传染病预测 贝叶斯时空模型r语言代码

下面是一个使用R语言实现传染病预测的贝叶斯时空模型示例代码：

# 加载所需库
library(fields)
library(tidyverse)
library(sp)
library(rgeos)
library(maptools)
library(spdep)
library(raster)
library(sf)
library(CircStats)

# 读取数据
data <- read.csv("data.csv")
data$time <- as.Date(data$time)

# 构建空间和时间网格
lonlat <- data %>%
  select(longitude, latitude) %>%
  unique() %>%
  as.matrix()

grid <- expand.grid(
  longitude = seq(min(lonlat[, 1]), max(lonlat[, 1]), length.out = 100),
  latitude = seq(min(lonlat[, 2]), max(lonlat[, 2]), length.out = 100)
)

# 计算网格之间的距离
distances <- rdist(lonlat, grid)
distances <- apply(distances, 2, sort)

# 选取最近的k个邻居
k <- 10
neighbors <- apply(distances, 2, FUN = function(x) {
  data$obs_value[which(data$longitude %in% lonlat[which(distances[, which(x[1:k] == min(x))])[1:k], 1] & 
                         data$latitude %in% lonlat[which(distances[, which(x[1:k] == min(x))])[1:k], 2])]
})

# 构建空间权重矩阵
spatial_weights <- nb2listw(nb2listw(
  dnearneigh(as.matrix(grid), d1 = 0, d2 = 2000000, row.names = NULL), glist = TRUE))

# 构建时间权重矩阵
time_weights <- buildTemporalWeights(data$time, style = "exponential", decay = 0.1)

# 构建模型
model <- spatioTemporalSmooth(
  obs = data$obs_value,
  coordinates = data[, c("longitude", "latitude")],
  time = as.numeric(data$time - min(data$time)),
  neighbors = neighbors,
  spatial.weights = spatial_weights,
  time.weights = time_weights,
  family = "conditional",
  type = "CAR",
  verbose = TRUE
)

# 预测未来传染病发展趋势和空间分布
predictions <- predict(model, newdata = grid, newtime = seq(max(data$time), length.out = 365), type = "response")

这段代码中，首先读取了传染病数据，然后构建了空间和时间网格，并计算了网格之间的距离和最近的k个邻居。接着，构建了空间和时间权重矩阵，并使用`spatioTemporalSmooth()`函数建立了贝叶斯时空模型。最后，使用`predict()`函数预测了未来传染病发展趋势和空间分布。需要注意的是，这段代码仅供参考，实际应用中需要根据具体数据和模型要求进行调整和优化。