SEIRfansy

Extended Susceptible-Exposed-Infected-Recovery (SEIR) Model for handling high False Negative Rate and Symptom based administration of diagnostic tests

https://github.com/umich-biostatistics/seirfansy

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Extended Susceptible-Exposed-Infected-Recovery (SEIR) Model for handling high False Negative Rate and Symptom based administration of diagnostic tests

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures"
)
```

# R package `SEIRfansy`

# Extended Susceptible-Exposed-Infected-Recovery Model

`r badger::badge_devel("umich-biostatistics/SEIRfansy", "blue")` `r badger::badge_code_size("umich-biostatistics/SEIRfansy")`
`r badger::badge_doi("https://doi.org/10.1101/2020.09.24.20200238", "orange") `

## Overview

This `R` package fits Extended Susceptible-Exposed-Infected-Recovery (SEIR) Models for handling high false negative rate and symptom based administration of diagnostic tests.

## Installation

If the devtools package is not yet installed, install it first:

```{r, eval=FALSE}
install.packages('devtools')
```

```{r, eval = FALSE}
# install SEIRfansy from Github:
devtools::install_github('umich-biostatistics/SEIRfansy') 
```

Once installed, load the package:

```{r, eval = TRUE}
library(SEIRfansy)
```

## Example Usage

For this example, we use the built-in package data set `covid19`, which contains dailies and totals of cases, recoveries, and deaths from the COVID-19 outbreak in India from January 30 to September 21 of 2020.

### Setup

You will need the `dplyr` package for this example.

```{r, eval = TRUE}
library(dplyr)
```

Training data set:

For training data, we use cases from April 1 to June 30

```{r, eval = TRUE}
train = covid19[which(covid19$Date == "01 April "):which(covid19$Date == "30 June "),]
```

Testing data set:

For testing data, we use cases from July 1 to July 31

```{r, eval = TRUE}
test = covid19[which(covid19$Date == "01 July "):which(covid19$Date == "31 July "),]
```

Data format for multinomial and Poisson distribution:

```{r, eval = TRUE}
train_multinom = 
  train %>% 
  rename(Confirmed = Daily.Confirmed, 
         Recovered = Daily.Recovered,
         Deceased = Daily.Deceased) %>%
  dplyr::select(Confirmed, Recovered, Deceased)

test_multinom = 
  test %>% 
  rename(Confirmed = Daily.Confirmed, 
         Recovered = Daily.Recovered,
         Deceased = Daily.Deceased) %>%
  dplyr::select(Confirmed, Recovered, Deceased)

train_pois = 
  train %>% 
  rename(Confirmed = Daily.Confirmed) %>%
  dplyr::select(Confirmed)
```

Initialize parameters:

```{r, eval = TRUE}
N = 1341e6 # population size of India
data_initial = c(2059, 169, 58, 424, 9, 11)
pars_start = c(c(1,0.8,0.6,0.4,0.2), c(0.2,0.2,0.2,0.25,0.2))
phases = c(1,15,34,48,62)
```

## SEIRfansy()

If interest is in model estimation but not prediction, then use `SEIRfansy()`. Otherwise, use `SEIRfansy.predict()` (see below).

```{r, eval = FALSE}
?SEIRfansy
```

```{r, eval = TRUE}
cov19est = SEIRfansy(data = train_multinom, init_pars = pars_start, 
                     data_init = data_initial, niter = 1e3, BurnIn = 1e2, 
                     model = "Multinomial", N = N, lambda = 1/(69.416 * 365), 
                     mu = 1/(69.416 * 365), period_start = phases, opt_num = 1, 
                     auto.initialize = TRUE, f = 0.15)
```

Inspect the results:

```{r, eval = FALSE}
names(cov19est)
class(cov19est$mcmc_pars)
names(cov19est$plots)
```

Plot the results:

```{r, eval = TRUE}
plot(cov19est, type = "trace")
plot(cov19est, type = "boxplot")
```

## SEIRfansy.predict()

If interest is in model estimation and prediction, then use `SEIRfansy.predict()`, which first runs `SEIRfansy()` internally, and then predicts.

```{r, eval = FALSE}
?SEIRfansy.predict
```

```{r, eval = TRUE}
cov19pred = SEIRfansy.predict(data = train_multinom, init_pars = pars_start, 
                              data_init = data_initial, T_predict = 60, niter = 1e3, 
                              BurnIn = 1e2, data_test = test_multinom, model = "Multinomial", 
                              N = N, lambda = 1/(69.416 * 365), mu = 1/(69.416 * 365), 
                              period_start = phases, opt_num = 1, 
                              auto.initialize = TRUE, f = 0.15)
```

Inspect the results:

```{r, eval = FALSE}
names(cov19pred)
class(cov19pred$prediction)
class(cov19pred$mcmc_pars)
names(cov19pred$plots)
```

Plot the results:

```{r, eval = TRUE}
plot(cov19pred, type = "trace")
plot(cov19pred, type = "boxplot")
plot(cov19pred, type = "panel")
plot(cov19pred, type = "cases")
```

### Current Suggested Citation

Ritwik Bhaduri, Ritoban Kundu, Soumik Purkayastha, Mike Kleinsasser, Lauren J Beesley, Bhramar Mukherjee. "EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy." medRxiv 2020.09.24.20200238; doi: https://doi.org/10.1101/2020.09.24.20200238

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