################################### ### Load and clean data sources ### ################################### #### ## Data independent of location #### titer_emp <- read.csv("data/host_response.csv") human_titer_emp <- read.csv("data/human_titre.csv") h_to_m_emp <- read.csv("data/mosquito_infection.csv") m_to_h_emp <- read.csv("data/mosquito_transmission.csv") ## Drop one row about sheep with too little information titer_emp <- titer_emp %>% filter(!is.na(sd.titre)) %>% mutate( infected.dose = as.numeric(infected.dose), titre.duration.days = as.numeric(titre.duration.days)) ## Raw calculated mosquito survival from Russel 1987 (not actually used because of many issues connecting these data to actual survival rates) #mosq_surv <- read.csv("data/mosq_surv_raw.csv") %>% dplyr::select(mos_species, trap_method, gonotrophic_cycle_days, SR1, SR2) %>% # rename(gon_len = gonotrophic_cycle_days) %>% # group_by(mos_species) %>% # summarize(SR1 = mean(SR1, na.rm = T), SR2 = mean(SR2, na.rm = T), gon_len = mean(gon_len)) %>% # mutate(mos_species = as.character(mos_species)) #mosq_surv <- rbind(mosq_surv # , data.frame(mos_species = "average", SR1 = mean(mosq_surv$SR1), SR2 = mean(mosq_surv$SR2), gon_len = 3)) #### ## Data dependent on location #### mosq_blood <- read.csv("data/mosquito_feeding.csv") %>% filter(Area.name == focal.location) %>% dplyr::select(-Reference, -Area.name, -Habitat, -Climatic.Zone, -Total, -mixed, -other) %>% rename(mos_species = Vector) %>% pivot_longer(-mos_species, names_to = "host_species", values_to = "prop") %>% group_by(mos_species, host_species) %>% summarize(prop = sum(prop, na.rm = T)) %>% pivot_wider(id_cols = mos_species, values_from = "prop", names_from = host_species) %>% rename(macropod = marsupial) host_prop <- read.csv("data/host_abundance.csv") %>% mutate(dens = count / area_km2) %>% filter(area == focal.location) %>% rename(host_species = species) %>% dplyr::select(host_species, dens) host_prop[is.na(host_prop$dens), ]$dens <- 0 mosq_prop <- read.csv("data/mosquito_abundance.csv") %>% dplyr::select(site, sp, count) %>% filter(site == focal.location) %>% dplyr::rename(mos_species = sp) %>% group_by(mos_species) %>% summarize(count = sum(count)) ## Drop all species for which we have no biting data and no observed host abundance no_blood <- which(mosq_blood %>% ungroup(mos_species) %>% dplyr::select(-mos_species) %>% colSums(.) == 0) %>% names() no_blood_no_obs <- no_blood[!(no_blood %in% host_prop$host_species)] no_blood_in_obs <- host_prop[which(host_prop$dens == 0), ]$host_species[host_prop[which(host_prop$dens == 0), ]$host_species %in% no_blood] hosts.remove <- c(no_blood_no_obs, no_blood_in_obs) mosq_blood <- mosq_blood %>% dplyr::select(-hosts.remove) host_prop <- host_prop %>% filter(host_species %!in% hosts.remove) ## For host seroprevalence we have to assume the same across locations. Combine "marsupial" because that is what we have for other data sources host_sero <- read.csv("data/host_seroprevalence.csv") host_sero.m <- host_sero %>% filter(host_species == "macropod" | host_species == "bandicoot") host_sero.m <- data.frame(host_species = "marsupial", prop_positive = mean(host_sero.m$prop_positive) , reference = "see raw data", note = "combined bandicoot and macropod") host_sero.o <- host_sero %>% filter(!(host_species == "macropod" | host_species == "bandicoot")) host_sero <- rbind(host_sero.o, host_sero.m) %>% mutate(host_species = mapvalues(host_species, to = "macropod", from = "marsupial"))