="language-r">## define vaccination rate
pars <- get_parameters(vax_rate = 0.005)
## run model
output <- run_model(pars)
## visualise prevalence
plot(output)
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12" />
<p class="caption">plot of chunk unnamed-chunk-12</p>
</div>
<p>We can see that the daily increase in number of vaccinated individuals, as
well as the impact on infection and disease severity.</p>
<h3 id="specifying-an-initial-state">Specifying an initial state</h3>
<p>The default initial state begins with an entirely susceptible, unvaccinated
population and a single infection. We can also specify a custom initial state by
passing a matrix specifying the number of individuals in each age-infection
compartment. In the example below, we extract the initial state from the
previous run, and modify the compartments so half the susceptible population is
assigned to the vaccinated compartment.</p>
<pre><code class="language-r">## extract starting point from previous run
state <- output$prevalence[1,,]
## assign half of susceptibles to vaccinated
state[,"S_u"] <- state[,"S_v"] <- state[,"S_u"]/2
## run model
output <- run_model(pars, init_state = state)
## visualise prevalence
plot(output)
</code></pre>
<div class="figure" style="text-align: center">
<img 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" alt="plot of chunk unnamed-chunk-13" />
<p class="caption">plot of chunk unnamed-chunk-13</p>
</div>
<p>We can see that the simulation now begins with a 50% vaccinated population,
significantly reducing the size and impact of the pandemic.</p>
<h3 id="timed-interventions-multiple-interventions">Timed interventions, multiple interventions</h3>
<p>Sometime we only want to introduce interventions at a certain time, or we want
to model multiple, successive interventions. We can do this easily by generating
a named list of parameters, where the name gives the time that parameter set
should be used from. In the example below, we introduce isolation measures with
an adherence of 50% on the 125th day.</p>
<pre><code class="language-r">## introduce isolation on the 125th day
parlist <- list(
"1" = get_parameters(),
"125" = get_parameters(isolation_adherence = 0.5)
)
## run model
output <- run_model(parlist)
## visualise prevalence
plot(output)
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14" />
<p class="caption">plot of chunk unnamed-chunk-14</p>
</div>
<p>Comparing this figure with the first model run with no interventions, we can see
the proportion of deaths drops from about 0.7% to 0.4%; a reduction in deaths of
more than 40%!</p>
<h3 id="comparing-scenarios">Comparing scenarios</h3>
<p>It is useful to directly compare different scenarios visually. The
<code>vis_comarison</code> function does exactly this, and accepts a named list of <code>simex</code>
objects. In the example below, we generate a named list of lists that compares
the default scenario (no interventions) with the a scenario where isolation is
introduced on the 125th day.</p>
<pre><code class="language-r">## define two scenarios, one without intervention and one with isolation
parlists <- list(
"No intervention" = get_parameters(),
"Isolation on day 125" = list(
"1" = get_parameters(),
"125" = get_parameters(isolation_adherence = 0.5)
)
)
## run model across set of parameter lists
outputs <- lapply(parlists, run_model)
## compare scenarios
vis_comparison(outputs)
</code></pre>
<div class="figure" style="text-align: center">
<img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15" />
<p class="caption">plot of chunk unnamed-chunk-15</p>
</div>
<h2 id="contributors">Contributors</h2>
<ul>
<li><a href="https://github.com/finlaycampbell">Finlay Campbell</a> (<a href="mailto:campbellf@who.int">campbellf@who.int</a>)</li>
<li>Prabasaj Paul (<a href="mailto:ppaul@who.int">ppaul@who.int</a>)</li>
</ul>
<p><strong>Maintainer:</strong> Finlay Campbell</p>
</div>
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