diff --git a/DESCRIPTION b/DESCRIPTION
index 9cd5179..4477460 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: jenner
 Title: Internal Montagu Helpers
-Version: 0.0.15
+Version: 0.0.16
 Description: Helpers for Montagu.
 License: MIT + file LICENSE
 Author: Rich FitzJohn
diff --git a/NEWS.md b/NEWS.md
index 4f04375..2523c55 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,5 @@
+### jenner 0.0.16
+ * VIMC-1738 modup method 1, method 2 verison 2 (gavi impact filtered from total impact by per-year-gavi-level)
 
 ### jenner 0.0.15
  * VIMC-14628 impact_method2 sql modification post Wes' touchstone fast forward
diff --git a/R/modified_update.R b/R/modified_update.R
index 86a2579..b2b88b2 100644
--- a/R/modified_update.R
+++ b/R/modified_update.R
@@ -9,26 +9,38 @@
 ##' @param touchstone_use Name of the touchstone that we are basing
 ##'   this off of
 ##'
+##' @param method two options: 1 = method1, 2 = method2
+##' method2_v2 is different from method2 in that, it takes per-year-gavi-level as an imput for gavi impact  
+##' 
 ##' @export
-modified_update_calculate <- function(con, touchstone_name_mod, touchstone_use) {
+modified_update_calculate <- function(con, touchstone_name_mod, touchstone_use, method = 2) {
+  if (!(method %in% 1:2)) {
+    stop("currently, only method 1 and 2 have been developed.")
+  }
   year_min <- 2001
   year_max <- 2030
-
+  
   sql <- paste("SELECT *",
                "  FROM touchstone",
                " WHERE touchstone_name = $1",
                sep = "\n")
   touchstone_mod <- DBI::dbGetQuery(con, sql, touchstone_name_mod)
-    i <- touchstone_mod$id != '201510gavi-42'
-    touchstone_mod <- touchstone_mod[which.max(touchstone_mod$version[i]), ]
+  ### touchstone 201510gavi-42 is imported from Rshiny, it contains Rubella only
+  ### It is not a target touchstone we are interested in.
+  i <- touchstone_mod$id != '201510gavi-42'
+  touchstone_mod <- touchstone_mod[which.max(touchstone_mod$version[i]), ]
   modup_by_itself <- any(touchstone_name_mod == touchstone_use)
   meta <- mu_prepare(con, touchstone_mod$id, modup_by_itself = modup_by_itself)
+  ## 201403gavi - SDF9 - does not have MCV1 coverage, we need to get rid of it to avoid triggering error message
+  if (touchstone_name_mod == "201403gavi") {
+    meta <- meta[meta$vaccine != "MCV1", ]
+  }
   meta <- mu_impact_metadata(meta, touchstone_use)
-
+  
   meta$touchstone_mod <-
     as.list(touchstone_mod[c("id", "touchstone_name", "version")])
   meta$touchstone_use <- touchstone_use
-
+  
   meta$group$touchstone_src <- meta$group$touchstone
   touchstone_src_native <- sub("-[0-9]{6}.*", "", meta$group$touchstone_name)
   meta$group$touchstone_name_dest <-
@@ -37,22 +49,24 @@ modified_update_calculate <- function(con, touchstone_name_mod, touchstone_use)
     "[%s] %s",
     ifelse(meta$group$activity_type == "routine", "Rout", "SIA"),
     meta$group$vaccine)
-
+  
   ## This is quite slow to run but we can just pull it once:
   fmt <- "SELECT * FROM v_target_pop_%s WHERE YEAR BETWEEN %d AND %d"
   pop <- list(
     total = DBI::dbGetQuery(con, sprintf(fmt, "total", year_min, year_max)),
     routine = DBI::dbGetQuery(con, sprintf(fmt, "routine", year_min, year_max)))
-
-
+  
+  
   index <- meta$group$index
   data <- lapply(index, function(i) mu_build_data(con, i, meta, pop))
-
+  
   data <- rbind_simple(data)
   ## Perform the update itself the update:
-  data$deaths_averted_new <- mu_scale("deaths_averted", data)
-  data$cases_averted_new <- mu_scale("cases_averted", data)
+  data$deaths_averted_new <- mu_scale("deaths_averted", data, method)
+  data$cases_averted_new <- mu_scale("cases_averted", data, method)
+  
   meta$data <- data
+  
   meta
 }
 
@@ -79,27 +93,27 @@ modified_update_summary_output <- function(con, res, path_meta) {
     x[j] <- tr[[to]][i[j]]
     x
   }
-
+  
   ## TODO: At the moment the metadata handling is very crude; the
   ## inputs are not properly documented or anything.
   meta_files <- c("gavi_country_data.csv", "tr_touchstone.csv",
                   "years_output.csv")
   stopifnot(all(file.exists(file.path(path_meta, meta_files))))
-
-
+  
+  
   ## Start on the meta
   group <- res$group
-
+  
   ## TODO: we need to filter this to only include the "gavi focal model"
   ## for each disease.  This will do for now:
   focal <- DBI::dbReadTable(con, "gavi_focal_model")
   focal$.code <- paste(focal$touchstone, focal$disease, focal$model, sep = "\n")
   group$is_focal <-
     paste(group$touchstone, group$disease, group$model, sep = "\n") %in%
-  focal$.code
-
+    focal$.code
+  
   tr_touchstone <- read_csv("meta/tr_touchstone.csv")
-
+  
   group_out <- data_frame(
     vaccine = group$vaccine,
     ## New columns
@@ -111,10 +125,10 @@ modified_update_summary_output <- function(con, res, path_meta) {
     model = group$model,
     touchstone = group$touchstone_name,
     vaccine_delivery = group$vaccine_delivery)
-
+  
   group_out$touchstone_short <-
     translate_vals(group$touchstone_name, tr_touchstone, "montagu_short")
-
+  
   ## The metadata that we have on countries differs from what the shiny
   ## app has (e.g WHO region of EMR vs EMRO for AFG), so we'll use this:
   ## cmp <- read.csv("../gavi-shiny-app/data_20170117.csv",
@@ -125,7 +139,7 @@ modified_update_summary_output <- function(con, res, path_meta) {
   ## write.csv(country, "meta/shiny/gavi_country_data.csv", row.names = FALSE)
   country <- read_csv(file.path(path_meta, "gavi_country_data.csv"))
   country$global <- NULL
-
+  
   ## Which leaves us with the rest:
   ##
   ## TODO: this should be target_pop_estimated_new which needs to come
@@ -146,17 +160,17 @@ modified_update_summary_output <- function(con, res, path_meta) {
                    cases_averted = "cases_averted_new",
                    fvps = "fvps_new",
                    population = "target_pop_given_new")
-
+  
   dat <- res$data[cols]
   names(dat) <- names(cols)
   tmp <- country[match(dat$country, country$country),
                  setdiff(names(country), "country")]
   dat <- cbind(dat, tmp)
   rownames(dat) <- NULL
-
+  
   dat_updated <- dat
   dat_updated[names(cols_update)] <- res$data[cols_update]
-
+  
   group_out_updated <- group_out
   ## Makeup the suffix from res$touchstone_mod$touchstone_name
   name_suffix <- unlist(strsplit(res$touchstone_mod$touchstone_name, "-"))
@@ -165,26 +179,26 @@ modified_update_summary_output <- function(con, res, path_meta) {
     paste0(sub("-[0-9]{6}.*", "", group_out$touchstone),"-",
            name_suffix )
   stopifnot(all(group_out_updated$touchstone %in%
-                tr_touchstone$montagu))
+                  tr_touchstone$montagu))
   group_out_updated$touchstone_short <-
     translate_vals(group_out_updated$touchstone,
                    tr_touchstone,
                    from = "montagu",
                    to = "montagu_short")
-
+  
   i <- match(res$data$index, res$group$index)
   d1 <- cbind(group_out[i, ], dat)
   d2 <- cbind(group_out_updated[i, ], dat_updated)
   d3 <- rbind(d1, d2)
   rownames(d3) <- NULL
-
+  
   ## Filter output to a set of years per touchstone:
   keep <- read_csv("meta/years_output.csv")
   i <- logical(nrow(d3))
   for (j in seq_len(nrow(keep))) {
     i[d3$touchstone == keep$touchstone[j] &
-      d3$year >= keep$year_from[j] &
-      d3$year <= keep$year_to[j]] <- TRUE
+        d3$year >= keep$year_from[j] &
+        d3$year <= keep$year_to[j]] <- TRUE
   }
   ## Exclude MCV1
   i[d3$vaccine == "MCV1"] <- FALSE
@@ -198,7 +212,7 @@ mu_prepare <- function(con, touchstone_new, modup_by_itself = FALSE) {
   ## this duplication later.
   ## sometimes we do modup for touchstone_old = touchstone_new
   ## the if-else clause is important to deal with this
-  if(modup_by_itself) {
+  if (modup_by_itself) {
     touchstone_new2 <- NULL
   } else {
     touchstone_new2 <- touchstone_new
@@ -213,13 +227,13 @@ mu_prepare <- function(con, touchstone_new, modup_by_itself = FALSE) {
     whisker::whisker.render(txt, list(touchstone_new2 = touchstone_new2, 
                                       touchstone_new = touchstone_new))
   }
-
+  
   views <- c("migrate_coverage", "burden_fvps", "impact", "coverage",
              "target_pop_total", "target_pop_routine")
   for (v in views) {
     DBI::dbExecute(con, temporary_view(v, read_sql(v)))
   }
-
+  
   DBI::dbGetQuery(con, read_sql("query_metadata"))
 }
 
@@ -229,14 +243,14 @@ mu_build_data <- function(con, index, meta, pop) {
   year_min <- min(pop$total$year)
   year_max <- max(pop$total$year)
   w <- 4L # 1/2 window for rolling average
-
+  
   ## Process our arguments a bit more:
   x <- meta$group[meta$group$index == index, ]; print(x)
   xd <- meta$impacts[meta$impacts$index == index, ]
   x_deaths <- xd[xd$impact_outcome == "deaths_averted", ]
   x_cases <- xd[xd$impact_outcome == "cases_averted", ]
   x_fvps <- xd[xd$impact_outcome == "fvps_added", ]
-
+  
   sql <- paste("SELECT country",
                "  FROM touchstone_country",
                " WHERE touchstone = $1",
@@ -244,22 +258,22 @@ mu_build_data <- function(con, index, meta, pop) {
                " ORDER BY country",
                sep = "\n")
   countries <- DBI::dbGetQuery(con, sql, list(x$touchstone, x$disease))[[1]]
-
+  
   if (length(countries) == 0L) {
     stop("Import error")
   }
-
+  
   window <- 2 * w + 1
   year_max2 <- year_max + w
   years <- year_min:year_max2
   n_years <- length(years)
-
+  
   dat <- expand.grid(year = years,
                      country = countries,
                      index = index,
                      stringsAsFactors = FALSE)[3:1]
   dat$.code <- paste(dat$country, dat$year, sep = "\r")
-
+  
   merge_in <- function(dat, d, cols) {
     d$.code <- paste(d$country, d$year, sep = "\r")
     i <- match(dat$.code, d$.code)
@@ -272,16 +286,16 @@ mu_build_data <- function(con, index, meta, pop) {
     }
     cbind(dat, keep)
   }
-
+  
   ## 1. impact:
   sql <- paste("SELECT * FROM v_impact",
                " WHERE YEAR BETWEEN 2001 AND $2",
                "   AND impact_estimate_set = $1")
-
+  
   d_deaths_averted <-
     DBI::dbGetQuery(con, sql, list(x_deaths$impact_estimate_set, year_max2))
   dat <- merge_in(dat, d_deaths_averted, c(deaths_averted = "value"))
-
+  
   if (nrow(x_cases) == 0) {
     dat$cases_averted <- NA_real_
   } else {
@@ -289,7 +303,7 @@ mu_build_data <- function(con, index, meta, pop) {
       DBI::dbGetQuery(con, sql, list(x_cases$impact_estimate_set, year_max2))
     dat <- merge_in(dat, d_cases_averted, c(cases_averted = "value"))
   }
-
+  
   if (nrow(x_fvps) > 0) {
     d_fvps_added <-
       DBI::dbGetQuery(con, sql, list(x_fvps$impact_estimate_set, year_max2))
@@ -302,7 +316,7 @@ mu_build_data <- function(con, index, meta, pop) {
     d_fvps <- DBI::dbGetQuery(con, sql, list(x$burden_estimate_set, year_max2))
     dat <- merge_in(dat, d_fvps, c(fvps = "value"))
   }
-
+  
   ## 3. old coverage
   sql <- paste("SELECT * FROM v_coverage",
                " WHERE YEAR BETWEEN 2001 and $2",
@@ -311,23 +325,23 @@ mu_build_data <- function(con, index, meta, pop) {
   dat <-
     merge_in(dat, mu_fix_coverage(d_cov_old),
              c(coverage_old = "coverage", coverage_target = "target"))
-
+  
   ## 4. new coverage
   if (is.na(x$coverage_set_new)) {
     stop("Import error: no new coverage found")
   } else {
-    if(meta$touchstone_mod$touchstone_name == meta$group$touchstone_name[meta$group$index == index])  {
+    if (meta$touchstone_mod$touchstone_name == meta$group$touchstone_name[meta$group$index == index])  {
       d_cov_new <- d_cov_old
     } else {
       d_cov_new <- DBI::dbGetQuery(con, sql, list(x$coverage_set_new, year_max2))
     }
-    dat <- merge_in(dat, mu_fix_coverage(d_cov_new),
-                    c(coverage_new = "coverage",
-                      coverage_target_new = "target"))
   }
-
-
-
+  dat <- merge_in(dat, mu_fix_coverage(d_cov_new),
+                  c(coverage_new = "coverage",
+                    coverage_target_new = "target"))
+  
+  
+  
   ## 5. population estimates
   pop_total <- pop$total[pop$total$demographic_source == x$demographic_source, ]
   pop_routine <- pop$routine[
@@ -335,9 +349,9 @@ mu_build_data <- function(con, index, meta, pop) {
       pop$routine$demographic_source == x$demographic_source, ]
   dat <- merge_in(dat, pop_total, c(pop_total = "value"))
   dat <- merge_in(dat, pop_routine, c(pop_routine = "value"))
-
+  
   ## 6. Impact rates
-
+  
   ## NOTE: There is some fiddling required here.  We might have cases
   ## where there is nonzero impact but zero fvps.  There are two forms
   ## of this!
@@ -354,8 +368,8 @@ mu_build_data <- function(con, index, meta, pop) {
   ## the coverage_target is not known; those cases I've opted to just
   ## zero the rate calculation but leave the impact in.
   ## (we have such cases for routines as well.)
-
-
+  
+  
   i <- is_blank(dat$fvps) &
     !(is_blank(dat$deaths_averted) & is_blank(dat$cases_averted)) &
     !is_blank(dat$coverage_target)
@@ -365,23 +379,23 @@ mu_build_data <- function(con, index, meta, pop) {
     ## Otherwise use pop_routine.
     dat$fvps[i] <- dat$coverage_old[i] * dat$coverage_target[i]
   }
-
+  
   ## There are cases where we have blank fvps_old, but positive coverage_old
   ## This caused the Inf/-Inf impact rate issue for CHN and TCD
   ## We create synthetic fvps for such cases when applicable.
   i <- is_blank(dat$fvps) & !is_blank(dat$coverage_old)
   if (any(i)) {
     message("Creating synthetic fvps for ", x$model)
-    if((x$activity_type == "campaign")){
+    if ((x$activity_type == "campaign")){
       dat$fvps[i] <- dat$coverage_old[i] * dat$coverage_target[i]
-    }else{
+    } else {
       dat$fvps[i] <- dat$coverage_old[i] * dat$pop_routine[i]
     }
   }
-
+  
   dat <- mu_calculate_rate("deaths", dat, window, n_years)
   dat <- mu_calculate_rate("cases", dat, window, n_years)
-
+  
   ## And another quantity needed
   ##
   ## NOTE: Here, we can run into trouble where the coverage is zero
@@ -390,10 +404,10 @@ mu_build_data <- function(con, index, meta, pop) {
   ## rolling calculation)
   dat$target_pop_estimated <- dat$fvps / dat$coverage_old
   dat$target_pop_estimated[dat$fvps > 0 & is_blank(dat$coverage_old)] <- NA
-
+  
   dat$target_pop_estimated_avg <-
     roll_mean_by(dat$target_pop_estimated, window, n_years)
-
+  
   if (x$activity_type == "routine") {
     dat$target_pop_given <- dat$pop_routine
     dat$target_pop_given_new <- dat$pop_routine
@@ -401,12 +415,12 @@ mu_build_data <- function(con, index, meta, pop) {
     dat$target_pop_given <- dat$coverage_target
     dat$target_pop_given_new <- dat$coverage_target_new
   }
-
+  
   ## We also want new fvps which we compute as coverage * target pop
   v <- if (x$activity_type == "campaign")
     "coverage_target_new" else "pop_routine"
   dat$fvps_new <- dat$coverage_new * dat[[v]]
-
+  
   ## At the moment we can do nothing for MHL, TUV and XK, because we have no routine pop for them
   i <- !is_blank(dat$coverage_new) & is_blank(dat$fvps_new) & dat$year < 2031#& !is_blank(dat$target_pop_estimated)
   if (any(i)) {
@@ -417,23 +431,23 @@ mu_build_data <- function(con, index, meta, pop) {
       stop("modified update error")
   }
   #For analysis purpose, a touchstone can be 'updated' by itself - assumes equal fvps
-  if(meta$touchstone_mod$touchstone_name == meta$group$touchstone_name[meta$group$index == index])
+  if (meta$touchstone_mod$touchstone_name == meta$group$touchstone_name[meta$group$index == index])
     dat$fvps_new <- dat$fvps
   ## drop excess temporary things
   dat$.code <- NULL
   dat <- dat[dat$year <= year_max, ]
-
+  
   dat
 }
 
 mu_impact_metadata <- function(meta, touchstone_use) {
   meta <- meta[meta$touchstone_name %in% touchstone_use, ]
   rownames(meta) <- NULL
-
+  
   vary <- c("impact_outcome", "impact_estimate_set", "impact_estimate_recipe",
             "focal_ingredient")
   same <- setdiff(names(meta), vary)
-
+  
   f <- function(y) {
     n <- vapply(y, function(x) length(unique(x)), integer(1))
     stopifnot(all(n[same] == 1))
@@ -443,16 +457,16 @@ mu_impact_metadata <- function(meta, touchstone_use) {
                      meta$vaccine, meta$activity_type, meta$support_type, meta$responsibility,
                      sep = "\r")
   meta_code <- factor(meta_code, unique(meta_code))
-
+  
   i1 <- unname(tapply(seq_along(meta_code), meta_code, min))
   group <- meta[i1, same]
   group$index <- seq_len(nrow(group))
   rownames(group) <- NULL
-
+  
   impacts <- meta[vary]
   impacts$index <- group$index[tapply(meta_code, meta_code)]
   impacts <- impacts[order(impacts$index, impacts$impact_outcome), ]
-
+  
   list(group = group, impacts = impacts)
 }
 
@@ -463,13 +477,26 @@ mu_impact_metadata <- function(meta, touchstone_use) {
 ##'
 ##' @param d Data: use impact_rate_tot (method 2)
 ##'
+##' @param method two options: method1 and method2
 ##' @export
-mu_scale <- function(name, d) {
-  ## This chunck becomes simpler, since only method 2 is used throughout.
-  rate_tot_old <- d[[paste0(name, "_rate_tot")]]
-  fvps_new <- d$fvps_new
-
-  ret <- fvps_new * rate_tot_old
+mu_scale <- function(name, d, method = 2) {
+  ## AS we need method 1 now. mu_scale includes both method 1 and method 2 now
+  ## This is method 1.
+  if (method == 1) {
+    # by default use impact_new = impact_old / coverage_old * coverage_new
+    ret <- d[[name]] / d$coverage_old * d$coverage_new
+    
+    # when coverage_old is NA or 0, use impact_rate_tot*fvps_new
+    i <- is.na(d[[name]]) | d[[name]] == 0
+    rate_tot_old <- d[[paste0(name, "_rate_tot")]]
+    fvps_new <- d$fvps_new
+    ret[i] <- fvps_new[i] * rate_tot_old[i]
+  } else {
+    ## This is method 2
+    rate_tot_old <- d[[paste0(name, "_rate_tot")]]
+    fvps_new <- d$fvps_new
+    ret <- fvps_new * rate_tot_old
+  }
   ret[!is.finite(ret)] <- NA
   j <- !is.na(d$coverage_new) & d$coverage_new == 0
   ret[j] <- 0
@@ -494,19 +521,19 @@ mu_fix_coverage <- function(d) {
     }
     v
   }
-
+  
   code <- make_code(args = d[c("coverage_set", "country", "year")])
   n <- table(code)
   fix <- code %in% as.integer(names(n[n > 1]))
-
+  
   d_fix <- d[fix, ]
   i <- code[fix]
-
+  
   tmp <- d_fix[tapply(seq_along(i), i, utils::head, 1), ]
   tmp$target <- unname(tapply(d_fix$target, i, sum, na.rm = TRUE))
   reached <- tapply(d_fix$coverage * d_fix$target, i, sum, na.rm = TRUE)
   tmp$coverage <- unname(reached / tmp$target)
-
+  
   ret <- rbind(d[!fix, ], tmp)
   ret[order(ret$coverage_set, ret$country, ret$year), ]
 }
@@ -518,19 +545,19 @@ mu_calculate_rate <- function(name, dat, window, n_years) {
   v_tot <- sprintf("%s_averted_rate_tot", name)
   v_use <- sprintf("%s_averted_rate", name)
   v_type <- sprintf("%s_averted_rate_type", name)
-
+  
   dat[[v_inst]] <- dat[[v_averted]] / dat$fvps
   dat[[v_inst]][
     is_blank(dat$fvps) & !is_blank(dat[[v_averted]])] <- NA
-
+  
   dat[[v_avg]] <-
     roll_sum_by(dat[[v_averted]], window, n_years) /
     roll_sum_by(dat$fvps, window, n_years)
-
+  
   dat[[v_tot]] <-
     roll_sum_by(dat[[v_averted]], Inf, n_years) /
     roll_sum_by(dat$fvps, Inf, n_years)
-
+  
   ## Some cases don't have any non-NA rates
   type <- rep(NA, nrow(dat))
   use <- rep(NA_real_, nrow(dat))
@@ -538,10 +565,10 @@ mu_calculate_rate <- function(name, dat, window, n_years) {
   i <- !is.na(dat[[v_tot]])
   type[i] <- "tot"
   use[i] <- dat[[v_tot]][i]
-
+  
   dat[[v_use]] <- use
   dat[[v_type]] <- type
-
+  
   dat
 }
 
@@ -562,15 +589,15 @@ mu_year_introduction <- function(con, dat, dat_summary){
   meta.group <- unique(group[meta.group])
   x <- merge(meta.group, dat_summary, by = c("touchstone", "modelling_group", "vaccine"), all.y=TRUE)
   stopifnot( nrow(x) == nrow(dat_summary) )
-
+  
   x$touchstone_src[ is.na(x$touchstone_src) ] <- dat$touchstone_mod$id
-
+  
   coverage.touchstone <- as.list(c(unique(x$touchstone_src)))
-
+  
   y <- do.call(rbind, lapply(coverage.touchstone, function(i) find_year_introduction(con, i)))
-
+  
   z <- merge(x, y, by=c("touchstone_src", "activity_type", "vaccine", "country"), all.x = TRUE)
-
+  
   z
 }
 
diff --git a/inst/sql/modified_update/migrate_coverage.sql b/inst/sql/modified_update/migrate_coverage.sql
index 09d4088..97c3bf8 100644
--- a/inst/sql/modified_update/migrate_coverage.sql
+++ b/inst/sql/modified_update/migrate_coverage.sql
@@ -9,6 +9,9 @@ LEFT JOIN (SELECT coverage_set.* FROM coverage_set
 JOIN touchstone ON touchstone.id = coverage_set.touchstone
            WHERE touchstone = '{{{touchstone_new}}}'
            AND (coverage_set.gavi_support_level = 'with'
-             OR coverage_set.gavi_support_level = 'bestminus') )AS coverage_new
+             OR coverage_set.gavi_support_level = 'bestminus') 
+           AND name NOT IN ('Rota: MCV1, with, routine',
+                            'Hib: MCV1, with, routine',
+                            'PCV: MCV1, with, routine'))AS coverage_new
 ON coverage_old.vaccine = coverage_new.vaccine AND
 coverage_old.activity_type = coverage_new.activity_type
diff --git a/man/modified_update_calculate.Rd b/man/modified_update_calculate.Rd
index fea71ea..4b131c8 100644
--- a/man/modified_update_calculate.Rd
+++ b/man/modified_update_calculate.Rd
@@ -4,7 +4,8 @@
 \alias{modified_update_calculate}
 \title{Do a modified update}
 \usage{
-modified_update_calculate(con, touchstone_name_mod, touchstone_use)
+modified_update_calculate(con, touchstone_name_mod, touchstone_use,
+  method = 2)
 }
 \arguments{
 \item{con}{Database connection}
@@ -14,6 +15,9 @@ that we are creating}
 
 \item{touchstone_use}{Name of the touchstone that we are basing
 this off of}
+
+\item{method}{two options: 1 = method1, 2 = method2
+method2_v2 is different from method2 in that, it takes per-year-gavi-level as an imput for gavi impact}
 }
 \description{
 Do a modified update
diff --git a/man/mu_scale.Rd b/man/mu_scale.Rd
index e8a3c5a..9b850d4 100644
--- a/man/mu_scale.Rd
+++ b/man/mu_scale.Rd
@@ -4,12 +4,14 @@
 \alias{mu_scale}
 \title{Calculate updated impact}
 \usage{
-mu_scale(name, d)
+mu_scale(name, d, method = 2)
 }
 \arguments{
 \item{name}{Impact type: deaths_averted or cases_averted}
 
 \item{d}{Data: use impact_rate_tot (method 2)}
+
+\item{method}{two options: method1 and method2}
 }
 \description{
 Calculate updated impact