Page not found (404) â¢ Rnaught

diff --git a/search.json b/search.json index 48857a4..c6f3190 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://MI2YorkU.github.io/Rnaught/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Sawitree Boonpatcharanon. Author. Jane Heffernan. Author. Hanna Jankowski. Author. Naeem Model. Author, maintainer.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Boonpatcharanon S, Heffernan J, Jankowski H, Model N (????). Rnaught: Estimate Basic Reproduction Number Infectious Diseases. R package version 0.1.0, https://github.com/MI2YorkU/Rnaught, https://MI2YorkU.github.io/Rnaught. Boonpatcharanon S, Heffernan JM, Jankowski H (2022). âEstimating basic reproduction number beginning outbreak.â PLOS ONE, 17(6), 1-24. doi:10.1371/journal.pone.0269306.","code":"@Manual{, title = {Rnaught: Estimate the Basic Reproduction Number of Infectious Diseases}, author = {Sawitree Boonpatcharanon and Jane Heffernan and Hanna Jankowski and Naeem Model}, note = {R package version 0.1.0, https://github.com/MI2YorkU/Rnaught}, url = {https://MI2YorkU.github.io/Rnaught}, } @Article{, doi = {10.1371/journal.pone.0269306}, author = {Sawitree Boonpatcharanon and Jane M. Heffernan and Hanna Jankowski}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Estimating the basic reproduction number at the beginning of an outbreak}, year = {2022}, month = {6}, volume = {17}, pages = {1-24}, number = {6}, }"},{"path":"https://MI2YorkU.github.io/Rnaught/index.html","id":"rnaught-","dir":"","previous_headings":"","what":"Estimate the Basic Reproduction Number of Infectious Diseases","title":"Estimate the Basic Reproduction Number of Infectious Diseases","text":"Rnaught R package web application estimating basic reproduction number (R0) infectious diseases. Currently, estimation can done web application via four different methods: White Panago (WP), sequential Bayes (seqB), incidence decay (ID), incidence decay exponential adjustment (IDEA). Datasets can uploaded .csv file, can entered manually application. data visualized application plots show case counts (either weekly daily). multiple datasets uploaded/manually entered, trends corresponding datasets populated plot can compared. plot can exported .png file. , dataset entered can exported .csv file. Two sample datasets included: weekly Canadian COVID-19 case count data March 3rd, 2020 March 31st, 2020, weekly Ontario COVID-19 case count data March 3rd, 2020 March 31st, 2020. estimate basic reproductive number, user can choose preferred estimator, applicable, must enter known serial interval prior estimation. multiple estimates basic reproductive number calculated, included table row represents estimate. multiple datasets considered, basic reproductive number estimated datasets columns table correspond different datasets uploaded application. table also consists column corresponding value serial interval (SI). table can exported .csv file. progressâ¦","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":null,"dir":"Reference","previous_headings":"","what":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"Daily national COVID-19 case counts Canada, start pandemic end 2023. Retrieved COVID-19 Canada Open Data Working Group 2024-05-11.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"","code":"COVIDCanada"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"data frame 1439 observations 3 variables: date date reporting YYYY-MM-DD format. cases daily number cases. cumulative_cases cumulative number cases.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"https://github.com/ccodwg/CovidTimelineCanada","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":null,"dir":"Reference","previous_headings":"","what":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"Daily COVID-19 case counts Canadian province territory, start pandemic end 2023. Retrieved COVID-19 Canada Open Data Working Group 2024-05-11.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"","code":"COVIDCanadaPT"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"data frame 16799 observations 4 variables: region two-letter code Canadian province territory. date date reporting YYYY-MM-DD format. cases daily number cases. cumulative_cases cumulative number cases.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"https://github.com/ccodwg/CovidTimelineCanada","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/app.html","id":null,"dir":"Reference","previous_headings":"","what":"Launch the Rnaught Web Application â app","title":"Launch the Rnaught Web Application â app","text":"Launch Rnaught Web Application","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Launch the Rnaught Web Application â app","text":"","code":"app()"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Decay (ID) â id","title":"Incidence Decay (ID) â id","text":"function implements least squares estimation method R0 due Fisman et al. (PloS One, 2013). See details implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Decay (ID) â id","text":"","code":"id(cases, mu)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Decay (ID) â id","text":"cases Vector case counts. vector must non-empty contain positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Decay (ID) â id","text":"estimate basic reproduction number (R0).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incidence Decay (ID) â id","text":"method based straightforward incidence decay model. estimate R0 value minimizes sum squares observed case counts cases counts expected model. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence Decay (ID) â id","text":"Fisman et al. (PloS One, 2013)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Decay (ID) â id","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. id(cases, mu = 5 / 7) #> [1] 1.245734 # Obtain R0 when the serial distribution has a mean of three days. id(cases, mu = 3 / 7) #> [1] 1.14092"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Decay and Exponential Adjustment (IDEA) â idea","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"function implements least squares estimation method R0 due Fisman et al. (PloS One, 2013). See details implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"","code":"idea(cases, mu)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"cases Vector case counts. vector must length least two contain positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"estimate basic reproduction number (R0).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"method closely related implemented id(). method based incidence decay model. estimate R0 value minimizes sum squares observed case counts case counts expected model. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"Fisman et al. (PloS One, 2013)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. idea(cases, mu = 5 / 7) #> [1] 1.419546 # Obtain R0 when the serial distribution has a mean of three days. idea(cases, mu = 3 / 7) #> [1] 1.233927"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Bayes (seqB) â seq_bayes","title":"Sequential Bayes (seqB) â seq_bayes","text":"function implements sequential Bayesian estimation method R0 due Bettencourt Riberio (PloS One, 2008). See details important implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Bayes (seqB) â seq_bayes","text":"","code":"seq_bayes(cases, mu, kappa = 20, post = FALSE)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Bayes (seqB) â seq_bayes","text":"cases Vector case counts. vector must contain non-negative integers, least two positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7. kappa Largest possible value uniform prior (defaults 20). must number greater equal 1. describes prior belief ranges R0, set higher value R0 believed larger. post Whether return posterior distribution R0 instead estimate R0 (defaults FALSE). must value identical TRUE FALSE.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Bayes (seqB) â seq_bayes","text":"post identical TRUE, list containing following components returned: supp - support posterior distribution R0 pmf - probability mass function posterior distribution R0 Otherwise, post identical FALSE, estimate R0 returned. Note estimate equal sum(supp * pmf) (.e., posterior mean).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sequential Bayes (seqB) â seq_bayes","text":"method sets uniform prior distribution R0 possible values 0 kappa, discretized fine grid. distribution R0 updated sequentially, one update new case count observation. final estimate R0 mean (last) posterior distribution. prior distribution initial belief distribution R0, uninformative uniform distribution values 0 kappa. Users can change value kappa (.e., prior distribution changed uniform). case counts observed, influence prior distribution lessen final estimate. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing. code modified provide estimate even case counts equal zero present time intervals. done grouping counts periods time. Without grouping, presence zero counts, estimate can provided.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Bayes (seqB) â seq_bayes","text":"Bettencourt Riberio (PloS One, 2008)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Bayes (seqB) â seq_bayes","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. seq_bayes(cases, mu = 5 / 7) #> [1] 1.026563 # Obtain R0 when the serial distribution has a mean of three days. seq_bayes(cases, mu = 3 / 7) #> [1] 1.015938 # Obtain R0 when the serial distribution has a mean of seven days, and R0 is # believed to be at most 4. estimate <- seq_bayes(cases, mu = 1, kappa = 4) # Same as above, but return the posterior distribution of R0 instead of the # estimate. posterior <- seq_bayes(cases, mu = 1, kappa = 4, post = TRUE) # Display the support and probability mass function of the posterior. posterior$supp #> [1] 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 #> [16] 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 #> [31] 0.30 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44 #> [46] 0.45 0.46 0.47 0.48 0.49 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 #> [61] 0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 #> [76] 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 #> [91] 0.90 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.00 1.01 1.02 1.03 1.04 #> [106] 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 #> [121] 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29 1.30 1.31 1.32 1.33 1.34 #> [136] 1.35 1.36 1.37 1.38 1.39 1.40 1.41 1.42 1.43 1.44 1.45 1.46 1.47 1.48 1.49 #> [151] 1.50 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59 1.60 1.61 1.62 1.63 1.64 #> [166] 1.65 1.66 1.67 1.68 1.69 1.70 1.71 1.72 1.73 1.74 1.75 1.76 1.77 1.78 1.79 #> [181] 1.80 1.81 1.82 1.83 1.84 1.85 1.86 1.87 1.88 1.89 1.90 1.91 1.92 1.93 1.94 #> [196] 1.95 1.96 1.97 1.98 1.99 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 #> [211] 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 #> [226] 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 #> [241] 2.40 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.50 2.51 2.52 2.53 2.54 #> [256] 2.55 2.56 2.57 2.58 2.59 2.60 2.61 2.62 2.63 2.64 2.65 2.66 2.67 2.68 2.69 #> [271] 2.70 2.71 2.72 2.73 2.74 2.75 2.76 2.77 2.78 2.79 2.80 2.81 2.82 2.83 2.84 #> [286] 2.85 2.86 2.87 2.88 2.89 2.90 2.91 2.92 2.93 2.94 2.95 2.96 2.97 2.98 2.99 #> [301] 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 3.12 3.13 3.14 #> [316] 3.15 3.16 3.17 3.18 3.19 3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 #> [331] 3.30 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39 3.40 3.41 3.42 3.43 3.44 #> [346] 3.45 3.46 3.47 3.48 3.49 3.50 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58 3.59 #> [361] 3.60 3.61 3.62 3.63 3.64 3.65 3.66 3.67 3.68 3.69 3.70 3.71 3.72 3.73 3.74 #> [376] 3.75 3.76 3.77 3.78 3.79 3.80 3.81 3.82 3.83 3.84 3.85 3.86 3.87 3.88 3.89 #> [391] 3.90 3.91 3.92 3.93 3.94 3.95 3.96 3.97 3.98 3.99 4.00 posterior$pmf #> [1] 4.396081e-14 6.866777e-14 1.069979e-13 1.663113e-13 2.578585e-13 #> [6] 3.987896e-13 6.151736e-13 9.465244e-13 1.452563e-12 2.223289e-12 #> [11] 3.393931e-12 5.167074e-12 7.845296e-12 1.187914e-11 1.793742e-11 #> [16] 2.700983e-11 4.055633e-11 6.072366e-11 9.065827e-11 1.349567e-10 #> [21] 2.003117e-10 2.964355e-10 4.373744e-10 6.433725e-10 9.435054e-10 #> [26] 1.379386e-09 2.010358e-09 2.920742e-09 4.229916e-09 6.106257e-09 #> [31] 8.786364e-09 1.260143e-08 1.801328e-08 2.566338e-08 3.643912e-08 #> [36] 5.156328e-08 7.271379e-08 1.021837e-07 1.430935e-07 1.996715e-07 #> [41] 2.776227e-07 3.846102e-07 5.308815e-07 7.300785e-07 1.000278e-06 #> [46] 1.365319e-06 1.856496e-06 2.514686e-06 3.393017e-06 4.560205e-06 #> [51] 6.104659e-06 8.139541e-06 1.080892e-05 1.429523e-05 1.882819e-05 #> [56] 2.469542e-05 3.225496e-05 4.194988e-05 5.432506e-05 7.004650e-05 #> [61] 8.992291e-05 1.149299e-04 1.462361e-04 1.852322e-04 2.335598e-04 #> [66] 2.931434e-04 3.662201e-04 4.553696e-04 5.635406e-04 6.940727e-04 #> [71] 8.507116e-04 1.037615e-03 1.259347e-03 1.520857e-03 1.827440e-03 #> [76] 2.184679e-03 2.598363e-03 3.074378e-03 3.618573e-03 4.236597e-03 #> [81] 4.933708e-03 5.714562e-03 6.582973e-03 7.541662e-03 8.591990e-03 #> [86] 9.733699e-03 1.096466e-02 1.228062e-02 1.367503e-02 1.513890e-02 #> [91] 1.666067e-02 1.822624e-02 1.981901e-02 2.142007e-02 2.300844e-02 #> [96] 2.456146e-02 2.605520e-02 2.746508e-02 2.876640e-02 2.993509e-02 #> [101] 3.094837e-02 3.178548e-02 3.242832e-02 3.286214e-02 3.307605e-02 #> [106] 3.306344e-02 3.282231e-02 3.235539e-02 3.167013e-02 3.077854e-02 #> [111] 2.969682e-02 2.844487e-02 2.704571e-02 2.552471e-02 2.390888e-02 #> [116] 2.222595e-02 2.050365e-02 1.876888e-02 1.704700e-02 1.536123e-02 #> [121] 1.373213e-02 1.217724e-02 1.071084e-02 9.343864e-03 8.083917e-03 #> [126] 6.935429e-03 5.899896e-03 4.976201e-03 4.160989e-03 3.449076e-03 #> [131] 2.833858e-03 2.307727e-03 1.862441e-03 1.489475e-03 1.180313e-03 #> [136] 9.266886e-04 7.207803e-04 5.553459e-04 4.238132e-04 3.203273e-04 #> [141] 2.397617e-04 1.777009e-04 1.304008e-04 9.473459e-05 6.812875e-05 #> [146] 4.849552e-05 3.416469e-05 2.381841e-05 1.643092e-05 1.121447e-05 #> [151] 7.572111e-06 5.057422e-06 3.340936e-06 2.182658e-06 1.410047e-06 #> [156] 9.006636e-07 5.687529e-07 3.550312e-07 2.190488e-07 1.335660e-07 #> [161] 8.047851e-08 4.791165e-08 2.817909e-08 1.637135e-08 9.394205e-09 #> [166] 5.323528e-09 2.978849e-09 1.645703e-09 8.975385e-10 4.831664e-10 #> [171] 2.566999e-10 1.345806e-10 6.961600e-11 3.552605e-11 1.788285e-11 #> [176] 8.878079e-12 4.346434e-12 2.098061e-12 9.984180e-13 4.683320e-13 #> [181] 2.165108e-13 9.863385e-14 4.427200e-14 1.957601e-14 8.526007e-15 #> [186] 3.657023e-15 1.544555e-15 6.422519e-16 2.628849e-16 1.059047e-16 #> [191] 4.198412e-17 1.637588e-17 6.283525e-18 2.371425e-18 8.801378e-19 #> [196] 3.211841e-19 1.152248e-19 4.063045e-20 1.407979e-20 4.794057e-21 #> [201] 1.603601e-21 5.268629e-22 1.699923e-22 5.385323e-23 1.674809e-23 #> [206] 5.112219e-24 1.531308e-24 4.500307e-25 1.297374e-25 3.668155e-26 #> [211] 1.016962e-26 2.764082e-27 7.363758e-28 1.922485e-28 4.917601e-29 #> [216] 1.232200e-29 3.023823e-30 7.265876e-31 1.709166e-31 3.935069e-32 #> [221] 8.865403e-33 1.954015e-33 4.212549e-34 8.880821e-35 1.830437e-35 #> [226] 3.687665e-36 7.260114e-37 1.396466e-37 2.623677e-38 4.813725e-39 #> [231] 8.622619e-40 1.507577e-40 2.572153e-41 4.281381e-42 6.950773e-43 #> [236] 1.100362e-43 1.698170e-44 2.554224e-45 3.743317e-46 5.343929e-47 #> [241] 7.429450e-48 1.005609e-48 1.324832e-49 1.698377e-50 2.118019e-51 #> [246] 2.568782e-52 3.029042e-53 3.471688e-54 3.866425e-55 4.182989e-56 #> [251] 4.394865e-57 4.482891e-58 4.438084e-59 4.263117e-60 3.972112e-61 #> [256] 3.588768e-62 3.143145e-63 2.667736e-64 2.193531e-65 1.746742e-66 #> [261] 1.346659e-67 1.004822e-68 7.254056e-70 5.065108e-71 3.419533e-72 #> [266] 2.231354e-73 1.406840e-74 8.567343e-76 5.037567e-77 2.859011e-78 #> [271] 1.565585e-79 8.268889e-81 4.210849e-82 2.066735e-83 9.773085e-85 #> [276] 4.450901e-86 1.951504e-87 8.234368e-89 3.342436e-90 1.304664e-91 #> [281] 4.895156e-93 1.764793e-94 6.110914e-96 2.031551e-97 6.481579e-99 #> [286] 1.983744e-100 5.821848e-102 1.637658e-103 4.413549e-105 1.139116e-106 #> [291] 2.814324e-108 6.652979e-110 1.504185e-111 3.251137e-113 6.714617e-115 #> [296] 1.324527e-116 2.494329e-118 4.482263e-120 7.682227e-122 1.255211e-123 #> [301] 1.954236e-125 2.897728e-127 4.090211e-129 5.493210e-131 7.015875e-133 #> [306] 8.517133e-135 9.822891e-137 1.075711e-138 1.117987e-140 1.102133e-142 #> [311] 1.030047e-144 9.121635e-147 7.649726e-149 6.072101e-151 4.559445e-153 #> [316] 3.236849e-155 2.171329e-157 1.375543e-159 8.224662e-162 4.638796e-164 #> [321] 2.466499e-166 1.235627e-168 5.828612e-171 2.587326e-173 1.080141e-175 #> [326] 4.238233e-178 1.562047e-180 5.404238e-183 1.754003e-185 5.337064e-188 #> [331] 1.521491e-190 4.061129e-193 1.014255e-195 2.368537e-198 5.168362e-201 #> [336] 1.053099e-203 2.002304e-206 3.550040e-209 5.865079e-212 9.022847e-215 #> [341] 1.291610e-217 1.719185e-220 2.126178e-223 2.441418e-226 2.600913e-229 #> [346] 2.568760e-232 2.350200e-235 1.990379e-238 1.559119e-241 1.128742e-244 #> [351] 7.546371e-248 4.655454e-251 2.647985e-254 1.387535e-257 6.692519e-261 #> [356] 2.968866e-264 1.210268e-267 4.529956e-271 1.555446e-274 4.895396e-278 #> [361] 1.410954e-281 3.720881e-285 8.970133e-289 1.975060e-292 3.968208e-296 #> [366] 7.268446e-300 1.212601e-303 1.840840e-307 2.540527e-311 3.184384e-315 #> [371] 3.621946e-319 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [376] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [381] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [386] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [391] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [396] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [401] 0.000000e+00 # Note that the following always holds: estimate == sum(posterior$supp * posterior$pmf) #> [1] TRUE"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":null,"dir":"Reference","previous_headings":"","what":"White and Pagano (WP) â wp","title":"White and Pagano (WP) â wp","text":"function implements R0 estimation due White Pagano (Statistics Medicine, 2008). method based maximum likelihood estimation Poisson transmission model. See details important implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"White and Pagano (WP) â wp","text":"","code":"wp( cases, mu = NA, serial = FALSE, grid_length = 100, max_shape = 10, max_scale = 10 )"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"White and Pagano (WP) â wp","text":"cases Vector case counts. vector must length least two contain positive integers. mu Mean serial distribution (defaults NA). must positive number NA. number specified, value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7. serial Whether return estimated serial distribution addition estimate R0 (defaults FALSE). must value identical TRUE FALSE. grid_length length grid grid search (defaults 100). must positive integer. used mu set NA. grid search go combinations shape scale parameters gamma distribution, grid_length evenly spaced values 0 (exclusive) max_shape max_scale (inclusive), respectively. Note larger values result longer search time. max_shape largest possible value shape parameter grid search (defaults 10). must positive number. used mu set NA. Note larger values result longer search time, may cause numerical instabilities. max_scale largest possible value scale parameter grid search (defaults 10). must positive number. used mu set NA. Note larger values result longer search time, may cause numerical instabilities.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"White and Pagano (WP) â wp","text":"serial identical TRUE, list containing following components returned: r0 - estimate R0 supp - support estimated serial distribution pmf - probability mass function estimated serial distribution Otherwise, serial identical FALSE, estimate R0 returned.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"White and Pagano (WP) â wp","text":"method based Poisson transmission model, hence may valid beginning epidemic. model, serial distribution assumed discrete finite number possible values. implementation, mu NA, serial distribution taken discretized version gamma distribution shape parameter 1 scale parameter mu (hence mean mu). mu NA, function implements grid search algorithm find maximum likelihood estimator possible gamma distributions unknown shape scale, restricting prespecified grid (see parameters grid_length, max_shape max_scale). cases, largest value support chosen cumulative distribution function original (pre-discretized) gamma distribution cumulative probability 0.999 value. serial distribution known (.e., mu NA), sensitivity testing mu strongly recommended. serial distribution unknown (.e., mu NA), likelihood function can flat near maximum, resulting numerical instability optimizer. mu NA, implementation takes considerably longer run. Users careful units time (e.g., counts observed daily weekly?) implementing. model developed White Pagano (2008) discrete, hence serial distribution finite discrete. implementation, input value mu continuous distribution. algorithm discretizes input, mean estimated serial distribution returned (serial set TRUE) differ mu somewhat. say, user notices input mu mean estimated serial distribution different, expected, caused discretization.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"White and Pagano (WP) â wp","text":"White Pagano (Statistics Medicine, 2008)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"White and Pagano (WP) â wp","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. wp(cases, mu = 5 / 7) #> [1] 1.107862 # Obtain R0 when the serial distribution has a mean of three days. wp(cases, mu = 3 / 7) #> [1] 1.067642 # Obtain R0 when the serial distribution is unknown. # Note that this will take longer to run than when `mu` is known. wp(cases) #> [1] 1.495574 # Same as above, but specify custom grid search parameters. The larger any of # the parameters, the longer the search will take, but with potentially more # accurate estimates. wp(cases, grid_length = 40, max_shape = 4, max_scale = 4) #> [1] 1.495574 # Return the estimated serial distribution in addition to the estimate of R0. estimate <- wp(cases, serial = TRUE) # Display the estimate of R0, as well as the support and probability mass # function of the estimated serial distribution returned by the grid search. estimate$r0 #> [1] 1.495574 estimate$supp #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 estimate$pmf #> [1] 0.3295449612 0.1855210503 0.1282030815 0.0920057871 0.0672100630 #> [6] 0.0496097863 0.0368701329 0.0275354532 0.0206388543 0.0155131855 #> [11] 0.0116867019 0.0088202295 0.0066670102 0.0050459517 0.0038232730 #> [16] 0.0028996361 0.0022009767 0.0016718921 0.0012708261 0.0009665381 #> [21] 0.0007354976 0.0005599523 0.0004264906 0.0003249676 0.0002477014"}] +[{"path":"https://MI2YorkU.github.io/Rnaught/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Naeem Model. Author, maintainer. Sawitree Boonpatcharanon. Author. Jane Heffernan. Author. Hanna Jankowski. Author. Tatiana Krikella. Contributor.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Model N, Boonpatcharanon S, Heffernan J, Jankowski H (????). Rnaught: Estimate Basic Reproduction Number (R0) Infectious Diseases. R package version 0.1.0, https://github.com/MI2YorkU/Rnaught, https://MI2YorkU.github.io/Rnaught. Boonpatcharanon S, Heffernan JM, Jankowski H (2022). âEstimating basic reproduction number beginning outbreak.â PLOS ONE, 17(6), 1-24. doi:10.1371/journal.pone.0269306.","code":"@Manual{, title = {Rnaught: Estimate the Basic Reproduction Number (R0) of Infectious Diseases}, author = {Naeem Model and Sawitree Boonpatcharanon and Jane Heffernan and Hanna Jankowski}, note = {R package version 0.1.0, https://github.com/MI2YorkU/Rnaught}, url = {https://MI2YorkU.github.io/Rnaught}, } @Article{, doi = {10.1371/journal.pone.0269306}, author = {Sawitree Boonpatcharanon and Jane M. Heffernan and Hanna Jankowski}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Estimating the basic reproduction number at the beginning of an outbreak}, year = {2022}, month = {6}, volume = {17}, pages = {1-24}, number = {6}, }"},{"path":"https://MI2YorkU.github.io/Rnaught/index.html","id":"rnaught-","dir":"","previous_headings":"","what":"Estimate the Basic Reproduction Number (R0) of Infectious Diseases","title":"Estimate the Basic Reproduction Number (R0) of Infectious Diseases","text":"Rnaught R package web application estimating basic reproduction number (R0) infectious diseases. Currently, estimation can done web application via four different methods: White Panago (WP), sequential Bayes (seqB), incidence decay (ID), incidence decay exponential adjustment (IDEA). Datasets can uploaded .csv file, can entered manually application. data visualized application plots show case counts (either weekly daily). multiple datasets uploaded/manually entered, trends corresponding datasets populated plot can compared. plot can exported .png file. , dataset entered can exported .csv file. Two sample datasets included: weekly Canadian COVID-19 case count data March 3rd, 2020 March 31st, 2020, weekly Ontario COVID-19 case count data March 3rd, 2020 March 31st, 2020. estimate basic reproductive number, user can choose preferred estimator, applicable, must enter known serial interval prior estimation. multiple estimates basic reproductive number calculated, included table row represents estimate. multiple datasets considered, basic reproductive number estimated datasets columns table correspond different datasets uploaded application. table also consists column corresponding value serial interval (SI). table can exported .csv file. progressâ¦","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":null,"dir":"Reference","previous_headings":"","what":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"Daily national COVID-19 case counts Canada, start pandemic end 2023. Retrieved COVID-19 Canada Open Data Working Group 2024-05-11.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"","code":"COVIDCanada"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"data frame 1439 observations 3 variables: date date reporting YYYY-MM-DD format. cases daily number cases. cumulative_cases cumulative number cases.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"COVID-19 Canada National Case Counts, 2020-2023 â COVIDCanada","text":"https://github.com/ccodwg/CovidTimelineCanada","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":null,"dir":"Reference","previous_headings":"","what":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"Daily COVID-19 case counts Canadian province territory, start pandemic end 2023. Retrieved COVID-19 Canada Open Data Working Group 2024-05-11.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"","code":"COVIDCanadaPT"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"data frame 16799 observations 4 variables: region two-letter code Canadian province territory. date date reporting YYYY-MM-DD format. cases daily number cases. cumulative_cases cumulative number cases.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"COVID-19 Canada Provincial and Territorial Case Counts, 2020-2023 â COVIDCanadaPT","text":"https://github.com/ccodwg/CovidTimelineCanada","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/Rnaught-package.html","id":null,"dir":"Reference","previous_headings":"","what":"Rnaught: Estimate the Basic Reproduction Number (R0) of Infectious Diseases â Rnaught-package","title":"Rnaught: Estimate the Basic Reproduction Number (R0) of Infectious Diseases â Rnaught-package","text":"R package web application estimating basic reproduction number (R0) infectious diseases.","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/Rnaught-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Rnaught: Estimate the Basic Reproduction Number (R0) of Infectious Diseases â Rnaught-package","text":"Maintainer: Naeem Model @nmode.ca Authors: Sawitree Boonpatcharanon Jane Heffernan Hanna Jankowski contributors: Tatiana Krikella [contributor]","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Decay (ID) â id","title":"Incidence Decay (ID) â id","text":"function implements least squares estimation method R0 due Fisman et al. (PloS One, 2013). See details implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Decay (ID) â id","text":"","code":"id(cases, mu)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Decay (ID) â id","text":"cases Vector case counts. vector must non-empty contain positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Decay (ID) â id","text":"estimate basic reproduction number (R0).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incidence Decay (ID) â id","text":"method based straightforward incidence decay model. estimate R0 value minimizes sum squares observed case counts cases counts expected model. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence Decay (ID) â id","text":"Fisman et al. (PloS One, 2013)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/id.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Decay (ID) â id","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. id(cases, mu = 5 / 7) #> [1] 1.245734 # Obtain R0 when the serial distribution has a mean of three days. id(cases, mu = 3 / 7) #> [1] 1.14092"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":null,"dir":"Reference","previous_headings":"","what":"Incidence Decay and Exponential Adjustment (IDEA) â idea","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"function implements least squares estimation method R0 due Fisman et al. (PloS One, 2013). See details implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"","code":"idea(cases, mu)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"cases Vector case counts. vector must length least two contain positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"estimate basic reproduction number (R0).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"method closely related implemented id(). method based incidence decay model. estimate R0 value minimizes sum squares observed case counts case counts expected model. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"Fisman et al. (PloS One, 2013)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/idea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incidence Decay and Exponential Adjustment (IDEA) â idea","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. idea(cases, mu = 5 / 7) #> [1] 1.419546 # Obtain R0 when the serial distribution has a mean of three days. idea(cases, mu = 3 / 7) #> [1] 1.233927"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":null,"dir":"Reference","previous_headings":"","what":"Sequential Bayes (seqB) â seq_bayes","title":"Sequential Bayes (seqB) â seq_bayes","text":"function implements sequential Bayesian estimation method R0 due Bettencourt Riberio (PloS One, 2008). See details important implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sequential Bayes (seqB) â seq_bayes","text":"","code":"seq_bayes(cases, mu, kappa = 20, post = FALSE)"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sequential Bayes (seqB) â seq_bayes","text":"cases Vector case counts. vector must contain non-negative integers, least two positive integers. mu Mean serial distribution. must positive number. value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7. kappa Largest possible value uniform prior (defaults 20). must number greater equal 1. describes prior belief ranges R0, set higher value R0 believed larger. post Whether return posterior distribution R0 instead estimate R0 (defaults FALSE). must value identical TRUE FALSE.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sequential Bayes (seqB) â seq_bayes","text":"post identical TRUE, list containing following components returned: supp - support posterior distribution R0 pmf - probability mass function posterior distribution R0 Otherwise, post identical FALSE, estimate R0 returned. Note estimate equal sum(supp * pmf) (.e., posterior mean).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sequential Bayes (seqB) â seq_bayes","text":"method sets uniform prior distribution R0 possible values 0 kappa, discretized fine grid. distribution R0 updated sequentially, one update new case count observation. final estimate R0 mean (last) posterior distribution. prior distribution initial belief distribution R0, uninformative uniform distribution values 0 kappa. Users can change value kappa (.e., prior distribution changed uniform). case counts observed, influence prior distribution lessen final estimate. method based approximation SIR model, valid beginning epidemic. method assumes mean serial distribution (sometimes called serial interval) known. final estimate can quite sensitive value, sensitivity testing strongly recommended. Users careful units time (e.g., counts observed daily weekly?) implementing. code modified provide estimate even case counts equal zero present time intervals. done grouping counts periods time. Without grouping, presence zero counts, estimate can provided.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Sequential Bayes (seqB) â seq_bayes","text":"Bettencourt Riberio (PloS One, 2008)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sequential Bayes (seqB) â seq_bayes","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. seq_bayes(cases, mu = 5 / 7) #> [1] 1.026563 # Obtain R0 when the serial distribution has a mean of three days. seq_bayes(cases, mu = 3 / 7) #> [1] 1.015938 # Obtain R0 when the serial distribution has a mean of seven days, and R0 is # believed to be at most 4. estimate <- seq_bayes(cases, mu = 1, kappa = 4) # Same as above, but return the posterior distribution of R0 instead of the # estimate. posterior <- seq_bayes(cases, mu = 1, kappa = 4, post = TRUE) # Display the support and probability mass function of the posterior. posterior$supp #> [1] 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 0.12 0.13 0.14 #> [16] 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 #> [31] 0.30 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 0.44 #> [46] 0.45 0.46 0.47 0.48 0.49 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 #> [61] 0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 #> [76] 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 #> [91] 0.90 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.00 1.01 1.02 1.03 1.04 #> [106] 1.05 1.06 1.07 1.08 1.09 1.10 1.11 1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 #> [121] 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29 1.30 1.31 1.32 1.33 1.34 #> [136] 1.35 1.36 1.37 1.38 1.39 1.40 1.41 1.42 1.43 1.44 1.45 1.46 1.47 1.48 1.49 #> [151] 1.50 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59 1.60 1.61 1.62 1.63 1.64 #> [166] 1.65 1.66 1.67 1.68 1.69 1.70 1.71 1.72 1.73 1.74 1.75 1.76 1.77 1.78 1.79 #> [181] 1.80 1.81 1.82 1.83 1.84 1.85 1.86 1.87 1.88 1.89 1.90 1.91 1.92 1.93 1.94 #> [196] 1.95 1.96 1.97 1.98 1.99 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 #> [211] 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 #> [226] 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 #> [241] 2.40 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.50 2.51 2.52 2.53 2.54 #> [256] 2.55 2.56 2.57 2.58 2.59 2.60 2.61 2.62 2.63 2.64 2.65 2.66 2.67 2.68 2.69 #> [271] 2.70 2.71 2.72 2.73 2.74 2.75 2.76 2.77 2.78 2.79 2.80 2.81 2.82 2.83 2.84 #> [286] 2.85 2.86 2.87 2.88 2.89 2.90 2.91 2.92 2.93 2.94 2.95 2.96 2.97 2.98 2.99 #> [301] 3.00 3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10 3.11 3.12 3.13 3.14 #> [316] 3.15 3.16 3.17 3.18 3.19 3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 3.28 3.29 #> [331] 3.30 3.31 3.32 3.33 3.34 3.35 3.36 3.37 3.38 3.39 3.40 3.41 3.42 3.43 3.44 #> [346] 3.45 3.46 3.47 3.48 3.49 3.50 3.51 3.52 3.53 3.54 3.55 3.56 3.57 3.58 3.59 #> [361] 3.60 3.61 3.62 3.63 3.64 3.65 3.66 3.67 3.68 3.69 3.70 3.71 3.72 3.73 3.74 #> [376] 3.75 3.76 3.77 3.78 3.79 3.80 3.81 3.82 3.83 3.84 3.85 3.86 3.87 3.88 3.89 #> [391] 3.90 3.91 3.92 3.93 3.94 3.95 3.96 3.97 3.98 3.99 4.00 posterior$pmf #> [1] 4.396081e-14 6.866777e-14 1.069979e-13 1.663113e-13 2.578585e-13 #> [6] 3.987896e-13 6.151736e-13 9.465244e-13 1.452563e-12 2.223289e-12 #> [11] 3.393931e-12 5.167074e-12 7.845296e-12 1.187914e-11 1.793742e-11 #> [16] 2.700983e-11 4.055633e-11 6.072366e-11 9.065827e-11 1.349567e-10 #> [21] 2.003117e-10 2.964355e-10 4.373744e-10 6.433725e-10 9.435054e-10 #> [26] 1.379386e-09 2.010358e-09 2.920742e-09 4.229916e-09 6.106257e-09 #> [31] 8.786364e-09 1.260143e-08 1.801328e-08 2.566338e-08 3.643912e-08 #> [36] 5.156328e-08 7.271379e-08 1.021837e-07 1.430935e-07 1.996715e-07 #> [41] 2.776227e-07 3.846102e-07 5.308815e-07 7.300785e-07 1.000278e-06 #> [46] 1.365319e-06 1.856496e-06 2.514686e-06 3.393017e-06 4.560205e-06 #> [51] 6.104659e-06 8.139541e-06 1.080892e-05 1.429523e-05 1.882819e-05 #> [56] 2.469542e-05 3.225496e-05 4.194988e-05 5.432506e-05 7.004650e-05 #> [61] 8.992291e-05 1.149299e-04 1.462361e-04 1.852322e-04 2.335598e-04 #> [66] 2.931434e-04 3.662201e-04 4.553696e-04 5.635406e-04 6.940727e-04 #> [71] 8.507116e-04 1.037615e-03 1.259347e-03 1.520857e-03 1.827440e-03 #> [76] 2.184679e-03 2.598363e-03 3.074378e-03 3.618573e-03 4.236597e-03 #> [81] 4.933708e-03 5.714562e-03 6.582973e-03 7.541662e-03 8.591990e-03 #> [86] 9.733699e-03 1.096466e-02 1.228062e-02 1.367503e-02 1.513890e-02 #> [91] 1.666067e-02 1.822624e-02 1.981901e-02 2.142007e-02 2.300844e-02 #> [96] 2.456146e-02 2.605520e-02 2.746508e-02 2.876640e-02 2.993509e-02 #> [101] 3.094837e-02 3.178548e-02 3.242832e-02 3.286214e-02 3.307605e-02 #> [106] 3.306344e-02 3.282231e-02 3.235539e-02 3.167013e-02 3.077854e-02 #> [111] 2.969682e-02 2.844487e-02 2.704571e-02 2.552471e-02 2.390888e-02 #> [116] 2.222595e-02 2.050365e-02 1.876888e-02 1.704700e-02 1.536123e-02 #> [121] 1.373213e-02 1.217724e-02 1.071084e-02 9.343864e-03 8.083917e-03 #> [126] 6.935429e-03 5.899896e-03 4.976201e-03 4.160989e-03 3.449076e-03 #> [131] 2.833858e-03 2.307727e-03 1.862441e-03 1.489475e-03 1.180313e-03 #> [136] 9.266886e-04 7.207803e-04 5.553459e-04 4.238132e-04 3.203273e-04 #> [141] 2.397617e-04 1.777009e-04 1.304008e-04 9.473459e-05 6.812875e-05 #> [146] 4.849552e-05 3.416469e-05 2.381841e-05 1.643092e-05 1.121447e-05 #> [151] 7.572111e-06 5.057422e-06 3.340936e-06 2.182658e-06 1.410047e-06 #> [156] 9.006636e-07 5.687529e-07 3.550312e-07 2.190488e-07 1.335660e-07 #> [161] 8.047851e-08 4.791165e-08 2.817909e-08 1.637135e-08 9.394205e-09 #> [166] 5.323528e-09 2.978849e-09 1.645703e-09 8.975385e-10 4.831664e-10 #> [171] 2.566999e-10 1.345806e-10 6.961600e-11 3.552605e-11 1.788285e-11 #> [176] 8.878079e-12 4.346434e-12 2.098061e-12 9.984180e-13 4.683320e-13 #> [181] 2.165108e-13 9.863385e-14 4.427200e-14 1.957601e-14 8.526007e-15 #> [186] 3.657023e-15 1.544555e-15 6.422519e-16 2.628849e-16 1.059047e-16 #> [191] 4.198412e-17 1.637588e-17 6.283525e-18 2.371425e-18 8.801378e-19 #> [196] 3.211841e-19 1.152248e-19 4.063045e-20 1.407979e-20 4.794057e-21 #> [201] 1.603601e-21 5.268629e-22 1.699923e-22 5.385323e-23 1.674809e-23 #> [206] 5.112219e-24 1.531308e-24 4.500307e-25 1.297374e-25 3.668155e-26 #> [211] 1.016962e-26 2.764082e-27 7.363758e-28 1.922485e-28 4.917601e-29 #> [216] 1.232200e-29 3.023823e-30 7.265876e-31 1.709166e-31 3.935069e-32 #> [221] 8.865403e-33 1.954015e-33 4.212549e-34 8.880821e-35 1.830437e-35 #> [226] 3.687665e-36 7.260114e-37 1.396466e-37 2.623677e-38 4.813725e-39 #> [231] 8.622619e-40 1.507577e-40 2.572153e-41 4.281381e-42 6.950773e-43 #> [236] 1.100362e-43 1.698170e-44 2.554224e-45 3.743317e-46 5.343929e-47 #> [241] 7.429450e-48 1.005609e-48 1.324832e-49 1.698377e-50 2.118019e-51 #> [246] 2.568782e-52 3.029042e-53 3.471688e-54 3.866425e-55 4.182989e-56 #> [251] 4.394865e-57 4.482891e-58 4.438084e-59 4.263117e-60 3.972112e-61 #> [256] 3.588768e-62 3.143145e-63 2.667736e-64 2.193531e-65 1.746742e-66 #> [261] 1.346659e-67 1.004822e-68 7.254056e-70 5.065108e-71 3.419533e-72 #> [266] 2.231354e-73 1.406840e-74 8.567343e-76 5.037567e-77 2.859011e-78 #> [271] 1.565585e-79 8.268889e-81 4.210849e-82 2.066735e-83 9.773085e-85 #> [276] 4.450901e-86 1.951504e-87 8.234368e-89 3.342436e-90 1.304664e-91 #> [281] 4.895156e-93 1.764793e-94 6.110914e-96 2.031551e-97 6.481579e-99 #> [286] 1.983744e-100 5.821848e-102 1.637658e-103 4.413549e-105 1.139116e-106 #> [291] 2.814324e-108 6.652979e-110 1.504185e-111 3.251137e-113 6.714617e-115 #> [296] 1.324527e-116 2.494329e-118 4.482263e-120 7.682227e-122 1.255211e-123 #> [301] 1.954236e-125 2.897728e-127 4.090211e-129 5.493210e-131 7.015875e-133 #> [306] 8.517133e-135 9.822891e-137 1.075711e-138 1.117987e-140 1.102133e-142 #> [311] 1.030047e-144 9.121635e-147 7.649726e-149 6.072101e-151 4.559445e-153 #> [316] 3.236849e-155 2.171329e-157 1.375543e-159 8.224662e-162 4.638796e-164 #> [321] 2.466499e-166 1.235627e-168 5.828612e-171 2.587326e-173 1.080141e-175 #> [326] 4.238233e-178 1.562047e-180 5.404238e-183 1.754003e-185 5.337064e-188 #> [331] 1.521491e-190 4.061129e-193 1.014255e-195 2.368537e-198 5.168362e-201 #> [336] 1.053099e-203 2.002304e-206 3.550040e-209 5.865079e-212 9.022847e-215 #> [341] 1.291610e-217 1.719185e-220 2.126178e-223 2.441418e-226 2.600913e-229 #> [346] 2.568760e-232 2.350200e-235 1.990379e-238 1.559119e-241 1.128742e-244 #> [351] 7.546371e-248 4.655454e-251 2.647985e-254 1.387535e-257 6.692519e-261 #> [356] 2.968866e-264 1.210268e-267 4.529956e-271 1.555446e-274 4.895396e-278 #> [361] 1.410954e-281 3.720881e-285 8.970133e-289 1.975060e-292 3.968208e-296 #> [366] 7.268446e-300 1.212601e-303 1.840840e-307 2.540527e-311 3.184384e-315 #> [371] 3.621946e-319 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [376] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [381] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [386] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [391] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [396] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [401] 0.000000e+00 # Note that the following always holds: estimate == sum(posterior$supp * posterior$pmf) #> [1] TRUE"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/web.html","id":null,"dir":"Reference","previous_headings":"","what":"Launch the Rnaught Web Application â web","title":"Launch the Rnaught Web Application â web","text":"entry point Rnaught web application, creates returns Shiny app object. invoked directly, web application launched.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/web.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Launch the Rnaught Web Application â web","text":"","code":"web()"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/web.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Launch the Rnaught Web Application â web","text":"Shiny app object Rnaught web application.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/web.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Launch the Rnaught Web Application â web","text":"following dependencies required run application: shiny bslib DT plotly packages missing launch, prompt appear install . configure settings port, host default browser, set Shiny's global options (see shiny::runApp()).","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":null,"dir":"Reference","previous_headings":"","what":"White and Pagano (WP) â wp","title":"White and Pagano (WP) â wp","text":"function implements R0 estimation due White Pagano (Statistics Medicine, 2008). method based maximum likelihood estimation Poisson transmission model. See details important implementation notes.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"White and Pagano (WP) â wp","text":"","code":"wp( cases, mu = NA, serial = FALSE, grid_length = 100, max_shape = 10, max_scale = 10 )"},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"White and Pagano (WP) â wp","text":"cases Vector case counts. vector must length least two contain positive integers. mu Mean serial distribution (defaults NA). must positive number NA. number specified, value match case counts time units. example, case counts weekly serial distribution mean seven days, mu set 1. case counts daily serial distribution mean seven days, mu set 7. serial Whether return estimated serial distribution addition estimate R0 (defaults FALSE). must value identical TRUE FALSE. grid_length length grid grid search (defaults 100). must positive integer. used mu set NA. grid search go combinations shape scale parameters gamma distribution, grid_length evenly spaced values 0 (exclusive) max_shape max_scale (inclusive), respectively. Note larger values result longer search time. max_shape largest possible value shape parameter grid search (defaults 10). must positive number. used mu set NA. Note larger values result longer search time, may cause numerical instabilities. max_scale largest possible value scale parameter grid search (defaults 10). must positive number. used mu set NA. Note larger values result longer search time, may cause numerical instabilities.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"White and Pagano (WP) â wp","text":"serial identical TRUE, list containing following components returned: r0 - estimate R0 supp - support estimated serial distribution pmf - probability mass function estimated serial distribution Otherwise, serial identical FALSE, estimate R0 returned.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"White and Pagano (WP) â wp","text":"method based Poisson transmission model, hence may valid beginning epidemic. model, serial distribution assumed discrete finite number possible values. implementation, mu NA, serial distribution taken discretized version gamma distribution shape parameter 1 scale parameter mu (hence mean mu). mu NA, function implements grid search algorithm find maximum likelihood estimator possible gamma distributions unknown shape scale, restricting prespecified grid (see parameters grid_length, max_shape max_scale). cases, largest value support chosen cumulative distribution function original (pre-discretized) gamma distribution cumulative probability 0.999 value. serial distribution known (.e., mu NA), sensitivity testing mu strongly recommended. serial distribution unknown (.e., mu NA), likelihood function can flat near maximum, resulting numerical instability optimizer. mu NA, implementation takes considerably longer run. Users careful units time (e.g., counts observed daily weekly?) implementing. model developed White Pagano (2008) discrete, hence serial distribution finite discrete. implementation, input value mu continuous distribution. algorithm discretizes input, mean estimated serial distribution returned (serial set TRUE) differ mu somewhat. say, user notices input mu mean estimated serial distribution different, expected, caused discretization.","code":""},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"White and Pagano (WP) â wp","text":"White Pagano (Statistics Medicine, 2008)","code":""},{"path":[]},{"path":"https://MI2YorkU.github.io/Rnaught/reference/wp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"White and Pagano (WP) â wp","text":"","code":"# Weekly data. cases <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4) # Obtain R0 when the serial distribution has a mean of five days. wp(cases, mu = 5 / 7) #> [1] 1.107862 # Obtain R0 when the serial distribution has a mean of three days. wp(cases, mu = 3 / 7) #> [1] 1.067642 # Obtain R0 when the serial distribution is unknown. # Note that this will take longer to run than when `mu` is known. wp(cases) #> [1] 1.495574 # Same as above, but specify custom grid search parameters. The larger any of # the parameters, the longer the search will take, but with potentially more # accurate estimates. wp(cases, grid_length = 40, max_shape = 4, max_scale = 4) #> [1] 1.495574 # Return the estimated serial distribution in addition to the estimate of R0. estimate <- wp(cases, serial = TRUE) # Display the estimate of R0, as well as the support and probability mass # function of the estimated serial distribution returned by the grid search. estimate$r0 #> [1] 1.495574 estimate$supp #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 estimate$pmf #> [1] 0.3295449612 0.1855210503 0.1282030815 0.0920057871 0.0672100630 #> [6] 0.0496097863 0.0368701329 0.0275354532 0.0206388543 0.0155131855 #> [11] 0.0116867019 0.0088202295 0.0066670102 0.0050459517 0.0038232730 #> [16] 0.0028996361 0.0022009767 0.0016718921 0.0012708261 0.0009665381 #> [21] 0.0007354976 0.0005599523 0.0004264906 0.0003249676 0.0002477014"}] diff --git a/sitemap.xml b/sitemap.xml index 1735532..1e122ee 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -8,11 +8,12 @@ https://MI2YorkU.github.io/Rnaught/index.html https://MI2YorkU.github.io/Rnaught/reference/COVIDCanada.html https://MI2YorkU.github.io/Rnaught/reference/COVIDCanadaPT.html -https://MI2YorkU.github.io/Rnaught/reference/app.html +https://MI2YorkU.github.io/Rnaught/reference/Rnaught-package.html https://MI2YorkU.github.io/Rnaught/reference/id.html https://MI2YorkU.github.io/Rnaught/reference/idea.html https://MI2YorkU.github.io/Rnaught/reference/index.html https://MI2YorkU.github.io/Rnaught/reference/seq_bayes.html +https://MI2YorkU.github.io/Rnaught/reference/web.html https://MI2YorkU.github.io/Rnaught/reference/wp.html