getdelaydata: Get delay data for one or more cities for delay analysis

In AlertaDengue/AlertTools: utility functions for the Infodengue Alert System

Get delay data for one or more cities for delay analysis

Description

Gets delay data for one or more cities. Internal function used in the delay fitting using inla.

Usage

getdelaydata(
  cities,
  nyears = 2,
  cid10 = "A90",
  lastday = Sys.Date(),
  datasource = con
)

Arguments

cities	vector with geocodes
nyears	number of years of data used for fitting: Default = 2 years
cid10	disease code, Default is dengue. "A92.0" for chik, "A92.8" for zika
lastday	last digitation day
datasource	valid connection to database

Details

# bayesnowcasting ——————————————————————— #'@description This function estimate the Bayesian nowcast (new version) #'@title Correct incidence data with notification delay (nowcasting). #'@export #'@param obj data.frame with individual cases, containing columns municipio_geocodigo, dt_notific, dt_sin_pri, dt_digita #'@param Dmax for the "bayesian" method. Maximum number of weeks that is modeled #'@param Fim date for the nowcasting (date). Default is today. #'@return data.frame with median and 95percent confidence interval for the #'predicted cases-to-be-notified) #'@examples #' # bayesian #'dd <- getdelaydata(cities=3167202, nyears=1, cid10="A90", datasource=con) #'resfit<-bayesnowcasting(dd) #'resfit

bayesnowcasting <- function(d, Dmax = 10, Fim = Sys.Date())

# check input if(is.null(names(d))) message("bayesnowcasting: no data, returning NULL") return(NULL)

if(nrow(d) < 50) message("bayesnowcasting: few data, returning NULL") return(NULL)

#d contains columns assert_that(all(c("municipio_geocodigo", "dt_notific", "dt_sin_pri", "dt_digita") municipio_geocodigo, dt_notific, dt_sin_pri, dt_digita")

d <- d dt_notific_epiweek = epiweek(dt_notific), dt_notific_aux = as.numeric(format(as.Date(dt_notific), " dt_notific_week = dt_notific + 6 - dt_notific_aux, dt_notific_epiyear = epiyear(dt_notific), dt_digita_epiweek = epiweek(dt_digita), dt_digita_epiyear = epiyear(dt_digita), delay_epiweek = ifelse( dt_digita_epiyear == dt_notific_epiyear, as.numeric(dt_digita_epiweek - dt_notific_epiweek), as.numeric(dt_digita_epiweek - dt_notific_epiweek) + 52 ) )

# casos observados por semama obs <- d group_by(dt_notific_week) summarize(casos = n())

Inicio <- min(d$dt_notific_week) # Ultimo dia com notificacao ou digitacao #Fim <- max(d$dt_digita, d$dt_notific, na.rm = T) #Fim <- max(d$dt_digita, d$dt_notific, na.rm = T) #Fim <- Fim + 6 - as.numeric(format(as.Date(Fim), "

# contruindo a matriz de atraso - running triang tibble(Date = c(Inicio,Fim) ) mutate(Weekday = weekdays(Date) )

tbl.dates <- tibble(dt_notific_week = seq(Inicio, Fim, by = 7)) rowid_to_column(var = "Time") Today = max(tbl.dates$Time)

dados.ag <- d filter( dt_digita <= Fim) mutate( delay_epiweek = ifelse(delay_epiweek > Dmax, NA, delay_epiweek) ) drop_na(delay_epiweek) group_by(dt_notific_week, delay_epiweek) dplyr::summarise( Casos = n() ) # Passando para o formato wide spread(key = delay_epiweek, value = Casos) # Adicianoando todas as data, algumas semanas nao houveram casos full_join( y = tbl.dates, by = "dt_notific_week" ) # Voltando para o formato longo gather(key = "Delay", value = Casos, -dt_notific_week, -Time) mutate( Delay = as.numeric(Delay), # Preparing the run-off triangle Casos = ifelse( test = (Time + Delay) <= Today, yes = replace_na(Casos, 0), no = NA) ) dplyr::rename( Date = dt_notific_week) # Sorting by date dplyr::arrange(Date)

dados.ag.full <- d group_by(dt_notific_week) dplyr::summarise( Total = n() ) # Adicianoando todas as data, algumas semanas nao houveram casos right_join( y = tbl.dates, by = "dt_notific_week" ) mutate( Total = replace_na(Total, 0) ) dplyr::rename( Date = dt_notific_week) # Sorting by date dplyr::arrange(Date)

# Model equation model.dengue <- Casos ~ 1 + f(Time, model = "rw2", constr = T #hyper = list("prec" = list(prior = "loggamma", param = c(0.001, 0.001) )) #hyper = list("prec" = list(prior = half_normal_sd(.1) )) ) + f(Delay, model = "rw1", constr = T # hyper = list("prec" = list(prior = "loggamma", param = c(0.001, 0.001) )) #hyper = list("prec" = list(prior = half_normal_sd(.1) )) ) + # Efeito tempo-atraso f(TimeDelay, model = "iid", constr = T # hyper = list("prec" = list(prior = "loggamma", param = c(0.001, 0.001) )) )

output.dengue <- nowcast.INLA( model.day = model.dengue, dados.ag = dados.ag mutate(TimeDelay = paste(Time, Delay)) )

pred.dengue <- nowcasting(output.dengue, dados.ag, Dm = Dmax, Fim = max(dados.ag$Date))

pred.dengue.summy <- pred.dengue dplyr::summarise( Mean = mean(Casos, na.rm = TRUE), Median = median(Casos, na.rm = TRUE), LI = quantile(Casos, probs = 0.025, na.rm = TRUE), LS = quantile(Casos, probs = 0.975, na.rm = TRUE) ) left_join(obs, by = c("Date" = "dt_notific_week"))

pred.dengue.summy$SE <- daySEday(pred.dengue.summy$Date)$SE

pred.dengue.summy

Value

list with d = data.frame with data.

Examples

dados <- getdelaydata(cities=3304557, nyears=1, cid10="A90", 
lastday = as.Date("2023-12-31"))  # Not run without connection

AlertaDengue/AlertTools documentation built on Nov. 27, 2024, 11:55 p.m.