01_update.Rmd

---
title: "Impact of COVID-19 on refugees and migrants, Update 1"
author: "Mixed Migration Centre, 27 April 2020"
date: ""
output: github_document
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
\  

**This is the first update on the situation for refugees and migrants on mixed migration routes around the world in light of the COVID-19 pandemic. Using data collected by the [Mixed Migration Centre](http://www.mixedmigration.org/), the objective of the global updates is to provide regular up-to-date findings on COVID-19 awareness, knowledge and risk perception, access to information, access to healthcare, assistance needs and the impact on refugees’ and migrants’ lives and migration journeys . This series provides an aggregated overview; more detailed, thematic and response-oriented COVID-19 snapshots are also developed in each of the MMC regional offices and available [here](http://www.mixedmigration.org/resource-type/covid-19/).**


## Key Messages

•	Interviewed refugees and migrants in Colombia, Peru, Libya and Tunisia show very high levels of awareness and knowledge (e.g. on symptoms, vulnerable groups and prevention) on COVID-19; hardly anyone has been tested

•	The national government is most often seen as a trustworthy source of information on COVID-19, but it is not always the most used. In Libya, for example, other migrants are the main source of information

•	Across the 4 countries, only 37% of interviewed refugees and migrants said they could access healthcare if they had coronavirus symptoms, although in Colombia more than half said they could

•	The main barriers to healthcare for the refugees and migrants are discrimination against foreigners, lack of money and lack of legal documents, while in Libya fear of being reported and general insecurity play a slightly larger role

•	Over 85% of respondents said they need additional assistance since the crisis began, but less than one-third on average had received additional assistance. Respondents primarily cite basic needs: food, water and shelter, but also cash and sanitary items

•	More than two-thirds of respondents said they had lost income due to COVID-19 restrictions, with highest percentages in Colombia and Peru. Respondents cite reduced access to work as the main impact of the crisis

•	Most respondents had not yet changed their migration plans due to the crisis, although respondents in North Africa report a greater impact of the COVID-19 crisis on their migration journeys than those in Colombia and Peru

## Respondents

692 respondents were interviewed between 7 and 20 April 2020, with 185 of them in Colombia (mean age: 34; 75% women), 212 in Libya (mean age: 31; 28% women,), 53 in Peru (mean age: 33; 49% women,), and 242 in Tunisia (mean age: 29; 33% women). In Colombia and Peru, all respondents were Venezuelan nationals. In Libya and Tunisia, more than 30 nationalities were represented, with more respondents from Sudan (15%), Nigeria (13%), and Côte d’Ivoire (11%). Out of all respondents, approximately 10% reported living in camps or informal settlements in the past six months (Colombia: 11%, Peru: 6%, Libya: 1%, Tunisia: 16%).  

A summary of the methodology utilized for this study can be [here](http://www.mixedmigration.org/4mi/4mi_faq/). Figures for Peru should be interpreted with caution, since the number of interviews in this country is low. All figures are rounded to the nearest whole number. This first global update only reports on Colombia, Peru, Libya and Tunisia, which is where MMC first rolled out the adapted 4Mi COVID-19 survey. Data collection has also started in West Africa, East Africa and Asia and future updates will include the data from these regions.

## Awareness, knowledge and risk perception

Knowledge and risk perception seem high amongst respondents. All 692 respondents reported they had heard of COVID-19, and across all countries more than 90% reported they have seen people acting more cautiously since the beginning of the crisis. Likewise, approximately 90% of them agreed or strongly agreed that they are worried about catching coronavirus (Colombia: 91%, Libya: 84%, Peru: 98%, Tunisia: 93%). Somewhat lower percentages also agreed or strongly agreed they are worried about transmitting coronavirus (Colombia: 79%, Peru: 81%, Libya: 59%, Tunisia: 79%).

Respondents also know coronavirus symptoms, with dry cough (80% to 92% of respondents across countries), fever (70% to 92%) and difficulties breathing (74% to 81%) being cited the most frequently. Respondents less frequently indicated that the virus can be asymptomatic (7% to 25%).  Furthermore, they know which groups are more at risk, with older people cited more frequently (Colombia: 95%, Peru: 89%, Libya: 81%, Tunisia: 84%), followed by people who are already ill with another condition, and health workers.

A vast majority of respondents take measures to protect themselves, with washing hands more regularly (Colombia: 80%, Peru: 68%, Libya: 86%, Tunisia: 82%) and staying at home or isolating from others (Colombia: 93%, Peru: 83%, Tunisia: 74%) being the most commonly cited. In Libya, however, far fewer respondents reported staying at home (37%). Also in Libya, 5% reported not taking any measures, while the proportion is close to 0 in the other countries. Overall, and except in Tunisia, respondents report they are able to keep the recommended 1.5 metre distance, see Figure 1. 
Almost no respondents were tested for coronavirus (Colombia: none, Peru: 1, Libya: 3 with 26 refused answers, Tunisia: 3).  
\  

**_Figure 1: Do you think you are able to practice 1.5 metre distancing?_**

```{r unfpa, include=FALSE}
library(tidyverse)
load("rda/01_cleaned_data_20200421.rda")
```

```{r 1, include=FALSE}
#FIGURE 1 29 Q60 Do you think you are able to practice the recommended 1.5 metre of distance between people?####
summary(data$Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.)


data$Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live. <- factor(data$Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live., levels = c("Yes", "No", "Don´t know", "Refused"))


levels(data$Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.)

safe_distance <- data %>% 
  group_by(Q2.What.country.are.you.in.right.now.) %>% 
  count(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.) %>% 
  mutate(Percent = round(n/sum(n)*100, digits = 1))
safe_distance

```

```{r 2, echo=FALSE, fig.align='left', fig.height=5, fig.width=5}
#PLOT1####
plot1 <- safe_distance %>% 
  filter(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.!="Refused") %>% 
  mutate(Country = Q2.What.country.are.you.in.right.now.) %>% 
  mutate(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.=recode(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live., 'Yes'="Yes", 'No'="No", 'Don´t know' = "Don't know", 'Refused'="Refused")) %>% 
  mutate(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live.=reorder(Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live., Percent)) %>% 
  ggplot(aes(fill=Country))+
  geom_col(aes(x = Q60.Do.you.think.you.are.able.to.practice.the.recommended.1.5.metre.of.distance.between.people.in.the.place.where.you.live., y=Percent), position = position_dodge2(preserve = "single", padding = 0), width = 0.4)+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 10))+
  theme(legend.position = c(0.80, 0.20), legend.direction = "vertical", legend.title = element_blank())+
  coord_flip()+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))

plot1
```

## Access to information

The national government and authorities were the most frequently cited sources of information on COVID-19 (Colombia: 65%, Peru: 83%, Tunisia: 63%), except in Libya, where ‘other migrants’ were cited as the main source of information (40% of respondents), followed by the government, at 34%.

Most participants received information on the virus via the media (Colombia: 86%, Peru: 91%, Libya: 64%, Tunisia: 65%),  and social media (Colombia: 61%, Peru: 72%, Libya: 53% , Tunisia: 79%), with Facebook (56% overall), WhatsApp (51%), and YouTube (21%) being the most frequently cited social media across countries.


The government is seen as the most trustworthy source of information (Peru: 74%, Libya: 44%, Tunisia: 73%), except in Colombia, where health officials (52%) are perceived as slightly more trustworthy than the government (45%), see Figure 2.
Interestingly, there are some differences between the sources of information that are more frequently used and those that are considered more trustworthy. For example, although the online community is used more (Colombia: 35%, Peru: 15%, Libya: 33%, Tunisia: 72%) than NGOs, NGOs and the UN are, overall, considered more trustworthy than the online community. Perhaps this is because the sources of information that respondents consider more trustworthy are simply not always available and that they have no choice but to rely on less trustworthy sources.  
\  

**_Figure 2: Who do you think is a trustworthy source of information on coronavirus?_**

```{r 3, include=FALSE}
c10  <- data  %>% 
  select(179, 47:61) %>% 
  pivot_longer(cols = 2:16, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  group_by(Q2.What.country.are.you.in.right.now.) %>% 
  count(Answer) %>% 
  mutate(Col = round(n/185*100, digits = 1), Per = round(n/53*100, digits = 1), Lib = round(n/212*100, digits = 1), Tun = round(n/242*100, digits = 1))%>%
  print(n=49)

c10_plot <- data  %>% 
  select(179, 47:61) %>% 
  pivot_longer(cols = 2:16, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  count(Q2.What.country.are.you.in.right.now., Answer) %>% 
  pivot_wider(values_from = 3, names_from = Answer) %>% 
  add_column(N_P = c(185, 53, 212, 242)) %>% 
  pivot_longer(cols=2:16, names_to = "Options", values_to = "Answer") %>% 
  mutate(Percent= round(Answer/N_P*100, digits = 1)) %>%
  filter(!is.na(Percent)) %>% 
  print(n=50)

```

```{r 4, echo=FALSE, fig.align='left', fig.width=7, fig.height=7}
#PLOT1####
#Thickness solved
plot2  <- c10_plot %>%
  mutate(Options = reorder(Options, Percent)) %>% 
  filter(Options != "I don't remember" & Options != "Refused") %>% 
  ggplot(aes(fill=Q2.What.country.are.you.in.right.now.))+
  geom_col(aes(x=Options, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.20), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
plot2
```


## Access to healthcare

Overall, only 37% of participants believe they would be able to access healthcare if they had coronavirus symptoms, but there are important differences between countries. In Colombia, more than half thought they could access services, but in other countries the figure was lower (Peru: 17%, Libya: 38%, Tunisia: 29%). In addition, another third of respondents across regions - with the exception of Colombia (11%) – reported that they simply do not know whether they would be able to access health services (Peru: 32%, Libya: 34%, Tunisia: 34%).

The respondents reported that the main barriers to accessing healthcare are a lack of money (Colombia: 39%, Peru: 36%, Libya: 26%, Tunisia: 52%) and discrimination against foreigners (Colombia: 17%, Libya: 29%), this reason seeming particularly important in Tunisia (57%) and Peru (55%), see Figure 3. Not having documentation also seems to be an important barrier, particularly in Colombia (60%). Finally, fear and insecurity seem more important in Libya (9%) than in the other countries.  
\  
  
**_Figure 3: What are the barriers to accessing healthcare?_**

```{r 5, include=FALSE}
c18 <- data  %>% 
  select(179, 96:107) %>% 
pivot_longer(cols = 2:13, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  group_by(Q2.What.country.are.you.in.right.now.) %>% 
  count(Answer) %>% 
  mutate(Col = round(n/185*100, digits = 1), Per = round(n/53*100, digits = 1), Lib = round(n/212*100, digits = 1), Tun = round(n/242*100, digits = 1))%>%
  print(n=49)

#plot

c18_plot <- data  %>% 
  select(179, 96:107) %>% 
  pivot_longer(cols = 2:13, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  count(Q2.What.country.are.you.in.right.now., Answer) %>% 
  pivot_wider(values_from = 3, names_from = Answer) %>% 
  add_column(N_P = c(185, 53, 212, 242)) %>% 
  pivot_longer(cols=2:13, names_to = "Options", values_to = "Answer") %>% 
  mutate(Percent= round(Answer/N_P*100, digits = 1)) %>% 
  filter(!is.na(Percent)) %>% 
  print(n=50)
```

```{r 6, echo=FALSE, fig.align='left', fig.width=7, fig.height=7}
plot3  <- c18_plot %>%
  mutate(Options = reorder(Options, Percent)) %>% 
  mutate(Options = recode(Options, 
                          'Discrimination against foreigners limits access to services' = "Discrimination against foreigners",
                          'General insecurity and conflict prevent me from accessing healthcare' = "General insecurity",
                          'I am afraid of being reported to authorities, or arrest, or deportation' = "Afraid of being reported",
                          'I don\'t have the money to pay for health services' = "I don't have the money",
                          'I don\'t have the right or the legal documentation to access health services here' = "I don't have the right/documentation",
                          'The advice for testing and treating coronavirus is unclear here' = "The advice is unclear here",
                          'Services are overwhelmed and access is difficult for everyone' = "Services are overwhelmed",
                          'I don\'t know where to go for healthcare' = "I don't know where to go")) %>% 
  filter(Options != "I don't remember" & Options != "Refused") %>% 
  ggplot(aes(fill=Q2.What.country.are.you.in.right.now.))+
  geom_col(aes(x=Options, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank()) + 
  theme(legend.position = c(0.80, 0.20), legend.direction = "vertical", legend.title = element_blank()) +
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))
plot3

```



## Assistance needs

More than 85% of all respondents stated that they are in need of extra help since the COVID-19 crisis began (Colombia: 92%, Peru: 74%, Libya: 77%, Tunisia: 93%). These respondents (n=598) mentioned that what they needed most was food, water, and shelter (76%), cash (71%), sanitary items (43%), access to work and livelihoods (28%), and access to health services (23%).

In Colombia and Peru, 35% and 32% of respondents, respectively, stated that they had received additional assistance since the coronavirus crisis began. In Libya and Tunisia, they were 7% and 22%, respectively.

Out of the total number of 149 respondents who received additional assistance, 79% received food, water and shelter; 29% received cash; and 20% received sanitary items such as sanitizer, mask, or gloves. The main providers of additional assistance were NGOs (35%), the local population (34%), and the government (29%).  


## Impact on refugees’ and migrants’ lives

Overall, more than two-thirds of respondents reported that they lost income due to coronavirus restrictions, and this is higher among those in Latin America than North Africa (Colombia: 89%, Peru: 87%, Libya: 61%, Tunisia: 59%). Respondents also reported that reduced access to work was the main impact on their day-to-day life (Colombia: 85%, Peru: 87%, Libya: 65%, Tunisia: 53%), followed by reduced availability of basic goods (62% overall), and stress (61% overall), see Figure 4.


\  
  
**_Figure 4: What impact has the crisis had on your day-to-day life?_**

```{r 7, include=FALSE}
c20  <- data  %>% 
  select(179, 118:123) %>% 
  pivot_longer(cols = 2:7, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  group_by(Q2.What.country.are.you.in.right.now.) %>% 
  count(Answer) %>% 
  mutate(Col = round(n/185*100, digits = 1), Per = round(n/53*100, digits = 1), Lib = round(n/212*100, digits = 1), Tun = round(n/242*100, digits = 1))%>%
  print(n=49)

c20_plot <- data  %>% 
  select(179, 118:123) %>% 
  pivot_longer(cols = 2:7, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  count(Q2.What.country.are.you.in.right.now., Answer) %>% 
  pivot_wider(values_from = 3, names_from = Answer) %>% 
  add_column(N_P = c(185, 53, 212, 242)) %>% 
  pivot_longer(cols=2:7, names_to = "Options", values_to = "Answer") %>% 
  mutate(Percent= round(Answer/N_P*100, digits = 1)) %>% 
  filter(!is.na(Percent)) %>% 
  print(n=50)
```

```{r 8, echo=FALSE, fig.align='left', fig.width=7, fig.height=7}
plot4 <- c20_plot %>%
  mutate(Options = reorder(Options, Percent)) %>% 
  filter(Options != "I don't remember" & Options != "Refused") %>% 
  ggplot(aes(fill=Q2.What.country.are.you.in.right.now.))+
  geom_col(aes(x=Options, y=Percent), width = 0.5, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.20), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))

plot4

```


## Impact on migration journeys

The impact of the crisis on refugees’ and migrants’ journeys differs between Colombia/Peru and Libya/Tunisia (see Figure 4). Although on average a majority of respondents stated that they had not changed their plans as a result of the coronavirus outbreak, these percentages are higher in Latin America than in North Africa (Colombia: 77%, Peru: 62%, Tunisia: 51%, Libya: 35%). Moreover, in Colombia and Peru, 42% and 60% of respondents stated that the coronavirus crisis had no impact on their migration journey, while these percentages are much lower in North Africa.  

In Libya and Tunisia, a much higher number of respondents report increased difficulty to move around, increased difficulty to cross borders, fear of moving and reduced access to smugglers. In Libya, in particular, respondents also cited increased risk of detention and deportation. One explanation for this difference between the two regions could be that Venezuelan refugees and migrants, as a community, in Colombia and Peru are more settled and more likely to consider these countries as (temporary) destinations. In Latin America, 87% of respondents said they had reached their destination, whereas in North Africa they were only 14%. Those who are more settled are therefore also more likely to report reduced access to work, as shown in the previous graph, while (primarily) sub-Saharan refugees and migrants in North Africa are more likely to be in transit, and thus report experiencing more of an impact of the crisis on their migration journeys. 

\  
  
**_Figure 5: What impact has the coronavirus crisis had on your migration journey?_**

```{r 9, include=FALSE}
c19  <- data  %>% 
  select(179, 108:117) %>% 
  pivot_longer(cols = 2:11, names_to = "Options", values_to = "Answer") %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  group_by(Q2.What.country.are.you.in.right.now.) %>% 
  count(Answer) %>% 
  mutate(Col = round(n/185*100, digits = 1), Per = round(n/53*100, digits = 1), Lib = round(n/212*100, digits = 1), Tun = round(n/242*100, digits = 1))%>%
  print(n=49)

View(c19)
  
c19_plot <- data  %>% 
  select(179, 108:117) %>% 
  pivot_longer(cols = 2:11, names_to = "Options", values_to = "Answer") %>% 
  mutate(Answer = recode(Answer, 
                         'I\'ve been delayed because I was sick, or because I had to stop and take care of people who got sick' = "Delayed because I or other people were sick",
                         'I feel too afraid to move (to continue my journey or return)' = "I feel to afraid to move",
                         'Increased difficulty moving around inside countries'="Increased difficulty moving around",
                         'I was going to be resettled, but this is now delayed'="About to be resettled, but now delayed",
                         'Disembarked / deported back to previous country'="Deported back to previous country"
  )) %>% 
  filter(Answer != "", !is.na(Answer)) %>% 
  count(Q2.What.country.are.you.in.right.now., Answer) %>% 
  pivot_wider(values_from = 3, names_from = Answer) %>% 
  add_column(N_P = c(185, 53, 212, 242)) %>% 
  pivot_longer(cols=2:11, names_to = "Options", values_to = "Answer") %>% 
  mutate(Percent= round(Answer/N_P*100, digits = 1)) %>% 
  filter(!is.na(Percent)) %>% 
  print(n=50)

```

```{r 10, echo=FALSE, fig.align='left', fig.width=7, fig.height=7}

  
plot5 <- c19_plot %>%
  mutate(Options = reorder(Options, Percent)) %>% 
  filter(Options != "I don't remember" & Options != "Refused") %>% 
  ggplot(aes(fill=Q2.What.country.are.you.in.right.now.))+
  geom_col(aes(x=Options, y=Percent), width = 0.6, position = position_dodge2(preserve = "single", padding = 0))+
  ylab("Percent")+
  xlab("")+
  theme_bw()+
  scale_y_continuous(breaks = seq(0, 100, by = 5)) + 
  coord_flip()+
  theme(legend.title = element_blank())+
  theme(legend.position = c(0.80, 0.20), legend.direction = "vertical", legend.title = element_blank())+
  scale_fill_discrete(guide = guide_legend(reverse = TRUE))


plot5
```

\  
  
### Acknowledgement 

The owner and source of the data used for this study is the [Mixed Migration Centre](http://www.mixedmigration.org/), and this study has been published first [here](http://www.mixedmigration.org/resource-type/covid-19/) on 27 April 2020. Data analyst: Jean-Luc Jucker, PhD  
\  
\  
\  
\