cancer-impact-covid/covid cases vs services.Rmd at main · CoV-IMPACT-C/cancer-impact-covid · GitHub

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---
title: "Serie de prestaciones, lme y ges vs serie de casos de COVID-19"
output:
---

#Librerías

```{r}
library(data.table)
library(tidyverse)
```

#Datos

```{r}
dat.covid <- fread("all services/FechaInicioSintomas_std.csv") %>%
  filter(`Semana Epidemiologica` < 202100) %>%
  mutate(week = `Semana Epidemiologica` - 202000) %>%
  group_by(week) %>%
  summarise(casos = sum(`Casos confirmados`),
            poblacion = sum(Poblacion))
```

```{r}
load("health services/output/DB/DB predichos nb sti.RData")

dat.prest <- dat.plots %>%
  filter(year == 2020,
         mod.int == "Additive") %>%
  mutate(week = as.numeric(week)) %>%
  group_by(week) %>%
  summarise(count_prest = sum(count),
            cf_prest = sum(cf))

rm(dat.plots)

load("sick leaves/output/DB/DB predichos nb sti.RData")

dat.lme <- dat.plots %>%
  filter(year == 2020,
         mod.int == "Additive") %>%
  mutate(week = as.numeric(week)) %>%
  group_by(week) %>%
  summarise(count_lme = sum(count),
            cf_lme = sum(cf))

rm(dat.plots)

dat.ges <- haven::read_dta("diag confirms/output/DB/all cancers observed predicted counterfactual.dta") %>%
  filter(ano == 2020) %>%
  group_by(semana) %>%
  summarise(count_ges = sum(`_freq`),
            cf_ges = sum(nhat0)) %>%
  mutate(week = semana-1,
         semana = NULL)
```


```{r}
dat <- full_join(dat.prest, dat.covid, by = "week")
dat <- full_join(dat.ges, dat, by = c("week"))
dat <- full_join(dat.lme, dat, by = "week")

dat <- dat %>%
  pivot_longer(cols = c(cf_lme, cf_ges, cf_prest, count_lme, count_ges, count_prest),
               names_to = c("scenario","data"),
               names_sep = c("_"),
               values_to = c("count")) %>%
  pivot_wider(names_from = scenario,
              values_from = count) %>%
  filter(week < 53) %>%
  mutate(caida_abs = cf-count,
         caida_rel = caida_abs*100 /cf,
         data = factor(data, levels = c("prest", "ges", "lme"),
                       labels = c("Ambulatory\nservices",
                                  "Diagnostic\n confirmations", "Sick\nleaves")))


#rm(dat.prest, dat.lme, dat.ges, dat.covid)
```

# Plot Caida relativa

```{r fig.height=3, fig.width=8}
scale.factor <- max(dat$caida_rel, na.rm = T) / max(dat$casos, na.rm = T)

g <- dat %>%
  ggplot(aes(x = week)) +
  geom_point(aes(y = casos*scale.factor, color = "COVID-19 new cases")) +
  geom_line(aes(y = casos*scale.factor, color = "COVID-19 new cases")) +

  geom_point(aes(y = caida_rel, color = "% reduction of cancer-related services")) +
  geom_line(aes(y = caida_rel, color = "% reduction of cancer-related services")) +

  scale_y_continuous(name = "% Cancer-related services reduction",
                     sec.axis = sec_axis(~./scale.factor, name = "New COVID-19 cases")) +
  geom_vline(xintercept = 11, linetype = "dotted", color = "red") +
  geom_hline(yintercept = 0, linetype = 3, color = "black") +
  facet_grid(data ~ .) +

  labs(x = "Week")+
  theme_minimal() +
  theme(legend.title = element_blank(), legend.position = "top")

g

ggsave(plot = g,
       filename = "all services/paper Fig0.pdf",
       device = "pdf",
       dpi = "retina",
       width = 8, height = 5)
```