## Characterizing semi-nomadic populations
## Script to generate figures for manuscript
## Date: February 3, 2024
## Author: Hannah Meredith


## load libraries and data

library(dplyr)
library(tidyr)
library(ggplot2)
library(epiDisplay)
library(lme4)
library(extrafont)
library(leaflet)
library(leaflet.esri)
library(geosphere)

source("make_table_functions.R")

# Load data
household <- read.csv("Household_details.csv")  # Malaria analysis: household data 
member <- read.csv("Member_details.csv")        # Malaria analysis: household member data 
night.tracks <- read.csv("Night_tracks.csv")    # GPS logger sub-analysis: tracks logged at night 
all.tracks <- read.csv("All_tracks.csv")        # GPS logger sub-analysis: tracks logged at any point    
any.traveler <- read.csv("Any_traveler.csv")    # Travel analysis: travel survey data

################################################################################
########## Categorizing and characterizing individual trip types ###############
################################################################################

trip.types = characterize.trip.types(all.tracks, night.tracks)
all.tracks = trip.types[[1]]  # updated file with trip.types - used for plotting
gps.point.stats = trip.types[[2]]  #summary of trip.type details- used for tables

############Table 1: GPS details #############
majority.long <- gps.point.stats %>% filter(trip.type == "Longterm")
quantile(majority.long$Trip_length)
quantile(majority.long$num.changes)
quantile(majority.long$num.campsites)
quantile(majority.long$Total_night_distance_km)
quantile(majority.long$Total_distance_km)

majority.transient <- gps.point.stats %>% filter(trip.type == "Transient")
quantile(majority.transient$Trip_length)
quantile(majority.transient$num.changes)
quantile(majority.transient$num.campsites)
quantile(majority.transient$Total_night_distance_km)
quantile(majority.transient$Total_distance_km)

one.location <- gps.point.stats %>% filter(trip.type == "Static")
quantile(one.location$Trip_length)
quantile(one.location$num.changes)
quantile(one.location$num.campsites)
quantile(one.location$Total_night_distance_km)
quantile(one.location$Total_distance_km)

one.location.daytrips <- gps.point.stats %>% filter(trip.type == "Day_trip")
quantile(one.location.daytrips$Trip_length)
quantile(one.location.daytrips$num.changes)
quantile(one.location.daytrips$num.campsites)
quantile(one.location.daytrips$Total_night_distance_km)
quantile(one.location.daytrips$Total_distance_km)


############# Table 1: Survey data for various subsets of travelers ###############
travel.data <- all.tracks %>% group_by(GPS_carrier_ID) %>% slice(1)
travel.data <- travel.data %>%
  group_by(GPS_carrier_ID) %>%
  mutate(Camps_reported = ifelse(!is.na(Trip1_travel_time) & !is.na(Trip2_travel_time) & !is.na(Trip3_travel_time), 3,
                                 ifelse(!is.na(Trip1_travel_time) & !is.na(Trip2_travel_time) & is.na(Trip3_travel_time), 2, 1)),
         AnyCamp_Travel_Time = mean(c(Trip1_travel_time, Trip2_travel_time, Trip3_travel_time), na.rm = T),
         AnyCamp_Stay_duration = mean(c(Stay1_duration, Stay2_duration, Stay3_duration), na.rm = T),
         AnyCamp_nonhousehold_members = ifelse(sum(c(Camp1_nonhousehold_members, Camp2_nonhousehold_members, Camp3_nonhousehold_members), na.rm = T) >= 1, 1, 0),
         AnyCamp_people_count = median(c(Camp1_people_count, Camp2_people_count, Camp3_people_count), na.rm = T),
         AnyCamp_Water_Open = ifelse(sum(Camp1_Water_open, Camp2_Water_open, Camp3_Water_open) > 0 & sum(Camp1_Water_closed, Camp2_Water_closed, Camp3_Water_closed) == 0, 1, 0),
         AnyCamp_Water_OpenClosed = ifelse(sum(Camp1_Water_open, Camp2_Water_open, Camp3_Water_open) > 0 & sum(Camp1_Water_closed, Camp2_Water_closed, Camp3_Water_closed) > 0, 1, 0),
         AnyCamp_Water_Closed = ifelse(sum(Camp1_Water_open, Camp2_Water_open, Camp3_Water_open) == 0 & sum(Camp1_Water_closed, Camp2_Water_closed, Camp3_Water_closed) > 0, 1, 0),
         AnyCamp_goats = ifelse(sum(c(Camp1_goats, Camp2_goats, Camp3_goats), na.rm = T) > 0, 1, 0),
         AnyCamp_sheep = ifelse(sum(c(Camp1_sheep, Camp2_sheep, Camp3_sheep), na.rm = T) > 0, 1, 0),
         AnyCamp_Camels = ifelse(sum(c(Camp1_camels, Camp2_camels, Camp3_camels), na.rm = T) > 0, 1, 0),
         AnyCamp_Cattle = ifelse(sum(c(Camp1_cattle, Camp2_cattle, Camp3_cattle), na.rm = T) > 0, 1, 0))

travel.data <- left_join(travel.data, gps.point.stats, by = c("GPS_carrier_ID", "Total_trip_duration"="Trip_length", "trip.type"))

majority.long.all <- travel.data %>% filter(prop.longterm > 0.5 & night.all.change.status != "Night.Stay")
quantile(majority.long.all$Camps_reported)
quantile(majority.long.all$AnyCamp_Travel_Time, na.rm = T)
quantile(majority.long.all$AnyCamp_Stay_duration, na.rm = T) 
table(majority.long.all$AnyCamp_nonhousehold_members); prop.table(table(majority.long.all$AnyCamp_nonhousehold_members))
quantile(majority.long.all$AnyCamp_people_count, na.rm = T)
table(majority.long.all$AnyCamp_Water_Open); prop.table(table(majority.long.all$AnyCamp_Water_Open))
table(majority.long.all$AnyCamp_Water_OpenClosed); prop.table(table(majority.long.all$AnyCamp_Water_OpenClosed))
table(majority.long.all$AnyCamp_Water_Closed); prop.table(table(majority.long.all$AnyCamp_Water_Closed))
table(majority.long.all$AnyCamp_goats); prop.table(table(majority.long.all$AnyCamp_goats))
table(majority.long.all$AnyCamp_sheep); prop.table(table(majority.long.all$AnyCamp_sheep))
table(majority.long.all$AnyCamp_Camels); prop.table(table(majority.long.all$AnyCamp_Camels))
table(majority.long.all$AnyCamp_Cattle); prop.table(table(majority.long.all$AnyCamp_Cattle))
table(majority.long.all$Gender); prop.table(table(majority.long.all$Gender))
quantile(majority.long.all$Age)
table(majority.long.all$Facility); prop.table(table(majority.long.all$Facility))
table(majority.long.all$Trip1_travel_time)
table(majority.long.all$Trip2_travel_time)
table(majority.long.all$Trip3_travel_time)


majority.transient.all <- travel.data %>% filter(prop.longterm <=0.5 & night.all.change.status != "Night.Stay")
quantile(majority.transient.all$Camps_reported)
quantile(majority.transient.all$AnyCamp_Travel_Time, na.rm = T)
quantile(majority.transient.all$AnyCamp_Stay_duration, na.rm = T)
table(majority.transient.all$AnyCamp_nonhousehold_members)
quantile(majority.transient.all$AnyCamp_people_count, na.rm = T)
table(majority.transient.all$AnyCamp_Water_Open); prop.table(table(majority.transient.all$AnyCamp_Water_Open))
table(majority.transient.all$AnyCamp_Water_OpenClosed); prop.table(table(majority.transient.all$AnyCamp_Water_OpenClosed))
table(majority.transient.all$AnyCamp_Water_Closed); prop.table(table(majority.transient.all$AnyCamp_Water_Closed))
table(majority.transient.all$AnyCamp_goats); prop.table(table(majority.transient.all$AnyCamp_goats))
table(majority.transient.all$AnyCamp_sheep); prop.table(table(majority.transient.all$AnyCamp_sheep))
table(majority.transient.all$AnyCamp_Camels); prop.table(table(majority.transient.all$AnyCamp_Camels))
table(majority.transient.all$AnyCamp_Cattle); prop.table(table(majority.transient.all$AnyCamp_Cattle))
table(majority.transient.all$Gender); prop.table(table(majority.transient.all$Gender))
quantile(majority.transient.all$Age)
table(majority.transient.all$Facility); prop.table(table(majority.transient.all$Facility))
table(majority.transient.all$Trip1_travel_time)
table(majority.transient.all$Trip2_travel_time)
table(majority.transient.all$Trip3_travel_time)


one.location.all <- travel.data %>% filter(combo.change.stat == "All.Stay.Night.Stay")
quantile(one.location.all$Camps_reported)
table(one.location.all$AnyCamp_nonhousehold_members);prop.table(table(one.location.all$AnyCamp_nonhousehold_members)) 
quantile(one.location.all$AnyCamp_people_count, na.rm = T)
table(one.location.all$AnyCamp_Water_Open); prop.table(table(one.location.all$AnyCamp_Water_Open))
table(one.location.all$AnyCamp_Water_OpenClosed); prop.table(table(one.location.all$AnyCamp_Water_OpenClosed))
table(one.location.all$AnyCamp_Water_Closed); prop.table(table(one.location.all$AnyCamp_Water_Closed))
table(one.location.all$AnyCamp_goats); prop.table(table(one.location.all$AnyCamp_goats))
table(one.location.all$AnyCamp_sheep); prop.table(table(one.location.all$AnyCamp_sheep))
table(one.location.all$AnyCamp_Camels); prop.table(table(one.location.all$AnyCamp_Camels))
table(one.location.all$AnyCamp_Cattle); prop.table(table(one.location.all$AnyCamp_Cattle))
table(one.location.all$Gender); prop.table(table(one.location.all$Gender))
quantile(one.location.all$Age)
table(one.location.all$Facility); prop.table(table(one.location.all$Facility))
table(one.location.all$Trip1_travel_time)
table(one.location.all$Trip2_travel_time)
table(one.location.all$Trip3_travel_time)

one.location.daytrips.all <- travel.data %>% filter(combo.change.stat == "All.Change.Night.Stay")
quantile(one.location.daytrips.all$Camps_reported)
table(one.location.daytrips.all$AnyCamp_nonhousehold_members);prop.table(table(one.location.daytrips.all$AnyCamp_nonhousehold_members)) 
quantile(one.location.daytrips.all$AnyCamp_people_count, na.rm = T)
table(one.location.daytrips.all$AnyCamp_Water_Open); prop.table(table(one.location.daytrips.all$AnyCamp_Water_Open))
table(one.location.daytrips.all$AnyCamp_Water_OpenClosed); prop.table(table(one.location.daytrips.all$AnyCamp_Water_OpenClosed))
table(one.location.daytrips.all$AnyCamp_Water_Closed); prop.table(table(one.location.daytrips.all$AnyCamp_Water_Closed))
table(one.location.daytrips.all$AnyCamp_goats); prop.table(table(one.location.daytrips.all$AnyCamp_goats))
table(one.location.daytrips.all$AnyCamp_sheep); prop.table(table(one.location.daytrips.all$AnyCamp_sheep))
table(one.location.daytrips.all$AnyCamp_Camels); prop.table(table(one.location.daytrips.all$AnyCamp_Camels))
table(one.location.daytrips.all$AnyCamp_Cattle); prop.table(table(one.location.daytrips.all$AnyCamp_Cattle))
table(one.location.daytrips.all$Gender); prop.table(table(one.location.daytrips.all$Gender))
quantile(one.location.daytrips.all$Age)
table(one.location.daytrips.all$Facility); prop.table(table(one.location.daytrips.all$Facility))
table(one.location.daytrips.all$Trip1_travel_time)
table(one.location.daytrips.all$Trip2_travel_time)
table(one.location.daytrips.all$Trip3_travel_time)

### All GPS carriers
quantile(travel.data$Camps_reported)
table(travel.data$AnyCamp_nonhousehold_members);prop.table(table(travel.data$AnyCamp_nonhousehold_members)) 
quantile(travel.data$AnyCamp_people_count, na.rm = T)
table(travel.data$AnyCamp_Water_Open); prop.table(table(travel.data$AnyCamp_Water_Open))
table(travel.data$AnyCamp_Water_OpenClosed); prop.table(table(travel.data$AnyCamp_Water_OpenClosed))
table(travel.data$AnyCamp_Water_Closed); prop.table(table(travel.data$AnyCamp_Water_Closed))
table(travel.data$AnyCamp_goats); prop.table(table(travel.data$AnyCamp_goats))
table(travel.data$AnyCamp_sheep); prop.table(table(travel.data$AnyCamp_sheep))
table(travel.data$AnyCamp_Camels); prop.table(table(travel.data$AnyCamp_Camels))
table(travel.data$AnyCamp_Cattle); prop.table(table(travel.data$AnyCamp_Cattle))
table(travel.data$Gender); prop.table(table(travel.data$Gender))
quantile(travel.data$Age)
quantile(travel.data$Total_trip_duration)
table(travel.data$Facility); prop.table(table(travel.data$Facility))

### Travelers who did not carry GPS loggers
traveler.no.gps <- any.traveler %>% filter(GPS_study_status == 0)
quantile(traveler.no.gps$Camps_reported)
table(traveler.no.gps$AnyCamp_nonhousehold_members);prop.table(table(traveler.no.gps$AnyCamp_nonhousehold_members)) 
quantile(traveler.no.gps$AnyCamp_people_count, na.rm = T)
table(traveler.no.gps$AnyCamp_Water_Open); prop.table(table(traveler.no.gps$AnyCamp_Water_Open))
table(traveler.no.gps$AnyCamp_Water_OpenClosed); prop.table(table(traveler.no.gps$AnyCamp_Water_OpenClosed))
table(traveler.no.gps$AnyCamp_Water_Closed); prop.table(table(traveler.no.gps$AnyCamp_Water_Closed))
table(traveler.no.gps$AnyCamp_goats); prop.table(table(traveler.no.gps$AnyCamp_goats))
table(traveler.no.gps$AnyCamp_sheep); prop.table(table(traveler.no.gps$AnyCamp_sheep))
table(traveler.no.gps$AnyCamp_Camels); prop.table(table(traveler.no.gps$AnyCamp_Camels))
table(traveler.no.gps$AnyCamp_Cattle); prop.table(table(traveler.no.gps$AnyCamp_Cattle))
table(traveler.no.gps$Gender); prop.table(table(traveler.no.gps$Gender))
quantile(traveler.no.gps$Age, na.rm = T)
quantile(traveler.no.gps$Total_trip_duration)
table(traveler.no.gps$Facility); prop.table(table(traveler.no.gps$Facility))
table(traveler.no.gps$Trip1_travel_time)
table(traveler.no.gps$Trip2_travel_time)
table(traveler.no.gps$Trip3_travel_time)
no.gps.travel.time <- c(rep(0,(15+11+1)), rep(1, (32+9+1)), rep(2, (138+21+4)), rep(3,(13+7+0)), rep(4, 4))
quantile(no.gps.travel.time)

### All travelers, regardless of GPS loggers
traveler.all <- any.traveler 
quantile(traveler.all$Camps_reported)
quantile(traveler.all$AnyCamp_Travel_Time, na.rm = T)
quantile(traveler.all$AnyCamp_Stay_duration, na.rm = T)
table(traveler.all$AnyCamp_nonhousehold_members);prop.table(table(traveler.all$AnyCamp_nonhousehold_members)) 
quantile(traveler.all$AnyCamp_people_count, na.rm = T)
table(traveler.all$AnyCamp_Water_Open); prop.table(table(traveler.all$AnyCamp_Water_Open))
table(traveler.all$AnyCamp_Water_OpenClosed); prop.table(table(traveler.all$AnyCamp_Water_OpenClosed))
table(traveler.all$AnyCamp_Water_Closed); prop.table(table(traveler.all$AnyCamp_Water_Closed))
table(traveler.all$AnyCamp_goats); prop.table(table(traveler.all$AnyCamp_goats))
table(traveler.all$AnyCamp_sheep); prop.table(table(traveler.all$AnyCamp_sheep))
table(traveler.all$AnyCamp_Camels); prop.table(table(traveler.all$AnyCamp_Camels))
table(traveler.all$AnyCamp_Cattle); prop.table(table(traveler.all$AnyCamp_Cattle))
table(traveler.all$Gender); prop.table(table(traveler.all$Gender))
quantile(traveler.all$Age, na.rm = T)
quantile(traveler.all$Total_trip_duration)
table(traveler.all$Facility); prop.table(table(traveler.all$Facility))

table(traveler.all$Trip1_travel_time)
table(traveler.all$Trip2_travel_time)
table(traveler.all$Trip3_travel_time)
all.travel.time <- c(rep(0,(17+16+2)), rep(1, (42+18+1)), rep(2, (179+27+7)), rep(3,(18+8+2)), rep(4, 4))
quantile(all.travel.time)

##################################################################################
###### Plot individual GPS details for Supplement figures and Figure 3 ###########
##################################################################################

# tiff("S2 Fig.tiff", units="in", width=7, height=2.625, res=300)
ggplot(all.tracks %>% filter(trip.type == "Day_trip"))+
  facet_wrap(~GPS_carrier_ID, ncol = 3, scales = "free")+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  geom_segment(aes(x = as.Date(date_enrolled), xend = as.Date(date_enrolled), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_followed_up), xend = as.Date(date_followed_up), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_report_depart), xend = as.Date(date_report_depart), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  geom_segment(aes(x = as.Date(date_report_return), xend = as.Date(date_report_return), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  theme_bw()+
  theme(legend.background = element_blank(),
        strip.text = element_text(size = 8))+
  labs(x = "Date", y = "Location ID", color = "", title = "Day trips")
# dev.off()

# tiff("Fig3_daytrip.tiff", units="in", width=3, height=2.5, res=300)
ggplot(all.tracks %>% filter(GPS_carrier_ID == "R 4"))+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  theme_bw()+
  theme(legend.position = c(0.13, 0.87),
        legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "")
# dev.off()

# tiff("S3 Fig.tiff", units="in", width=9, height=10.5, res=300)
ggplot(all.tracks %>% filter(trip.type == "Static"))+
  facet_wrap(~GPS_carrier_ID, scales = "free", ncol = 3)+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  geom_segment(aes(x = as.Date(date_enrolled), xend = as.Date(date_enrolled), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_followed_up), xend = as.Date(date_followed_up), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_report_depart), xend = as.Date(date_report_depart), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  geom_segment(aes(x = as.Date(date_report_return), xend = as.Date(date_report_return), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  theme_bw()+
  theme(legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "", title = "Static")
# dev.off()

# tiff("Fig3_static.tiff", units="in", width=3, height=2.5, res=300)
ggplot(all.tracks %>% filter(GPS_carrier_ID == "L 9"))+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  theme_bw()+
  theme(legend.position = c(0.13, 0.87),
        legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "")
# dev.off()

# tiff("S4 Fig.tiff", units="in", width=15, height=10.5, res=300)
ggplot(all.tracks %>% filter(trip.type == "Transient"))+
  facet_wrap(~GPS_carrier_ID, scales = "free", ncol = 5)+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  geom_segment(aes(x = as.Date(date_enrolled), xend = as.Date(date_enrolled), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_followed_up), xend = as.Date(date_followed_up), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_report_depart), xend = as.Date(date_report_depart), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  geom_segment(aes(x = as.Date(date_report_return), xend = as.Date(date_report_return), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  theme_bw()+
  theme(legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "", title = "Transient")
# dev.off()

# tiff("Manuscripts/Fig3_transient.tiff", units="in", width=3, height=2.5, res=300)
ggplot(all.tracks %>% filter(GPS_carrier_ID == "N 5"))+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  theme_bw()+
  theme(legend.position = c(0.13, 0.87),
        legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "")
# dev.off()

# tiff("Manuscripts/S5 Fig.tiff", units="in", width=15, height=10.5, res=300)
ggplot(all.tracks %>% filter(trip.type == "Longterm"))+
  facet_wrap(~GPS_carrier_ID, scales = "free", ncol = 5)+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  geom_segment(aes(x = as.Date(date_enrolled), xend = as.Date(date_enrolled), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_followed_up), xend = as.Date(date_followed_up), y = 0, yend = max(ClusterID)), color = "blue")+
  geom_segment(aes(x = as.Date(date_report_depart), xend = as.Date(date_report_depart), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  geom_segment(aes(x = as.Date(date_report_return), xend = as.Date(date_report_return), y = 0, yend = max(ClusterID)), color = "blue", linetype = "dashed")+
  theme_bw()+
  theme(
    legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "", title = "Long term")
# dev.off()

# tiff("Fig3_longterm.tiff", units="in", width=3, height=2.5, res=300)
ggplot(all.tracks %>% filter(GPS_carrier_ID == "K 7"))+
  geom_point(aes(as.Date(Date_adj), ClusterID, color = Night), alpha = 0.5, size = 2)+
  theme_bw()+
  theme(legend.position = c(0.13, 0.87),
        legend.background = element_blank())+
  labs(x = "Date", y = "Location ID", color = "")
# dev.off()


##################################################################################
######## Traveler and Remainer characterization and malaria analysis #############
##################################################################################

#### Table 2: Household characteristics #####
household.table <- household.epi(df = household, time.pt = "enroll")

#### Table 3: Traveler vs Remainer characteristics ######

herder <- subset(member, Travel_status == 1)
herder$Water_open <- ifelse(herder$Camp1_Water_open == 1 | herder$Camp2_Water_open == 1 | herder$Camp3_Water_open == 1, 1, 0)  # Open water = River, lake, dam, spring, hand dug water pit
herder$Water_closed <- ifelse(herder$Camp1_Water_closed == 1 | herder$Camp2_Water_closed == 1 | herder$Camp3_Water_closed == 1, 1, 0) # Closed water = Tap water, well or borehole,
herder.all <- member.epi.follow(subset(herder))
herder.f.PCR.pos <- member.epi.follow.diff.denom(herder)

remainer <- subset(member, Travel_status == 0)
remainer$Water_open <- ifelse(remainer$Household_water %in% c("Open only", "Open or closed"), 1, 0)  # Open water = River, lake, dam, spring, hand dug water pit
remainer$Water_closed <- ifelse(remainer$Household_water %in% c("Closed only", "Open or closed"), 1, 0)  # Closed water = Tap water, well or borehole,
remainer.all <- member.epi.follow(subset(remainer))#, rdt_e_hm == 1))
remainer.f.PCR.pos <- member.epi.follow.diff.denom(remainer)

travel.remainer.table <- cbind(herder.all$member, herder.f.PCR.pos$member, remainer.all$member, remainer.f.PCR.pos$member)
colnames(travel.remainer.table) <- c("Traveler: All", "Traveler: PCR+", "Remainer: All", "Remainer: PCR+")
rownames(travel.remainer.table) <- rownames(herder.all)

#### Table 4: models ####
model.travel <- glm(PCR_result_Falciparum ~ Travel_status, data = member, family = "binomial")
exp(cbind(coef(model.travel), confint(model.travel))) 

model.age <- glm(PCR_result_Falciparum ~ Age_bin, data = member, family = "binomial")
exp(cbind(coef(model.age), confint(model.age))) 

model.Gender <- glm(PCR_result_Falciparum ~ Gender, data = member, family = "binomial")
exp(cbind(coef(model.Gender), confint(model.Gender))) 

model.Facility <- glm(PCR_result_Falciparum ~ Facility, data = member, family = "binomial")
exp(cbind(coef(model.Facility), confint(model.Facility))) 

model.water <- glm(PCR_result_Falciparum ~ Household_water, data = member, family = "binomial")
exp(cbind(coef(model.water), confint(model.water))) 

model.adjusted <- glm(PCR_result_Falciparum ~ Travel_status + Age_bin + Gender, data = member, family = "binomial")
exp(cbind(coef(model.adjusted), confint(model.adjusted)))